ITC Journal 1998-1 Imaging spectrometry for monitoring tree damage caused by volcanic activity in the Long Valley caldera, California Steven M de Jong1 ABSTRACT canic activity, which causes significant damage to the pine and fir species. Multitemporal images acquired by AVIRIS were used to survey damage to pine and fir trees, and to map the spatial extent of diffuse volcanic gas emissions. AVIRIS acquires images at an altitude of 20 km in the spectral range of 400 to 2500 nm, with a pixel size of 20 x 20 m. It has 224 spectral bands with a nominal bandwidth of 10 nm (Figure 1). Developments in detector technology have triggered a new remote sensing technology: imaging spectrometry. Imaging spectrometers measure reflected solar radiance on a pixel-by-pixel basis in many narrow spectral bands, allowing the identification of materials or their properties by diagnostic absorption features. To date, only airborne imaging spectrometers are available, but several imaging spectrometers are planned for the next generation of space platforms. The abundance of information available in the continuous spectral coverage makes it possible to address questions in numerous environmental disciplines. This paper describes a study in the Sierra Nevada, using multitemporal images acquired by the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) for monitoring tree damage by volcanic activity. Diffuse volcanic gas emanations deprive the roots of oxygen, resulting in trees that are under stress and ultimately die. Imaging spectrometry yields important information on tree conditions and on the presence of dead vegetative material. The spatial and temporal extent of the dead and stressed tree areas were mapped using AVIRIS data. The use of imaging spectrometry to map the diffuse volcanic gas emissions was less successful. Although the images yield noisy spatial patterns of carbon dioxide, it is difficult to separate atmospheric gases from the diffuse soil emanations. STUDY AREA The research area is situated around Mammoth Mountain. Mammoth Mountain is a volcanic cone rising up to 3300 m; it forms the western rim of the Long Valley caldera in the Sierra Nevada, California (Figure 2). The Long Valley caldera measures approximately 17 x 32 km, and was formed by a large eruption about 760,000 years ago . After a period of rest (the last signs of activity from Mammoth Mountain occurred roughly 500 years ago), the area has since 1980 been suffering from frequent earthquakes, hydrothermal activity and gas emissions [22, 26, 32]. Furthermore, the resurgent dome in the center of the Long Valley caldera is inflating; the U.S. Geological Survey has measured an uplift of approximately 60 cm since 1980. In 1990, areas of dying forests were found on the flanks of Mammoth Mountain . At first, the cause of tree die-off was sought in the persisting drought of the preceding years. However, trees died regardless of age or species, as shown in Figure 3. Research  revealed that high concentrations of carbon dioxide (30 In the last decennia, a new remote sensing technique was developed through significant advances in detector technology: imaging spectrometry. An imaging spectrometer collects narrow spectral bands on a pixel-by-pixel basis, aiming to identify surface materials by using diagnostic absorption features [12, 23, 37]. Figure 1 shows the concept of imaging spectrometry. Conventional broadband sensors such as Spot-XS, Landsat MSS and Landsat TM are not very suitable for mapping minerals or soil properties because their bandwidth of 70 to 240 nm cannot resolve diagnostic spectral features of terrestrial materials. Often, absorption features of interest have bandwidths of only 20 nm or less. Since the construction of the first spectrometer, the technique and the sensors have been further developed and refined, and software especially designed to analyze the large data volumes generated by imaging spectrometers have become available [31, 39]. These developments have led to the successful applications of imaging spectrometry in several environmental disciplines, such as atmospheric science , ecology [36, 38, 44, 46, 47], geology [29, 30, 31,37, 45], soil science [11, 15, 16], hydrology and oceanography [5, 25, 35]. The importance of these types of instrument may be indicated by the fact that several proposals for launching spaceborne spectrometers in the near future have been approved. This paper presents a practical application of imaging spectrometry for vegetation survey in the Long Valley