KINGS COLLEGE OF ENGINEERING DEPARTMENT OF CIVIL ENGINEERING Question Bank Sub. Code/Name: CE1304 Fundamentals of Remote sensing and GIS Year/Sem: III / V UNIT I EMR AND ITS INTERACTION WITH ATMOPHERE AND EARTH MATERIAL PART A 1.What is remote sensing? Remote sensing is the science and art of obtaining information about on object, area, or phenomena through the analysis of data acquired by a device that is not in contact with the object, area, or phenomena under investigation. 2.What are all the applications of remote sensing? In many respects, remote sensing can be thought of as a reading process. Using various sensors, we remotely collect data that may be analyzed to obtain information about the objects, areas, or phenomena being investigated. The remotely collected data can be of many forms, including variations in force distributions, acoustic wave distributions, or electromagnetic energy distributions. 3.Write the physics of remote sensing ? Visible light is only one of many forms of electromagnetic energy. Radio waves, heat, ultraviolet rays, and X-rays are other familiar forms. All this energy is inherently similar and radiates in accordance with basic wave theory. This theory describes electromagnetic energy as traveling in harmonic, sinusoidal fashion at the “velocity of light” c. The distance from one wave peak to the next is the wave length ψ, and the number of peaks passing a fixed point in space per unit time is the wave frequency V. From basic physics, wave obey the general equation C=vy 4.What are the Components of Remote Sensing ? 5.What is Electromagnetic radiation? Electromagnetic (EM) radiation is a self-propagating wave in space or through matter. EM radiation has an electric and magnetic field component which oscillate in phase perpendicular to each other and to the direction of energy propagation. 6.Write the type of Electromagnetic radiation? Electromagnetic radiation is classified into types according to the frequency of the wave, these types include (in order of increasing frequency): radio waves, microwaves, terahertz radiation, infrared radiation, visible light, ultraviolet radiation, X-rays and gamma rays. 7.Draw the quantum theory interaction? A quantum theory of the interaction between electromagnetic radiation and matter such as electrons is described by the theory of quantum electrodynamics. 8.Write about refraction? In refraction, a wave crossing from one medium to another of different density alters its speed and direction upon entering the new medium. The ratio of the refractive indices of the media determines the degree of refraction, and is summarized by Snell's law. Light disperses into a visible spectrum as light is shone through a prism because of refraction. 9.Draw the Wave model? 10.Write Planck’s equation? The frequency of the wave is proportional to the magnitude of the particle's energy. Moreover, because photons are emitted and absorbed by charged particles, they act as transporters of energy. The energy per photon can be calculated by Planck's equation: where E is the energy, h is Planck's constant, and f is frequency. 11.What is Black body ? By definition a black body is a material that absorbs all the radiant energy that strikes it. A black body also radiates the maximum amount of energy, which is dependent on the kinetic temperature. 12.Write Stefan Boltzman law? According to the Stefan-Boltzman law the radiant flux of a black body, Fb, at a kinetic temperature, Tkin, is Fb = s* Tkin 4 where s is the Stefan- Boltzman constant, 5.67*10-12 W*cm-2*°K-4. 13.What is emissivity? Emissivity is a measure of the ability of a material to both radiate and absorb energy. Materials with a high emissivity absorb and radiate large proportions of incident and kinetic energy, respectively (and vice-versa). 14.Write Wein’s Displacement law? For an object at a constant temperature the radiant power peak refers to the wavelength at which the maximum amount of energy is radiated, which is expressed as lmax. The sun, with a surface temperature of almost 6000°K, has its peak at 0.48mm (wavelength of yellow). The average surface temperature of the earth is 290°K (17°C), which is also called the ambient temperature; the peak concentration of energy emitted from the earth is at 9.7mm.This shift to longer wavelengths with decreasing temperature is described by Wien’s displacement law, which states: lmax = 2,897mm°K / Trad°K . 15.Write Planck’s Law? The primary law governing blackbody radiation is the Planck Radiation Law, which governs the intensity of radiation emitted by unit surface area into a fixed direction (solid angle) from the blackbody as a function of wavelength for a fixed temperature. The Planck Law can be expressed through the following equation. 16.What is Scattering? Scattering occurs when particles or large gas molecules present in the atmosphere interact with and cause the electromagnetic radiation to be redirected from its original path. How much scattering takes place depends on several factors including the wavelength of the radiation, the abundance of particles or gases, and the distance the radiation travels through the atmosphere. There are three (3) types of scattering which take place. 17.What are the types of scattering? (i) Rayleigh scattering occurs when particles are very small compared to the wavelength of the radiation. (ii) Mie scattering It occurs when the particles are just about the same size as the wavelength of the radiation. (iii) Non Selective Scattering The final scattering mechanism of importance is called nonselective scattering. This occurs when the particles are much larger than the wavelength of the radiation. 18.What is Atmospheric Windows? The areas of the spectrum which are not severely influenced by atmospheric absorption and thus, are useful to remote sensors, are called atmospheric windows. PART B 1. Discuss on spectral signature and its rule in identifying objects with suitable diagrams. 2. Explain the principle of working of remote sensing? 3. With a suitable diagram explain the Electromagnetic Spectrums and its characteristics used in remote sensing? 4. Explain on the different types of interactions of EMR with atmosphere? UNIT II PLATFORMS AND SENSORS PART A 1.What is passive sensors? Passive sensors can only be used to detect energy when the naturally occurring energy is available. For all reflected energy, this can only take place during the time when the sun is illuminating the Earth. There is no reflected energy available from the sun at night. Energy that is naturally emitted (such as thermal infrared) can be detected day or night, as long as the amount of energy is large enough to be recorded. 2.What is Active sensors? On the other hand, provide their own energy source for illumination. The sensor emits radiation which is directed toward the target to be investigated. The radiation reflected from that target is detected and measured by the sensor. 3.Write the advantages of active sensors? Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season. Active sensors can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. However, active systems require the generation of a fairly large amount of energy to adequately illuminate targets. Some examples of active sensors are a laser fluorosensor and a synthetic aperture radar (SAR). 4. What are the types of Platforms? The vehicle or carrier for remote sensor is borne is called the Platform.” The typical platforms are satellite and aircraft, but they can also include radio controlled airplanes, balloons, pigeons, and kites for low altitude remote sensing, as well as ladder and cherry pickers for ground investigation. 5.Differentiate Geostationary orbit and Polar sun synchronous orbit. Geostationary orbit . High altitude (36,000km) Remains in same position above the Earth Used by meteorological and communications satellites Sees Earth disk (between third and quarter of Earths surface) High temporal frequency (c.30 mins typical) Polar sun synchronous orbit Low altitude (200-1000km) Goes close to poles Higher spatial resolution than geostationary Lower temporal resolution than geostationary 6. What is Resolution? In general resolution is defined as the ability of an entire remote-sensing system, including lens antennae, display, exposure, processing, and other factors, to render a sharply defined image.It is the resolving power of the sensor to detect the smallest meaningful elemental area in different spectral bands in s defined gray level at a regular interval. 7.What are the elements of resolution? The four elements of resolutions are Spatial, Spectral, Radiometric and Temporal. 8. Write short notes about Spatial resolution. It is the minimum elemental area the sensor can detect or measure. The resolution element is called pixel (picture element). Example: IRS LISS 1-72.5m; LISS II-36.25m Land sat MSS-80m; Land sat TM-30m SPOT MSS HRV-120m; SPOT MSS HRV II-10m 9. Write short notes about Spectral resolution. It refers to the sensing and recording power of the sensor in different bands of EMR. The sensors can observe an object separately in different bands or colors. Examples: IRS-4 bands; Land sat MSS-4 bands; Land sat MSS TM-7 bands SPOT-4 bands It is the ability if the sensor to distinguish the finer variation of the reflected radiation from different objects. 10. Write short notes on Radiometric resolutions. It is the smallest amount of energy that can be detected by sensor and differentiate the same in a defined scale. It is recorded in digital number (DN) for different bands of the satellite. The radiometric value of the pixel is the average of the values coming from every part of the pixel. Example: IRS-128 gray level; Land sat MSS-64; Land sat TM-256; SPOT-256(it is to be noted that ‘0’is also a value in the gray scale). 11. Write short notes on Temporal resolution. It is the time interval between two successive surveys of a particular place of the earth by the sensor or satellite. Examples: IRS-22days; Land sat 16/18days; SPOT-16days. 12. Write the types of Microwave Sensors? Active microwave sensors are generally divided into two distinct categories: imaging and non-imaging. The most common form of imaging active microwave sensors is RADAR. 13.What is RADAR? RADAR is an acronym for RAdio Detection And Ranging, which essentially characterizes the function and operation of a radar sensor. The sensor transmits a microwave (radio) signal towards the target and detects the backscattered portion of the signal. 14. What are the types of DATA products? The data for all the sensors of IRS -1C/1D are supplied on digital media like a) Computer copatible tapes (CCTs) b) Cartridge tapes c) Floppies d) CD-ROM products PART B 1.What is resolution of a sensor? Describe all sensor resolutions. 2.Write short notes on the Indian remote sensing programme. 3.What is the role of a scanner in remote sensing and describe the different types of scanners used in remote sensing. 4.Discuss the thermal infrared in remote sensing? 5. Give details and examples about platforms and sensors. 6. What are the two type of sensors and discuss detail? UNIT III IMAGE INTERPRETATION AND ANALYSIS PART A 1.What is image interpretation? Image interpretation is defined as the extraction of qualitative and quantitative information in the form of a map, about the shape, location, structure, function, quality, condition, relationship of and between objects, etc. by using human knowledge or experience. 2. What are all the Types of image interpretation? Photo interpretation photographic interpretation and image interpretation are the terms used to interpret the Visual Image Interpretation. 3. What is Visual Image interpretation? Visual Image interpretation is the act of examining photographs/images for the purpose of identifying objects and judging their significance” 4. What is Photo interpretation? Photo interpretation is defined as the process of identifying objects or conditions in aerial photographs and determining their meaning or significance. 4.What is image reading? Image reading is an elemental form of image interpretation. It corresponds to simple identification of objects using such elements as shape, size, pattern, tone, texture, color, shadow and other associated relationships. Image reading is usually implemented with interpretation keys with respect to each object . 5.What is image measurement? Image measurement is the extraction of physical quantities, such as length, location, height, density, temperature and so on, by using reference data or calibration data deductively or inductively. 6. What is image analysis? Image analysis is the understanding of the relationship between interpreted information and the actual status or phenomenon, and to evaluate the situation. 7. What is thematic map? Extracted information will be finally represented in a map form called an interpretation map or a thematic map. 8. What are the Image interpretation elements ? The eigtht elements of image interpretation are shape ,size ,tone,shadows,texture ,site,pattern and association. 9. What is Digital Image Processing? Digital Image Processing is a collection of techniques for the manipulation of digital images by computers. The raw data received from the imaging sensors on the satellite platforms contains flaws and deficiencies. To overcome these flaws and deficiencies inorder to get the originality of the data, it needs to undergo several steps of processing. This will vary from image to image depending on the type of image format, initial condition of the image and the information of interest and the composition of the image scene. 10. What are the general steps of image processing? The three steps of image processing are , • Pre-processing • Display and enhancement • Information extraction 11.Write about pre processing? In the preprocessing ,prepare data for subsequent analysis that attempts to correct or compensate for systematic errors. 12. What is Image Enhancement? The operations are carried out to improve the interpretability of the image by increasing apparent contrast among various features in the scene. The enhancement techniques depend upon two factors mainly l The digital data (i.e. with spectral bands and resolution) 14. Write the objectives of interpretation? The objectives of interpretation as an image enhancement technique often drastically alters the original numeric data, it is normally used only for visual (manual) interpretation and not for further numeric analysis. Common enhancements include image reduction, image rectification, image magnification, transect extraction, contrast adjustments, band ratioing, spatial filtering, Fourier transformations, principal component analysis and texture transformation. 15.What is digital image? Digital Image is the matrix of “Digital Numbers”. A digital image is composed of thousands of pixels. Each pixel represents the brightness of small region on the earth surface.Digital Image processing involves the manipulation and interpretation of digital image with the aid of computer. 16.What is filtering? Filtering means the smoothening of an image using different Masks or Kernels.\ 17.What is spatial filtering? “ Spatial Filtering can be described as selectively emphasizing or suppressing information at different spatial scales over an image. “ Spatial operation consists in changing the values of each pixels according to the values of the pixels in the neighborhoods. 18.What is convolution? A convolution is an integral which expresses the amount of overlap of one function g as it is shifted over another function f. “ PART B 1.Write a detailed description on the elements of visual interpretation quoting suitalble examples for each. 2.Give a detailed description on the how the flaws and deficiency in remote sensing data can be removed. 3.Describe the different digital image processing techniques used. 4.Give a deatailed description on image classification and analysis of a remotely sensed data.What is the use of classifying image. UNIT IV GEOGRAPHIC INFORMATION SYSTEM PART A 1.What is map? A map is usually considered to be a drawing to scale of the whole or a part of the surface of the earth on a plane surface; it is a manually or mechanically drawn picture of the earth showing the location and distribution of various natural and cultural phenomena.A map is a symbolic representation of an area. 2.Write the two types of maps? The two maps are topographical and thematic maps. 3.Write about topographical map? It is a reference tool, showing the outlines of selected natural and man-made features of the Earth – often acts as a frame for other information "Topography" refers to the shape of the surface, represented by contours and/or shading, but topographic maps also show roads and other prominent features. 4.Write about thematic map? It is a tool to communicate geographical themes such as, the distribution of population & densities, climatic variables and land use etc. 5.What are the thematic maps in GIS? a) choropleth map b) area class map c) isopleth map 6.What are the characteristics of map? • maps are often stylized, generalized or abstracted, requiring careful interpretation • usually out of date • show only a static situation - one slice in time • often highly elegant/artistic • easy to use to answer certain types of questions: – how do I get there from here? – what is at this point? • difficult or time-consuming to answer other types: – what is the area of this lake? – what places can I see from this TV tower? – what does that thematic map show at the point I'm interested in on this topographic map? 7.Write the necessity of map projection? Projection is necessary one because spatial entities locate in two dimensions. The method by which the “world is laid flat” is use to help projection. Doing the process introduce error into spatial data. Spatial data character varies depending on the projection method chosen. Shape and distance are distorted the accuracy world is spherical shape visualize the two dimension in flat surface is difficult. 8.Write the types of map projection? 1.Cylindrical projection 2. Azimuthal projection 3. Conical projection 9.Write few lines about cylindrical projection? Countries near the equator in true relative portion Distance increases between countries located towards top and bottom of mage. The view of the poles is very distorted Area for the most part is preserved 10.Write few lines about conical projection? Area is distorted. Distance is very distorted towards the bottom of the image. Scale for the most part is preserved 11.Write few lines about azimuthal projection? Only a part of the earth surface is visible. The view will be of half the globe or less. Distortion will occur at all four edges. Distance for the more part is preserved. 12.What is referencing system? Referencing system is used to locate a feature on the earth’s surface or a two dimension representation of this surface such as a map. 13.What are the methods of spatial referencing systems? Several methods of spatial referencing exist all of which can be grouped into three categories. Geographical co-ordinate system Rectangular co-ordinate system Non-co-ordinate system 14. What is Geographic Co-Ordinate System? This is a one of true co-ordinate system .the location of any point on the earth surface can be defined by a reference using latitude and longitude. 15.What is QTM? The quaternary triangular mesh refrenshing system tries to deal with irregularities in the earth surface. 16.What is GIS? It’s a computer based information system primarily aims in collecting, classifying, crosschecking, manipulating, interpreting, retrieving and displaying data which are spatially referred to the earth in an appealing way. 17.What are the components of GIS? i) The Computer System (Hardware and Operating System) ii) The Software iii) Spatial Data iv) Data Management and analysis procedures v) The People to operate the GIS 18.What are the GIS softwares used? Standard GIS Softwares • ARCGIS • ARCVIEW • ARCINFO • MAPINFO • ERDAS • ENVI • AUTOCADMAP • IDRISI PART B 1.What is map projection and explain the differentiate types of map projections with their characteristics. 2.Explain in detail on the different types of data utilized in GIS technology. 3.Explain the different classification of maps. 4.Explain DBMS ,with emphasis on the differentiate types of DBMS used in GIS functioning. UNIT V DATA - ENTRY ,STORAGE AND ANALYSIS PART A 1.What is Data model? Data Models: Vector and Raster Spatial data in GIS has two primary data formats: raster and vector. Raster uses a grid cell structure, whereas vector is more like a drawn map. Raster and Vector Data Vector format has points, lines, polygons that appear normal, much like a map. Raster format generalizes the scene into a grid of cells, each with a code to indicate the feature being depicted. The cell is the minimum mapping unit. Raster has generalized reality: all of the features in the cell area are reduced to a single cell identity. 2.What is raster data? Raster is a method for the storage, processing and display of spatial data. Each area is divided into rows and columns, which form a regular grid structure. Each cell must be rectangular in shape, but not necessarily square. Each cell within this matrix contains location co-ordinates as well as an attribute value. The origin of rows and column is at the upper left corner of the grid. Rows function as the “y”coordinate and column as”x”coordinate in a two dimensional system. A cell is defined by its location in terms of rows and columns. 3.What is vector data? • Vector data uses two dimensional Cartesian coordinates to store the shape of spatial entity. Vector based features are treated as discrete geometric objects over the space. • In the vector data base point is the basic building block from which all the spatial entities are constructed. • The vector spatial entity ,the point is represented by a single x,y coordinate pair. Line and area entities are constructed by a series of points into chains and 4. What is Raster? The raster cell’s value or code represents all of the features within the grid, it does not maintain true size, shape, or location for individual features. Even where “nothing” exists (no data), the cells must be coded. 5.What is Vector? vectors are data elements describing position and direction. In GIS, vector is the maplike drawing of features, without the generalizing effect of a raster grid. Therefore, shape is better retained. Vector is much more spatially accurate than the raster format. 6.What is raster coding? In the data entry process, maps can be digitized or scanned at a selected cell size and each cell assigned a code or value. The cell size can be adjusted according to the grid structure or by ground units, also termed resolution. There are three basic and one advanced scheme for assigning cell codes. Presence/Absence: is the most basic method and to record a feature if some of it occurs in the cell space. 7. What is Cell Center? The cell center involves reading only the center of the cell and assigning the code accordingly. Not good for points or lines. 8.What is Dominant Area? To assign the cell code to the feature with the largest (dominant) share of the cell. This is suitable primarily for polygons. 9.What is Percent Coverage? A more advanced method. To separate each feature for coding into individual themes and then assign values that show its percent cover in each cell. 10.Different methods of data input? Key board entry O.C.R. Digitizing Manual digitizing Automatic digitizing Scanning Automatic line follower Electronic data transfer 11.What is digitizing? The most common method employed in encoding data from a paper map. Manual digitizing Automatic digitizing Scanning Automatic line follower 12.Write the errors in digitizing? Scale and resolution of the source/base map. Quality of the equipment and the software used. Incorrect registration. A shaky hand. Line thickness. Overshoot. Under shoot. Spike. Displacement. Polygonal knot. Psychological errors. 13.What is scanning? piece of hard ware for converting an analogue source of document into digital raster format (a light sensitive device). Most commonly used method. When raster data are there to be encoded scanning is the most appropriate option. There are three different types of scanners available in usage : Flat-bed scanners (a PC peripheral). Rotating drum scanners. Large format feed scanners 14.Write the important components of scanner? A light source. A back ground. A lens. 15.Write the practical problems in scanning? Possibility of optical distortion associated with the usage of flat bed scanners. Automatic scanning of unwanted information. Selection of appropriate scanning tolerance to ensure important data are encoded, and background data ignored. The format of files produced and the input of data into G.I.S. software. The amount of editing required to produce data suitable for analysis. PART B 1.What is data model ?Enumerate different types of GIS data. 2.Write short notes on: (i) Overlaying (ii) Buffering and GIS 3.What are the possible techniques best adopted for better storage of raster data that would avoid repetition of characters. 4.Explain on the different methods of data input in GIS. ● QUESTIONS 1. Describe detaily about Electromagnetic Spectrums? 2. Write detaily the Energy Intraction with atmosphere. 3. Give details and examples about platforms and sensors 4. What are the two type of sensors and discuss detaily. 