Mapping species diversity patterns in the Kansas shortgrass region by integrating remote sensing and vegetation analysis

Abstract

Abstract. Field reconnaissance data are used in a supervised classification of a 1989 Landsat Thematic Mapper (TM) scene to create a digital database of high and low quality grasslands for northwestern Kansas. To test the classification of grassland quality, plot‐based vegetation data collected from 32 sites are analyzed for differences in species composition, and evaluated for relationships between TM data and plant diversity. Significant differences between predicted high and low quality grassland sites are identified for the following variables: cover of the dominant and common species, overall species richness, number of forbs, number of grasses, and plant diversity using Shannon's index. Linear regression analysis reveals a significant relationship (r2= 0.61) between species diversity and the prediction of grassland quality from the supervised classification. The addition of spectral data to this model did not improve the prediction of species diversity, but spectral brightness is identified as a key feature in mapping shortgrass vegetation diversity patterns with TM data.