caldera in the Sierra Nevada, California. This area suffers from vol- each pixel has an associated, continuous spectrum that can be used to identify the surface materials 224 spectral bands crosstrack (614 pixels x 20 m/pixel) 20 m Reflectance along track (512 pixels per scene) 100 kaolinite 50 10 0.4 1 Department of Physical Geography, Utrecht University, PO Box 80 115, 3508 TC Utrecht, The Netherlands 1 1.5 2 2.5 Wavelength (µm) FIGURE 1 The concept of imaging spectrometry 1 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 Mountain. The study area is covered by an open type of montane forest and the dominant species are the lodgepole pine (Pinus contorta) and the red fir (Abies magnifica). The soil surface near Horseshoe Lake is generally fairly bright. The surface is covered by glacial deposits (till) consisting mainly of weathered granitic rocks. As a result of the volcanic gas emission around Mammoth Mountain, there is an excellent sequence of dead, stressed and healthy trees and bare surfaces. This site provides excellent opportunities to study: - the capabilities of imaging spectrometry for mapping stressed (and dead) pine and fir species - methods of separating the vigorous vegetation, stressed vegetation and dead vegetation from the soil background of glacial deposits and crystalline rocks - the potential of AVIRIS to detect areas of volcanic gas emissions. N Reno Sacramento Mammoth San Francisco Fresno Las Vegas Californië Bakersfield Santa Barbara Los Angeles FIELD AND IMAGE SPECTRA In October 1995, spectral field measurements were carried out in two of the four dead tree sites, using a portable field spectrometer . The instrument covers the 350 to 2500 nm spectral region, using three individual spectrometers. It samples every 2 nm, with a variable resolution between 10 and 11 nm. Together with each set of target measurements, a white reference plate and the instrument's dark current were measured, and multiple target measurements were averaged to increase the signal-to-noise ratio. The dark current was subtracted from the raw spectral measurements and the white reference measurements were used to convert the raw data to reflectance. The spectral behaviour of several surfaces was measured: healthy pine and fir species, stressed and dead trees, the litter layer, dead trunks, bare soil surfaces, rock outcrops and some reference sites for radiometric image corrections. Trees were considered "stressed" if several branches were dead or if at least some of the needles had lost colour. The information content of the field spectra was then analyzed in respect to healthy, stressed and dead trees, and the soil background. The analysis methods used included standard normalization procedures, first derivative transform, convex hull transform and automatic absorption feature finding [16, 24]. First derivative analysis is a common method of enhancing abrupt changes in the spectral curve of objects, eg, the red edge San Diego 0 250km FIGURE 2 Location of the Mammoth study area in the Sierra Nevada, California to 96 vol percent) in the soil profile are the most probable cause of tree death. Although not much is known about the effect on roots of high levels of carbon dioxide in the soil profile, it is most likely that the trees are dying because of oxygen deprivation: the CO2 drives the oxygen out of the soil [20, 33]. The most probable gas source is the magma body approximately 7 km beneath the Long Valley caldera. The gas surfaces via the fumaroles and as diffuse soil emanations. It is only recently that research at Vulcano Island in southern Italy has shown that volcanoes release abundant carbon dioxide from their flanks as diffuse soil emanations . To date, four dead tree areas have been mapped out around Mammoth Mountain. The two largest dying tree sites are located near Horseshoe Lake and near Mammoth Mountain Main Lodge, covering areas of approximately 10 ha. Figure 4 shows an AVIRIS image cube of Mammoth Mountain, acquired on 23 August 1994. Snow-covered Mammoth Mountain is visible in the center of the image. Horseshoe Lake and the largest dying tree areas are located just south of Mammoth A B FIGURE 3 The southern flank of Mammoth Mountain at Horseshoe Lake; (A) the forest in excellent health; (B) trees dying of carbon dioxide asphyxia  2 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 Normalized Reflectance (%) Reflectance (%) for vegetation [13, 27, 46]. The convex hull transform is a method of normalizing spectra [16, 41]. The convex