5. Discuss detaily about image interpetation keys and Techniques. UNIT-II DATA PROCESSING VISUAL INTERPRETATION AND DIGITAL IMAGE PROCESSING Elements of Visual Interpretation As we noted in the previous section, analysis of remote sensing imagery involves the identification of various targets in an image, and those targets may be environmental or artificial features which consist of points, lines, or areas. Targets may be defined in terms of the way they reflect or emit radiation. This radiation is measured and recorded by a sensor, and ultimately is depicted as an image product such as an air photo or a satellite image. What makes interpretation of imagery more difficult than the everyday visual interpretation of our surroundings? For one, we lose our sense of depth when viewing a two-dimensional image, unless we can view it stereoscopically so as to simulate the third dimension of height. Indeed, interpretation benefits greatly in many applications when images are viewed in stereo, as visualization (and therefore, recognition) of targets is enhanced dramatically. Viewing objects from directly above also provides a very different perspective than what we are familiar with. Combining an unfamiliar perspective with a very different scale and lack of recognizable detail can make even the most familiar object unrecognizable in an image. Finally, we are used to seeing only the visible wavelengths, and the imaging of wavelengths outside of this window is more difficult for us to comprehend. Recognizing targets is the key to interpretation and information extraction. Observing the differences between targets and their backgrounds involves comparing different targets based on any, or all, of the visual elements of tone, shape, size, pattern, texture, shadow, and association. Visual interpretation using these elements is often a part of our daily lives, whether we are conscious of it or not. Examining satellite images on the weather report, or following high speed chases by views from a helicopter are all familiar examples of visual image interpretation. Identifying targets in remotely sensed images based on these visual elements allows us to further interpret and analyze. The nature of each of these interpretation elements is described below, along with an image example of each. Tone refers to the relative brightness or colour of objects in an image. Generally, tone is the fundamental element for distinguishing between different targets or features. Variations in tone also allows the elements of shape, texture, and pattern of objects to be distinguished. Shape refers to the general form, structure, or outline of individual objects. Shape can be a very distinctive clue for interpretation. Straight edge shapes typically represent urban or agricultural (field) targets, while natural features, such as forest edges, are generally more irregular in shape, except where man has created a road or clear cuts. Farm or crop land irrigated by rotating sprinkler systems would appear as circular shapes. Size of objects in an image is a function of scale. It is important to assess the size of a target relative to other objects in a scene, as well as the absolute size, to aid in the interpretation of that target. A quick approximation of target size can direct interpretation to an appropriate result more quickly. For example, if an interpreter had to distinguish zones of land use, and had identified an area with a number of buildings in it, large buildings such as factories or warehouses would suggest commercial property, whereas small buildings would indicate residential use. Pattern refers to the spatial arrangement of visibly discernible objects. Typically an orderly repetition of similar tones and textures will produce a distinctive and ultimately recognizable pattern. Orchards with evenly spaced trees, and urban streets with regularly spaced houses are good examples of pattern. Texture refers to the arrangement and frequency of tonal variation in particular areas of an image. Rough textures would consist of a mottled tone where the grey levels change abruptly in a small area, whereas smooth textures would have very little tonal variation. Smooth textures are most often the result of uniform, even surfaces, such as fields, asphalt, or grasslands. A target with a rough surface and irregular structure, such as a forest canopy, results in a rough textured appearance. Texture is one of the most important elements for distinguishing features in radar imagery. Shadow is also helpful in interpretation as it may provide an idea of the profile and relative height of a target or targets which may make identification easier. However, shadows can also reduce or eliminate interpretation in their area of influence, since targets within shadows are much less (or not at all) discernible from their surroundings. Shadow is also useful for enhancing or identifying topography and landforms, particularly in radar imagery. Association takes into account the relationship between other recognizable objects or features in proximity to the target of interest. The identification of features that one would expect to associate with other features may provide information to facilitate identification. In the example given above, commercial properties may be associated with proximity to major transportation routes, whereas residential areas would be associated with schools, playgrounds, and sports fields. In our example, a lake is associated with boats, a marina, and adjacent recreational land. Digital Image Processing In today's world of advanced technology where most remote sensing data are recorded in digital format, virtually all image interpretation and analysis involves some element of digital processing. Digital image processing may involve numerous procedures including formatting and correcting of the data, digital enhancement to facilitate better visual interpretation, or even automated classification of targets and features entirely by computer. In order to process remote sensing imagery digitally, the data must be recorded and available in a digital form suitable for storage on a computer tape or disk. Obviously, the other requirement for digital image processing is a computer system, sometimes referred to as an image analysis system, with the appropriate hardware and software to process the data. Several commercially available software systems have been developed specifically for remote sensing image processing and analysis. For discussion purposes, most of the common image processing functions available in image analysis systems can be categorized into the following four categories: 1. 2. 3. 4. Preprocessing Image Enhancement Image Transformation Image Classification and Analysis Preprocessing functions involve those operations that are normally required prior to the main data analysis and extraction of information, and are generally grouped as radiometric or geometric corrections. Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor. Geometric corrections include correcting for geometric distortions due to sensor-Earth geometry variations, and conversion of the data to real world coordinates (e.g. latitude and longitude) on the Earth's surface. The objective of the second group of image processing functions grouped under the term of image enhancement, is solely to improve the appearance of the imagery to assist in visual interpretation and analysis. Examples of enhancement functions include contrast stretching to increase the tonal distinction between various features in a scene, and spatial filtering to enhance (or suppress) specific spatial patterns in an image. Image transformations are operations similar in concept to those for image enhancement. However, unlike image enhancement operations which are normally applied only to a single channel of data at a time, image transformations usually involve combined processing of data from multiple spectral bands. Arithmetic operations (i.e. subtraction, addition, multiplication, division) are performed to combine and transform the original bands into "new" images which better display or highlight certain features in the scene. We will look at some of these operations including various methods of spectral or band ratioing, and a procedure called principal components analysis which is used to more efficiently represent the information in multichannel imagery. Image classification and analysis operations are used to digitally identify and classify pixels in the data. Classification is usually performed on multichannel data sets (A) and this process assigns each pixel in an image to a particular class or theme (B) based on statistical characteristics of the pixel brightness values. There are a variety of approaches taken to perform digital classification. We will briefly describe the two generic approaches which are used most often, namely supervised and unsupervised classification. In the following sections we will describe each of these four categories of digital image processing functions in more detail. Pre-processing Pre-processing operations, sometimes referred to as image restoration and rectification, are intended to correct for sensor- and platform-specific radiometric and geometric distortions of data. Radiometric corrections may be necessary due to variations in scene illumination and viewing geometry, atmospheric conditions, and sensor noise and response. Each of these will vary depending on the specific sensor and platform used to acquire the data and the conditions during data acquisition. Also, it may be desirable to convert and/or calibrate the data to known (absolute) radiation or reflectance units to facilitate comparison between data. Variations in illumination and viewing geometry between images (for optical sensors) can be corrected by modeling the geometric relationship and distance between the area of the Earth's surface imaged, the sun, and the sensor. This is often required so as to be able to more readily compare images collected by different sensors at different dates or times, or to mosaic multiple images from a single sensor while maintaining uniform illumination conditions from scene to scene. As we learned in Chapter 1, scattering of radiation occurs as it passes through and interacts with the atmosphere. This scattering may reduce, or attenuate, some of the energy illuminating the surface. In addition, the atmosphere will further attenuate the signal propagating from the target to the sensor. Various methods of atmospheric correction can be applied ranging from detailed modeling of the atmospheric conditions during data acquisition, to simple calculations based solely on the image data. An example of the latter method is to examine the observed brightness values (digital numbers), in an area of shadow or for a very dark object (such as a large clear lake - A) and determine the minimum value (B). The correction is applied by subtracting the minimum observed value, determined for each specific band, from all pixel values in each respective band. Since scattering is wavelength dependent the minimum values will vary from band to band. This method is based on the assumption that the reflectance from these features, if the atmosphere is clear, should be very small, if not zero. If we observe values much greater than zero, then they are considered to have resulted from atmospheric scattering. Noise in an image may be due to irregularities or errors that occur in the sensor response and/or data recording and transmission. Common forms of noise include systematic striping or banding and dropped lines. Both of these effects should be corrected before further enhancement or classification is performed. Striping was common in early Landsat MSS data due to variations and drift in the response over time of the six MSS detectors. The 'drift' was different for each of the six detectors, causing the same brightness to be represented differently by each detector. The overall appearance was thus a 'striped' effect. The corrective process made a relative correction among the six sensors to bring their apparent values in line with each other. Dropped lines occur when there are systems errors which result in missing or defective data along a scan line. Dropped lines are normally 'corrected' by replacing the line with the pixel values in the line above or below, or with the average of the two. For many quantitative applications of remote sensing data, it is necessary to convert the digital numbers to measurements in units which represent the actual reflectance or emittance from the surface. This is done based on detailed knowledge of the sensor response and the way in which the analog signal (i.e. the reflected or emitted radiation) is converted to a digital number, called analogto-digital (A-to-D) conversion. By solving this relationship in the reverse direction, the absolute radiance can be calculated for each pixel, so that comparisons can be accurately made over time and between different sensors. we learned that all remote sensing imagery are inherently subject to geometric distortions. These distortions may be due to several factors, including: the perspective of the sensor optics; the motion of the scanning system; the motion of the platform; the platform altitude, attitude, and velocity; the terrain relief; and, the curvature and rotation of the Earth. Geometric corrections are intended to compensate for these distortions so that the geometric representation of the imagery will be as close as possible to the real world. Many of these variations are systematic, or predictable in nature and can be accounted for by accurate modeling of the sensor and platform motion and the geometric relationship of the platform with the Earth. Other unsystematic, or random, errors cannot be modeled and corrected in this way. Therefore, geometric registration of the imagery to a known ground coordinate system must be performed. The geometric registration process involves identifying the image coordinates (i.e. row, column) of several clearly discernible points, called ground control points (or GCPs), in the distorted image (A - A1 to A4), and matching them to their true positions in ground coordinates (e.g. latitude, longitude). The true ground coordinates are typically measured from a map (B - B1 to B4), either in paper or digital format. This is image-to-map registration. Once several welldistributed GCP pairs have been identified, the coordinate information is processed by the computer to determine the proper transformation equations to apply to the original (row and column) image coordinates to map them into their new ground coordinates. Geometric registration may also be performed by registering one (or more) images to another image, instead of to geographic coordinates. This is called image-to-image registration and is often done prior to performing various image transformation procedures, which will be discussed in section 4.6, or for multitemporal image comparison. In order to actually geometrically correct the original distorted image, a procedure called resampling is used to determine the digital values to place in the new pixel locations of the corrected output image. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. Nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the simplest method and does not alter the original values, but may result in some pixel values being duplicated while others are lost. This method also tends to result in a disjointed or blocky image appearance. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. This may be undesirable if further processing and analysis, such as classification based on spectral response, is to be done. If this is the case, resampling may best be done after the classification process. Cubic convolution resampling goes even further to calculate a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, these two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. Image Enhancement Enhancements are used to make it easier for visual interpretation and understanding of imagery. The advantage of digital imagery is that it allows us to manipulate the digital pixel values in an image. Although radiometric corrections for illumination, atmospheric influences, and sensor characteristics may be done prior to distribution of data to the user, the image may still not be optimized for visual interpretation. Remote sensing devices, particularly those operated from satellite platforms, must be designed to cope with levels of target/background energy which are typical of all conditions likely to be encountered in routine use. With large variations in spectral response from a diverse range of targets (e.g. forest, deserts, snowfields, water, etc.) no generic radiometric correction could optimally account for and display the optimum brightness range and contrast for all targets. Thus, for each application and each image, a custom adjustment of the range and distribution of brightness values is usually necessary. In raw imagery, the useful data often populates only a small portion of the available range of digital values (commonly 8 bits or 256 levels). Contrast enhancement involves changing the original values so that more of the available range is used, thereby increasing the contrast between targets and their backgrounds. The key to understanding contrast enhancements is to understand the concept of an image histogram. A histogram is a graphical representation of the brightness values that comprise an image. The brightness values (i.e. 0-255) are displayed along the x-axis of the graph. The frequency of occurrence of each of these values in the image is shown on the y-axis. By manipulating the range of digital values in an image, graphically represented by its histogram, we can apply various enhancements to the data. There are many different techniques and methods of enhancing contrast and detail in an image; we will cover only a few common ones here. The simplest type of enhancement is a linear contrast stretch. This involves identifying lower and upper bounds from the histogram (usually the minimum and maximum brightness values in the image) and applying a transformation to stretch this range to fill the full range. In our example, the minimum value (occupied by actual data) in the histogram is 84 and the maximum value is 153. These 70 levels occupy less than one-third of the full 256 levels available. A linear stretch uniformly expands this small range to cover the full range of values from 0 to 255. This enhances the contrast in the image with light toned areas appearing lighter and dark areas appearing darker, making visual interpretation much easier. This graphic illustrates the increase in contrast in an image before (top) and after (bottom) a linear contrast stretch. A uniform distribution of the input range of values across the full range may not always be an appropriate enhancement, particularly if the input range is not uniformly distributed. In this case, a histogram-equalized stretch may be better. This stretch assigns more display values (range) to the frequently occurring portions of the histogram. In this way, the detail in these areas will be better enhanced relative to those areas of the original histogram where values occur less frequently. In other cases, it may be desirable to enhance the contrast in only a specific portion of the histogram. For example, suppose we have an image of the mouth of a river, and the water portions of the image occupy the digital values from 40 to 76 out of the entire image histogram. If we wished to enhance the detail in the water, perhaps to see variations in sediment load, we could stretch only that small portion of the histogram represented by the water (40 to 76) to the full grey level range (0 to 255). All pixels below or above these values would be assigned to 0 and 255, respectively, and the detail in these areas would be lost. However, the detail in the water would be greatly enhanced. Spatial filtering encompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is related to the concept of image texture, which we discussed in section 4.2. It refers to the frequency of the variations in tone that appear in an image. "Rough" textured areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while "smooth" areas with little variation in tone over several pixels, have low spatial frequencies. A common filtering procedure involves moving a 'window' of a few pixels in dimension (e.g. 3x3, 5x5, etc.) over each pixel in the image, applying a mathematical calculation using the pixel values under that window, and replacing the central pixel with the new value. The window is moved along in both the row and column dimensions one pixel at a time and the calculation is repeated until the entire image has been filtered and a "new" image has been generated. By varying the calculation performed and the weightings of the individual pixels in the filter window, filters can be designed to enhance or suppress different types of features. A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. Average and median filters, often used for radar imagery are examples of low-pass filters. High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image. One implementation of a high-pass filter first applies a low-pass filter to an image and then subtracts the result from the original, leaving behind only the high spatial frequency information. Directional, or edge detection filters are designed to highlight linear features, such as roads or field boundaries. These filters can also be designed to enhance features which are oriented in specific directions. These filters are useful in applications such as geology, for the detection of linear geologic structures. Image Transformations Image transformations typically involve the manipulation of multiple bands of data, whether from a single multispectral image or from two or more images of the same area acquired at different times (i.e. multitemporal image data). Either way, image transformations generate "new" images from two or more sources which highlight particular features or properties of interest, better than the original input images. Basic image transformations apply simple arithmetic operations to the image data. Image subtraction is often used to identify changes that have occurred between images collected on different dates. Typically, two images which have been geometrically registered are used with the pixel (brightness) values in one image (1) being subtracted from the pixel values in the other (2). Scaling the resultant image (3) by adding a constant (127 in this case) to the output values will result in a suitable 'difference' image. In such an image, areas where there has been little or no change (A) between the original images, will have resultant brightness values around 127 (mid-grey tones), while those areas where significant change has occurred (B) will have values higher or lower than 127 - brighter or darker depending on the 'direction' of change in reflectance between the two images . This type of image transform can be useful for mapping changes in urban development around cities and for identifying areas where deforestation is occurring, as in this example. Image division or spectral ratioing is one of the most common transforms applied to image data. Image ratioing serves to highlight subtle variations in the spectral responses of various surface covers. By ratioing the data from two different spectral bands, the resultant image enhances variations in the slopes of the spectral reflectance curves between the two different spectral ranges that may otherwise be masked by the pixel brightness variations in each of the bands. The following example illustrates the concept of spectral ratioing. Healthy vegetation reflects strongly in the near-infrared portion of the spectrum while absorbing strongly in the visible red. Other surface types, such as soil and water, show near equal reflectances in both the near-infrared and red portions. Thus, a ratio image of Landsat MSS Band 7 (Near-Infrared - 0.8 to 1.1 m) divided by Band 5 (Red 0.6 to 0.7 m) would result in ratios much greater than 1.0 for vegetation, and ratios around 1.0 for soil and water. Thus the discrimination of vegetation from other surface cover types is significantly enhanced. Also, we may be better able to identify areas of unhealthy or stressed vegetation, which show low nearinfrared reflectance, as the ratios would be lower than for healthy green vegetation. Another benefit of spectral ratioing is that, because we are looking at relative values (i.e. ratios) instead of absolute brightness values, variations in scene illumination as a result of topographic effects are reduced. Thus, although the absolute reflectances for forest covered slopes may vary depending on their orientation relative to the sun's illumination, the ratio of their reflectances between the two bands should always be very similar. More complex ratios involving the sums of and differences between spectral bands for various sensors, have been developed for monitoring vegetation conditions. One widely used image transform is the Normalized Difference Vegetation Index (NDVI) which has been used to monitor vegetation conditions on continental and global scales using the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the NOAA series of satellites. Different bands of multispectral data are often highly correlated and thus contain similar information. For example, Landsat MSS Bands 4 and 5 (green and red, respectively) typically have similar visual appearances since reflectances for the same surface cover types are almost equal. Image transformation techniques based on complex processing of the statistical characteristics of multi-band data sets can be used to reduce this data redundancy and correlation between bands. One such transform is called principal components analysis. The objective of this transformation is to reduce the dimensionality (i.e. the number of bands) in the data, and compress as much of the information in the original bands into fewer bands. The "new" bands that result from this statistical procedure are called components. This process attempts to maximize (statistically) the amount of information (or variance) from the original data into the least number of new components. As an example of the use of principal components analysis, a seven band Thematic Mapper (TM) data set may be transformed such that the first three principal components contain over 90 percent of the information in the original seven bands. Interpretation and analysis of these three bands of data, combining them either visually or digitally, is simpler and more efficient than trying to use all of the original seven bands. Principal components analysis, and other complex transforms, can be used either as an enhancement technique to improve visual interpretation or to reduce the number of bands to be used as input to digital classification procedures, discussed in the next section. Image Classification and Analysis A human analyst attempting to classify features in an image uses the elements of visual interpretation to identify homogeneous groups of pixels which represent various features or land cover classes of interest. Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. This type of classification is termed spectral pattern recognition. In either case, the objective is to assign all pixels in the image to particular classes or themes (e.g. water, coniferous forest, deciduous forest, corn, wheat, etc.). The resulting classified image is comprised of a mosaic of pixels, each of which belong to a particular theme, and is essentially a thematic "map" of the original image. When talking about classes, we need to distinguish between information classes and spectral classes. Information classes are those categories of interest that the analyst is actually trying to identify in the imagery, such as different kinds of crops, different forest types or tree species, different geologic units or rock types, etc. Spectral classes are groups of pixels that are uniform (or nearsimilar) with respect to their brightness values in the different spectral channels of the data. The objective is to match the spectral classes in the data to the information classes of interest. Rarely is there a simple one-to-one match between these two types of classes. Rather, unique spectral classes may appear which do not necessarily correspond to any information class of particular use or interest to the analyst. Alternatively, a broad information class (e.g. forest) may contain a number of spectral sub-classes with unique spectral variations. Using the forest example, spectral sub-classes may be due to variations in age, species, and density, or perhaps as a result of shadowing or variations in scene illumination. It is the analyst's job to decide on the utility of the different spectral classes and their correspondence to useful information classes. Common classification procedures can be broken down into two broad subdivisions based on the method used: supervised classification and unsupervised classification. In a supervised classification, the analyst identifies in the imagery homogeneous representative samples of the different surface cover types (information classes) of interest. These samples are referred to as training areas. The selection of appropriate training areas is based on the analyst's familiarity with the geographical area and their knowledge of the actual surface cover types present in the image. Thus, the analyst is "supervising" the categorization of a set of specific classes. The numerical information in all spectral bands for the pixels comprising these areas are used to "train" the computer to recognize spectrally similar areas for each class. The computer uses a special program or algorithm (of which there are several variations), to determine the numerical "signatures" for each training class. Once the computer has determined the signatures for each class, each pixel in the image is compared to these signatures and labeled as the class it most closely "resembles" digitally. Thus, in a supervised classification we are first identifying the information classes which are then used to determine the spectral classes which represent them. Unsupervised classification in essence reverses the supervised classification process. Spectral classes are grouped first, based solely on the numerical information in the data, and are then matched by the analyst to information classes (if possible). Programs, called clustering algorithms, are used to determine the natural (statistical) groupings or structures in the data. Usually, the analyst specifies how many groups or clusters are to be looked for in the data. In addition to specifying the desired number of classes, the analyst may also specify parameters related to the separation distance among the clusters and the variation within each cluster. The final result of this iterative clustering process may result in some clusters that the analyst will want to subsequently combine, or clusters that should be broken down further - each of these requiring a further application of the clustering algorithm. Thus, unsupervised classification is not completely without human intervention. However, it does not start with a pre-determined set of classes as in a supervised classification. Data Integration and Analysis In the early days of analog remote sensing when the only remote sensing data source was aerial photography, the capability for integration of data from different sources was limited. Today, with most data available in digital format from a wide array of sensors, data integration is a common method used for interpretation and analysis. Data integration fundamentally involves the combining or merging of data from multiple sources in an effort to extract better and/or more information. This may include data that are multitemporal, multiresolution, multisensor, or multi-data type in nature. Multitemporal data integration has already been alluded to in section 4.6 when we discussed image subtraction. Imagery collected at different times is integrated to identify areas of change. Multitemporal change detection can be achieved through simple methods such as these, or by other more complex approaches such as multiple classification comparisons or classifications using integrated multitemporal data sets. Multiresolution data merging is useful for a variety of applications. The merging of data of a higher spatial resolution with data of lower resolution can significantly sharpen the spatial detail in an image and enhance the discrimination of features. SPOT data are well suited to this approach as the 10 metre panchromatic data can be easily merged with the 20 metre multispectral data. Additionally, the multispectral data serve to retain good spectral resolution while the panchromatic data provide the improved spatial resolution. Data from different sensors may also be merged, bringing in the concept of multisensor data fusion. An excellent example of this technique is the combination of multispectral optical data with radar imagery. These two diverse spectral representations of the surface can provide complementary information. The optical data provide detailed spectral information useful for discriminating between surface cover types, while the radar imagery highlights the structural detail in the image. Applications of multisensor data integration generally require that the data be geometrically registered, either to each other or to a common geographic coordinate system or map base. This also allows other ancillary (supplementary) data sources to be integrated with the remote sensing data. For example, elevation data in digital form, called Digital Elevation or Digital Terrain Models (DEMs/DTMs), may be combined with remote sensing data for a variety of purposes. DEMs/DTMs may be useful in image classification, as effects due to terrain and slope variability can be corrected, potentially increasing the accuracy of the resultant classification. DEMs/DTMs are also useful for generating threedimensional perspective views by draping remote sensing imagery over the elevation data, enhancing visualization of the area imaged. Combining data of different types and from different sources, such as we have described above, is the pinnacle of data integration and analysis. In a digital environment where all the data sources are geometrically registered to a common geographic base, the potential for information extraction is extremely wide. This is the concept for analysis within a digital Geographical Information System (GIS) database. Any data source which can be referenced spatially can be used in this type of environment. A DEM/DTM is just one example of this kind of data. Other examples could include digital maps of soil type, land cover classes, forest species, road networks, and many others, depending on the application. The results from a classification of a remote sensing data set in map format, could also be used in a GIS as another data source to update existing map data. In essence, by analyzing diverse data sets together, it is possible to extract better and more accurate information in a synergistic manner than by using a single data source alone. There are a myriad of potential applications and analyses possible for many applications. In the next and final chapter, we will look at examples of various applications of remote sensing data, many involving the integration of data from different sources. QUESTIONS 1) what are important elements for visual interpretation? 2) describe image classification and analysis? 3) Define detail about image enhancement techniques? 4) Describe about image transformation? 5) What are the important keys for image interpretations? UNIT-III GIS TECHNIQUES AND DATA INPUT Map A map is a graphic representation of a portion of the earth's surface drawn to scale. It uses colors, symbols, and labels to represent features found on the ground. The ideal representation would be realized if every feature of the area being mapped could be shown in true shape. Obviously this is impossible, and an attempt to plot each feature true to scale would result in a product impossible to read even with the aid of a magnifying glass. a. Therefore, to be understandable, features must be represented by conventional signs and symbols. To be legible, many of these must be exaggerated in size, often far beyond the actual ground limits of the feature represented. On a 1:250,000 scale map, the prescribed symbol for a building covers an area about 500 feet square on the ground; a road symbol is equivalent to a road about 520 feet wide on the ground; the symbol for a single-track railroad (the length of a cross-tie) is equivalent to a railroad cross-tie about 1,000 feet on the ground. b. The portrayal of many features requires similar exaggeration. Therefore, the selection of features to be shown, as well as their portrayal, is in accord with the guidance established by the Defense Mapping Agency. A map is a visual representation of an area a symbolic depiction highlighting relationships between elements of that space such as objects, regions, and themes. Many maps are static two-dimensional, geometrically accurate representations of three-dimensional space, while others are dynamic or interactive, even threedimensional. Although most commonly used to depict geography, maps may represent any space, real or imagined, without regard to context or scale; e.g. Brain mapping, DNA mapping, and extraterrestrial mapping. PURPOSE A map provides information on the existence, the location of, and the distance between ground features, such as populated places and routes of travel and communication. It also indicates variations in terrain, heights of natural features, and the extent of vegetation cover. With our military forces dispersed throughout the world, it is necessary to rely on maps to provide information to our combat elements and to resolve logistical operations far from our shores. Soldiers and materials must be transported, stored, and placed into operation at the proper time and place. Much of this planning must be done by using maps. Therefore, any operation requires a supply of maps; however, the finest maps available are worthless unless the map user knows how to read them TYPES OF MAPS Thematic map A map that displays the spatial distribution of an attribute that relates to a single topic, theme, or subject of discourse. Usually, a thematic map displays a single attribute (a "univariate map") such as soil type, vegetation, geology, land use, or landownership. For attributes such as soil type or land use ("nominal" variables), shaded maps that highlight regions ("polygons") by employing different colors or patterns is generally wanted. For other attributes (like population density - a "metric" variable), a shaded map in which each shade corresponds to a range of population densities is generally wanted. Thematic maps are used to display geographical concepts such as density, distribution, relative magnitudes, gradients, spatial relationships and movements. Also called geographic, special purpose, distribution, parametric, or planimetric maps. A thematic map displays spatial pattern of a theme or series of attributes. In contrast to reference maps which show many geographic features (forests, roads, political boundaries), thematic maps emphasize spatial variation of one or a small number of geographic distributions. These distributions may be physical phenomena such as climate or human characteristics such as population density and health issues. These types of maps are sometimes referred to as graphic essays that portray spatial variations and interrelationships of geographical distributions. Location, of course, is also important to provide a reference base of where selected phenomena are occurring. Barbara B. Petchenik described the difference as "in place, about space." While general reference maps show where something is in space, thematic maps tell a story about that place. 1.TOPOGRAPHIC MAPS A topographic map is a type of map characterized by large-scale detail and quantitative representation of relief, usually using contour lines in modern mapping, but historically using a variety of methods. Traditional definitions require a topographic map to show both natural and man-made features. The Centre for Topographic Information provides this definition of a topographic map: "A topographic map is a detailed and accurate graphic representation of cultural and natural features on the ground." However, in the vernacular and day to day world, the representation of relief (contours) is popularly held to define the genre, such that even small-scale maps showing relief are commonly (and erroneously, in the technical sense) called "topographic." According to Cartographer's Kraak and Ormeling, "Traditionally, the main division of maps is into topographic and thematic maps. Topographic maps supply a general image of the earth's surface: roads, rivers, buildings, often the nature of the vegetation, the relief and the names of the various mapped objects.The study or discipline of topography, while interested in relief, is actually a much broader field of study which takes into account all natural and man made features of terrain. Topographic maps show a 3 dimensional world in 2 dimensions by using contour lines. Many people have trouble reading these maps, because they have mountains and valleys are represented with concentric circles and lines. Many hikers use topographic maps, especially in areas where there are no roads with signs. Geologists depend on topographic maps to record the types of rocks. Engineers use topographic maps when they are planning roads, buildings, or other human–made structures. Imagine designing a city without considering where hills and valleys are located! 2.GEOLOGICAL MAPS A geologic map or geological map is a special-purpose map made to show geological features. The stratigraphic contour lines are drawn on the surface of a selected deep stratum, so that they can show the topographic trends of the strata under the ground. It is not always possible to properly show this when the strata are extremely fractured, mixed, in some discontinuities, or where they are otherwise disturbed. Strike and dip symbols consist of (at minimum) a long line, a number, and a short line which are used to indicate tilted beds. The long line is the strike line, which shows the true horizontal direction along the bed, the number is the dip or number of degrees of tilt above horizontal, and the short line is the dip line, which shows the direction of tilt. A geologic map is a map of the different types of rocks that are on the surface of the Earth. By mapping different rock types, geologists can determine the relationships between different rock formations which can then be used to find mineral resources, oil, and gravel deposits. Also, you want to know what type of rock you are building on or else you might have a Leaning Tower of Pisa or a pile of rubble after a strong earthquake. 3. Geographic maps Cartography, or map-making is the study and, often, practice, of crafting representations of the Earth upon a flat surface and one who makes maps is called a cartographer. Road maps are perhaps the most widely used maps today, and form a subset of navigational maps, which also include aeronautical and nautical charts, railroad network maps, and hiking and bicycling maps. In terms of quantity, the largest number of drawn map sheets is probably made up by local surveys, carried out by municipalities, utilities, tax assessors, emergency services providers, and other local agencies. Many national surveying projects have been carried out by the military, such as the British Ordnance Survey (now a civilian government agency internationally renowned for its comprehensively detailed work). A map can also be any document giving information as to where or what something is. 4. BIO-GEOGRAPHICAL MAPS Scientists involved in the study of animals, plants, and other living organisms use maps to illustrate where these groups live or migrate. It is important to many zoologists to know where the organisms that they study live and where they move to. People who monitor endangered species need to know if the ranges of migration have become larger or smaller through time. 5. ENVIRONMENTAL MAPS These types of maps include maps that look at human's activity in urban and metropolitan areas and the environment in which we all live. Maps that illustrate physiographic features such as forests, grassland, woodland, tundra, grazing land, ocean floors, and ocean sediments could be included in this large grouping. Meteorological maps that show climate, weather and wind are types of environmental maps. Meteorologists, oceanographers, geographers, city planners, and many other professionals depend greatly on these maps to record and forecast their specific field. 6.Orthophoto maps These maps show land features using color-enhanced photographic images which have been processed to show detail in true position. They may or may not include contours. Because imagery naturally depicts an area in a more true-to-life manner than the conventional line map, the orthophoto map provides an excellent portrayal of extensive areas of sand, marsh, or flat agricultural areas. 7.Physical maps Physical maps show the earth's landforms and bodies of water. The maps use lines, shading, tints, spot elevations, and different colors to show elevation and distinguish the mountains from the lowlands. This kind of map often has some road, city and cultural information but mostly functions as a view of the land surface. Often these maps make very attractive framed pieces for the den or office. 8.Political maps Political maps show boundaries that divide one political entity from another, such as townships, counties, cities, and states. Some maps emphasize the boundaries by printing the areas of each political division in different colors, for example world maps usually show each country in a different color. A political map can be made of any area from the local county, municipal levels all the way up to the world level. In general, most maps are political with far fewer being produced as physical maps. 9.Relief maps: Shaded Relief and Raised Relief Relief maps are maps that show relief data using contour lines, colors, and/or shading to evidence the elevation. Shaded relief maps show topographic features by using shading to simulate the appearance of sunlight and shadows. Steep mountains will have dark shadows, while flat lands will have no shadows. Raised-relief maps are three-dimensional plastic or vinyl maps portraying the physical features of a region. Raised relief maps can have as much as 2-3 inches of vertical relief, while this type of map is neat to look at they are all but impossible to ship so we cannot offer them on this site. In fact we rarely carry them in our store as we had upwards of 50% of them arrive in the "flattened relief" condition. 10.Road maps Michelin in France and Gulf Oil in America produced the first road maps to encourage people to travel more, thus consuming more tires and oil. Such maps were usually free until the oil crisis of 1973, when service stations began to charge for their maps. A road map is published primarily to assist travelers in moving from one place to another. Some road maps show only interstate highways, while others show a detailed network of roads, including the back roads. Generally, only large-scale maps - such as a topographic map, a Gem Trek map, Trails Illustrated map, or a DeLorme Atlas and Gazetteer - will show unimproved roads. Some road maps specify distances between various points on the map. Others show various cultural geography features such as colleges and universities, airports, museums, historical sights, and information to make a journey more interesting. You will discover several publishers that have produced entire series of road maps for given regions. Examples include the Michelin series for France or the Mairs series for Germany. Road atlases are frequently a good choice for a traveler who is going to be covering a large region. There are two main types of road atlases: state or national atlases, and city street atlases. Geographic information system A geographic information system (GIS), also known as a geographical information system or geospatial information system, is any system for capturing, storing, analyzing, managing and presenting data and associated attributes which are spatially referenced to Earth. In certain countries such as Canada, GIS is more well known as Geomatics. The other definition is, "GIS is a system or tool or computer based methodology to collect, store, manipulate, retrieve and analyse spatially (georeferenced) data." In the strictest sense, it is any information system capable of integrating, storing, editing, analyzing, sharing, and displaying geographically referenced information. In a more generic sense, GIS is a tool that allows users to create interactive queries (user created searches), analyze the spatial information, edit data, maps, and present the results of all these operations. Geographic information science is the science underlying the geographic concepts, applications and systems, taught in degree and GIS Certificate programs at many universities.Geographic information system technology can be used for scientific investigations, resource management, asset management, environmental impact assessment, urban planning, cartography, criminology, history, sales, marketing, and logistics. For example, GIS might allow emergency planners to easily calculate emergency response times in the event of a natural disaster, GIS might be used to find wetlands that need protection from pollution, or GIS can be used by a company to site a new business location to take advantage of a previously underserved market. History of development About 15,500 years ago, on the walls of caves near Lascaux, France, CroMagnon hunters drew pictures of the animals they hunted. Associated with the animal drawings are track lines and tallies thought to depict migration routes. While simplistic in comparison to modern technologies, these early records mimic the two-element structure of modern geographic information systems, an image associated with attribute information. In 1854, John Snow depicted a cholera outbreak in London using points to represent the locations of some individual cases, possibly the earliest use of the geographic method.His study of the distribution of cholera led to the source of the disease, a contaminated water pump within the heart of the cholera outbreak. E. W. Gilbert's version (1958) of John Snow's 1855 map of the Soho cholera outbreak showing the clusters of cholera cases in the London epidemic of 1854 While the basic elements of topology and theme existed previously in cartography, the John Snow map was unique, using cartographic methods not only to depict but also to analyze clusters of geographically dependent phenomena for the first time. The early 20th century saw the development of "photo lithography" where maps were separated into layers. Computer hardware development spurred by nuclear weapon research would lead to general purpose computer "mapping" applications by the early 1960s. The year 1962 saw the development of the world's first true operational GIS in Ottawa Ontario, Canada by the federal Department of Forestry and Rural Development. Developed by Dr. Roger Tomlinson, it was called the "Canada Geographic Information. System" (CGIS) and was used to store, analyze, and manipulate data collected for the Canada Land Inventory (CLI)—an initiative to determine the land capability for rural Canada by mapping information about soils, agriculture, recreation, wildlife, waterfowl, forestry, and land use at a scale of 1:50,000. A rating classification factor was also added to permit analysis. CGIS was the world's first "system" and was an improvement over "mapping" applications as it provided capabilities for overlay, measurement, and digitizing/scanning. It supported a national coordinate system that spanned the continent, coded lines as "arcs" having a true embedded topology, and