Remote Sensing (Oct 2021)

Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China

  • Fang Wang,
  • Xingzhong Yuan,
  • Lilei Zhou,
  • Shuangshuang Liu,
  • Mengjie Zhang,
  • Dan Zhang

DOI
https://doi.org/10.3390/rs13214248
Journal volume & issue
Vol. 13, no. 21
p. 4248

Abstract

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Ecosystem services (ESs) are highly vulnerable to human activities. Understanding the relationships among multiple ESs and driving mechanisms are crucial for multi-objective management in complex social-ecological systems. The goals of this study are to quantitatively evaluate and identify ESs hotspots, explore the relationships among ESs and elucidate the driving mechanisms. Taking central urban area Chongqing municipality as the study area, biodiversity (BI), carbon fixation (CF), soil conservation (SC) and water conservation (WC) were evaluated based on the InVEST model and ESs hotspots were identified. The complex interactions among multiple ESs were determined by utilizing multiple methods: spearman correlation analysis, bivariate local spatial autocorrelation and K-means clustering. The linear or nonlinear relationships between ESs and drivers were discussed by generalized additive models (GAMs). The results showed that during 2000–2018, except for CF that exhibited no obvious change, all other ESs showed a decrease tendency. High ESs were clustered in mountains, while ESs in urban areas were lowest. At administrative districts scale, ESs were relatively higher in Beibei, Banan and Yubei, and drastically decreased in Jiangbei. Multiple ES hotspots demonstrated clear spatial heterogeneity, which were mainly composed of forestland and distributed in mountainous areas with high altitude and steep slope. The relationships between ES pairs were synergistic at the entire scale. However, at grid scale, the synergies were mainly concentrated in the high-high and low-low clusters, i.e., mountainous areas and urban central areas. Five ESs bundles presented the interactions among multiple ESs, which showed well correspondence with social-ecological conditions. GAMs indicated that forestland and grassland had positive impact on BI and CF. Additionally, SC was mainly determined by geomorphological factors, while WC were mainly influenced by precipitation. Furthermore, policy factors were confirmed to have a certain positive effect on ESs. This study provides credible references for ecosystem management and urban planning.

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