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. 2024 Oct 5;14(1):23208.
doi: 10.1038/s41598-024-74506-1.

Landscape ecological risk assessment and driving factor analysis in southwest china

Hui Chen  1   2 , Hongxing Chen  1   2 , Xiaoyun Huang  1   2 , Song Zhang  1   2 , Tengbing He  1 , Zhenran Gao  3   4
Affiliations

Landscape ecological risk assessment and driving factor analysis in southwest china

Hui Chen et al. Sci Rep. .

Abstract

Landscape ecological risk assessment and ecological network construction are of great significance for optimizing territorial functions and reducing regional ecological risks. Based on the production-living-ecological space perspective, this study evaluated the spatiotemporal differentiation characteristics of landscape ecological risk and its driving mechanism in Southwest China and constructed a landscape ecological network. The results showed that the proportions of ecological space, production space and living space to the total space in 2020 were 74.35%, 24.55% and 1.10%, respectively. The industrial production space had the highest growth rate, increasing by 9.8 times from 2000 to 2020. During the study period, the average value of the ecological risk index ranged from 0.2 to 0.21 for the whole landscape. The geographical distribution of ecological risk zones showed significant differences, with risk zones showing a transition from high-risk and low-risk to medium-risk zones. A total of 105 ecological corridors and 156 ecological nodes have been constructed in the 2020 ecological network. The northeastern part of the study area needs better landscape connectivity and should be focused on ecological protection. Random Forest (RF) and Geodetector modeling showed that anthropogenic disturbance and land use levels have strong explanatory power for the evolution of ecological risk in the landscape. The interactions between anthropogenic disturbance, natural climate and regional economy are essential factors in the spatiotemporal differentiation of ecological risk. This study provides scientific references for ecological risk research and the promotion of high-quality development in Southwest China.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Location of the southwest China. (created by ArcMap, version 10.7, http://www.arcgis.com/).
Fig. 2
Fig. 2
Distribution of the PLEs in Southwest China in (a)2000, (b) 2010, and (c)2020. (created by ArcMap, version 10.7, http://www.arcgis.com/).
Fig. 3
Fig. 3
Spatial distribution of landscape ecological risks in Southwest China in (a) 2000, (b) 2010, and (c) 2020, (d) shows the area proportion statistics of each risk level. (created by ArcMap, version 10.7, http://www.arcgis.com/).
Fig. 4
Fig. 4
Sankey diagram of landscape ecological risk transition matrix from 2000 to 2020.
Fig. 5
Fig. 5
Scatter plot of the Moran’s I values in the study area in (a) 2000, (b) 2010, and (c) 2020.
Fig. 6
Fig. 6
LASA aggregation map of the landscape ecological risks in the study area in (a) 2000, (b) 2010, and (c) 2020. (created by Geoda, version 1.16, https://geodacenter.github.io/).
Fig. 7
Fig. 7
Spatial distribution of minimum cumulative resistance surface (a) and ecological networks (b) in the study area. (created by ArcMap, version 10.7, http://www.arcgis.com/).
Fig. 8
Fig. 8
Correlation analysis of ecological risk indices with driver indicators. E: elevation, S: slope, N: NDVI, AP: annual precipitation, AT: average annual temperature, LU: degree of land use, G: GDP, PI: output value of the primary industry, SI: output value of the secondary industry, TI: output value of the tertiary industry, GP: GDP per capita, P: population density, AD: degree of anthropogenic disturbance, GS: grain sown area.
Fig. 9
Fig. 9
The radial histogram of the driving factors obtained by RF and Geodetector in (a) 2000, (b) 2010 and (c) 2020. E: elevation, S: slope, N: NDVI, AP: annual precipitation, AT: average annual temperature, LU: degree of land use, G: GDP, PI: output value of the primary industry, SI: output value of the secondary industry, TI: output value of the tertiary industry, GP: GDP per capita, P: population density, AD: degree of anthropogenic disturbance, GS: grain sown area.
Fig. 10
Fig. 10
Detection results of the interaction of driving factors obtained by Geodetector in (a) 2000, (b) 2010, and (c) 2020. E: elevation, S: slope, N: NDVI, AP: annual precipitation, AT: average annual temperature, LU: degree of land use, G: GDP, PI: output value of the primary industry, SI: output value of the secondary industry, TI: output value of the tertiary industry, GP: GDP per capita, P: population density, AD: degree of anthropogenic disturbance, GS: grain sown area.
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