Hyperspectral Image Based Vegetation Index (HSVI): A New Vegetation Index for Urban Ecological Research

As the source of urban ecology, urban green space (UGS) has always been the focus of urban ecological research. The complex urban surface structure causes great interference to UGS extraction. In areas with high vegetation density, the vegetation index becomes rapidly saturated. Existing vegetation indices are not effective for the two problems due to that these indices do not make full use of the rich spectral information contained in hyperspectral image. To remedy these issues, a hyperspectral image based vegetation index (HSVI) is proposed. In the formulation of the HSVI numerator, we chose four new bands combination sensitive to vegetation to improve the identification of vegetation. We opt the sum of the red edge and green bands as the HSVI denominator and reconstruct the easily saturable band (760nm) in the form of exponential function to weaken the saturation problem. We use the hyperspectral image from Shanghai Theatre Academy and University of Houston with different geomorphological features to verify the effect of HSVI. The performance of HSVI is compared with three widely adopted vegetation indices, i.e., the normalized difference vegetation index (NDVI), the optimized soil-adjusted vegetation index (OSVAI) and the wide-dynamic-range vegetation index (WDRVI). The results show that the UGS extraction accuracy of HSVI is more than 90%, which is significantly better than the other indices. Meanwhile, HSVI can also solve the problem of vegetation index saturation. It can be proved that HSVI can fulfill the requirements of urban ecological research on a fine scale.