hull technique is analogous to fitting a rubber band over a spectrum to form a continuum. Figure 5 shows the concept of the convex hull transform. The difference between the hull and the original spectrum is subtracted from a constant to obtain a hull difference. Such a normalization of the spectra allows the application of quantitative absorption feature characterization in terms of feature depth, surface area and asymmetry. Figure 6 shows some examples of the collected field spectra for dead, stressed and healthy lodgepole pines. Figure 7 shows the first derivative of the spectra in Figure 6. The derivative computation tends to enhance not only the absorption features but also the noise . Both figures clearly show the presence/absence of chlorophyll absorption near 680 nm in the healthy and dead lodgepole pine spectra, respectively. Although the red edge [13, 16], the steep spectral transition zone between chlorophyll absorption at FIGURE 4 AVIRIS image cube of Mammoth Mountain (acquired on 23 August 1994 and covering an area of approximately 12 x 12 km). X and Y axes show the geo680 nm and the high near-infrared graphic position in the scene; the Z axis shows the spectral bands (224). Snowreflectance at 720 nm, is not very covered Mammoth Mountain is visible in the center of the image; Horseshoe Lake pronounced, it is visible in the origand the largest dying tree areas are just south of Mammoth Mountain inal and derivative spectra. Figure 6 also illustrates the effect of increasing brightness between 1400 and 1700 nm with green canopy obscures the presence of woody material. respect to the reduced water content of healthy pines as Furthermore, a convex hull transform was computed compared with dead pines. Within the same spectral from the field spectra and the feature-finding algorithm range (about 1720 nm), absorption features associated [16, 24] was applied. The results are presented in Table with lignin and cellulose can be seen for the dead pines 1; water is the most dominant absorption feature (1900 and litter spectra [36, 44, 48]. These features are not and 1400 nm) identified by the algorithm. Compared visible in the case of the healthy spectrum because the with the healthy lodgepole pine, the stressed tree shows 90 85 80 75 70 100 99 98 97 96 95 94 65 93 60 92 55 91 50 90 45 89 A B 88 40 87 0.60 35 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 2.20 1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 2.50 Wavelength (µm) Hull Wavelength (µm) Hull 0.80 2.40 2.50 Transformed Spectrum Original Spectrum FIGURE 5 Concept of the convex hull transform; (A) a hull fitted over the original spectrum; (B) the transformed spectrum. The example shows a laboratory spectrum of a weathered limestone rock with absorption features for iron near 900 nm, for water near 1400 and 1900 nm, for clay minerals near 2200 nm and for calcite near 2350 nm  3 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 TABLE 1 Results of the automatic absorption feature search on field spectra after convex hull normalization Wavelength Depth Area Asymmetry Healthy lodgepole pine (Pinus contorta) 1.8040 1.4540 0.6860 1.9860 1.9700 25.2592 22.2009 18.4455 13.6994 13.6013 3.6183 4.7954 2.2596 0.3587 0.1553 0.0314 0.4094 2.6633 0.1768 1.9560 Stressed lodgepole pine (Pinus contorta) 1.8040 1.4460 0.6880 1.9640 1.7940 FIGURE 6 Examples of field reflectance spectra (400 to 1800 nm) for healthy, dead and stressed lodgepole pines (Pinus contorta). The spectra were collected near Horseshoe Lake and each spectrum is an average of eight measurements 18.5023 17.5540 14.2796 12.0939 11.8013 2.6410 3.5220 1.9255 0.2501 0.1222 0.0441 0.3128 3.1891 0.3853 4.3238 Dead lodgepole pine (Pinus contorta) 1.8040 1.9640 1.9560 1.4540 2.1060 1.7880 25.1266 14.9782 14.1485 13.5969 11.1379 10.8838 3.5749 0.1669 0.0831 2.3908 0.2055 0.1313 0.0278 0.5096 0.5139 0.3469 1.5145 0.5303 31.5548 11.5030 11.3799 9.1239 8.0103 4.6171 0.0454 0.1287 0.0661 1.1761 0.0400 1.0037 0.2122 0.8646 0.3211 4.6679 1.0844 3.4357 2.9338 0.2219 0.0950 20.5118 0.0349 2.9247 1.3108 Litter 1.8040 1.9560 1.9600 1.7880 1.4460 Healthy red fir (Abies magnifica) 1.4460 1.4280 1.8040 0.6880 1.9640 First derivative spectra (500 to 850 nm) of field measurements of dead, stressed and healthy lodgepole pines (Pinus contorta) FIGURE 7 25.3815 25.2224 24.0184 23.6689 16.5759 a decrease in chlorophyll absorption of approximately 15 percent. The convex hull transform is a promising tool to quantitatively analyze field and image spectra. Based