Estimation of fractional cover based on NDVI-VISI response space using visible-near infrared satellite imagery

Abstract

Remote sensing observations of green vegetation (GV), impervious surface (IS), and bare soil (BS) fractional cover are essential for understanding climate change, characterizing ecosystem functions, monitoring urbanization process. As an important indicator of urbanization, the continuous increase of impervious surfaces alters the radiative transfer process at the surface, causing a series of environmental problems. Therefore, timely and accurate monitoring of the spatial and temporal changes in impervious surfaces and their impact on the ecological environment is of great significance for a comprehensive understanding of the process of urbanization as well as for the planning and construction of future cities. This study aims to propose a generalized method for the accurate estimation of GV, IS, and BS coverage. In this study, the visible impervious surface index (VISI), ${(B}_r-B_g)/{(B}_r+B_g)$, was developed using measured spectral data of GV, IS, and BS, and analyzing their spectral characteristics to determine the spectral bands where they can be distinguished. Furthermore, the VISI combined with the NDVI was utilized to establish a triangular space for linear unmixing of the satellite image data to estimate the coverage of its GV, IS, and BS. Finally, the generalizability of this method was verified using UAV and satellite image data, with pearson correlation coefficient > 0.69. The results demonstrate that the VISI index proposed in this study is feasible for long-term series of multispectral imagery and large-scale coverage estimation.