Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland

Abstract

Hyperspectral remotely sensed data are useful for studying ecosystem processes and patterns. However, spatial characterization of such remotely sensed images is needed to optimize sampling procedures and address scaling issues. We have investigated spatial scaling in ground-based and airborne hyperspectral data for canopy- to watershed-level ecosystem studies of southern California chaparral and grassland vegetation. Three optical reflectance indices, namely, Normalized Difference Vegetation Index (NDVI), Water Band Index (WBI) and Photochemical Reflectance Index (PRI) were used as indicators of biomass, plant water content and photosynthetic activity, respectively. Two geostatistical procedures, the semivariogram and local variance, were used for the spatial scaling analysis of these indices. The results indicate that a pixel size of 6 m or less would be optimal for studying functional properties of southern California grassland and chaparral ecosystems using hyperspectral remote sensing. These results provide a guide for selecting the spatial resolution of future airborne and satellite-based hyperspectral sensors.