on these encouraging results with the field spectra, the first derivative was computed for a transect in the AVIRIS image spectra located over the dead tree area near Horseshoe Lake (Figure 3B). Figure 8 shows the 460 to 950 nm range of these first derivative computations. The transect covers a quarry, an area with stressed trees, an area with dead trees, a bare soil region, another zone with stressed and dead trees and finally an area of healthy trees. The variation in the peak height in the center of the graph shows the differences in chlorophyll absorption. The results of the transect analysis show that information on the varying shape of the chlorophyll absorption features of vegetation is present in the AVIRIS images. Similar results were reported by Elvidge et al  and Roberts et al . IMAGE ANALYSIS Three AVIRIS images (acquisition dates: 21 May 1994, 23 August 1994 and 22 June 1995) of the Mammoth Mountain region were analyzed for this study. The AVIRIS radiance data were converted to reflectance by using the empirical line method: a linear regression FIGURE 8 3D plot of the first derivative spectra of a transect in the AVIRIS image of 23 August 1994 near Horseshoe Lake (Figure 4). The transect crosses a gravel pit and healthy, stressed and dead vegetation (Figure 3B). The X, Y and Z axes represent the distance along the transect in pixels of 20 m, the AVIRIS spectral range from 460 to 950 nm and the first derivative of reflectance, respectively 4 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 function was computed between the field reflectance and corresponding AVIRIS pixels for a dark and a bright surface (a large asphalt parking lot and a gravel field). The field spectra for empirical line correction were not collected on the same dates that the images were acquired, thus hampering the multitemporal comparison of the AVIRIS images. Furthermore, differences in snow cover and the phenological state of the vegetation played a role. A comparison of the reflectance curves of a number of reference sites in the three multitemporal images showed that the deviation of these multitemporal spectra was smaller than 0.05, which was considered acceptable for further analyses . Multitemporal analyses and spectral unmixing techniques were then used to survey the temporal changes of the dead tree areas and to separate stressed, dead and healthy pine trees from the soil background. aims at unravelling the complex satellite spectrum by using the pure endmember spectra, and is a useful tool for surveying. The mathematical notation of the unmixing procedure is as follows : n R i = ∑ (F j × RE ij )+ ε i n =1  where: R i = reflectance value of a pixel in band i F j = fraction of endmember j (in terms of percentage of the pixel surface) RE ij = reflectance value for band i of endmember j ei = residual error in band i n = number of endmembers. Spectral unmixing techniques have been described and applied in various contexts by several authors [1, 2, 28, 38]. Two approaches were followed when using spectral unmixing techniques in this study: SPECTRAL UNMIXING - field spectra were used as endmember spectra Spectral unmixing is a technique to evaluate the large - endmember spectra were collected from the airborne volume of spectral data in a spatial context. The basic images. assumption of the unmixing technique is that the signal Selected endmembers from the field spectra included recorded by the sensor is for each pixel, a linear comdead, stressed and healthy lodgepole pines and bare soil. bination of spectral signatures of pure components Endmembers selected from the images comprised areas weighted to their abundance in that pixel [1, 2, 38]. The of dead, stressed and healthy pine species and bare soil. components (or endmembers) are those elements that Unconstrained unmixing (ie, abundances of endmembers represent the spectral variability of the landscape (eg, are not forced to sum to one and may assume negative vegetation, soil and shade). The unmixing approach values) was applied in this investigation. The unmixing algorithm was applied to a spectral subset from 400 to 1800 nm for all AVIRIS images because the 1800 to 2500 nm range suffers from noise, which is detrimental to the algorithm. Single endmember images were colour-coded for visual interpretation and combinations of three endmember images were displayed in red, green and blue to study the quantitative contribution of each endmember. Figure 9 shows the promising unmixing results for the dead tree