it stored the attribute and locational information in separate files. As a result of this, Tomlinson has become known as the "father of GIS," particularly for his use of overlays in promoting the spatial analysis of convergent geographic data. CGIS lasted into the 1990s and built the largest digital land resource database in Canada. It was developed as a mainframe based system in support of federal and provincial resource planning and management. Its strength was continent-wide analysis of complex data sets. The CGIS was never available in a commercial form. In 1964, Howard T Fisher formed the Laboratory for Computer Graphics and Spatial Analysis at the Harvard Graduate School of Design (LCGSA 19651991), where a number of important theoretical concepts in spatial data handling were developed, and which by the 1970s had distributed seminal software code and systems, such as 'SYMAP', 'GRID', and 'ODYSSEY' -- which served as literal and inspirational sources for subsequent commercial development -- to universities, research centers, and corporations worldwide. By the early 1980s, M&S Computing (later Intergraph), Environmental Systems Research Institute (ESRI) and CARIS (Computer Aided Resource Information System) emerged as commercial vendors of GIS software, successfully incorporating many of the CGIS features, combining the first generation approach to separation of spatial and attribute information with a second generation approach to organizing attribute data into database structures. In parallel, the development of a public domain GIS was begun in 1982 by the U.S. Army Corp of Engineering Research Laboratory (USA-CERL) in Champaign, Illinois, a branch of the U.S. Army Corps of Engineers to meet the need of the United States military for software for land management and environmental planning. The later 1980s and 1990s industry growth were spurred on by the growing use of GIS on Unix workstations and the personal computer. By the end of the 20th century, the rapid growth in various systems had been consolidated and standardized on relatively few platforms and users were beginning to export the concept of viewing GIS data over the Internet, requiring data format and transfer standards. More recently, there is a growing number of free, open source GIS packages which run on a range of operating systems and can be customized to perform specific tasks. Components of a GIS A GIS can be divided into five components: People, Data, Hardware, Software, and Procedures. All of these components need to be in balance for the system to be successful. No one part can run without the other. People The people are the component who actually makes the GIS work. They include a plethora of positions including GIS managers, database administrators, application specialists, systems analysts, and programmers. They are responsible for maintenance of the geographic database and provide technical support. People also need to be educated to make decisions on what type of system to use. People associated with a GIS can be categorized into: viewers, general users, and GIS specialists. Viewers are the public at large whose only need is to browse a geographic database for referential material. These constitute the largest class of users. General Users are people who use GIS to conducting business, performing professional services, and making decisions. They include facility managers, resource managers, planners, scientists, engineers, lawyers, business entrepreneurs, etc. GIS specialists are the people who make the GIS work. They include GIS managers, database administrators, application specialists, systems analysts, and programmers. They are responsible for the maintenance of the geographic database and the provision of technical support to the other two classes of users. (Lo, 2002) Procedures Procedures include how the data will be retrieved, input into the system, stored, managed, transformed, analyzed, and finally presented in a final output. The procedures are the steps taken to answer the question needs to be resolved. The ability of a GIS to perform spatial analysis and answer these questions is what differentiates this type of system from any other information systems. The transformation processes includes such tasks as adjusting the coordinate system, setting a projection, correcting any digitized errors in a data set, and converting data from vector to raster or raster to vector. (Carver, 1998). Hardware Hardware consists of the technical equipment needed to run a GIS including a computer system with enough power to run the software, enough memory to store large amounts of data, and input and output devices such as scanners, digitizers, GPS data loggers, media disks, and printers. (Carver, 1998). Software There are many different GIS software packages available today. All packages must be capable of data input, storage, management, transformation, analysis, and output, but the appearance, methods, resources, and ease of use of the various systems may be very different. Today’s software packages are capable of allowing both graphical and descriptive data to be stored in a single database, known as the object-relational model. Before this innovation, the georelational model was used. In this model, graphical and descriptive data sets were handled separately. The modern packages usually come with a set of tools that can be customized to the users needs (Lo, 2002). The producers and the main products of GIS Software are the following: 1. 2. 3. 4. 5. 6. 7. 8. Environmental Systems Research Institute ( ESRI ): ArcInfo, ArcView. Autodesk: AutoCAD Map Clark Labs: IDRISI International Institute for Aerospace Survey and Earth Sciences: ILWIS Mapinfo Corporation: Mapinfo. Bentley Systems: Microstation. PCI Geomatics: PAMAP TYDAC Inc. : SPANS Data Perhaps the most time consuming and costly aspect of initiating a GIS is creating a database. There are several things to consider before acquiring geographic data. It is crucial to check the quality of the data before obtaining it. Errors in the data set can add many unpleasant and costly hours to implementing a GIS and the results and conclusions of the GIS analysis most likely will be wrong. Several guidelines to look at include: Lineage – This is a description of the source material from which the data were derived, and the methods of derivation, including all transformations involved in producing the final digital files. This should include all dates of the source material and updates and changes made to it. (Guptill, 1995) Positional Accuracy – This is the closeness of an entity in an appropriate coordinate system to that entity’s true position in the system. The positional accuracy includes measures of the horizontal and vertical accuracy of the features in the data set. (Guptill, 1995) Attribute Accuracy – An attribute is a fact about some location, set of locations, or features on the surface of the earth. This information often includes measurements of some sort, such as temperature or elevation or a label of a place name. The source of error usually lies within the collection of these facts. It is vital to the analysis aspects of a GIS that this information be accurate. Logical Consistency - Deals with the logical rules of structure and attribute rules for spatial data and describes the compatibility of a datum with other data in a data set. There are several different mathematical theories and models used to test logical consistency such as metric and incidence tests, topological and order related tests. These consistency checks should be run at different stages in the handling of spatial data. (Guptill, 1995). Completeness – This is a check to see if relevant data is missing with regards to the features and the attributes. This could deal with either omission errors or spatial rules such as minimum width or area that may limit the information. (Guptill, 1995) (Chrisman,1999). TYPES OF DATA SPATIAL DATA Also known as geospatial data or geographic information it is the data or information that identifies the geographic location of features and boundaries on Earth, such as natural or constructed features, oceans, and more. Spatial data is usually stored as coordinates and topology, and is data that can be mapped. Spatial data is often accessed, manipulated or analyzed through Geographic Information Systems (GIS). Definition 1: The conversion or abstraction of the earth and it’s properties to a database that defines location and properties of individual features of interest. Definition 2: Duplicating the real world in the computer by collecting information about things and where these things are located. Spatial Data is: An inventory of assets - Landcover, Landuse and other natural resources can be considered assets. A ‘Snapshot’ in time - Information loses value if not maintained. A “living document” type of resource if you chose to keep it up to date. Spatial Data = Spatial (Where) + Data (What) NON-SPATIAL DATAS Non-spatial data may be joined to geocoded files with matching attributes and displayed as regular maps. This is common in Geographic Information Systems (GIS). For example census information such as race or income, noninherently spatial data, can be displayed as maps. Unfortunately non-spatial data often has no corresponding geocoded representation; yet valuable information may still be derived if the right representation can be found. By drawing on cartographic metaphors and representing non-spatial data as maps, or "information maps," the information in non-spatial data can be "spatialized," analyzed, browsed, and processed using GIS and cartographic methods, then shared on the web using internet map servers. Additional non-spatial data can also be stored besides the spatial data represented by the coordinates of a vector geometry or the position of a raster cell. In vector data, the additional data are attributes of the object. For example, a forest inventory polygon may also have an identifier value and information about tree species. In raster data the cell value can store attribute information, but it can also be used as an identifier that can relate to records in another table. Spatial analysis with GIS Given the vast range of spatial analysis techniques that have been developed over the past half century, any summary or review can only cover the subject to a limited depth. This is a rapidly changing field, and GIS packages are increasingly including analytical tools as standard built-in facilities or as optional toolsets, add-ins or 'analysts'. In many instances such facilities are provided by the original software suppliers (commercial vendors or collaborative non commercial development teams), whilst in other cases facilities have been developed and are provided by third parties. Furthermore, many products offer software development kits (SDKs), programming languages and language support, scripting facilities and/or special interfaces for developing one’s own analytical tools or variants. The website Geospatial Analysis and associated book/ebook attempt to provide a reasonably comprehensive guide to the subject. LINE SEGMENTS In geometry, a line segment is a part of a line that is bounded by two distinct end points, and contains every point on the line between its end points. Examples of line segments include the sides of a triangle or square. More generally, when the end points are both vertices of a polygon, the line segment is either an edge (of that polygon) if they are adjacent vertices, or otherwise a diagonal. When the end points both lie on a curve such as a circle, a line segment is called a chord (of that curve). POLYGON In geometry a polygon is traditionally a plane figure that is bounded by a closed path or circuit, composed of a finite sequence of straight line segments (i.e., by a closepolygonal chain). These segments are called its edges or sides, and the points where two edges meet are the polygon's vertices or corners. The interior of the polygon is sometimes called its body. A polygon is a 2-dimensional example of the more general polytope in any number of dimensions. Usually two edges meeting at a corner are required to form an angle that is not straight (180°); otherwise, the line segments will be considered parts of a single edge. The basic geometrical notion has been adapted in various ways to suit particular purposes. For example in the computer graphics (image generation) field, the term polygon has taken on a slightly altered meaning, more related to the way the shape is stored and manipulated within the computer. Data representation GIS data represents real world objects (roads, land use, elevation) with digital data. Real world objects can be divided into two abstractions: discrete objects (a house) and continuous fields (rain fall amount or elevation). There are two broad methods used to store data in a GIS for both abstractions: Raster and Vector. VECTOR A simple vector map, using each of the vector elements: points for wells, lines for rivers, and a polygon for the lake. I n a GIS, geographical features are often expressed as vectors, by considering those features as geometrical shapes. Different geographical features are expressed by different types of geometry: Points Zero-dimensional points are used for geographical features that can best be expressed by a single point reference; in other words, simple location. For example, the locations of wells, peak elevations, features of interest or trailheads. Points convey the least amount of information of these file types. Points can also be used to represent areas when displayed at a small scale. For example, cities on a map of the world would be represented by points rather than polygons. No measurements are possible with point features. Lines or polylines One-dimensional lines or polylines are used for linear features such as rivers, roads, railroads, trails, and topographic lines. Again, as with point features, linear features displayed at a small scale will be represented as linear features rather than as a polygon. Line features can measure distance. Polygons Two-dimensional polygons are used for geographical features that cover a particular area of the earth's surface. Such features may include lakes, park boundaries, buildings, city boundaries, or land uses. Polygons convey the most amount of information of the file types. Polygon features can measure perimeter and area. Each of these geometries is linked to a row in a database that describes their attributes. For example, a database that describes lakes may contain a lake's depth, water quality, pollution level. This information can be used to make a map to describe a particular attribute of the dataset. For example, lakes could be coloured depending on level of pollution. Different geometries can also be compared. For example, the GIS could be used to identify all wells (point geometry) that are within 1-mile (1.6 km) of a lake (polygon geometry) that has a high level of pollution. Vector features can be made to respect spatial integrity through the application of topology rules such as 'polygons must not overlap'. Vector data can also be used to represent continuously varying phenomena. Contour lines and triangulated irregular networks (TIN) are used to represent elevation or other continuously changing values. TINs record values at point locations, which are connected by lines to form an irregular mesh of triangles. The face of the triangles represent the terrain surface. VECTOR A raster data type is, in essence, any type of digital image. Anyone who is familiar with digital photography will recognize the pixel as the smallest individual unit of an image. A combination of these pixels will create an image, distinct from the commonly used scalable vector graphics which are the basis of the vector model. While a digital image is concerned with the output as representation of reality, in a photograph or art transferred to computer, the raster data type will reflect an abstraction of reality. Aerial photos are one commonly used form of raster data, with only one purpose, to display a detailed image on a map or for the purposes of digitization. Other raster data sets will contain information regarding elevation, a DEM, or reflectance of a particular wavelength of light, LANDSAT. Digital elevation model, map (image), and vector data Raster data type consists of rows and columns of cells, with each cell storing a single value. Raster data can be images (raster images) with each pixel (or cell) containing a color value. Additional values recorded for each cell may be a discrete value, such as land use, a continuous value, such as temperature, or a null value if no data is available. While a raster cell stores a single value, it can be extended by using raster bands to represent RGB (red, green, blue) colors, colormaps (a mapping between a thematic code and RGB value), or an extended attribute table with one row for each unique cell value. The resolution of the raster data set is its cell width in ground units. Raster data is stored in various formats; from a standard file-based structure of TIF, JPEG, etc. to binary large object (BLOB) data stored directly in a relational database management system (RDBMS) similar to other vector-based feature classes. Database storage, when properly indexed, typically allows for quicker retrieval of the raster data but can require storage of millions of significantly-sized records. Advantages and disadvantages There are advantages and disadvantages to using a raster or vector data model to represent reality. Raster data sets record a value for all points in the area covered which may require more storage space than representing data in a vector format that can store data only where needed. Raster data also allows easy implementation of overlay operations, which are more difficult with vector data. Vector data can be displayed as vector graphics used on traditional maps, whereas raster data will appear as an image that, depending on the resolution of the raster file, may have a blocky appearance for object boundaries. Vector data can be easier to register, scale, and re-project. This can simplify combining vector layers from different sources. Vector data are more compatible with relational database environment. They can be part of a relational table as a normal column and processes using a multitude of operators. The file size for vector data is usually much smaller for storage and sharing than raster data. Image or raster data can be 10 to 100 times larger than vector data depending on the resolution. Another advantage of vector data is it can be easily updated and maintained. For example, a new highway is added. The raster image will have to be completely reproduced, but the vector data, "roads," can be easily updated by adding the missing road segment. In addition, vector data allow much more analysis capability especially for "networks" such as roads, power, rail, telecommunications, etc. For example, with vector data attributed with the characteristics of roads, ports, and airfields, allows the analyst to query for the best route or method of transportation. In the vector data, the analyst can query the data for the largest port with an airfield within 60 miles and a connecting road that is at least two lane highway. Raster data will not have all the characteristics of the features it displays. DATABASE STRUCTURES There are five kinds of data to be represented in a GIS, see figure 1. Point features Eg., location of soil samples, boreholes, manholes, rain gauges, burst water mains, pumping stations, trees, buildings. The points consist of a number of nodes with no thickness and is often referred to as zero dimensional. One method to store a point feature in a GIS is as a table in the data base management system Point ID X coordinate Y Coordinate Pointer data 1 30123.6 19782.4 Point 1 2 30167.3 19745.7 Point 2 3 34952.2 19648.1 Point 3 to attribute Where the pointer to attribute data is a link into another data base table when other data about that point is kept, for example that it represents an access chamber to a sewer system and that it has properties such as date of construction, condition, size, material etc. This is a link into a full data base management system so further relations are permitted from this point Linear features Eg. roads (on small scale maps), rivers, pipe lines, power lines, elevation contours. The nodes are linked with arcs, each with a number of vertices (the simple arc is a straight line). Between vertices the arc is usually considered a straight line but curved links are possible. Line data can either be non-branching lines, or tree or network structures. In a network there are more than one routes between two nodes. This data has one dimension, that is, it does not have thickness and care must be taken in the definition of the system that a loop is not confused with a polygon. A simple structure for a line feature or network is: line reference Attribute Pointer arc 1 arc 2 --etc Arc reference X coordinate Y Coordinate Node 1 30123.6 19782.4 Node 2 30167.3 19745.7 Vertex 3 30952.2 19648.1 etc Areas (polygons) with common properties, e.g. pressure zones, catchments, contributing areas, soil association mapping units, climate zones, administrative district areas, buildings and other land cover. The polygon consists of a number of arcs or linear features that form a closed loop without crossing over one another. The arcs are usually straight between vertices but may be curved. Polygon Reference Attribute Pointer 3 Point 1 X coordinate Y Coordinate 30123.6 19782.4 30167.3 19745.7 30952.2 19648.1 30123.6 19782.4 Simple polygon structure. The simple polygon representation shown above where a quadrangle is represented, as used in CAD (or DXF format), is of little use in GIS. The 3 major problems with simple polygons are: The boundary between 2 polygon needs to be stored twice. There is always a possibility that the nodes for each boundary polygon are in slightly different positions resulting in artificial gaps between polygons, or slivers where an area is assigned to two or more polygons (see figure 3). These problems are particularly acute when manually digitising. 