A new sensor system, the Airborne Imaging Spectrometer (AIS), has been developed to provide both high spectral and spatial resolution information. This system is potentially more useful for discriminating arid and semiarid shrub communities (Goetz, 1985; Goetz et al., 1983; 1985). The first generation instrument, the AIS-1, has 128 near and shortwave infrared channels averaging approximately 10 nm in width per band. The AVIRIS (Airborne Visible Infrared Imaging Spectrometer), available in 1987-1988, will have 224 channels covering the visible to shortwave infrared regions. These instruments provide the first opportunity to produce high spectral resolution images capable of detecting subtle changes in canopy water or pigment content, to detect changes in abundance or density of dominant species among communities, and to detect narrow-band absorption features potentially useful for the unique identification of surface materials.
In this paper we investigate the spatial and spectral resolution of
AIS-1 data and provide a preliminary assessment regarding its potential
for discriminating vegetation in semiarid environments. Specifically, AIS
results are compared with a corresponding analysis of Landsat TM data to
examine the information content of AIS data relative to the TM. The data
presented here was obtained during two seasons when shrub vegetation were
either at minimum or maximum physiological activity.
Percent vegetation cover, height, and species composition on the burned
and unburned surfaces of the younger alluvial substrate and on the older
alluvial substrate were determined by 50 m line transects (n = 18-21) using
a stratified random sampling design (Table 1;
data from Ustin et al., 1986b). The following perennial species vary in
proportion from site to site but comprise 93-98% of the total vegetation
cover: Coleogyne ramosissima (Blackbrush), Eriogonum fasiculatum
(California Buckwheat), Artemisia tridentata (Great Basin Sagebrush),
Chrysothamnus teretifolius (Long Leaf Rabbitbush), Haplopappus
Cooperi (Cooper's Goldenbush). Annuals and grasses were an insignificant
proportion of the total cover during 1984-1985. Soil mapping was performed
by the Bureau of Land Management (Vaughn, 1983) and Soil Conservation Service,
Bishop, CA (F. Fischer, personal communication). Both soil units are classified
as loamy-skeletal, mixed, thermic Xeralfic Haplargids and differ principally
in age and weathering. All soils are gravelly with scattered boulderfields.
Within the region of wavelength overlap between the two AIS data sets, the comparisons between dates were primarily centered on two wavelength regions: 1.21-1.29 mm and 1.53-1.69 mm (Fig. 2). These correspond roughly to reflectance peaks in the near and middle infrared reflectance range, common to the two dates but excluding wavelengths having atmospheric water absorption (1.30-1.52 mm) (Fig. 3). These spectral regions do, however, include the shoulders of wavelength regions affected by atmospheric water absorption. Murray et al. (1986) found the AIS spectral region between 1.53 and 1.63 mm to best characterize between-class differences in a desert shrub community in central Oregon.
Digital TM data covering the Owens Valley area were acquired from a
16 May 1985 scene (row 41, pass 35) with a 09.45 h overpass time. These
data were obtained 1 week prior to the AIS acquisition. All six reflected
visible and infrared bands having eight-bit spectral resolution (256 levels)
and 30 m on a side ground resolution per pixel were included in the data
set. These bands covered the following wavelength regions: 0.45-0.52 mm
(blue), 0.52-0.60 mm (green), 0.63-0.69 mm
(red), 0.76-0.90 mm (near infrared, NIR), 1.55-1.75
mm and 2.08-2.35 mm
(shortwave infrared, SWIR). In addition a thermal band (10.3-12.5 mm)
having a 120 m on-a-side ground resolution was also obtained as part of
the standard Landsat digital TM data set.
The eight destriped AIS bands in the May data covering the 1.21-1.28 mm wavelength region were treated as one set of variables. The 16 bands covering the 1.54-1.69 mm region were separated into two eight-band sets of variables. The October data was treated similarly except that the data selected was displaced one band toward shorter wavelengths as a result of rounding-error uncertainty. Each band set was subjected to statistical analysis using the Earth Resources Laboratory Applications Software (ELAS) (Graham et al., 1984) as implemented at U.C. Berkeley under Version 4.2 bsd UNIX. In this case, the Supervised Encounter (SUPE) module was used to produce a mean vector (by summation over pixels), covariance matrix, and correlation matrix for each band set. Principal components analysis (e.g., Anderson, 1958; Morrison, 1976) was performed with the Statistical Analysis System (SAS, 1982), using the correlation matrix to give eight eigenvalues and eigenvectors for each set. The eigenvectors (containing the direction cosines) were then multiplied by appropriate destriped AIS bands in ELAS (via the Programmable Calculator module, PCAL) to compute the first and second principal component (PC) scores for each AIS pixel. A similar computation of PC scores was performed for 48 contiguous AIS bands, divided into eight-band sets, covering the 0.91-1.38 mm wavelength region in May and the 1.21-1.69 mm region in October.
