Using multiple image endmember spectral mixture analysis to study chaparral regrowth in southern California

Abstract

This research tested the ability of a multiple endmember (EM) spectral mixture analysis (SMA) approach, applied to multi-temporal Landsat Thematic Mapper (TM) data, to produce realistic and meaningful EM fractions for the study of post-fire regrowth in a southern California chaparral landscape. Eight different image EMs were used, two types for each EM class of interest (green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade); the best EM combination was selected for each pixel. These EM fractions were validated with fractions derived from 1 m Airborne Data Acquisition and Registration multi-spectral image data. The EM fractions from the two datasets were similar (r=0.873, 0.776, 0.790 for GV, NPV, and soil, respectively). Chaparral stands were delineated using vegetation type, fire history and slope aspect GIS layers. Mean EM fractions were calculated for each stand, and analysis of variance was performed to determine if EM fractions were different for stands of different age. Short-term trajectories of individual stands appeared to exhibit trends consistent with trends reported in the literature. However, only the youngest and oldest stands were consistently significantly different.