Land Surface Eco-Environmental Situation Index (LSEESI) Derived From Remote Sensing

Efficient and accurate monitoring of land surface eco-environmental situation (LSEES) is critical to promoting the sustainable development of global society. This study utilizes satellite data from EOS/MODIS to derive the land surface eco-environmental index (LSEESI) through the covariance-based principal component analysis method. Four strategies are used to evaluate the performance of this methodology. The stability, reasonability, comprehensive representation, and regional adaptability of this model are approved. LSEESI is also compared with the remote sensing ecological index (RSEI) and shows that LSEESI better indicates the LSEES (R ² = 0.674 for LSEESI, 0.437 for RSEI). Application of the LSEESI model in Yangtze River Delta during 2001-2021 shows the conclusions as follows: 1) Overall, the LSEES in Yangtze River Delta is stable or improving, and the annual average LSEESI increased from 0.572 to 0.593. 2) There were significant spatial differences in LSEES in Yangtze River Delta. Areas with relatively poor LSEES were mainly in Suzhou-Wuxi-Changzhou urban agglomeration, Hangzhou-Jiaxing-Ningbo urban agglomeration, and Shanghai. Regions with deteriorating LSEES were also mainly concentrated in the above urban agglomerations around Lake Taihu. 3) The contribution of temperature, precipitation, and NTL to LSEES was 0.07, 0.38, and 0.55, respectively, suggesting that LSEES change in Yangtze River Delta in recent 21 years might have been influenced primarily by human activity, with only some parts of Anhui Province affected mainly by climate change. This study demonstrated that the proposed LSEESI model can effectively monitor and quantitatively evaluate LSEES change, and provide the information necessary for monitoring and managing eco-environmental systems.