Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research

Abstract

Accurately monitoring the quantity and quality of urban vegetation contributes to regional greening efforts and improves the understanding of vegetation's impact on the environment. However, factors such as building shadows and synthetic materials can greatly obstruct vegetation estimates. Additionally, vegetation indices (VIs) saturate quickly in high biomass conditions, complicating vegetation quality assessments. To address these issues, we propose a new vegetation index, namely the hyperspectral image-based vegetation index (HSVI). HSVI is built in three steps: band selection, saturable band reconstruction, and index structure redefinition. Firstly, we select four representative bands and construct an enhanced vegetation index (EVI) to eliminate the interference of complex urban surface factors. Secondly, we reconstruct the easily saturable band (760 nm) through an exponential function to form an optimized enhanced vegetation index (OEVI). Finally, the index structure is redefined by adding the denominator of the sum of the red edge (689 nm) and green (520 nm) bands to OEVI to further enhance the spectral information of vegetation and eliminate the saturation problem typical of VIs. Three datasets with different geomorphological features (Shanghai Theatre Academy, Dazhu Mountain, and the University of Houston) are used to compare the performance of HSVI with six VIs widely adopted in urban ecological research, i.e., the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the simple ratio (SR), the optimized soil-adjusted vegetation index (OSVAI), the modified transformed vegetation index (MTVI2) and the wide-dynamic-range vegetation index (WDRVI). The results show that the vegetation extraction accuracy of HSVI is more than 90%, which is significantly higher than those of the other VIs. Additionally, HSVI also solves the VIs saturation issue. Therefore, HSVI shows high potential usefulness for urban ecological research applications.