1. When manually digitising it is possible to accidentally cross over from one polygon to another creating a totally false polygon or to pass from one node to another in incorrect order giving rise to a weird polygon (see figure 4) 1. Complex geographical objects are difficult to represent, for example islands or disjointed polygons (see figure 5). If we consider an example from urban drainage where a garden area is completely surrounded by car park, as, for example, at a prestige office complex, then if we calculate the area of simple polygons the area of the car park will include, erroneously, the area of the gardens and grossly overestimate the impermeable area. In GIS therefore area data is represented as topological structure in one of a number of ways. The Arc/Info method of storing this information is shown in figure 7. A separate list is used to hold information about islands and disjointed structures. Different themes can be represented on the same coverage and there is no requirement that polygons do not overlap. For example a single coverage may contain polygons representing landcover, whereas another other polygons may contain the contributing areas to inlet nodes of a storm water drainage system, see figure 6. The polygons naturally overlap and the intersections of these polygons provides one of the main uses of GIS and is known as overlay to reflect the graphical process of overlaying one theme upon another. Actual or potential surfaces, e.g. ground elevation, variation of mean annual temperature, spatial distributions of rainfall, population densities. These are discussed in detail in the section on the digital elevation model (DEM) Temporal elements, e.g. changes in land use over time, changes to a pipe network, rainfall records or streamflow records. These are not well represented in current GIS technology, but newer object oriented GIS should make this more readily available Raster Representation Figure 6 shows two polygons intersecting. The numerical calculation required to calculate either the intersection or the join of the 2 polygons is quite intensive. The whole process is made much simpler if the polygons are all the same shape and size, preferably rectangular. This use of rectangular polygons is known as a cell, grid or raster representation and provides one of the simplest representations for GIS and spatial statistical modelling. Figure 7 shows the same polygon data represented as a vector and as a raster. Note that the individual cell values can be either numbers for computation, such as elevations or pointers to a database with further attributes. The ease of programming raster GIS systems and low computational overheads makes them very suitable for natural or environmental modelling. The size of cells used in GIS modelling requires careful thought before data entry and modelling can begin. I have used cells of 1m square for urban drainage work where we were only interested in a small catchment and 250m square for land evaluation where we were studying the whole of Ghana. There is always error in the representation of real world structures as small cells and it is important to realise the trade off between small cells that accurately represent the real world but carry a lot of computational overhead and large cells that are much more efficient but introduce large errors. Fortunately computers are getting more powerful and disk drives much larger every year so these problems become less important and we can select cell sizes to represent the natural variation we observe. For example a soil association boundary will never be known on the ground to better than 50m accuracy, therefore using any cell size less than 50m is pointless. My recommendations on cell size are as follows: Data derived from 1:50 000 maps 50m Data derived from 1:10 000 maps 10m Data derived from 1:1250 maps 1m Any modelling with satellite remote resolution of the sensor (often 30m) sensing Nation wide land evaluation 250m Studies involving geodemographics 200m 20-40m (it is debatable whether it is truly physically based at this Physically based rainfall runoff modelling resolution but this will allow realistic computation times) Flood plane studies 50m Most GIS that use raster data have some means of compressing the data using either run length encoding, quad trees or any of the loss less schemes for computer graphics. Unless you intent to write your own modules and one of the big attractions of raster GIS is that you can write your own modules then, then the compression technique is irrelevant to the user. However, it does mean that raster GIS data bases can be as small as their vector counterparts. With some raster GIS all overlays must be carried out with identically sized cells and all resampling must be carried out manually before the overlay modelling begins. With other GIS the resampling is carried out dynamically to either the largest grid size of all the overlays in the model or some user specified grid size. Raster and vector GIS are traditionally compared and the author states his preference for one or the other, but most modern GIS have vector and raster components which can often be inter linked seamlessly. Many tasks are easier to carry out in each form, for example cadasteral work requires the accuracy and precision of a vector GIS, whereas determining the water requirements of a region can be best done using a raster representation. GIS software comes in a variety of packages. The two main types, as already described, are the vector based system and the raster based system. More modern systems permit the total integration of raster and vector data, allowing the advantages of both methods to be enjoyed, with few of the disadvantages. Vector systems are often supported by traditional DataBase Management Systems (DBMS). The most common conform to the relational model, see Avison (1992). Arc-Info, the most widely used vector GIS package, follows this approach, Info being a relational DBMS in its own right The relational model is the basis of most DBMS used in organisations and businesses. This underlies the vector model's principle use as an asset or resource inventory system. A DBMS should allow access to appropriate parts of the database to different types of user, and prevent unauthorised viewing or changing. It should also maintain data concurrency, provide archive facilities and present a simple interface to the user for manipulating the data. Raster systems generally do not employ such strict data management. They have developed from image processing systems and are often used by a single user. Clearly these are generalisations, and many packages will embody aspects of both systems. The most up-to-date systems are described as 'object oriented'. The distinction of object oriented systems is that all data items are described as being of one or more object type; e.g. a linear feature, a point, a vector polygon, a regular raster, a raster cell, a TIN, a DEM, etc. In addition to storing the description of the object, the methods of displaying, plotting and general manipulation are also carried with the object type, this is known as encapsulation. Objects are hierarchical; rivers, roads and pipes will be objects that are descended from the linear object, each will, therefore, have the properties, behaviour and methods inherited from the linear feature, such as length. However they will each have behaviour and properties that are distinct; roads will have classes (i.e. 'A' roads and motorways); pipes and roads will not be able to connect to form a network. The object oriented paradigm is currently of great interest to the computer science community. Object oriented programming languages, databases and, of course, GIS are under development, (see Worboys et al, 1990). There are several advantages that are stressed by advocates of the object oriented approach; (i) it is intuitive as people naturally think in terms of objects; (ii) by specifying behaviour, inconsistencies in the database can be reduced, for example sewers and water mains objects exhibit different behaviour and should not be part of the same network; (iii) developing applications is easy; by having a hierarchical structure new objects are easily created. There are a variety of ways of storing geographical data and different ways of processing the data. The choice of data structure is largely dictated by the use the data is to be put to, the capabilities of the GIS being used and, to a large extent by the existing data formats. Figure 7(a) Simple vector representation, using the topologic model presented by Dangermond (1982), more complex structures are used to improve access times. (b) Raster representation, a raster layer is required for each attribute to be represented. QUESTIONS 1) define map and explain different type of maps? 2) Define geographical information systems and how can it is help to create digital maps? 3) What are the components of GIS? 4) Write detail about spatial and non-spatial datas? 5) Write vector and raster data base structure? UNIT-IV DATA ANALYSIS AND MODELLING DATA RETRIEVAL AND QUERYING Perhaps the initial GIS analysis that any user undertakes is the retrieval and/or reclassification of data. Retrieval operations occur on both spatial and attribute data. Often data is selected by an attribute subset and viewed graphically. Retrieval involves the selective search, manipulation, and output of data without the requirement to modify the geographic location of the features involved. The ability to query and retrieve data based on some user defined criteria is a necessary feature of the data storage and retrieval subsystem. Data retrieval involves the capability to easily select data for graphic or attribute editing, updating, querying, analysis and/or display. The ability to retrieve data is based on the unique structure of the DBMS and command interfaces are commonly provided with the software. Most GIS software also provides a programming subroutine library, or macro language, so the user can write their own specific data retrieval routines if required. Querying is the capability to retrieve data, usually a data subset, based on some user defined formula. These data subsets are often referred to as logical views. Often the querying is closely linked to the data manipulation and analysis subsystem. Many GIS software offerings have attempted to standardize their querying capability by use of a Standard Query Language (SQL). This is especially true with systems that make use of an external relational DBMS. Through the use of SQL, GIS software can interface to a variety of different DBMS packages. This approach provides the user with the flexibility to select their own DBMS. This has direct implications if the organization has an existing DBMS that is being used for to satisfy other business requirements. Often it is desirable for the same DBMS to be utilized in the GIS applications. This notion of integrating the GIS software to utilize an existing DBMS through standards is referred to as corporate or enterprise GIS. With the migration of GIS technology from being a research tool to being a decision support tool there is a requirement for it to be totally integrated with existing corporate activities, including accounting, reporting, and business functions. There is a definite trend in the GIS marketplace towards a generic interface with external relational DBMS's. The use of an external DBMS, linked via a SQL interface, is becoming the norm. A flexibility as such is a strong selling point for any GIS. SQL is quickly becoming a standard in the GIS software marketplace. Spatial analysis In statistics, spatial analysis or spatial statistics includes any of the formal techniques which study entities using their topological, geometric, or geographic properties. The phrase properly refers to a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, to chip fabrication engineering, with its use of 'place and route' algorithms to build complex wiring structures. The phrase is often used in a more restricted sense to describe techniques applied to structures at the human scale, most notably in the analysis of geographic data. The phrase is even sometimes used to refer to a specific technique in a single area of research, for example, to describe geostatistics. The history of spatial analysis starts with early mapping, surveying and geography at the beginning of history, although the techniques of spatial analysis were not formalized until the later part of the twentieth century. Modern spatial analysis focuses on computer based techniques because of the large amount of data, the power of modern statistical and geographic information science (GIS) software, and the complexity of the computational modeling. Spatial analytic techniques have been developed in geography, biology, epidemiology, statistics, geographic information science, remote sensing, computer science, mathematics, and scientific modelling. Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. The most fundamental of these is the problem of defining the spatial location of the entities being studied. For example, a study on human health could describe the spatial position of humans with a point placed where they live, or with a point located where they work, or by using a line to describe their weekly trips; each choice has dramatic effects on the techniques which can be used for the analysis and on the conclusions which can be obtained. Other issues in spatial analysis include the limitations of mathematical knowledge, the assumptions required by existing statistical techniques, and problems in computer based calculations. Classification of the techniques of spatial analysis is difficult because of the large number of different fields of research involved, the different fundamental approaches which can be chosen, and the many forms the data can take. Common errors in spatial analysis The fundamental issues in spatial analysis lead to numerous problems in analysis including bias, distortion and outright errors in the conclusions reached. These issues are often interlinked but various attempts have been made to separate out particular issues from each other. The locational fallacy The locational fallacy is a phrase used to describe an error due to the particular spatial characterization chosen for the elements of study, in particular choice of placement for the spatial presence of the element. Spatial characterizations may be simplistic or even wrong. Studies of humans often reduce the spatial existence of humans to a single point, for instance their home address. This can easily lead to poor analysis, for example, when considering disease transmission which can happen at work or at school and therefore far from the home. The spatial characterization may implicitly limit the subject of study. For example, the spatial analysis of 'crime' data has recently become popular but these studies can only describe the particular kinds of crime which can be described spatially. This leads to many maps of assault but not to any maps of embezzlement with political consequences in the conceptualization of crime and the design of policies to address the issue. The atomic fallacy This describes errors due to treating elements as separate 'atoms' outside of their spatial context. The ecological fallacy The ecological fallacy describes errors due to performing analyses on aggregate data when trying to reach conclusions on the individual units. It is closely related to the Modifiable Areal Unit Problem. The modifiable areal unit problem. The modifiable areal unit problem (MAUP) is an issue in the analysis of spatial data arranged in zones, where the conclusion depends on the particular shape of the zones used in the analysis. Spatial analysis and modeling often involves aggregate spatial units such as census tracts and traffic analysis zones. These units may reflect data collection and/or modeling convenience rather than homogeneous, cohesive regions in the real world. The spatial units are therefore arbitrary or modifiable and contain artifacts related to the degree of spatial aggregation or the placement of boundaries. The problem arises because it is known that results derived an analysis of these zones depends directly on the zones being studied. It has been shown that the aggregation of point data into zones of different shape can lead to opposite conclusions. Various solutions have been proposed to address the MAUP, including repeated analysis and graphical techniques but the issue cannot yet be considered to be solved. One strategy is to assess its effects in a sensitivity analysis by changing the aggregation or boundaries and comparing results from the analysis and modeling under these different schemes. A second strategy is to develop optimal spatial units for the analysis. OVERLAY GIS analysis functions use the spatial and non-spatial attribute data to answer questions about real-world. It is the spatial analysis functions that distinguishes GIS from other information systems. When use GIS to address real-world problems, you'll come up against the question that which analysis function you want to use and to solve the problems. In this case, you should be aware that wisely using functions will lead to high quality of the information produced from GIS and individual analysis functions must be used in the context of a complete analysis strategy. (Stan Aronoff, 1989) 1. Spatial Data Functions Spatial data refers to information about the location and shape of, and relationships among, geographic features, usually stored as coordinates and topology. Spatial data functions are used to transform spatial data files, such as digitized map, edit them, and assess their accuracy. They are mainly concerned with the spatial data. Format Transformations Format is the pattern into which data are systematically arranged for use on a computer. Format transformations are used to get data into acceptable GIS format. Digital Files must be transformed into the data format used by the GIS, such as transforming from raster to vector data structure. A raster data often requires no re-formatting. A vector data often requires topology to be built from coordinate data, such as arc/node translations. Transformation can be very costly and time-consuming with poor coordinate data. Geometric Transformations Geometric transformations are used to assign ground coordinates to a map or data layer within the GIS or to adjust one data layer so it can be correctly overlayed on another of the same area. The procedure used to accomplish this correction is termed registration. Two approaches are used in registration: the adjustment of absolute positions and the adjustment of relative position. Relative Position refers to the location of features in relation to a geographic coordinate system. Rubber sheeting (registration by Relative Position) is the procedure using "slave" and "master" mathematical transformations to adjust coverage features in a nonuniform manner. Links representing from- and to-locations are used to define the adjustment. It needs easily identifiable, accurate, well distributed control points. Absolute Position is the location in relation to the ground. This registration is done by individual layers. The advantage is that it does not propagate errors. Projection Transformations Map projection is a mathematical transformation that is used to represent a spherical surface on a flat map. The transformation assigns to each location on a spherical surface a unique location on a 2-dimensional map. Map projections always causes some distortion: area, shape, distance, or direction distortion. GIS commonly supports several projections and has software to transform data from one projection to another. The map projections most commonly used for mapping at scales of 1:500,000 or larger in North America is the UTM(Universal Transverse Mercator) Projection. For maps of continental extent, the Albers, Lambert's Azimuthal, and Polyconic projections are commonly used. Conflation Conflation is the procedure of reconciling the positions of corresponding features in different data layers. Conflation functions are used to reconcile these differences so that the corresponding features overlay precisely. This is important when data from several data layers are used in an analysis. Edge-matching Edge matching is a procedure to adjust the position of features extending across map sheet boundaries. This function ensures that all features that cross adjacent map sheets have the same edge locations. Links are used when matching features in adjacent coverages. Editing Functions Editing functions are used to add, delete, and change the geographic position of features. Sliver or splinter polygons are thin polygons that are occurring along the borders of polygons following digitizing and the topological overlay of two or more coverages. Address Matching is a mechanism for relating two files using address as the relate item. Geographic coordinates and attributes can be transferred from one address to the other. For example, a data file containing student addresses can be matched to a street coverage that contains addresses creating a point coverage of where the students live. Line Coordinate Thinning The Thinning function reviews all the coordinate data in a file, identifies and then removes unnecessary coordinates. Depending on scale, a number of coordinate pairs can often be significantly reduced without a perceived loss of detail. This function is used to reduce the quantity of coordinate data that must be stored by the GIS. Coordinate thinning, by reducing the number of coordinate points, reduces the size of the data file, thereby reducing the volume of data to be stored and processed in the GIS. 2. Attribute Data Functions Attribute Data is relate to the description of the map items. It is typically stored in tabular format and linked to the feature by a user-assigned identifier (e.g., the attributes of a well might include depth and gallons per minute). Retrieval(selective search) Retrieval operations on the spatial and attribute data involve the selective search manipulation, and output of data without the need to modify the geographic location of features or to create new spatial entities. These operations work with the spatial elements as they were entered in the data base. I nformation from database tables can be accessed directly through the map, or new maps can be created using information in the tabular database. Both graphic and tabular data must be stored in formats the computer can recognize and retrieve. Classification Classification is the procedure of identifying a set of features as belonging to a group and defining patterns. Some form of classification function is provided in every GIS. In a raster-based GIS, numerical values are often used to indicate classes. Classification is important because it defines patterns. One of the important functions of a GIS is to assist in recognizing new patterns. Classification is done using single data layers, as well as with multiple data layers as part of an overlay operation. Generalization, also called map dissolve, is the process of making a classification less detailed by combining classes. Generalization is often used to reduce the level of classification detail to make an underlying pattern more apparent. Verification Verification is a procedure for checking the values of attributes for all records in a database against their correct values. (Keith C. Clarke, 1997) 3. Integrated Analysis of Spatial and Attribute Data Overlay Overlay is a GIS operation in which layers with a common, registered map base are joined on the basis of their occupation of space. (Keith C. Clarke, 1997). The overlay function creates composite maps by combining diverse data sets. The overlay function can perform simple operations such as laying a road map over a map of local wetlands, or more sophisticated operations such as multiplying and adding map attributes of different value to determine averages and co-occurrences. Raster and vector models differ significantly in the way overlay operations are implemented. Overlay operations are usually performed more efficiently in raster-based systems. In many GISs a hybrid approach is used that takes advantage of the capabilities of both data models. A vector-based system may implement some functions in the raster domain by performing a vector-to-raster conversion on the input data, doing the processing as a raster operation, and converting the raster result back to a vector file. Region Wide Overlay: "Cookie Cutter Approach" The region wide, or "cookie cutter," approach to overlay analysis allows natural features, such as forest stand boundaries or soil polygons, to become the spatial area(s) which will be analyzed on another map. For example ( see figures above): given two data sets, forest patches and slope, what is the area-weighted average slope within each separate patch of forest? To answer this question, the GIS overlays each patch of forest from the forest patch data set onto the slope map and then calculates the area-weighted average slope for each individual forest patch. Topological Overlay: Co-Occurrence mapping in a vector GIS is accomplished by topological overlaying. Any number of maps may be overlayed to show features occurring at the same location. To accomplish this, the GIS first stacks maps on top of one another and finds all new intersecting lines. Second, new nodes (point features where three or more arcs, or lines, come together) are set at these new intersections. Lastly, the topologic structure of the data is rebuilt and the multifactor attributes are attached to the new area features. Neighborhood Function Neighborhood Function analyzes the relationship between an object and similar surrounding objects. For example, in a certain area, analysis of a kind of land use is next to what kinds of land use can be done by using this function. This type of analysis is often used in image processing. A new map is created by computing the value assigned to a location as a function of the independent values surrounding that location. Neighborhood functions are particularly valuable in evaluating the character of a local area. Point-in-Polygon and Line-In-Polygon Point-in-Polygon is a topological overlay procedure which determines the spatial coincidence of points and polygons. Points are assigned the attributes of the polygons within which they fall. For example, this function can be used to analyze an address and find out if it (point) is located within a certain zip code area (polygon). Line-in-Polygon is a spatial operation in which lines in one coverage are overlaid with polygons of another coverage to determine which lines, or portions of lines, are contained within the polygons. Polygon attributes are associated with corresponding lines in the resulting line coverage. For example, this function can be used to find out who will be affected when putting in a new powerline in an area. In a vector-based GIS, the identification of points and lines contained within a polygon area is a specialized search function. In a raster-based GIS, it is essentially an overlay operation, with the polygons in one data layer and the points and/or lines in a second data layer. Topographic Functions Topography refers to the surface characteristics with continuously changing value over an area such as elevations, aeromagnetics, noise levels, income levels, and pollution levels. The topography of a land surface can be represented in a GIS by digital elevation data. An alternative form of representation is the Triangulated Irregular Network or TIN used in vectorbased systems. Topographic functions are used to calculate values that describe the topography at a specific geographic location or in the vicinity of the location. The two most commonly used terrain parameters are the slope and aspect, which are calculated using the elevation data of the neighbouring points. Slope is the measure of change in surface value over distance, expressed in degrees or as a percentage. For example, a rise of 2 meters over a distance of 100 meters describes a 2% slope with an angle of 1.15. Mathematically, slope is referred to as the first derivative of the surface. The maximum slope is termed the gradient. In a raster format DEM, another grid where each cell is the slope at a certain position can be created, then the maximun difference can be found and the gradient can be determined. Aspect is the direction that a surface faces. Aspect is defined by the horizontal and vertical angles that the surface faces. In a raster format DEM, another grid can be created for aspect and a number can be assigned to a specific direction. Sun intensity is the combination of slope and aspect. Illumination portrays the effect of shining a light onto a 3-dimensional surface. (Stan Aronoff, 1989). Thiessen Polygons Thiessen or voronoi polygons define individual areas of influence around each of a set of points. Thiessen polygons are polygons whose boundaries define the area that is closest to each point relative to all other points. Thiessen polygons are generated from a set of points. They are mathematically defined by the perpendicular bisectors of the lines between all points. A tin structure is used to create Thiessen polygons. Interpolation Interpolation is the procedure of predicting unknown values using the known values at neighboring locations. The quality of the interpolation results depends on the accuracy, number, and distribution of the known points used in the calculation and on how well the mathematical function correctly