Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas

Xinyan Zou, Chen Wang, Xiang Que (), Xiaogang Ma, Zhe Wang, Quanli Fu, Yuting Lai and Xinhan Zhuang
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Xinyan Zou: College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Chen Wang: Fujian Geologic Surveying and Mapping Institute, Fuzhou 350000, China
Xiang Que: College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Xiaogang Ma: Department of Computer Science, University of Idaho, Moscow, ID 83844, USA
Zhe Wang: Department of Computer Science, University of Idaho, Moscow, ID 83844, USA
Quanli Fu: College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Yuting Lai: College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Xinhan Zhuang: College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Sustainability, 2024, vol. 16, issue 8, 1-18

Abstract: With the acceleration of urbanization, the ecosystem around cities is facing severe challenges. The drastic changes in the landscape pattern, especially in urban–suburban areas, are usually regarded as one of the main drivers. However, the spatiotemporal heterogeneous impacts of landscape patterns on the ecosystem services in this region remain unclear. To address this issue, we propose a novel framework integrating the InVEST-based ecosystem service assessment and spatiotemporal weighted regression (STWR)-based analysis of the spatiotemporal heterogeneity in urban–suburban areas, and apply it to the empirical study of Fuzhou City from 2000 to 2020. It first utilized the InVEST model to build a comprehensive ecosystem service index (CES) from five aspects (i.e., habitat quality, carbon storage, water yield, soil retention, and water purification capacity). Then, four landscape pattern indices (LPIs) (i.e., patch density (PD), area-weighted mean fractal dimension (FRAC_AM), splitting (SPLIT), and Shannon’s diversity (SHDI) index) were selected to build the STWR model. We compared and analyzed the differences in the spatial coefficient surfaces and significance tests generated by the STWR model in urban, urban–suburban, and rural areas. Results show that the following: (1) The CES in Fuzhou shows an upward trend from the urban area to the urban–suburban and rural areas, with significant gradient differences. (2) Compared with other areas, the LPIs in urban–suburban areas show more fragmentation, discreteness, and diversity, indicating more socioeconomic activities. (3) Although LPIs’ impacts on CES change over time (increasing from 2005 to 2010 and 2020 but decreasing in 2015), their effects are relatively low in urban–suburban areas, significantly lower than in urban areas. (4) Interestingly, the LPI coefficients near the urban–suburban boundary seem more significant. (5) This framework can effectively reveal the spatiotemporal heterogeneous relationships between various LPIs and CES, thus guiding concrete policies and measures that support decision-making for improving the ecosystem services surrounding cities through shaping landscape patterns.

Keywords: ecosystem services; landscape pattern; urban–suburban area; spatiotemporal weighted regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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