Three-season Hyperspectral Mapping of Land Cover in Northern California using Multiple-Endmember Spectral Mixture Analysis

Abstract:

The Hyperspectral Infrared Imager (HyspIRI) mission is a full-range hyperspectral and thermal satellite being considered for development by NASA (hyspiri.jpl.nasa.gov). A hyperspectral satellite, such as HyspIRI, will provide detailed spectral and temporal information at global scales that could greatly improve our ability to map land cover with greater class detail and spatial and temporal accuracy than possible with conventional multispectral satellites (e.g., Landsat OLI). The broad goal of our research is to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping across a range of environmental and anthropogenic gradients in California. In this study, we mapped FAO Land Cover Classification System (LCCS) classes over 30,000 km² in Northern California using multi-temporal HyspIRI imagery simulated from the AVIRIS airborne sensor. The three-season hyperspectral data were used to extract image endmembers for input into a Multiple-Endmember Spectral Mixture Analysis (MESMA). Classified maps are compared to that from Landsat 8 OLI. We hypothesize that the inclusion of phenological spectral variability from three-seasons, and the ability to resolve spectral features in the hyperspectral HyspIRI imagery, will improve the final classified map, especially with respect to deciduous tree species.