endmember of the 23 August 1994 image (Figure 4). The dead tree endmember abundance is shown colour-coded in blue-green-red-yellow. The dead tree areas near Horseshoe Lake (south of Mammoth Mountain; see Figure 3) and northwest of Mammoth Mountain clearly show up in yellow. The triangular yellow spot west of Mammoth Mountain is a 1992 forest fire area and a tree-logging site. The yellow spot at the right edge of the figure is brown/yellow pastureland. The spectral responses of these land cover types show some similarity with the dead tree endmember and are identified as such. The 23 FIGURE 9 Unmixing results for the dead tree endmember of the 23 August 1994 August 1994 image yields the best AVIRIS image shown in Figure 4. The dead tree endmember abundance is colourresults because at this time of the coded in blue-green-red-yellow. The dead tree areas near Horseshoe Lake (south year the vegetation is most vigorof Mammoth Mountain; see Figure 3) and northwest of Mammoth Mountain clearly show up in yellow ous and, hence, the contrast 5 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 between dead trees, stressed trees and healthy trees is at its maximum. The somewhat poor results for the other two images can most probably be attributed to the snow cover and the dormant phenological condition of the trees. Furthermore, the sun angle and, consequently, the illumination conditions are not optimal for the last two images. The results for the endmember images, using field spectra as the reference for the unmixing algorithm, are less satisfying, although the radiometric correction using the empirical line method yielded fairly satisfactory results. Confusion occurs between the healthy and stressed tree endmembers and between the dead vegetation and soil. The fractions for healthy vegetation, for example, are often underestimated. area, but field checks revealed that it was a 1992 forest fire area. The multitemporal analyses of the AVIRIS scenes proved valuable for surveying the dynamics of the dead tree areas. STRESS INDICATORS The detection of early vegetation stress by optical remote sensing depends largely on the capabilities of the sensor to detect the subtle changes in the reflectance in spectral regions that are most responsive to unfavourable growth conditions. One of the first signs that vegetation is exposed to stress (eg, water deficiency, senescence or herbicides) is a decrease in the absorption of radiance by chlorophyll in the visible wavelengths [7, 8, 9, 10, 27, 40, 50]. The decrease in absorption at the edges of the chlorophyll absorption feature around 605 nm and 695 nm is particularly suitable as an early stress indicator. The field spectra of Figures 6 and 7 illustrate this phenomenon. Two approaches, with the same theoretical basis, are feasible: (1) to determine the blue shift (or red edge), ie, the spectral shift towards the blue by a decrease in chlorophyll activity [9, 14, 27, 50] and, hence, an indication of vegetation under stress (2) to use the ratios 5of spectral bands located at the critical spectral position at the edges of chlorophyll absorption [7, 8]. The second method was applied to the field and image spectra to determine its suitability for mapping the stressed pines near Mammoth. When reflectance ratios are used as stress indicators, as suggested by Carter , bands 694 and 760 nm or 695 and 420 nm are used. Applying Carter's first stress indicator, R694/R760, to MULTITEMPORAL IMAGE ANALYSIS Multitemporal analysis refers to the comparison of remote sensing images acquired at different times. The three available AVIRIS images were used to survey the temporal changes of the dead tree areas. A prerequisite for successful multitemporal comparison is a proper geometric match and radiometric correction of the images. The images were geometrically matched to one another using ground control points, a first degree polynomial warping algorithm and a nearest neighbourhood resampling technique. Multitemporal analysis included: - comparing the spectral unmixing results - comparing NDVI (Normalized Difference Vegetation Index) values for each AVIRIS scene. Large NDVI values indicate pixels with high proportions of green biomass; low values indicate pixels of bare soil, water bodies or built-up areas; and intermediate values give an indication of differences in coverage for green vegetation [17, 43, 50]. The three multitemporal unmixed images and the three NDVI images were displayed as red, green and blue combinations for visual