First and second PC images were produced for the two AIS data sets.
The first PC scores in the 1.21-1.28 m m) 1.54-1.69
mm wavelength regions were then subjected to
unsupervised clustering in ELAS using the Search (SRCH) module. Initial
clustering was performed with a minimum required intercluster distance
(sdis) of 1.5. Final clusters were defined using various minimum required
intercluster distances between 0.375 and 1.5, according to the dynamic
range and number of distinct spectral classes available in each data set.
Resulting cluster statistics (mean vector and covariance matrix) were used
to classify each pixel in each of the 32 x 512 pixel data sets to one unique
class using the ELAS Maximum Likelihood Classifier (MAXL).
All AIS and TM classification maps were displayed using ELAS and examined
for relative level of classification detail and approximate spatial correspondence
with ground cover and soil types. In addition to examination of the classification
statistics, the location and relative distribution of individual AIS classes
were evaluated by displaying each class individually to determine the spatial
coherence and fidelity with known ground features. Because this analysis
of the classification maps was based on comparisons with preexisting ground
data, no attempts were made to locate specific pixels on the ground. The
surface characteristics considered were the mean and variance of the vegetation
cover, height, and species composition (based on transect data) from each
of the four surface types. Additionally, the positions of terrain features
such as streams and washes were examined on the AIS data using 35-mm black/white
and color infrared ground-track photos obtained from the AIS flights and
from black/white aerial photos (1973 and 1983) obtained from the U.S.D.A.
Forest Service.
Note that the atmospheric water bands (centered at 1.4 and 1.9 mm) showed greater absorptance in May relative to October. This suggests that the AIS is sensitive to variations in atmospheric water vapor concentration. The greater atmospheric water absorption in May also affects the shoulders of the water bands, making the region between the water bands appear narrower. In addition to these major absorption features, secondary water absorption bands are seen at 0.94, 1.13, 1.25, 1.54, and 1.67 mm) Two smaller CO2 absorptance bands occur at 2.00 and 2.05 mm and another on the shoulder of the water band, at 1.46 mm. Secondary water and CO2 absorptance bands also show deeper absorption features in May relative to October.
In general, the two data sets show similar patterns for the overlapping spectral regions (Fig. 3). The spectra on both days basically follow the same signature patterns, with differences between surface types being expressed principally through albedo. In fact, the primary difference between the dates is in curve amplitude (digital numbers, DN), with maximum reflectance values in May data about 82% DN reflectance values in October (the proportion varied across the spectrum between 60 and 90%). This pattern is consistent with field measured spectra which showed increased reflectance during winter compared to summer (Ustin et al., 1985; 1986b; unpublished data). Observations on the field-collected spectral data suggest that the seasonal trend is linked with changes in the solar angle, possibly through altered canopy shading.
In both October and May, the relative brightness shows that the unburned shrub community on the older soil surface (TIN) had the lowest DN values, while the recent burned surface (SCB) on the younger soil terrace had the highest DN values (Fig. 3, Table 1). Both the unburned shrub community surface (LUT) and the older burned surface (OVB) on the younger soil terrace had similar spectral signatures and intermediate DN values. One of the important differences between dates is the ability to separate the soils of different ages (TIN and LUT) in October but not in May. In contrast, the May AIS image allowed better separation of the older burn surface (OVB) from the unburned shrub surface (LUT) which were only marginally separable in October. Although statistical tests were not performed, these results are consistent with numerous spectral plots examined on these data. It appears that the ability to distinguish spectrally similar surface types is somewhat dependent on the timing of data collection and possibly related to the phenological activity of the species. In our AIS data we have not attempted to separate sun angle effects from seasonal phenological differences in vegetation.
