Multiscale mapping of species diversity under changed land use using imaging spectroscopy.

Land use changes are one of the most important factors causing environmental transformations and species diversity alterations. The aim of the current study was to develop a geoinformatics-based framework to quantify alpha and beta diversity indices in two sites in Israel with different land uses, i.e., an agricultural system of fruit orchards, an afforestation system of planted groves, and an unmanaged system of groves. The framework comprises four scaling steps: (1) classification of a tree species distribution (SD) map using imaging spectroscopy (IS) at a pixel size of 1 m; (2) estimation of local species richness by calculating the alpha diversity index for 30-m grid cells; (3) calculation of beta diversity for different land use categories and sub-categories at different sizes; and (4) calculation of the beta diversity difference between the two sites. The SD was classified based on a hyperspectral image with 448 bands within the 380-2500 nm spectral range and a spatial resolution of 1 m. Twenty-three tree species were classified with high overall accuracy values of 82.57% and 86.93% for the two sites. Significantly high values of the alpha index characterize the unmanaged land use, and the lowest values were calculated for the agricultural land use. In addition, high values of alpha indices were found at the borders between the polygons related to the "edge-effect" phenomenon, whereas low alpha indices were found in areas with high invasion species rates. The beta index value, calculated for 58 polygons, was significantly lower in the agricultural land use. The suggested framework of this study succeeded in quantifying land use effects on tree species distribution, evenness, and richness. IS and spatial statistics techniques offer an opportunity to study woody plant species variation with a multiscale approach that is useful for managing land use, especially under increasing environmental changes.