空间分析
空间生态学
生态学
比例(比率)
环境资源管理
地理
地质统计学
环境科学
自然地理学
空间变异性
地图学
统计
数学
生物
遥感
作者
Shaokun Li,Bing Tu,Z. Zhang,Lei Wang,Zhang Zhi,Xiaoqian Che,Zhuangzhuang Wang
标识
DOI:10.1016/j.jclepro.2024.142633
摘要
Scientific assessment of landscape ecological risk (LER) aims to optimize land use patterns and mitigate regional ecological hazards. However, accurate LER assessment requires the selection of a feasible spatial analysis scale. This study identified the optimal spatial scale by analyzing high-resolution data from 2006, 2012, and 2019. Within the "production-living-ecological" spatial (PLES) perspective, this study employed six methods to depict the pattern changes and spatiotemporal variations of LER. These included the Markov transfer matrix model, the geoscience information atlas–gravity center model (GIAGC), the LER index model, Min/Max Autocorrelation Factors, the Sequential Gaan simulation (MAF-SGS) multivariate geostatistical model, and spatial autocorrelation. The results are as follows: 1) A granularity of 10 meters and a magnitude of 200 meters are the most effective parameters for LER assessment. 2) From 2006 to 2019, there was a shift in the structure of the landscape pattern from "ecological space" to "productive space", accompanied by a tendency for the center of gravity to shift towards the southwest. 3) The non-structural spatial factors contributing to LER are gradually increasing, and the maximum variability is steadily rising. Medium-low risk areas dominate the basin, and the spatial distribution of LER exhibits an overall pattern: high in the center, low in the southwest, and low in the northwest. Specifically, a low-low spatial clustering pattern is predominant; however, there is a general upward trend in LER. In summary, this study provides a scientific basis for adjusting basin land use structures, resolving ecological risks, and promoting high-quality sustainable development.
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