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The geographical patterns of species richness and underlying mechanisms are among the central issues of ecology. The Himalaya, a global biodiversity hotspot, lacks spatially explicit representation of plant richness patterns and predictor environmental covariates. The rugged Himalayan terrain limits large-scale field surveys, we, therefore, disentangle the role of remotely sensed environmental proxies for characterization of Plant Functional Types (PFTs) and prediction of plant richness in an alpine area in WesternHimalaya. Alpine plant richness was recorded in cluster plots (10 random quadrants of 1m(2) in approximately 1-ha area) across 97 sites in Pithoragarh district in part of the Western Himalaya(India). The dominant PFTs were mapped based on support vector machine classification of Landsat 8 image. Thesatellite-derived climate, landscape,and topographic variables were correlated to plant richness using generalized linear model (GLM) with poisson distribution to unravel species richness-environment linkages in the study area. The dominant PFTs mapped were herbaceous meadow, Danthonia grassland, Kobresia sedge meadow, moist scrub, and dry scrub. The GLM based plant richness model explained 70% variation in alpine plant richness. The environmental factorssuch as vegetation vigor, elevation, landscape diversity and moisturewere observed to influence alpine plant richness of the study area. The study presents a valuable baseline spatial database for judicious management of alpine plant resources and climate change studies.