Nonlinear Interactions and Dynamic Analysis of Ecosystem Resilience and Human Activities in China’s Potential Urban Agglomerations - Xinyu Wang; Shidong Ge; Yaqiong Xu; László Kollányi; Tian Bai (Remote Sensing)
Understanding the nonlinear relationship between human activity intensity (HAI) and ecosystem resilience (ER) is crucial for sustainability, yet underdeveloped areas are often overlooked. This study examines the Xuzhou Urban Agglomeration (XZUA) from 2012 to 2022, creating a framework to assess both ER and HAI. Both frameworks utilize multi-source datasets, such as remote sensing, statistical yearbooks, and geospatial data. The ER framework uniquely combines dynamic and static indicators, while the HAI framework differentiates explicit and implicit human activity dimensions. We used spatial analysis, the Optimal Parameter Geodetector (OPGD), and Multi-Scale Geographically Weighted Regression (MGWR) to examine the nonlinear spatiotemporal interaction between HAI and ER. Results show the following: (1) ER exhibited a “shock-recovery” pattern with a net decline of 3.202%, while HAI followed a nonlinear “rise-fall” trend with a net decrease of 0.800%. (2) Spatial mismatches between HAI and ER intensified over time. (3) The negative correlation in high-HAI regions remained stable, whereas neighboring low-HAI areas deteriorated, indicating a spillover effect. (4) OPGD identified the change in HAI (Sen’s slope) as the primary driver of ER change (q = 0.512), with the strongest interaction observed between HAI Sen’s slope and precipitation (q = 0.802). (5) Compared to HAI intensity (mean), its temporal variation had a more spatially stable influence on ER. These findings offer insights for ecological management and sustainable planning in underdeveloped regions, highlighting the need for targeted HAI and ER interventions.