models the phenomenon. DATA ANALYSIS Most GIS's provide the capability to build complex models by combining primitive analytical functions. Systems vary as to the complexity provided for spatial modelling, and the specific functions that are available. However, most systems provide a standard set of primitive analytical functions that are accessible to the user in some logical manner. VECTOR DATA AND RASTER DATA ANALYSIS A geodatabase is a database that is in some way reference to locations on the earth. Coupled with this data is usually data known as attribute data. Attribute data generally defined as additional information, which can then be tied to spatial data. Geodatabases are grouped into two different types: vector and raster. Most GIS software applications mainly focus on the usage and manipulation of vector geodatabases with added components to work with raster-based geodatabases. Vector data Vector data is split into three types: polygon, line (or arc) and point data. Polygon data is used to represent areas. Polygon features are most commonly distinguished using either a thematic mapping symbology (color schemes), patterns, or in the case of numeric gradation, a color gradation scheme could be used. In this view of a polygon based dataset, frequency of fire in an area is depicted showing a graduate color symbology. Line (or arc) data is used to represent linear features. Common examples would be road centerlines and hydrology. Symbology most commonly used to distinguish arc features from one another are line types (solid lines versus dashed lines) and combinations using colors and line thicknesses. In the example below roads are distinguished from the stream network but designated the roads as a solid black line and the hydrology a dashed blue line. Point data is most commonly used to represent nonadjacent features. Examples would be schools, points of interest, and in the example below, bridge and culvert locations. Point features are also used to represent abstract points. For instance, point locations could represent city locations or place names. Both line and point feature data represent polygon data at a much smaller scale. They help reduce clutter by simplifying data locations. As the features are zoomed in, the point location of a school is more realistically represented by a series of building footprints showing the physical location of the campus. Line features of a street centerline file only represent the physical location of the street. If a higher degree of spatial resolution is needed, a street curbwidth file would be used to show the width of the road as well as any features such as medians and right-of-ways (or sidewalks). Raster Data Raster data are cell-based spatial datasets. There are also three types of raster datasets: thematic data, spectral data, and pictures. This example of a thematic raster dataset is called a Digital Elevation Model (DEM). Each cell presents a 30m pixel size with an elevation value assigned to that cell. The area shown is the Topanga Watershed in California and gives the viewer and understand of the topography of the region. This image shows a portion of Topanga, California taken from a USGS DOQ. Each cell contains one value representing the dominate value of that cell. Raster datasets are intrinsic to most spatial analysis. Data analysis such as extracting slope and aspect from Digital Elevation Models occurs with raster datasets. Spatial hydrology modeling such as extracting watersheds and flow lines also uses a raster-based system. Spectral data presents aerial or satellite imagery which is then often used to derive vegetation geologic information by classifying the spectral signatures of each type of feature. Vegetation classification raster data. The vegetation data was derived from NDVI classification of a satellite image. What results from the effect of converting spatial data location information into a cell based raster format is called stairstepping. The name derives from the image of exactly that, the square cells along the borders of different value types look like a staircase viewed from the side. Unlike vector data, raster data is formed by each cell receiving the value of the feature that dominates the cell. The stairstepping look comes from the transition of the cells from one value to another. In the image above the dark green cell represents chamise vegetation. This means that the dominate feature in that cell area was chamise vegetation. Other features such as developed land, water or other vegetation types may be present on the ground in that area. As the feature in the cell becomes more dominantly urban, the cell is attributed the value for developed land, hence the pink shading. GIS and modeling Using GIS to prepare data, display results loosely coupled to modeling code Model and GIS working off the same database component-based software architecture tight coupling Writing the model in the GIS's scripting language embedding performance problems for dynamic models Modeling Tools in GIS Modeling lies at the very core of analytical applications in GIS.Species habitat modeling, soil erosion modeling, vulnerability modeling, and so on - all have the common element of deriving new maps of the likely occurrence or magnitude of some phenomenon based on an established relation between existing map layers.Given the importance of this activity, it is not surprising then to see that GIS continues to evolve in its modeling tools.The latest developments, however, promise to take GIS to dramatic new levels of functionality. The earliest modeling tools were macro-scripting languages (e.g., Arc/Info AML, ERDAS EML and IDRISI IML).Macro languages allow one to develop and save a sequence of GIS operational commands, either as sequences of command line statements or through the use of a special-purpose macro language, in some cases incorporating some of the control and interface design elements of a programming language (e.g., ArcView Avenue).Macro languages can be very powerful, but the sequences are often tedious to construct.In addition, each scripting language tends to be system specific, requiring a substantial investment of learning when several systems are used. A typical map calculator tool.Map calculators are excellent for the implementation of models that can be expressed as equations using the operations typically associated with a scientific calculator. The next tool to be developed was the map calculator (Figure 1).Using the analogy of a scientific calculator, these tools offer the ability to enter and save algebraic or logical equations using map layers as variables.Map Dynamic modeling - calculators are popular because of their immediate a somewhat new familiarity and the ease with which complex equations but extremely can be entered and executed.However, they typically do important not offer the functionality of a macro scripting language, development in the being limited to the kinds of operations found on a history of GIS scientific calculator. modeling. Most recently we have seen the development of two new modeling approaches that offer not only the ability to automate complex tasks, but the promise of profoundly changing the nature of GIS modeling itself.These are the COM client/server model and the graphical modeling medium. COM is Microsoft's acronym for the Component Object Model - a language independent specification for the manner in which software components communicate.COM is an outgrowth of the developments in Object Linking and Embedding (OLE) and the component model that underlies most visual programming languages (VBX/OCX/ActiveX).Today it provides the very foundation of Windows software development.Significantly, we are seeing the transformation of most major GIS software systems into COM servers. A typical sequence in accessing the exposed procedures of a COM server.Once the server (IDRISI32 in this example) has been registered with your programming software (in this case, Microsoft's Visual Basic for Applications), typing the server reference followed by a dot is enough to list the available properties and methods (top illustration).Then, when a method has been selected, code completion lists the parameters and their data types (bottom illustration). A COM server, such as IDRISI32 or the latest release of ArcGIS, is an application that exposes elements of its functionality to other applications (clients).Through the standardized interfaces of COM technology, it is possible to use a visual programming language such as Visual Basic, Delphi or Visual C++ to write programs that control the server application like a puppet.Although there is some investment in learning one of these languages, the payoff is substantial the ability to create complex models with customized interfaces based on the capabilities of the host GIS software (Figures 2 and 3).Further, since the interface is standard across many applications (unlike system specific scripting languages), it is possible to marshal the capabilities of several tools in a single model.The advantages for third-party software developers are clear.However, for the individual user and agency workgroup, the potential is also significant.With only the most fundamental knowledge of visual programming (something that can be gained from a self-help book in a couple of days) it is possible to construct models of a complexity that would be almost inconceivable to do by hand. The land cover change prediction module illustrated here was developed as a COM client program - a program that makes calls to modules in the COM server to do the actual work.In this case, the program code associated with the OK button simply serves to direct the sequence of operations among standard IDRISI32 modules exposed by the COM interface.The model is complex (a typical run might involve more than two thousand GIS operations), but the programming knowledge required to develop it is minimal. While COM gets at the internals of the system, graphical modeling tools go in a very different direction (Figure 4).By placing an additional layer onto the top of the system they provide a very simple and powerful medium for expressing the relationship between operations that form the sequence of a model.They also offer a very flexible means of model development: add a step; test it; add another; test it; modify it if necessary; and so on. Graphical modeling environments provide a very direct means of visualizing the sequence of operations in a model.This example illustrates a cellular automata process of urban growth using a feedback loop (the red link).The result is a dynamic model - a major new phase in GIS model development.The continued development of control structures in graphical modeling environments (such as conditional branches of control and iteration structures) suggest that graphical programming environments may rival current programming environments for many modeling activities. Current graphical modeling environments in GIS are largely confined to the tree structure of traditional cartographical modeling (although those built on scripting structures do sometimes offer non-graphical control structures).However, it is perhaps not surprising that we are beginning to see the introduction of alternative control structures.For example, the illustration in Figure 4 shows the use of a Dynalink in IDRISI32 - a feedback loop that replaces input layers with the outputs of previous iterations.The result is a form of dynamic modeling - a somewhat new but extremely important development in the history of GIS modeling. Perhaps the surprising thing about graphical modeling in GIS is that the direction in which these tools are heading promises to replicate much of the power of COM-based visual programming in the not too distant future.We are already seeing the introduction of feedback and iteration structures that allow for elementary dynamic modeling and highly flexible iteration and batch processes.However, the introduction of true graphical conditional branches of control and other basic elements of programming languages are not far on the horizon.Coupled with suitable interface development tools, it is not beyond reason to expect that these may replace conventional programming languages for model development for the majority of GIS professionals. Digital elevation model 3D rendering of a DEM of Tithonium Chasma on Mars A digital elevation model (DEM) is a digital representation of ground surface topography or terrain. It is also widely known as a digital terrain model (DTM). A DEM can be represented as a raster (a grid of squares) or as a triangular irregular network. DEMs are commonly built using remote sensing techniques, however, they may also be built from land surveying. DEMs are used often in geographic information systems, and are the most common basis for digitallyproduced relief maps. Production Digital elevation models may be prepared in a number of ways, but they are frequently obtained by remote sensing rather than direct survey. One powerful technique for generating digital elevation models is interferometric synthetic aperture radar; two passes of a radar satellite (such as RADARSAT-1) suffice to generate a digital elevation map tens of kilometers on a side with a resolution of around ten meters. One also obtains an image of the surface cover. Another powerful technique for generating a Digital Elevation Model is using the digital image correlation method. It implies two optical images acquired with different angles taken from the same pass of an airplane or an Earth Observation Satellite (such as the HRS instrument of SPOT5). Older methods of generating DEMs often involve interpolating digital contour maps that may have been produced by direct survey of the land surface; this method is still used in mountain areas, where interferometry is not always satisfactory. Note that the contour data or any other sampled elevation datasets (by GPS or ground survey) are not DEMs, but may be considered digital terrain models. A DEM implies that elevation is available continuously at each location in the study area. The quality of a DEM is a measure of how accurate elevation is at each pixel (absolute accuracy) and how accurately is the morphology presented (relative accuracy). Several factors play an important role for quality of DEM-derived products: • • • • • • terrain roughness; sampling density (elevation data collection method); grid resolution or pixel size; interpolation algorithm; vertical resolution; terrain analysis algorithm; Methods for obtaining elevation data to used to create DEMs • • • Real Time Kinematic GPS stereo photogrammetry LIDAR • there are others that must be added, please help... Uses Common uses of DEMs include: • • • • • • • • extracting terrain parameters modeling water flow or mass movement (for example avalanches) creation of relief maps rendering of 3D visualizations. creation of physical models (including raised-relief maps) rectification of aerial photography or satellite imagery. reduction (terrain correction) of gravity measurements (gravimetry, physical geodesy). terrain analyses in geomorphology and physical geography Integration with-GIS Instant Access to Critical Information Laserfiche Integration Express-GIS unites Laserfiche solutions with ESRI ArcMap 8.x, allowing users tosel ect map elements – parcels, streets, water mains, for example – and immediately access associated documents. An intelligent search tool helps userspin point the specific type of document needed in seconds. Police department case files, historical maps, work orders, business licenses and other documents become instantly available to GIS users in support of effective decision making organization-wide. Complement Homeland Security Initiatives Laserfiche solutions form the archival and retrieval core of informationrelated homeland security initiatives. Working in conjunction with ESRI GIS, Laserfiche enhances emergency response and preparedness with ready access to building plans, hazardous materials reports and other documents essential to a rapid, effective response. Laserfiche Integration Express-GIS is the key, making available this paperbound information that otherwise would remain inaccessible to dispatchers, wireless-equipped first responders and other field personnel A Streamlined Solution for IT As demand for information access increases, IT staff are charged with making disparate systems work together to solve real-world problems. Laserfiche Integration Express-GIS is a complete, packaged integration solution. It delivers unified information access benefits to staff who rely on GIS and document management solutions without taxing IT resources with excessive customization work. Integration Express-GIS Highlights 1. Improve service by bridging the gap between document management and GIS. 2. Access supporting documents directly within the ESRI interface. 3. Enhance homeland security effectiveness and empower first-responders with instant information access. 4. Conserve IT resources with this packaged integration solution. 5. Leverage standard-setting Laserfiche solutions to deliver information access and protection benefits organization-wide. QUESTIONS 1) write detaily about spatial analysis? 2) Define data retrival? 3) How can use the GIS technology for modeling? 4) Define DEM (Digital elevation model) and give brief discuss? 5) How can use the GIS techniques for Artificial intelligence? 6) How can derive the cost and path analysis in GIS? UNIT-V GIS APLICATION IN RESOURCE MANAGEMENT Introduction As we learned in the section on sensors, each one was designed with a specific purpose. With optical sensors, the design focuses on the spectral bands to be collected. With radar imaging, the incidence angle and microwave band used plays an important role in defining which applications the sensor is best suited for. Each application itself has specific demands, for spectral resolution, spatial resolution, and temporal resolution. To review, spectral resolution refers to the width or range of each spectral band being recorded. As an example, panchromatic imagery (sensing a broad range of all visible wavelengths) will not be as sensitive to vegetation stress as a narrow band in the red wavelengths, where chlorophyll strongly absorbs electromagnetic energy. Spatial resolution refers to the discernible detail in the image. Detailed mapping of wetlands requires far finer spatial resolution than does the regional mapping of physiographic areas. Temporal resolution refers to the time interval between images. There are applications requiring data repeatedly and often, such as oil spill, forest fire, and sea ice motion monitoring. Some applications only require seasonal imaging (crop identification, forest insect infestation, and wetland monitoring), and some need imaging only once (geology structural mapping). Obviously, the most timecritical applications also demand fast turnaround for image processing and delivery - getting useful imagery quickly into the user's hands. In a case where repeated imaging is required, the revisit frequency of a sensor is important (how long before it can image the same spot on the Earth again) and the reliability of successful data acquisition. Optical sensors have limitations in cloudy environments, where the targets may be obscured from view. In some areas of the world, particularly the tropics, this is virtually a permanent condition. Polar areas also suffer from inadequate solar illumination, for months at a time. Radar provides reliable data, because the sensor provides its own illumination, and has long wavelengths to penetrate cloud, smoke, and fog, ensuring that the target won't be obscured by weather conditions, or poorly illuminated. Often it takes more than a single sensor to adequately address all of the requirements for a given application. The combined use of multiple sources of information is called integration. Additional data that can aid in the analysis or interpretation of the data is termed "ancillary" data. The applications of remote sensing described in this chapter are representative, but not exhaustive. We do not touch, for instance, on the wide area of research and practical application in weather and climate analysis, but focus on applications tied to the surface of the Earth. The reader should also note that there are a number of other applications that are practiced but are very specialized in nature, and not covered here (e.g. terrain trafficability analysis, archeological investigations, route and utility corridor planning, etc.). Multiple sources of information Each band of information collected from a sensor contains important and unique data. We know that different wavelengths of incident energy are affected differently by each target - they are absorbed, reflected or transmitted in different proportions. The appearance of targets can easily change over time, sometimes within seconds. In many applications, using information from several different sources ensures that target identification or information extraction is as accurate as possible. The following describe ways of obtaining far more information about a target or area, than with one band from a sensor. Multispectral The use of multiple bands of spectral information attempts to exploit different and independent "views" of the targets so as to make their identification as confident as possible. Studies have been conducted to determine the optimum spectral bands for analyzing specific targets, such as insect damaged trees. Multisensor Different sensors often provide complementary information, and when integrated together, can facilitate interpretation and classification of imagery. Examples include combining high resolution panchromatic imagery with coarse resolution multispectral imagery, or merging actively and passively sensed data. A specific example is the integration of SAR imagery with multispectral imagery. SAR data adds the expression of surficial topography and relief to an otherwise flat image. The multispectral image contributes meaningful colour information about the composition or cover of the land surface. This type of image is often used in geology, where lithology or mineral composition is represented by the spectral component, and the structure is represented by the radar component. Multitemporal Information from multiple images taken over a period of time is referred to as multitemporal information. Multitemporal may refer to images taken days, weeks, or even years apart. Monitoring land cover change or growth in urban areas requires images from different time periods. Calibrated data, with careful controls on the quantitative aspect of the spectral or backscatter response, is required for proper monitoring activities. With uncalibrated data, a classification of the older image is compared to a classification from the recent image, and changes in the class boundaries are delineated. Another valuable multitemporal tool is the observation of vegetation phenology (how the vegetation changes throughout the growing season), which requires data at frequent intervals throughout the growing season. 