The primary change observed between the dates was in the region between the 1.4 and 1.9 mm water absorption bands (Fig. 3). This difference is principally seen in the negative slope between 1.51 and 1.70 mm in October and the relatively flat response across this region in May. If these differences were due to atmospheric water absorption on the shoulder of the 1.9 mm water band, the slope was expected to have been greater in May rather than October. However, other physical bases for the slope change cannot be readily determined since, in this case, all surface types responded similarly. Nonetheless, slope changes in reflectance in the NIR to SWIR region have been related to ecological differences by other authors, and have been consistently observed in comparisons among various vegetation surfaces and community types, as reported for marshes (Gross and Klemas, 1985, 1986), agriculture (Wood and Wrigley, 1985), urban (Vanderbilt, 1985), forest (Milton et al., 1986), and desert marsh and meadow communities (Ustin et al., 1986a).
In general, areas with dense vegetation tend to have positive slopes
rather than negative slopes in the 1.5-1.7 mm
region. Thus, in these data the general spectral pattern appears to be
responding predominantly to soil features rather than vegetation per se.
However, because these communities have similar species composition and
structure they primarily differ only in total vegetation cover (Table
1). This is spectrally evident by the decrease in brightness as canopy
cover increases. Ustin and Rock (1985) and Ustin et al. (1986a) suggested
that the predominant effect of semiarid vegetation on these fans is to
lower spectral brightness nearly equally in all AIS bands. This effect
is at least partially due to the canopy geometry of semiarid shrubs, having
low leaf area and relatively sparse, open branching (deliquescent structures)
that cast shadows and shade the soil surface but have relatively low biomass.
Based on an analysis of multidimensional TM spectral space in linear mixing
models of this fan region, Smith and Adams (Univ. of Washington, personal
communication) have found that the mixing space in their vegetation models
occurs along a similar trajectory with the shadow-shade spectral mixing
space. As a consequence, separation of vegetation and shadowed surfaces
is difficult in semiarid communities with a physiogonomic structure such
as these.
Table 3 gives the percentage of the total variance over all bands in each eight-band data set accounted for by the first two principal components. Differences in percentage of variance accounted for by each PC may be explained by the inclusion of some AIS channels affected by atmospheric water in the analyses [see Fig. 3(a)-(b) for position and width of water bands]. This assumption is consistent with examination of the PC statistics and inspection of the PC images. For instance, the steep shoulders on the 1.4 mm water absorption band affect few spectral channels in the May image and, thus, have little effect on the PCs. This is confirmed by the relatively small proportion of variance accounted for by the second PC in May. On the other hand, the gentler slope of the water absorption regions in the October data trig. 3(a)] affected more of the spectral: bands, therefore, creating second and subsequent PCs accounting for a significant proportion of the variance in the NIR and second SWIR region.
Referring again to Table 2, note that when the first PC dominates (i.e.:, accounts for a large percentage of the variance) that the PC score is formed by an approximately even weighting (using the direction cosines) of the digital response from each original AIS band. When the first PC is less dominant, the weighting is more uneven. Less dominance in the first PC reflects inclusion of the edge of an atmospheric absorption window or choppiness in the spectral response curve due to plant or soil absorption, or other phenomena such as shading. However, direction cosines for the second PC nearly always represented a contrast (i.e., a differencing) between the first three to four AIS channels and the remaining bands in the set suggesting atmospheric water absorption. Even when the second PC accounted for greater than 5-20% of the variance, the dynamic range of DN counts was primarily restricted to fewer than five. Thus, in each case, only one PC was significant. A similar pattern was evident in consecutive eight-band groups referred to in Table 3. Other studies have reported that a significant proportion of the variance was attributable to brightness. For instance, Murray (1983) found that the first two PCs accounted for 91% of scene variance in Landsat multispectral data.
When a larger number of AIS channels are included in the PCA, other
authors (Smith and Adams, 1985; Murray et al., 1986) found additional PCs
to be significant. Murray et al. (1986) evaluated 32 channels in the 1.5-1.8
mm region obtained over desert sagebrush and
soil targets in central Oregon. They found the largest proportion of variance
was accounted for by the first two PCs which were successfully used to
separate the major cover classes. The next four PCs, however, were found
to contain a mix of real factor and noise components. Contamination of
these subsequent PCs with noise supports our decision to destripe data
and eliminate wavelength regions affected by atmospheric water absorption
prior to statistical analyses. Unfortunately, because of analytical restrictions,
we could not perform a PCA on our entire data set simultaneously, so we
cannot eliminate the possibility that a larger number of significant PCs
could be identified. In contrast, Smith and Adams (1985) analyzed 32-band
AIS data in the 2.1-2.4 mm region (which does
not include atmospheric water bands) from a site having greater geologic
diversity at Cuperite, NV. They found additional significant PCs that were
useful in separating six surface mineral and vegetation types. Subsequently,
instead of using their PCs to define an image classification map as done
in this study, they utilized the spectral dimensions associated with the
PCs to decompose the matrix into spectral signatures. They demonstrated
that it was possible to interpret these signatures by comparisons to spectra
of various ground categories. This approach is similar to that proposed
by Huete (1986) using ground-based radiometry.