interpretation. Figure 10 shows the multitemporal NDVI values in red, green and blue, corresponding to image acquisition dates of 21 May 1994, 23 August 1994 and 22 June 1995, respectively. Results show that the Horseshoe Lake dead tree area was fairly constant over the time span studied. Only the western part of this region shows some minor enlargement. Interviews with the forest rangers revealed that this site rapidly expanded in the period late 1989 to late 1992 but then came to a standstill. The second dead tree area, further uphill from Horseshoe Lake, shows a significant decrease in NDVI values over time as a red spot in Figure 10, indicating a significant enlargement of the dead tree area since May 1994. Another area of reduced green coverage was located in the image, uphill on the FIGURE 10 Multitemporal NDVI scene of the Mammoth Mountain area. Red, green western flank of Mammoth and blue are assigned to AVIRIS images acquired on 21 May 1994, 23 August 1994 Mountain. It was first identified as and 22 June 1995, respectively. The reddish border is caused by the incomplete coverage of the three scenes. Red tints indicate a loss of vegetative cover over time a fifth, so far unknown dead tree 6 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 the spectra of Figure 6 yields values for healthy, stressed and dead pines of 0.22, 0.25 and 0.45, respectively. These values are in quite good agreement with results reported by Carter ; his laboratory values range from 0.13 to 0.47. The same is true for the transect of Figure 8; values for the stressed/healthy trees range from 0.24 to 0.51 (bare surface areas were not considered). Table 2 shows some results obtained using the stress indicators. The performance of the second index (R694/R420) is less satisfactory. Values are much larger than those reported by Carter . These stress indicators were then applied to the AVIRIS 23 August 1994 scene. Table 3 shows the results of Carter's index applied to this image for some areas with known healthy, stressed and dead trees. Although their absolute value is a bit high, the relative values for the R694/R760 index are quite good; they seem to represent the field situation. The performance of the other index (R694/R420) is less satisfactory; all values are too low. A possible explanation for these somewhat poor results might be that the empirical line correction does not sufficiently compensate for the strong atmospheric effects in the short wavelengths around 420 nm. The next step in this study was to compute a stress index for the entire 24 August 1994 scene and display it as a colour-coded image for visual interpretation (see Figure 11). As expected, an irrigated golf course close to Mammoth village does not show any signs of vegetation stress. The results are also very promising for the triangular forest fire area in the left part of the image, and for the dead tree area at Horseshoe Lake, where the complete gradient from healthy to severely stressed trees is shown in conformity with field observations. However, areas such as lakes and the snow-covered summit of Mammoth Mountain show up as "stressed vegetation" because the spectral signatures of these objects show a decrease in reflectance similar to that of stressed vegetation. Further research is needed to refine the stress indicator approach and to develop methods to separate vegetation-covered areas from other surfaces. A combination of spectral unmixing and stress indicators might be useful in tackling this problem. MAPPING VOLCANIC GAS EMISSIONS The next step in this research was to investigate whether AVIRIS is capable of locating the areas of volcanic gas emissions in the study area. The volcanic gas emissions near Mammoth Mountain contain mainly water vapour, carbon diox- ide and some minor gases such as methane and sulphur dioxide [19, 22, 42]. Two approaches can be followed to map the volcanic fluxes: - to detect volcanic gas types not normally present in the atmosphere TABLE 2 Carter's stress index computed from the field spec- tra H LP S LP D LP R694/R420 CV n R694/R760 CV n 4.53 3.21 3.17 0.16 0.20 0.22 8 6 7 0.21 0.27 0.62 0.23 0.47 0.11 8 6 7 H LP = healthy lodgepole pine; S LP = stressed lodgepole pine; D LP = dead lodgepole pine; CV = coefficient of variance; n = number of samples TABLE 3 Carter's stress index computed for image spectra (23 August 1994) dominated by lodgepole pines H TR S TR D TR R694/R420 CV n R694/R760 CV n 0.81 1.04 1.37 0.23 0.10 0.04 55 51 51 0.33 0.63 0.87 0.23 0.08 0.03 55 51 51 H