Remote Sensing (Dec 2022)
Spatial-Temporal Evolution and Influencing Factors Analysis of Ecosystem Services Value: A Case Study in Sunan Canal Basin of Jiangsu Province, Eastern China
Abstract
The continuing degradation of ecosystem functioning, particularly in areas of fast-growing economies, is a common challenge facing humanity today and a focus of global research on sustainable development. A typical fast-developing economic region in Southeastern China, namely Sunan Canal Basin of Jiangsu Province (SCBJ), was selected for this study. Based on remote sensing monitoring data of land use for five periods of 2000, 2005, 2010, 2015, and 2019, the ecosystem service value (ESV) of SCBJ was measured using the equivalent factor method, and the spatial and temporal evolution of the ESV was analyzed using contribution and spatial statistical methods. Panel quantile regression was employed to explore in depth the segmental effects of the ecosystem service influencing factors and to increase the understanding of ecosystem service influencing mechanisms. Our results showed the following: (1) From 2000 to 2019, the land use structure was stable, and the comprehensive land use dynamic degree was continuously reduced. There were significant differences in the single land use dynamic degree of each land type, especially for built-up land. (2) During the study period, the total ESV increased first and then decreased, with the decreasing regions mainly distributed in the southeast of SCBJ and the urban rapid expansion areas on both sides of the canal. (3) In areas with different levels of ESV, the influencing factors had different impact effects. The mean proximity index (MPI), i.e., land use integrity, had a significant enhancement effect at the low ESV region and a significant dampening effect at the high ESV region. As the quantile points increase, the positive effects of the annual mean temperature (ATE), annual mean precipitation (APR), and net primary productivity (NPP) on ecosystem services gradually increase, which increased the gap between high and low ESV areas, creating a “natural Matthew effect”, while the negative effects of economic density (GDP) and population density (POP) on ecosystem services gradually decreased.
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