May vs. October AIS data. Greater resolution of classification detail is apparent in the May map when compared with the October maps [Plate VII (a)-(d)]. However, not all clusters could be associated with ground surface features. The effect of the clustering algorithm, in this case, may have been almost equivalent to density slicing the PC spectral data belonging to these highly correlated ground density and cover classes. Inspection of the maximum likelihood results [Plate VII (a)-(d)] relative to known ground cover within each remote sensing data set showed the following: Despite apparent similarity of spectral signatures, May AIS data provided much better discrimination between surfaces and considerably more subtle structure. The number of distinct, spectrally meaningful (nonnoise) classes in the May AIS data appeared to be two to three times that of the October AIS data at all intercluster distances (sdis). For example, 10-15 such spectral classes were found on the more recent burn (SCB) in May in contrast to approximately half that number in the October data. The same pattern held on the more recent soil unit (LUT) and the older soil unit (TIN). Both the Symmes Creek and Independence Creeks were evident on the May image, whereas only the latter creek was detectable in October. Also the OVB area was more difficult to resolve in October. These observations regarding the October AIS classification results are reflected in the low rankings shown in Table 4 for spatial data coherence, correspondence with labels, and adequacy of spectral partitioning to represent ground cover categories. Of the October classifications, the eight-class resolution presented the best representation of ground categories. The 51-class product had many classes which lacked spatial continuity and which appeared to represent noise.
Although a third of the May classification may represent noise classes (e.g., classes having speckled, vertical or horizontal distributions, or classes of especially small size), an equivalent proportion of the class structure seen in the May AIS data did not appear to be noise and may in fact represent real variation in surface components. While the ground data was not sufficiently detailed to validate this conclusion, the spatial pattern inherent in the assignment of the pixels (when examined on a class-by-class basis) suggested that at least 25-35 classes were potentially nonnoise classes. Their spatial patterns were consistent with features (rock, shadow, riparian and shrub vegetation, and combinations of these) known to exist within the plant communities surveyed. An analysis of subpixel site variation (5 x 5 m plots) revealed that cover could vary greatly between adjacent plots (e.g., 15-30%). Additionally, sample sizes of 20 50-m transects were insufficient to produce accurate estimates of the mean cover of the dominant species individually. In fact, the variance of the total shrub cover averaged nearly 25%. Therefore, the considerable spatial heterogeneity of the fans could account for this level of class distinction. Thus, the May AIS 99-class classification, produced with an intercluster distance of 1.50, was ranked in Table 4 as "generally sufficient," in terms of class spatial coherence and correspondence to ground class labels. The exceeding degree of detail represented by the large number of classes considered to be nonnoise may be a product of slight variations in the proportion of various ground class components, such as vegetation density, composition, or surface texture.
TM data. As expected, the clustering and classification of the May TM data produced successively more detailed partitions of the landscape as the minimum intercluster distance was reduced (Table 4). At an sdis of 0.75, the TIN soil units and the SCB burn were accurately distinguished from other surface types. This classification map [Plate VII (e)] is similar to the less detailed July 1984 TM scene reported by Ustin et al. (1986c). Considerably more classes were produced from TM data when all visible and reflected infrared bands were used compared to just the two band shortwave infrared data sets. Use of six bands produced good class resolution (broad category) and the best spatial coherence of any data set examined. However, at sdis below 0.75, the classification categories were partitioned into units having much less spatial organization. A similar pattern was observed in comparison of six-, three-, and two-band July TM data of this region (unpublished data). Plate VII (f) illustrates the loss of resolution when only the two SWIR TM bands are used. When visible bands were included in the analysis, significantly better resolution of broad category classes was developed at all sdis. This suggests that the AVIRIS system, which will have visible and IR bands combined with an intermediate ground resolution between the AIS-1 and TM (i.e., 20 x 20 m pixels) will produce better spatial pattern categories than either AIS1 or TM data. Plate VII (g) is an aerial color infrared photo obtained from the October AIS flight. Comparison of this figure with the AIS and TM maps facilitates image comparison with ground features.