'Multitemporal information' is acquired from the interpretation of images taken over the same area, but at different times. The time difference between the images is chosen so as to be able to monitor some dynamic event. Some catastrophic events (landslides, floods, fires, etc.) would need a time difference counted in days, while much slower-paced events (glacier melt, forest regrowth, etc.) would require years. This type of application also requires consistency in illumination conditions (solar angle or radar imaging geometry) to provide consistent and comparable classification results. The ultimate in critical (and quantitative) multitemporal analysis depends on calibrated data. Only by relating the brightnesses seen in the image to physical units, can the images be precisely compared, and thus the nature and magnitude of the observed changes be determined. Agriculture Agriculture plays a dominant role in economies of both developed and undeveloped countries. Whether agriculture represents a substantial trading industry for an economically strong country or simply sustenance for a hungry, overpopulated one, it plays a significant role in almost every nation. The production of food is important to everyone and producing food in a costeffective manner is the goal of every farmer, large-scale farm manager and regional agricultural agency. A farmer needs to be informed to be efficient, and that includes having the knowledge and information products to forge a viable strategy for farming operations. These tools will help him understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions. Commodity brokers are also very interested in how well farms are producing, as yield (both quantity and quality) estimates for all products control price and worldwide trading. Satellite and airborne images are used as mapping tools to classify crops, examine their health and viability, and monitor farming practices. Agricultural applications of remote sensing include the following: • • • • • • crop type classification crop condition assessment crop yield estimation mapping of soil characteristics mapping of soil management practices compliance monitoring (farming practices) Crop Type Mapping Background Identifying and mapping crops is important for a number of reasons. Maps of crop type are created by national and multinational agricultural agencies, insurance agencies, and regional agricultural boards to prepare an inventory of what was grown in certain areas and when. This serves the purpose of forecasting grain supplies (yield prediction), collecting crop production statistics, facilitating crop rotation records, mapping soil productivity, identification of factors influencing crop stress, assessment of crop damage due to storms and drought, and monitoring farming activity. Key activities include identifying the crop types and delineating their extent (often measured in acres). Traditional methods of obtaining this information are census and ground surveying. In order to standardize measurements however, particularly for multinational agencies and consortiums, remote sensing can provide common data collection and information extraction strategies. Remote sensing offers an efficient and reliable means of collecting the information required, in order to map crop type and acreage. Besides providing a synoptic view, remote sensing can provide structure information about the health of the vegetation. The spectral reflection of a field will vary with respect to changes in the phenology (growth), stage type, and crop health, and thus can be measured and monitored by multispectral sensors. Radar is sensitive to the structure, alignment, and moisture content of the crop, and thus can provide complementary information to the optical data. Combining the information from these two types of sensors increases the information available for distinguishing each target class and its respective signature, and thus there is a better chance of performing a more accurate classification. Interpretations from remotely sensed data can be input to a geographic information system (GIS) and crop rotation systems, and combined with ancillary data, to provide information of ownership, management practices etc. Datarequirements Crop identification and mapping benefit from the use of multitemporal imagery to facilitate classification by taking into account changes in reflectance as a function of plant phenology (stage of growth). This in turn requires calibrated sensors, and frequent repeat imaging throughout the growing season. For example, crops like canola may be easier to identify when they are flowering, because of both the spectral reflectance change, and the timing of the flowering. Multisensor data are also valuable for increasing classification accuracies by contributing more information than a sole sensor could provide. VIR sensing contributes information relating to the chlorophyll content of the plants and the canopy structure, while radar provides information relating to plant structure and moisture. In areas of persistent cloud cover or haze, radar is an excellent tool for observing and distinguishing crop type due to its active sensing capabilities and long wavelengths, capable of penetrating through atmospheric water vapour. Canadavs.International Although the principles of identifying crop type are the same, the scale of observation in Europe and Southeast Asia is considerably smaller than in North America, primarily due to smaller field parcel sizes. Cloud cover in Europe and tropical countries also usually limits the feasibility of using high-resolution optical sensors. In these cases high-resolution radar would have a strong contribution. The sizable leaves of tropical agricultural crops (cocoa, banana, and oil palm) have distinct radar signatures. Banana leaves in particular are characterized by bright backscatter (represented by "B" in image). Monitoring stages of rice growth is a key application in tropical areas, particularly Asian countries. Radar is very sensitive to surface roughness, and the development of rice paddies provides a dramatic change in brightness from the low returns from smooth water surfaces in flooded paddies , to the high return of the emergent rice crop. Casestudy(example) The countries involved in the European Communities (EC) are using remote sensing to help fulfil the requirements and mandate of the EC Agricultural Policy, which is common to all members. The requirements are to delineate, identify, and measure the extent of important crops throughout Europe, and to provide an early forecast of production early in the season. Standardized procedures for collecting this data are based on remote sensing technology, developed and defined through the MARS project (Monitoring Agriculture by Remote Sensing). The project uses many types of remotely sensed data, from low resolution NOAA-AVHRR, to high-resolution radar, and numerous sources of ancillary data. These data are used to classify crop type over a regional scale to conduct regional inventories, assess vegetation condition, estimate potential yield, and finally to predict similar statistics for other areas and compare results. Multisource data such as VIR and radar were introduced into the project for increasing classification accuracies. Radar provides very different information than the VIR sensors, particularly vegetation structure, which proves valuable when attempting to differentiate between crop type. One the key applications within this project is the operational use of high resolution optical and radar data to confirm conditions claimed by a farmer when he requests aid or compensation. The use of remote sensing identifies potential areas of non-compliance or suspicious circumstances, which can then be investigated by other, more direct methods. As part of the Integrated Administration and Control System (IACS), remote sensing data supports the development and management of databases, which include cadastral information, declared land use, and parcel measurement. This information is considered when applications are received for area subsidies. This is an example of a truly successfully operational crop identification and monitoring application of remote sensing. Crop Monitoring & Damage Assessment Background Assessment of the health of a crop, as well as early detection of crop infestations, is critical in ensuring good agricultural productivity. Stress associated with, for example, moisture deficiencies, insects, fungal and weed infestations, must be detected early enough to provide an opportunity for the farmer to mitigate. This process requires that remote sensing imagery be provided on a frequent basis (at a minimum, weekly) and be delivered to the farmer quickly, usually within 2 days. Also, crops do not generally grow evenly across the field and consequently crop yield can vary greatly from one spot in the field to another. These growth differences may be a result of soil nutrient deficiencies or other forms of stress. Remote sensing allows the farmer to identify areas within a field which are experiencing difficulties, so that he can apply, for instance, the correct type and amount of fertilizer, pesticide or herbicide. Using this approach, the farmer not only improves the productivity from his land, but also reduces his farm input costs and minimizes environmental impacts. There are many people involved in the trading, pricing, and selling of crops that never actually set foot in a field. They need information regarding crop health worldwide to set prices and to negotiate trade agreements. Many of these people rely on products such as a crop assessment index to compare growth rates and productivity between years and to see how well each country's agricultural industry is producing. This type of information can also help target locations of future problems, for instance the famine in Ethiopia in the late 1980's, caused by a significant drought which destroyed many crops. Identifying such areas facilitates in planning and directing humanitarian aid and relief efforts. Remote sensing has a number of attributes that lend themselves to monitoring the health of crops. One advantage of optical (VIR) sensing is that it can see beyond the visible wavelengths into the infrared, where wavelengths are highly sensitive to crop vigour as well as crop stress and crop damage. Remote sensing imagery also gives the required spatial overview of the land. Recent advances in communication and technology allow a farmer to observe images of his fields and make timely decisions about managing the crops. Remote sensing can aid in identifying crops affected by conditions that are too dry or wet, affected by insect, weed or fungal infestations or weather related damage . Images can be obtained throughout the growing season to not only detect problems, but also to monitor the success of the treatment. In the example image given here, a tornado has destroyed/damaged crops southwest of Winnipeg, Manitoba. Healthy vegetation contains large quantities of chlorophyll, the substance that gives most vegetation its distinctive green colour. In referring to healthy crops, reflectance in the blue and red parts of the spectrum is low since chlorophyll absorbs this energy. In contrast, reflectance in the green and nearinfrared spectral regions is high. Stressed or damaged crops experience a decrease in chlorophyll content and changes to the internal leaf structure. The reduction in chlorophyll content results in a decrease in reflectance in the green region and internal leaf damage results in a decrease in near-infrared reflectance. These reductions in green and infrared reflectance provide early detection of crop stress. Examining the ratio of reflected infrared to red wavelengths is an excellent measure of vegetation health. This is the premise behind some vegetation indices, such as the normalized differential vegetation index (NDVI) (Chapter 4). Healthy plants have a high NDVI value because of their high reflectance of infrared light, and relatively low reflectance of red light. Phenology and vigour are the main factors in affecting NDVI. An excellent example is the difference between irrigated crops and non-irrigated land. The irrigated crops appear bright green in a real-colour simulated image. The darker areas are dry rangeland with minimal vegetation. In a CIR (colour infrared simulated) image, where infrared reflectance is displayed in red, the healthy vegetation appears bright red, while the rangeland remains quite low in reflectance. Examining variations in crop growth within one field is possible. Areas of consistently healthy and vigorous crop would appear uniformly bright. Stressed vegetation would appear dark amongst the brighter, healthier crop areas. If the data is georeferenced, and if the farmer has a GPS (global position satellite) unit, he can find the exact area of the problem very quickly, by matching the coordinates of his location to that on the image. Data requirements Detecting damage and monitoring crop health requires high-resolution imagery and multispectral imaging capabilities. One of the most critical factors in making imagery useful to farmers is a quick turnaround time from data acquisition to distribution of crop information. Receiving an image that reflects crop conditions of two weeks earlier does not help real time management nor damage mitigation. Images are also required at specific times during the growing season, and on a frequent basis. Remote sensing doesn't replace the field work performed by farmers to monitor their fields, but it does direct them to the areas in need of immediate attention. Canada vs. International Efficient agricultural practices are a global concern, and other countries share many of the same requirements as Canada in terms of monitoring crop health by means of remote sensing. In many cases however, the scale of interest is smaller smaller fields in Europe and Asia dictate higher resolution systems and smaller areal coverage. Canada, the USA, and Russia, amongst others, have more expansive areas devoted to agriculture, and have developed, or are in the process of developing crop information systems (see below). In this situation, regional coverage and lower resolution data (say: 1km) can be used. The lower resolution facilitates computer efficiency by minimizing storage space, processing efforts and memory requirements. As an example of an international crop monitoring application, date palms are the prospective subject of an investigation to determine if remote sensing methods can detect damage from the red palm weevil in the Middle East. In the Arabian Peninsula, dates are extremely popular and date crops are one of the region's most important agricultural products. Infestation by the weevil could quickly devastate the palm crops and swallow a commodity worth hundreds of millions of dollars. Remote sensing techniques will be used to examine the health of the date crops through spectral analysis of the vegetation. Infested areas appear yellow to the naked eye, and will show a smaller near infrared reflectance and a higher red reflectance on the remotely sensed image data than the healthy crop areas. Authorities are hoping to identify areas of infestation and provide measures to eradicate the weevil and save the remaining healthy crops. Case study (example) Canadian Crop Information System: A composite crop index map is created each week, derived from composited NOAA-AVHRR data. Based on the NDVI, the index shows the health of crops in the prairie regions of Manitoba through to Alberta. These indices are produced weekly, and can be compared with indices of past years to compare crop growth and health. In 1988, severe drought conditions were prevalent across the prairies. Using NDVI values from NOAA AVHRR data, a drought area analysis determined the status of drought effects on crops across the affected area. Red and yellow areas indicate those crops in a weakened and stressed state, while green indicates healthy crop conditions. Note that most of the healthy crops are those in the cooler locations, such as in the northern Alberta (Peace River) and the higher elevations (western Alberta). Non-cropland areas (dry rangeland and forested land) are indicated in black, within the analysis region. Forestry Forests are a valuable resource providing food, shelter, wildlife habitat, fuel, and daily supplies such as medicinal ingredients and paper. Forests play an important role in balancing the Earth's CO2 supply and exchange, acting as a key link between the atmosphere, geosphere, and hydrosphere. Tropical rainforests, in particular, house an immense diversity of species, more capable of adapting to, and therefore surviving, changing environmental conditions than monoculture forests. This diversity also provides habitat for numerous animal species and is an important source of medicinal ingredients. The main issues concerning forest management are depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation. Humans generally consider the products of forests useful, rather than the forests themselves, and so extracting wood is a wide-spread and historical practice, virtually global in scale. Depletion of forest resources has long term effects on climate, soil conservation, biodiversity, and hydrological regimes, and thus is a vital concern of environmental monitoring activities. Commercial forestry is an important industry throughout the world. Forests are cropped and re-harvested, and the new areas continually sought for providing a new source of lumber. With increasing pressure to conserve native and virgin forest areas, and unsustainable forestry practices limiting the remaining areas of potential cutting, the companies involved in extracting wood supplies need to be more efficient, economical, and aware of sustainable forestry practices. Ensuring that there is a healthy regeneration of trees where forests are extracted will ensure a future for the commercial forestry firms, as well as adequate wood supplies to meet the demands of a growing population. Non-commercial sources of forest depletion include removal for agriculture (pasture and crops), urban development, droughts, desert encroachment, loss of ground water, insect damage, fire and other natural phenomena (disease, typhoons). In some areas of the world, particularly in the tropics, (rain) forests, are covering what might be considered the most valuable commodity - viable agricultural land. Forests are burned or clear-cut to facilitate access to, and use of, the land. This practice often occurs when the perceived need for long term sustainability is overwhelmed by short-term sustenance goals. Not only are the depletion of species-rich forests a problem, affecting the local and regional hydrological regime, the smoke caused by the burning trees pollutes the atmosphere, adding more CO2, and furthering the greenhouse effect. Of course, monitoring the health of forests is crucial for sustainability and conservation issues. Depletion of key species such as mangrove in environmentally sensitive coastline areas, removal of key support or shade trees from a potential crop tree, or disappearance of a large biota acting as a CO2 reservoir all affect humans and society in a negative way, and more effort is being made to monitor and enforce regulations and plans to protect these areas. International and domestic forestry applications where remote sensing can be utilized include sustainable development, biodiversity, land title and tenure (cadastre), monitoring deforestation, reforestation monitoring and managing, commercial logging operations, shoreline and watershed protection, biophysical monitoring (wildlife habitat assessment), and other environmental concerns. General forest cover information is valuable to developing countries with limited previous knowledge of their forestry resources. General cover type mapping, shoreline and watershed mapping and monitoring for protection, monitoring of cutting practices and regeneration, and forest fire/burn mapping are global needs which are currently being addressed by Canadian and foreign agencies and companies employing remote sensing technology as part of their information solutions in foreign markets. Forestry applications of remote sensingg include the following: 1) reconnaissance mapping: Objectives to be met by national forest/environment agencies include forest cover updating, depletion monitoring, and measuring biophysical properties of forest stands. • • forest cover type discrimination agroforestry mapping 2) Commercial forestry: Of importance to commercial forestry companies and to resource management agencies are inventory and mapping applications: collecting harvest information, updating of inventory information for timber supply, broad forest type, vegetation density, and biomass measurements. • • • • • • clear cut mapping / regeneration assessment burn delineation infrastructure mapping / operations support forest inventory biomass estimation species inventory 3) Environmental monitoring Conservation authorities are concerned with monitoring the quantity, health, and diversity of the Earth's forests. • • • • • deforestation (rainforest, mangrove colonies) species inventory watershed protection (riparian strips) coastal protection (mangrove forests) forest health and vigour Canadian requirements for forestry application information differ considerably from international needs, due in part to contrasts in tree size, species diversity (monoculture vs. species rich forest), and agroforestry practices. The level of accuracy and resolution of data required to address respective forestry issues differs accordingly. Canadian agencies have extensive a priori knowledge of their forestry resources and present inventory and mapping needs are often capably addressed by available data sources. For Canadian applications requirements, high accuracy (for accurate information content), multispectral information, fine resolution, and data continuity are the most important. There are requirements for large volumes of data, and reliable observations for seasonal coverage. There is a need to balance spatial resolution with the required accuracy and costs of the data. Resolution capabilities of 10 m to 30 m are deemed adequate for forest cover mapping, identifying and monitoring clearcuts, burn and fire mapping, collecting forest harvest information, and identifying general forest damage. Spatial coverage of 100 - 10000 km2 is appropriate for district to provincial scale forest cover and clear cut mapping, whereas 1-100 km2 coverage is the most appropriate for site specific vegetation density and volume studies. Tropical forest managers will be most concerned with having a reliable data source, capable of imaging during critical time periods, and therefore unhindered by atmospheric conditions. Hydrology Hydrology is the study of water on the Earth's surface, whether flowing above ground, frozen in ice or snow, or retained by soil. Hydrology is inherently related to many other applications of remote sensing, particularly forestry, agriculture and land cover, since water is a vital component in each of these disciplines. Most hydrological processes are dynamic, not only between years, but also within and between seasons, and therefore require frequent observations. Remote sensing offers a synoptic view of the spatial distribution and dynamics of hydrological phenomena, often unattainable by traditional ground surveys. Radar has brought a new dimension to hydrological studies with its active sensing capabilities, allowing the time window of image acquisition to include inclement weather conditions or seasonal or diurnal darkness. Examples of hydrological applications include: • • • • • • • • • • • wetlands mapping and monitoring, soil moisture estimation, snow pack monitoring / delineation of extent, measuring snow thickness, determining snow-water equivalent, river and lake ice monitoring, flood mapping and monitoring, glacier dynamics monitoring (surges, ablation) river /delta change detection drainage basin mapping and watershed modelling irrigation canal leakage detection • irrigation scheduling Flood Delineation & Mapping Background: A natural phenomenon in the hydrological cycle is flooding. Flooding is necessary to replenish soil fertility by periodically adding nutrients and fine grained sediment; however, it can also cause loss of life, temporary destruction of animal habitat and permanent damage to urban and rural infrastructure. Inland floods can result from disruption to natural or man-made dams, catastrophic melting of ice and snow (jökulhlaups in Iceland), rain, river ice jams and / or excessive runoff in the spring. Remote sensing techniques are used to measure and monitor the areal extent of the flooded areas , to efficiently target rescue efforts and to provide quantifiable estimates of the amount of land and infrastructure affected. Incorporating remotely sensed data into a GIS allows for quick calculations and assessments of water levels, damage, and areas facing potential flood danger. Users of this type of data include flood forecast agencies, hydropower companies, conservation authorities, city planning and emergency response departments, and insurance companies (for flood compensation). The identification and mapping of floodplains, abandoned river channels, and meanders are important for planning and transportation routing. Datarequirements: Many of these users of remotely sensed data need the information during a crisis and therefore require "near-real time turnaround". Turnaround time is less demanding for those involved in hydrologic modelling, calibration/validation studies, damage assessment and the planning of flood mitigation. Flooding conditions are relatively short term and generally occur during inclement weather, so optical sensors, although typically having high information content for this purpose, can not penetrate through the cloud cover to view the flooded region below. For these reasons, active SAR sensors are particularly valuable for flood monitoring. RADARSAT in particular offers a high turnaround interval, from when the data is acquired by the sensor, to when the image is delivered to the user on the ground. The land / water interface is quite easily discriminated with SAR data, allowing the flood extent to be delineated and mapped. The SAR data is most useful when integrated with a pre-flood image, to highlight the flood-affected areas, and then presented in a GIS with cadastral and road network information. Canada vs. International Requirements for this application are similar the world over. Flooding can affect many areas of the world, whether coastal or inland, and many of the conditions for imaging are the same. Radar provides excellent water/land discrimination and is reliable for imaging despite most atmospheric limitations. Case study (example): RADARSAT MAPS THE MANITOBA SEA: THE FLOODS OF 1997 In 1997, the worst Canadian flood of the 20th century inundated prairie fields and towns in the states of Minnesota, North Dakota, and the Canadian province of Manitoba. By May 5th, 25,000 residents of Manitoba had been evacuated from their homes, with 10,000 more on alert. The watershed of the Red River, flowing north from the United States into Canada, received unusually high winter snowfalls and heavy precipitation in April. These factors, combined with the northward flow into colder ground areas and very flat terrain beyond the immediate floodplain, caused record flooding conditions, with tremendous damage to homes and property, in addition to wildlife and livestock casualties. For weeks emergency response teams, area residents, and the media monitored the extent of the flood, with some input from remote sensing techniques. It is impossible to imagine the scale of flooding from a ground perspective, and even video and photographs from aircraft are unable to show the full extent. Spectacular satellite images however, have shown the river expand from a 200 m wide ribbon, to a body of water measuring more than 40 km across. Towns protected by sand-bag dikes, were dry islands in the midst of what was described as the "Red Sea". Many other towns weren't as fortunate, and home and business owners were financially devastated by their losses. Insurance agents faced their own flood of claims for property, businesses, and crops ruined or damaged by the Red River flood. To quickly assess who is eligible for compensation, the insurance companies can rely on remotely sensed data to delineate the flood extent, and GIS databases to immediately identify whose land was directly affected. City and town planners could also use the