TR = healthy pine trees; S TR = stressed pine trees; D TR = dead pine trees; CV = coefficient of variance; n = number of samples FIGURE 11 Carter’s [7, 8] stress index computed from the AVIRIS scene acquired on 23 August 1994. Values range from high to low, from yellow to red, green, blue and black. The irrigated golf course at the center right of the image is completely black, as might be expected. The stressed tree area of Figure 3 and the forest-fire areas are yellow, which also matches expectations. Water and snow-covered Mammoth Mountain also show up in yellow because of their spectral signature 7 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 - to image the abundance of gases normally present in order to detect spatial anomalies. The most promising approach to trace the volcanic emissions is using carbon dioxide (CO2) and/or methane (CH4), because both gases have absorption features in the AVIRIS spectral range [37, 49]. Figure 12 shows a field spectrum of the gas discharge of a fumarole near Mammoth Mountain. Clear absorption features show up for CO2; the concentration of CH4, however, is probably small as no features show up for this gas (confirmed by Sorey et al ). The radiance images and reflectance images of the three available AVIRIS images of the Mammoth Mountain region were analyzed. The continuum interpolated band algorithm (CIBR) was used to enhance CO2 absorption at 2005 and 2055 nm. The CIBR algorithm uses the "shoulders" at either side of the absorption feature to interpolate a radiance continuum over the absorption feature of interest. A ratio is then computed between the absorption band and the interpolated continuum : B2 CIBR =  c1 B1 + c2 B2 ble with AVIRIS to locate the CO2 gas emission spots or to separate atmospheric CO2 from the diffuse soil emanations of CO2. When using AVIRIS, several problems may hamper the mapping of CO2 emissions: (1) the temporal and spatial distribution of gases in the atmosphere (2) the unknown spatial and temporal variation of the diffuse CO2 soil flux at Mammoth (the flux depends, among other things, on magma activity, barometric pressure and changes in soil moisture content) (3) the low-energy solar irradiance at the 2000 to 2500 nm spectral region, resulting in a low signal-to-noise ratio around the carbon dioxide absorption bands. CONCLUSIONS The use of remote sensing in environmental studies has always been hampered by the difficulties in quantifying image-derived information. Reflectance properties of broad-band sensors are not easy to correlate quantitatively with field or laboratory measurements. The development of imaging spectrometers is an important step forward in remote sensing because it allows the identification and quantitative assessment of several environmental characteristics. The technique of imaging spectrometry is currently finding its way from the specialized laboratories into the environmental science community to serve as a powerful tool in studying earth science problems. In this case study, AVIRIS images were used to monitor the deteriorating effect of diffuse volcanic gas emissions on the conditions of trees. Field spectral measurements revealed the presence of chlorophyll and lignincellulose associated absorption features in the spectra of healthy, stressed and dead pine species. Spectral unmixing techniques proved useful in mapping areas of dead and stressed trees. Multitemporal techniques were successfully applied to determine which dead tree areas were still expanding and which were unchanged over the period studied. However, the different image acquisition dates presented problems, eg, snow cover and the dormant state of the vegetation in some images hampered the spectral identification of tree conditions. The first results with image-based vegetation stress indicators are promising, although further research is necessary to refine this approach. Using AVIRIS to trace diffuse volcanic gas emissions was less satisfactory. Field spectra and image spectra contain distinct absorption features (eg, for carbon dioxide), but so far it has not been possible to find a distinct correlation between areas showing vegetation stress and the diffuse soil emanations, or to separate the atmospheric CO2 from the diffuse soil emanations, despite the considerable volume of the soil flux. The solar energy reflected around the 2003 absorption feature might be too small, and the resulting AVIRIS spectrum too noisy to reveal the subtle variations in CO2 concentration. Another explanation