Quantifying Spatiotemporal Characteristics and Identifying Influential Factors of Ecosystem Fragmentation in Karst Landscapes: A Comprehensive Analytical Framework

Abstract

Guanling-Zhenfeng County, a microcosm of the ecologically fragile karst area in southwest China, experiences rapid population growth and urban expansion which intensifies land use transformation and ecological landscape fragmentation. Exploring the spatiotemporal characteristics of landscape fragmentation and its causes in Guanling-Zhenfeng County is of great significance in maintaining the stability of the ecosystem and ecological protection in karst areas. In this study, a comprehensive landscape fragmentation index (FI), geographic probe, multi-scale geographically weighted regression (MGWR), and PLUS model were used to quantitatively explore the spatiotemporal characteristic heterogeneity, causes, and future scenario projections of landscape fragmentation in Guanling-Zhenfeng County from 2000 to 2020. The results showed that: (1) the distribution of each landscape index was characterized by obvious spatial differentiation. Among them, the spatial distribution trends of patch density (PD) and largest patch index (LPI) were opposite and the distribution trends of Shannon diversity index (SHDI) and Shannon evenness index (SHEI) were similar. There were fewer heterogeneous patches in the study area from 2000 to 2020, and the landscape shape was more regular and less fragmented. (2) The overall landscape fragmentation in Guanling-Zhenfeng County from 2000 to 2020 was dominated by moderate fragmentation, with the smallest percentage of extreme fragmentation, and heavy fragmentation was mainly distributed in the north-central part of the study area. (3) Natural and social factors jointly affect the landscape fragmentation in Guanling-Zhenfeng County, and there is a significant interactive enhancement effect among the factors, with population density being the most important influence factor. In addition, the effects of the factors on landscape fragmentation showed significant spatial non-stationarity. (4) The characteristics of landscape fragmentation changes in Guanling-Zhenfeng County under different scenarios varied significantly, with the largest percentage of increase in heavy landscape fragmentation under the business-as-usual scenario (BAU), the next under the land use planning scenario (LUP), and the smallest under the ecological protection scenario (ESP).

Analyzing Spatial–Temporal Characteristics and Influencing Mechanisms of Landscape Changes in the Context of Comprehensive Urban Expansion Using Remote Sensing

“…Given the complex spatial heterogeneity inherent in natural and social systems, drivers exert differing effects on landscape patterns depending on their geographical locations [43,60]. Wu et al found that the distribution of each landscape index had obvious spatial differentiation [61]. Thus, accounting for the spatial heterogeneity of driving mechanisms is crucial for a more accurate depiction of these mechanisms across diverse geographical contexts.…”

“…However, the impact of these two factor types on landscape patterns is not isolated; rather, they interact synergistically. Wu et al, found that there was a significant interaction enhancement effect between natural and social factors in influencing landscape fragmentation [61]. Nie et al, found the digital elevation model (DEM) and GDP to be the main drivers of landscape patterns using Geodetectors [8].…”