MAPPING CHAPARRAL IN THE SANTA-MONICA MOUNTAINS USING MULTIPLE ENDMEMBER SPECTRAL MIXTURE-MODELS

A new technique, called multiple endmember spectral mixture analysis ( MESMA), was developed and tested in the Santa Monica Mountains, using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired in the fall of 1994 to map California chaparral. The technique models remotely measured spectra as linear combinations of pure spectra, call ed endmembers, while allowing the types and number of endmembers to va ry on a per pixel basis. In this manner, vegetation is characterized b y a unique set of endmembers as well as by the fractions. Reference en dmembers were selected from a library of field and laboratory measured spectra of leaves, canopies, nonphotosynthetic materials (e.g., stems ), and soils and used to develop a series of candidate models. Each ca ndidate model was applied to the image, then, on a per pixel basis, as sessed in terms of fractions, root mean squared (RMS) error, and resid uals. If a model met all criteria, it was listed as a candidate for th at pixel. For this study, selection criteria included fractions betwee n -0.01 and 1.01, an RMS less than 0.025 and a residual less than 0.02 5 in seven or more contiguous bands. A total of 889 two-endmember mode ls were evaluated and used to generate 276 three-endmember models. To facilitate model selection from a large pool of candidates, an optimal set was selected to provide maximal areal coverage. A total of 24 two -endmember and 12 three-endmember models were chosen. These models wer e used to generate fraction images and vegetation maps showing evergre en and drought deciduous or senesced vegetation. We found that a major ity of the image could be modeled as two-endmember models. Three-endme mber models provided greater areal coverage, yet provided poorer veget ation discrimination due to an increase in model overlap (two or more model candidates modeling the same pixel). The vegetation maps demonst rate that the technique is capable of discriminating a large number of spectrally distinct types of vegetation while capturing the mosaic-li ke spatial distribution typical of chaparral. However, additional rese arch is required to fully evaluate the technique and validate the vege tation maps that were produced. (C)Elsevier Science Inc., 1998