images to study potential locations for future dike reinforcement and construction, as well as residential planning. Both NOAA-AVHRR and RADARSAT images captured the scale and extent of the flood. The AVHRR sensors onboard the NOAA satellites provided smallscale views of the entire flood area from Lakes Manitoba and Winnipeg south to the North Dakota - South Dakota border. Some of the best images are those taken at night in the thermal infrared wavelengths, where the cooler land appears dark and the warmer water (A) appears white. Manmade dikes, such as the Brunkild Dike (B), were quickly built to prevent the flow of water into southern Winnipeg. Dikes are apparent on the image as very regular straight boundaries between the land and floodwater. Although the city of Winnipeg (C) is not clearly defined, the Winnipeg floodway (D) immediately to the east, paralleling the Red River at the northeast end of the flood waters, is visible since it is full of water. The floodway was designed to divert excess water flow from the Red River outside of the city limits. In this case, the volume of water was simply too great for the floodway to carry it all, and much of the flow backed up and spread across the prairie. RADARSAT provided some excellent views of the flood, because of its ability to image in darkness or cloudy weather conditions, and its sensitivity to the land/water differences. In this image, the flood water (A) completely surrounds the town of Morris (B), visible as a bright patch within the dark flood water. The flooded areas appear dark on radar imagery because very little of the incident microwave energy directed toward the smooth water surface returns back to the sensor. The town however, has many angular (corner) reflectors primarily in the form of buildings, which cause the incident energy to "bounce" back to the sensor. Transportation routes can still be observed. A railroad, on its raised bed, can be seen amidst the water just above (C), trending southwest - northeast. Farmland relatively unaffected by the flood (D) is quite variable in its backscatter response. This is due to differences in each field's soil moisture and surface roughness. Soil Moisture Background Soil moisture is an important measure in determining crop yield potential in Canada and in drought-affected parts of the world (Africa) and for watershed modelling. The moisture content generally refers to the water contained in the upper 1-2m of soil, which can potentially evaporate into the atmosphere. Early detection of dry conditions which could lead to crop damage, or are indicative of potential drought, is important for amelioration efforts and forecasting potential crop yields, which in turn can serve to warn farmers, prepare humanitarian aid to affected areas, or give international commodities traders a competitive advantage. Soil moisture conditions may also serve as a warning for subsequent flooding if the soil has become too saturated to hold any further runoff or precipitation. Soil moisture content is an important parameter in watershed modelling that ultimately provides information on hydroelectric and irrigation capacity. In areas of active deforestation, soil moisture estimates help predict amounts of run-off, evaporation rates, and soil erosion. Why remote sensing? Remote sensing offers a means of measuring soil moisture across a wide area instead of at discrete point locations that are inherent with ground measurements. RADAR is effective for obtaining qualitative imagery and quantitative measurements, because radar backscatter response is affected by soil moisture, in addition to topography, surface roughness and amount and type of vegetative cover. Keeping the latter elements static, multitemporal radar images can show the change in soil moisture over time. The radar is actually sensitive to the soil's dielectric constant, a property that changes in response to the amount of water in the soil. Users of soil moisture information from remotely sensed data include agricultural marketing and administrative boards, commodity brokers, large scale farming managers, conservation authorities, and hydroelectric power producers. Datarequirements Obviously, a sensor must be sensitive to moisture conditions, and radar satisfies this requirement better than optical sensors. Frequent and regular (repeated) imaging is required during the growing season to follow the change in moisture conditions, and a quick turnaround is required for a farmer to respond to unsuitable conditions (excessive moisture or dryness) in a timely manner. Using high resolution images, a farmer can target irrigation efforts more accurately. Regional coverage allows an overview of soil and growing conditions of interest to agricultural agencies and authorities. Canadavs.International Data requirements to address this application are similar around the world, except that higher resolution data may be necessary in areas such as Europe and Southeast Asia, where field and land parcel sizes are substantially smaller than in North America. CaseStudy(example) Rainfall distribution , Melfort, Saskatchewan, Canada As with most Canadian prairie provinces, the topography of Saskatchewan is quite flat. The region is dominated by black and brown chernozemic soil characterized by a thick dark organic horizon, ideal for growing cereal crops such as wheat. More recently, canola has been introduced as an alternative to cereal crops. Shown here is a radar image acquired July 7, 1992 by the European Space Agency (ESA) ERS-1 satellite. This synoptic image of an area near Melfort, Saskatchewan details the effects of a localized precipitation event on the microwave backscatter recorded by the sensor. Areas where precipitation has recently occurred can be seen as a bright tone (bottom half) and those areas unaffected by the event generally appear darker (upper half). This is a result of the complex dielectric constant which is a measure of the electrical properties of surface materials. The dielectric property of a material influences its ability to absorb microwave energy, and therefore critically affects the scattering of microwave energy. The magnitude of the radar backscatter is proportional to the dielectric constant of the surface. For dry, naturally occurring materials, this is in the range of 3 - 8 , and may reach values as high as 80 for wet surfaces. Therefore the amount of moisture in the surface material directly affects the amount of backscattering. For example, the lower the dielectric constant, the more incident energy is absorbed, the darker the object will be on the image. Land Cover & Land Use Although the terms land cover and land use are often used interchangeably, their actual meanings are quite distinct. Land cover refers to the surface cover on the ground, whether vegetation, urban infrastructure, water, bare soil or other. Identifying, delineating and mapping land cover is important for global monitoring studies, resource management, and planning activities. Identification of land cover establishes the baseline from which monitoring activities (change detection) can be performed, and provides the ground cover information for baseline thematic maps. Land use refers to the purpose the land serves, for example, recreation, wildlife habitat, or agriculture. Land use applications involve both baseline mapping and subsequent monitoring, since timely information is required to know what current quantity of land is in what type of use and to identify the land use changes from year to year. This knowledge will help develop strategies to balance conservation, conflicting uses, and developmental pressures. Issues driving land use studies include the removal or disturbance of productive land, urban encroachment, and depletion of forests. It is important to distinguish this difference between land cover and land use, and the information that can be ascertained from each. The properties measured with remote sensing techniques relate to land cover, from which land use can be inferred, particularly with ancillary data or a priori knowledge. Land cover / use studies are multidisciplinary in nature, and thus the participants involved in such work are numerous and varied, ranging from international wildlife and conservation foundations, to government researchers, and forestry companies. Regional (in Canada, provincial) government agencies have an operational need for land cover inventory and land use monitoring, as it is within their mandate to manage the natural resources of their respective regions. In addition to facilitating sustainable management of the land, land cover and use information may be used for planning, monitoring, and evaluation of development, industrial activity, or reclamation. Detection of long term changes in land cover may reveal a response to a shift in local or regional climatic conditions, the basis of terrestrial global monitoring. Ongoing negotiations of aboriginal land claims have generated a need for more stringent knowledge of land information in those areas, ranging from cartographic to thematic information. Resource managers involved in parks, oil, timber, and mining companies, are concerned with both land use and land cover, as are local resource inventory or natural resource agencies. Changes in land cover will be examined by environmental monitoring researchers, conservation authorities, and departments of municipal affairs, with interests varying from tax assessment to reconnaissance vegetation mapping. Governments are also concerned with the general protection of national resources, and become involved in publicly sensitive activities involving land use conflicts. Land use applications of remote sensing include the following: • • • • • • • • natural resource management wildlife habitat protection baseline mapping for GIS input urban expansion / encroachment routing and logistics planning for seismic / exploration / resource extraction activities damage delineation (tornadoes, flooding, volcanic, seismic, fire) legal boundaries for tax and property evaluation target detection - identification of landing strips, roads, clearings, bridges, land/water interface Land Use Change (Rural / Urban) Background As the Earth's population increases and national economies continue to move away from agriculture based systems, cities will grow and spread. The urban sprawl often infringes upon viable agricultural or productive forest land, neither of which can resist or deflect the overwhelming momentum of urbanization. City growth is an indicator of industrialization (development) and generally has a negative impact on the environmental health of a region. The change in land use from rural to urban is monitored to estimate populations, predict and plan direction of urban sprawl for developers, and monitor adjacent environmentally sensitive areas or hazards. Temporary refugee settlements and tent cities can be monitored and population amounts and densities estimated. Analyzing agricultural vs. urban land use is important for ensuring that development does not encroach on valuable agricultural land, and to likewise ensure that agriculture is occurring on the most appropriate land and will not degrade due to improper adjacent development or infrastructure. With multi-temporal analyses, remote sensing gives a unique perspective of how cities evolve. The key element for mapping rural to urban landuse change is the ability to discriminate between rural uses (farming, pasture forests) and urban use (residential, commercial, recreational). Remote sensing methods can be employed to classify types of land use in a practical, economical and repetitive fashion, over large areas. Datarequirements Requirements for rural / urban change detection and mapping applications are 1) high resolution to obtain detailed information, and 2) multispectral optical data to make fine distinction among various land use classes. Sensors operating in the visible and infrared portion of the spectrum are the most useful data sources for land use analysis. While many urban features can be detected on radar and other imagery (usually because of high reflectivity), VIR data at high resolution permits fine distinction among more subtle land cover/use classes. This would permit a confident identification of the urban fringe and the transition to rural land usage. Optical imagery acquired during winter months is also useful for roughly delineating urban areas vs. non-urban. Cities appear in dramatic contrast to smooth textured snow covered fields. Radar sensors also have some use for all urban/rural delineation applications, due to the ability of the imaging geometry to enhance anthropogenic features, such as buildings, in the manner of corner reflectors. The optimum geometric arrangement between the sensor and urban area is an orientation of linear features parallel to the sensor movement, perpendicular to the incoming incident EM energy. Generally, this type of application does not require a high turnaround rate, or a frequent acquisition schedule. Canadavs.International Throughout the world, requirements for rural/urban delineation will differ according to the prevalent atmospheric conditions. Areas with frequently cloudy skies may require the penetrating ability of radar, while areas with clear conditions can use airphoto, optical satellite or radar data. While the land use practices for both rural and urban areas will be significantly different in various parts of the world, the requirement for remote sensing techniques to be applied (other than the cloud-cover issue) will be primarily the need for fine spatial detail. Casestudy(example) This image of land cover change provides multitemporal information in the form of urban growth mapping. The colours represent urban land cover for two different years. The green delineates those areas of urban cover in 1973, and the pink, urban areas for 1985. This image dramatically shows the change in expansion of existing urban areas, and the clearing of new land for settlements over a 12 year period. This type of information would be used for upgrading government services, planning for increased transportation routes, etc Land Cover / Biomass Mapping Background Land cover mapping serves as a basic inventory of land resources for all levels of government, environmental agencies, and private industry throughout the world. Whether regional or local in scope, remote sensing offers a means of acquiring and presenting land cover data in a timely manner. Land cover includes everything from crop type, ice and snow, to major biomes including tundra, boreal or rainforest, and barren land. Regional land cover mapping is performed by almost anyone who is interested in obtaining an inventory of land resources, to be used as a baseline map for future monitoring and land management. Programs are conducted around the world to observe regional crop conditions as well as investigating climatic change on a regional level through biome monitoring. Biomass mapping provides quantifiable estimates of vegetation cover, and biophysical information such as leaf area index (LAI), net primary productivity (NPP) and total biomass accumulations (TBA) measurements - important parameters for measuring the health of our forests, for example. There is nothing as practical and cost efficient for obtaining a timely regional overview of land cover than remote sensing techniques. Remote sensing data are capable of capturing changes in plant phenology (growth) throughout the growing season, whether relating to changes in chlorophyll content (detectable with VIR) or structural changes (via radar). For regional mapping, continuous spatial coverage over large areas is required. It would be difficult to detect regional trends with point source data. Remote sensing fulfills this requirement, as well as providing multispectral, multisource, and multitemporal information for an accurate classification of land cover. The multisource example image shows the benefit of increased information content when two data sources are integrated. On the left is TM data, and on the right it has been merged with airborne SAR. Datarequirements For continental and global scale vegetation studies, moderate resolution data (1km) is appropriate, since it requires less storage space and processing effort, a significant consideration when dealing with very large area projects. Of course the requirements depend entirely on the scope of the application. Wetland mapping for instance, demands a critical acquisition period and a high resolution requirement. Coverage demand will be very large for regional types of surveying. One way to adequately cover a large area and retain high resolution, is to create mosaics of the area from a number of scenes. Land cover information may be time sensitive. The identification of crops, for instance canola, may require imaging on specific days of flowering, and therefore, reliable imaging is appropriate. Multi-temporal data are preferred for capturing changes in phenology throughout the growing season. This information may be used in the classification process to more accurately discriminate vegetation types based on their growing characteristics. While optical data are best for land cover mapping, radar imagery is a good replacement in very cloudy areas. Case study (example) NBIOME: Classification of Canada's LandCover A major initiative of the Canada Centre for Remote Sensing is the development of an objective, reproducible classification of Canada's landcover. This classification methodology is used to produce a baseline map of the major biomes and land cover in Canada, which can then be compared against subsequent classifications to observe changes in cover. These changes may relate to regional climatic or anthropogenic changes affecting the landscape. The classification is based on NOAA-AVHRR LAC (Local Area Coverage) (1km) data. The coarse resolution is required to ensure efficient processing and storage of the data, when dealing with such a large coverage area. Before the classification procedure, cloud -cover reduced composites of the Canadian landmass, each spanning 10 day periods are created. In the composite, the value for each pixel used is the one most cloud free of the ten days. This is determined by the highest normalized difference vegetation index (NDVI) value, since low NDVI is indicative of cloud cover (low infrared reflectance, high visible reflectance). The data also underwent a procedure to minimize atmospheric, bidirectional, and contamination effects. The composites consist of four channels, mean reflectance of AVHRR channels 1 and 2, NDVI and area under the (temporal NDVI) curve. 16 composites (in 1993) were included in a customized land cover classification procedure (named: classification by progressive generalization), which is neither a supervised nor unsupervised methodology, but incorporates aspects of both. The classification approach is based on finding dominant spectral clusters and conducting progressive merging methodology. Eventually the clusters are labelled with the appropriate land cover classes. The benefit is that the classification is more objective than a supervised approach, while not controlling the parameters of clustering, which could alter the results. The result of this work is an objective, reproducible classification of Canada's land cover. Digital Elevation Models Background The availability of digital elevation models (DEMs) is critical for performing geometric and radiometric corrections for terrain on remotely sensed imagery, and allows the generation of contour lines and terrain models, thus providing another source of information for analysis. Present mapping programs are rarely implemented with only planimetric considerations. The demand for digital elevation models is growing with increasing use of GIS and with increasing evidence of improvement in information extracted using elevation data (for example, in discriminating wetlands, flood mapping, and forest management). The incorporation of elevation and terrain data is crucial to many applications, particularly if radar data is being used, to compensate for foreshortening and layover effects, and slope induced radiometric effects. Elevation data is used in the production of popular topographic maps. Elevation data, integrated with imagery is also used for generating perspective views, useful for tourism, route planning, to optimize views for developments, to lessen visibility of forest clearcuts from major transportation routes, and even golf course planning and development. Elevation models are integrated into the programming of cruise missiles, to guide them over the terrain. Resource management, telecommunications planning, and military mapping are some of the applications associated with DEMs. There are a number of ways to generate elevation models. One is to create point data sets by collecting elevation data from altimeter or Global Positioning System (GPS) data, and then interpolating between the points. This is extremely time and effort consuming. Traditional surveying is also very time consuming and limits the timeliness of regional scale mapping. Generating DEMs from remotely sensed data can be cost effective and efficient. A variety of sensors and methodologies to generate such models are available and proven for mapping applications. Two primary methods if generating elevation data are 1. Stereogrammetry techniques using airphotos (photogrammetry), VIR imagery, or radar data (radargrammetry), and 2. Radar interferometry. Stereogrammetry involves the extraction of elevation information from stereo overlapping images, typically airphotos, SPOT imagery, or radar. To give an example, stereo pairs of airborne SAR data are used to find point elevations, using the concept of parallax. Contours (lines of equal elevation) can be traced along the images by operators constantly viewing the images in stereo. The potential of radar interferometric techniques to measure terrain height, and to detect and measure minute changes in elevation and horizontal base, is becoming quickly recognized. Interferometry involves the gathering of precise elevation data using successive passes (or dual antenna reception) of spaceborne or airborne SAR. Subsequent images from nearly the same track are acquired and instead of examining the amplitude images, the phase information of the returned signals is compared. The phase images are coregistered, and the differences in phase value for each pixel is measured, and displayed as an interferogram. A computation of phase "unwrapping" or phase integration, and geometric rectification are performed to determine altitude values. High accuracies have been achieved in demonstrations using both airborne (in the order of a few centimetres) and spaceborne data (in the order of 10m). Primary applications of interferometry include high quality DEM generation, monitoring of surface deformations (measurement of land subsidence due to natural processes, gas removal, or groundwater extraction; volcanic inflation prior to eruption; relative earth movements caused by earthquakes), and hazard assessment and monitoring of natural landscape features and fabricated structures, such as dams. This type of data would be useful for insurance companies who could better measure damage due to natural disasters, and for hydrology-specialty companies and researchers interested in routine monitoring of ice jams for bridge safety, and changes in mass balance of glaciers or volcano growth prior to an eruption. From elevation models, contour lines can be generated for topographic maps , slope and aspect models can be created for integration into (land cover) thematic classification datasets or used as a sole data source, or the model itself can be used to orthorectify remote sensing imagery and generate perspective views. Datarequirements The basic data requirement for both stereogrammetric and interferometric techniques is that the target site has been imaged two times, with the sensor imaging positions separated to give two different viewing angles. In virtually all DEM and topographic map generation applications, cartographic accuracy is the important limiting factor. Turnaround time is not critical and repeat frequency is dependent on whether the application involves change detection, and what the temporal scope of the study is. Canadavs.International Aerial photography is the primary data source for DEM generation in Canada for national topographic mapping. For other applications of DEMs, there are additional satellite sources such as SPOT, with its pointable sensors and 10m panchromatic spatial resolution, producing adequate height information at scales smaller than 1:50,000. The height accuracy requirement for 1:50,000 mapping in Canada is between 5 and 20 m. In developing countries it is typically 20 m. The original elevation information used in the Canadian National Topographic Series Maps was provided from photogrammetric techniques. In foreign markets, airborne radar mapping is most suited for approximately 1:50,000 scale topographic mapping. Spaceborne radar systems will be able to provide data for the generation of coarser DEMs through radargrammetry, in areas of cloud cover and with less stringent accuracy requirements. Stereo data in most modes of operation will be available because of the flexible incidence angles, allowing most areas to be captured during subsequent passes. Interferometry from airborne and spaceborne systems should meet many mapping requirements. QUESTIONS 1) how can use the GIS Techniques to natural resources? 2) How can creat the Agriculture map using GIS. What are the Spatial features from Agriculture Resource? 3) Write detaily the water Resources Application using in GIS Techniques. 4) How can do the aerial survey for surface water bodies? 5) How can implement the GIS techniques to waste managements? Define and decribe LIS?