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Changes in climate and land management practices in the San Pedro River basin have altered the vegetation patterns and dynamics. Therefore, there is a need to map the spatial and temporal distribution of the vegetation community in order to understand how climate and human activities affect the ecosystem in the arid and semi-arid region. Remote sensing provides a means to derive vegetation properties such as fractional green vegetation cover (fc) and green leaf area index (GLAI). However, to map such vegetation properties using multitemporal remote sensing imagery requires ancillary data for atmospheric corrections that are often not available. In this study, we developed a new approach to circumvent atmospheric effects in deriving spatial and temporal distributions of fc and GLAI. The proposed approach employed a concept, analogous to the pseudoinvariant object method that uses objects void of vegetation as a baseline to adjust multitemporal images. Imagery acquired with Landsat TM, SPOT 4 VEGETATION, and aircraft based sensors was used in this study to map the spatial and temporal distribution of fractional green vegetation cover and GLAI of the San Pedro River riparian corridor and southwest United States. The results suggest that remote sensing imagery can provide a reasonable estimate of vegetation dynamics using multitemporal remote sensing imagery without atmospheric corrections.
