The spectral library endmembers were measured in a Varian Cary SE spectrometer of soil, dry grass, and other plant samples (leaves, bark) collected within the Berryessa study area. The GPS locations of these materials were included in our GIS data base. Specifically we used a Heteromeles arbutifolia (Toyon) leaf as the green foliar endmember, a Sehorn Clay Series soil, and a mixture of dry annual grass leaves for the dry grass endmember. Bark from Quercus agrifolia function of wavelength was used as a "woody" endmember.
We used the ARP and lignin/cellulose analysis program by Gao and Goetz
(1990) to compare against the dry grass endmember fraction. We assumed
that the cellulose/lignin estimate was a reasonable approximation of the
biochemistry of dry leaf residues in this study. This analysis produced
images having a more speckled appearance and larger variability between
adjacent pixels then did the SMA results.
The topographically developed classification scheme does well at separating the grassland from the oak woodlands, due to their aspect dependence, and also identifies riparian zones using the accumulation and elevation layers. Rock outcroppings are over-estimated and chaparral, the most abundant community in the region, is under-represented. This resulted because the initial chaparral classification was relegated to the areas unoccupied by the more topographically distinct grasslands and oak woodlands.
The SMA fractions were also used as data layers in the GIS for classification of the SCCP. The SMA-based MLS classification does a better job separating the dry grasslands from the other communities. This is not surprising since one of the endmembers selected for the SMA was of dry grass. Even so, the accuracy of the prediction is striking. The SMA method was less successful discriminating oak woodlands and riparian woodlands which have similar endmember fractions. This effect might be improved if a multiple endmember approach like that of Roberts et al., (1992) were adopted. The rock outcroppings were over-predicted and the chaparral regions were under-predicted. The lower specificity of chaparral may be due to a varying spectral signature.
Finally, the DEM layers and the SMA fraction layers were combined to determine a final "actual vegetation" map. The resulting map is a remarkably good representation of the vegetation type distributions based on comparisons against the SCCP vegetation map and against aerial photographs. In fact, the combined map is better at defining the vegetation type distributions than the more simplified ground-based vegetation map. The grassland predictions suffered slightly, due to topographic variables driving some predictions toward other vegetation types even when the dry grass fraction identifies grasslands. However, the chaparral distribution improves in this map compared to the previous maps. Also, the rock outcrops show closer agreement with the field-based map. The riparian and oak woodlands are also well separated and accurately located when compared with the field-based map.
The landscape parameter maps developed for the SCCP subset were then created for the larger area covered by the two AVIRIS overflights and the same classifications were performed for the entire region. A significant portion of the total image was classified as grassland (Table 1). However, we still require a map of the spatial variation in dry biomass to monitor dry grass residues. To determine biomass distribution in the dry grasslands, non-grasslands and areas where grazing is unlikely were masked.
The histogram of dry vegetation fractions for the whole area and for
the areas classified as grassland are shown in Fig.
3. Both the 1991 and 1992 AVIRIS overflights show close agreement.
As seen from the figure, most pixels having low dry vegetation fractions
were classified as other vegetation types. To quantify the ranges of dry
grass biomass, endmember fractions were divided into five frequency classes.
These biomass classifications are consistent with spatial patterns in grassland
biomass variation predicted. Predicted area coverage for all vegetation
types is shown in Table 1.
3. Masking non-grassland areas improves the ability to evaluate spatial variations in dry grass abundance.
4. Spectral measures alone are communities.
5. Combined SMA and DEM data in a GIS produced vegetation maps as good
or better then those based on field surveys.
Ustin, S.L., M.O. Smith, J.B. Adams, 1993, Remote Sensing of Ecological Processes: A strategy for developing and testing ecological models using spectral mixture analysis, in: J. Ehlringer and C. Field (eds.) Scaling Physiological Processes: Leaf to Globe. Acad. Press, New York, pp. 339-357.
Wessman, C.A., 1990. Evaluation of canopy biochemistry. In: R.J. Hobbs and H.A. Mooney (eds.), Remote Sensing of Biosphere Functioning, Springer-Verlag, New York, pp. 135-156.