AIS vs. TM data. AIS maps show greater detail than TM maps, especially when the comparison is restricted to the TM two-band SWIR wavelength region. The May AIS data contained at least three times the spatially coherent classification detail as the corresponding May TM data. For example, six-channel TM classifications had three to four classes for the SCB burn compared to at least 10 or more meaningful classes in the AIS data. A similar ratio held on the older soil type mentioned previously and on the LUT soil type. Small washes and creeks running through the study site were not detectable with TM but could be resolved with both the October and May AIS data. Classes covering small areas but having coherent spatial patterns within the burns and in other vegetated areas could be distinguished with the May AIS data but not with the TM data. The significance of this AIS detail could not be determined, however, since it exceeded that of the corresponding ground sample data.
The situation was generally reversed with the October AIS data. May six-channel TM data provided both a more detailed and a more spatially coherent representation of ground cover phenomena. The two band, 11-class TM data set also produced classification results [Plate VII (f)] which were somewhat more spatially coherent (for broad scale patterns) and consistent with ground data than those obtained with the October AIS set. However, the October AIS data did enable better detection of fine scale spatial pattern, particularly the smaller streams and washes, than either of the two TM data sets.
The increased information content of the May AIS vs. May TM data sets
may be due to a combination of better spatial resolution (ca. 10 m vs.
30 m), spectral resolution, and/or the location of the wavelength regions
utilized. The relative importance of these factors will require additional
research, which should include determination of the pixel-level mixtures
of vegetation, rock, soil, and shade, contributing to the reflectance.
Linear mixing models of reflectance data have been utilized by Adams and
colleagues (Adams et al., 1986) to examine Viking Lander and TM data and
by Huete (1986) using field radiometry measurements to identify spectral
surface types in spatially averaged data. Mixing models have been examined
by Adams and colleagues for TM data from the fan region studied here (Ustin
et al., 1986c). Such an approach may prove useful in establishing a physical
basis for the detailed information apparent in this initial analysis of
AIS data for mapping arid land surfaces and vegetation cover.
Nonetheless, it was shown that despite the apparent similarity of spectral signatures between vegetation and soil surfaces, a sufficient degree of fine-scale spectral structure existed in AIS wavelength regions to define major and minor surface features. The degree to which differentiation was possible varied with time of year, being greater in spring. The level of potentially useful ground class information in the May AIS data was found to significantly exceed that in approximately date-matched TM six-channel data. One objective of further work should be determining what these small spatially coherent class patterns represent.
In the AIS data analyzed here, only the first PCs were significant and these were related to scene brightness. Even though they were developed from small eight-band sets covering a limited portion of the SWIR spectrum, these PCs produced scene classifications at, or exceeding, knowledge of the surface conditions on the fan. A PCA over a larger portion of the data set could reveal additional PCs related to physical condition. Further improvements in extractable information might result from other partitionings and/or transformations of the data. For example, selection of individual or very small groups of AIS bands to take advantage of the microphenomena seen in the AIS spectral plots might enable a more accurate identification of subtle vegetation condition differences. Or construction of band ratios, and generation of greenness or other spectral indices related to biomass or vegetation condition, may be possible. In addition, inspection of our data suggests that information recovery from AIS for many purposes may be improved by further normalization of the raw data.
Our results represent a preliminary assessment of the usefulness of
high resolution radiometry systems for arid vegetation studies. At present,
facilities for processing and analysis of the large data arrays present
the most severe limitation for use of such data. The SPAM software package,
developed by the JPL, provides the most widely used method of analysis.
While the capabilities within this package have grown, it remains limited
because of the difficulty in interfacing the data easily with readily available
statistical packages or other software useful for specific research purposes.
Even when such packages are available, because of the large number of AIS
bands, software programs commonly used for other remotely sensed data are
inadequate. Unlike analysis of mineralogical features which depend on identification
of distinctly different absorption features across the spectrum, analysis
of vegetation features depends upon more subtle changes in spectral patterns.
This becomes extremely critical in arid regions where low vegetation density
makes mere identification difficult. Better analytical methods, particularly
for curve shape, are required before the full potential of this data is
realized.
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