Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models

Abstract

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, called endmembers, while allowing the types and number of endmembers to vary on a per pixel basis. In this manner, vegetation is characterized by a unique set of endmembers as well as by the fractions. Reference endmembers 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 candidate model was applied to the image, then, on a per pixel basis, assessed in terms of fractions, root mean squared (RMS) error, and residuals. If a model met all criteria, it was listed as a candidate for that pixel. For this study, selection criteria included fractions between −0.01 and 1.01, an RMS less than 0.025 and a residual less than 0.025 in seven or more contiguous bands. A total of 889 two-endmember models 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 were used to generate fraction images and vegetation maps showing evergreen and drought deciduous or senesced vegetation. We found that a majority of the image could be modeled as two-endmember models. Three-endmember models provided greater areal coverage, yet provided poorer vegetation discrimination due to an increase in model overlap (two or more model candidates modeling the same pixel). The vegetation maps demonstrate that the technique is capable of discriminating a large number of spectrally distinct types of vegetation while capturing the mosaic-like spatial distribution typical of chaparral. However, additional research is required to fully evaluate the technique and validate the vegetation maps that were produced.