might be that the temporal variability of the CO2 does not coincide with the three AVIRIS data acquisition dates. where: B2 = radiance at the absorption feature position B1 , B3 = radiance values at the shoulders c1, c2 = symmetry coefficients. Values for c1 and c2 depend on the symmetry of the continuum at either side of the absorption feature. Complete symmetry of the continuum results in c1 and c2 values of 0.5. The analysis results for the radiance and reflectance images of all three data acquisition dates are poor. A noisy salt-and-pepper pattern is visible, with a dim spatial pattern. It is striking is that all the lakes in the images show high CIBR values. This is not caused by high CO2 concentrations in the lakes or the fluxes forming the lakes, but is merely because the noise in the low radiance spectrum for water is strongly enhanced. It was expected that the dead tree areas would be clearly identified on the CIBR images since field surveys  yielded a strong correlation between dead/stressed trees and gas emanations. Unfortunately, these areas do not show up in any of the three radiance or reflectance images. Although the volumes of the volcanic gas flux are considerable (estimated at 120 ton/day/ha by Farrar et al ), the results show that in this case it was not possi- ACKNOWLEDGMENTS The research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of FIGURE 12 Field spectrum of the gas discharge of a fuma- role near Mammoth Mountain 8 Imaging spectrometry for monitoring tree damage ITC Journal 1998-1 Technology, Pasadena, California, and was financially supported by the Netherlands Organization for Scientific Research (NWO Talent Stipend S76-175). 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The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentration. Rem Sens of Environ 49, pp 81-91. RESUMEN Los desarrollos en la tecnología de detectores han fomentado una nueva tecnología de teledetección: la espectrometría de formación de imágenes. Los espectrómetros miden la radiancia solar reflectada sobre una base de pixel por pixel en un gran número de bandas espectrales estrechas, permitiendo la identificación de materiales o de sus propiedades gracias a rasgos diagnósticos de absorción. Hoy en día, se dispone solamente de espectrómetros aeroportados, pero varios espectrómetros están previstos para la próxima generación de plataformas espaciales. La abundancia de la información disponible a través de la cobertura espectral continua permite analizar problemas referentes a numerosas disciplinas ambientales. Este artículo describe un estudio en la Sierra Nevada, en el cual se usaron imágenes multitemporales adquiridas por el Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) para monitorear daños causados a árboles por actividad volcánica. Las emanaciones difusas de gas volcánico privan las raíces de oxígeno, lo cual pone los árboles bajo tensión y últimamente causa su muerte. La espectrometría genera información importante sobre las condiciones de los árboles y sobre la presencia de material vegetativo muerto. Mediante el uso de datos de AVIRIS, se mapeó la extensión espacial y temporal de las áreas con árboles estressados y muertos. El uso de la espectrometría para mapear las emisiones difusas de gas volcánico fue menos exitoso. Aun cuando las imágenes producen patrones espaciales confusos de dióxido de carbono, resulta difícil separar los gases atmosféricos de las emanaciones difusas saliendo de los suelos. RESUME Les développements dans la technologie des détecteurs ont déclenché une nouvelle technologie de télé-détection: la spectrométrie d’image. Les spectromètres d’images mesurent la radiance solaire reflétée sur la base de pixel par pixel dans plusieurs bandes spectrales étroites, en permettant l’identification de matériels ou de leurs propriétés par le diagnostic de leurs caractéristiques d’absorption. Aujourd’hui, seuls les spectromètres d’images aériens sont disponibles, mais plusieurs de ces spectromètres d’images sont prévus pour la prochaine géneration de plates-formes spatiales. L’abondance d’information disponible dans la couverture continue spectrale permet de poser des questions dans de nombreuses disciplines d’environnement. Cet article décrit une étude dans la Sierra Nevada, avec l’utilisation d’images multi-temporelles acquises par Spectromètre aérien d’image visible infra-rouge (AVIRIS) pour l’observation de trois lieux sinistrés suite à l’activité volcanique. 10
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