Improved vegetation maps are required for fire management and biodiversity assessment, from critical inputs for hydrological and biogeochemical models and represent a means for scaling-up point measurements. At scales greater than 10 meters, vegetation communities are typically mixed consisting of leaves, branches, exposed soil and shadows. To map mixed vegetation, many researchers employ spectral mixture analysis (SMA). In most SMA applications, a single set of spectra consisting of green vegetation, soil, non- photosynthetic vegetation and shade are used to 'unmix' images. However, because most scenes contain more than four components, this simple approach leads to fraction errors and may fail to differentiate many vegetation types. In this work, we apply a new approach called multiple endmember spectral mixture analysis, in which the number and types of endmembers vary per-pixel. Using this approach, hundreds of unique models are generated that account for community specific differences in plant chemistry, physical attributes and phenology. Additionally, we describe a new strategy for developing and organizing regionally specific spectral libraries. We present result from a study in the Santa Monica Mountains using AVIRIS data, in which we map grassland and chaparral communities, mapping species dominance in some cases to a high degree of accuracy.