ESTIMATING BURN AREA SEVERITY USING SPATIAL AUTOCORRELATION ANALYSIS

Traditional methods of recording fire burned areas and fire severity involve expensive and time - consuming field surveys. The available remote sensing technologies may allow us to develop standardized burn-severity maps for evaluating fire effects and addressing post fire management activities. This paper is fo-cused on the characterization of burn severity using ASTER (Advanced Spaceborne Thermal Emission and Re-flection Radiometer). satellite pictures have been processed using geo-statistic analyses to capture pattern features of burned areas. Even if in last decades different authors tried to integrate geo-statistics and remote sensing image processing methods used since now are only variograms, semivariograms and kriging. In this paper, we propose an approach based on the use of spatial indicators of global and local autocorrelation. Spatial autocorrelation statistics, such as Moran’s I, Geary’s C, and Getis-Ord Local Gi index (see Anselin 1995; Getis and Ord 1992), were used to measure and analyze the degree of dependency among spectral features of burned areas. This approach enables the characterization of the pattern features of burned area and improves the estimation of burn severity.