基于XGBoost-SHAP方法的天山中段景观生态风险时空演变及驱动因素

doi:10.12118/j.issn.1000-6060.2025.703

Abstract

Abstract:

Landscape ecological risk refers to the potential adverse effects on ecological processes due to changes in regional landscape structure. Understanding the spatiotemporal dynamics and driving factors of landscape ecological risk is crucial for optimizing territorial spatial planning. Focusing on the middle section of the Tianshan Mountains, this study applied a landscape ecological risk assessment model to analyze the spatiotemporal evolution of risk from 2005 to 2023. The extreme gradient boosting-Shapley additive explanations method was employed to identify the driving factors and their interactions. The results revealed the following: (1) Temporally, landscape ecological risk in the study area remained generally stable between 2005 and 2023, with a slight decrease in the ecological risk index. Although 66.12% of the area maintained consistent risk levels, 33.88% experienced transitions between risk levels, indicating overall stability with localized sensitivity. (2) Spatially, landscape ecological risk exhibited a polarized “low in the north, high in the south” pattern, roughly delineated by the main ridge of the Tianshan Mountains in a “人”-shaped configuration. High-risk areas were concentrated in the southern Taklimakan Desert, whereas low-risk areas were predominantly in the northern Ili River Valley. Landscape composition significantly influenced this spatial differentiation. (3) In terms of driving factors, single-factor analysis indicated that natural geographical factors, particularly the normalized difference vegetation index (NDVI) and temperature, primarily determined the spatial pattern of landscape ecological risk in the middle Tianshan Mountains. Interaction analysis showed that high-risk areas are collectively driven by the coupled effects of “low NDVI and relatively high temperature” and “low GDP and high population density”. These multi-threshold nonlinear feedback mechanisms warrant particular attention for ecological risk management in arid regions.

Key words: landscape ecological risk, XGBoost-SHAP, spatiotemporal evolution, driving factors, middle section of the Tianshan Mountains

BI Wenjun, YAN Qingwu, ZHAO Fuxin, YI Minghao, WU Zihao. Spatiotemporal evolution and driving factors of landscape ecological risk in the middle section of the Tianshan Mountains based on the XGBoost-SHAP method[J].Arid Land Geography, 2026, 49(9): 1826-1839.

Figures/Tables 11

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数据类型	数据名称	数据来源
自然地理因素	NDVI	国家青藏高原科学数据中心（https://data.tpdc.ac.cn）
	坡度	由高程在ArcGIS中计算得到
	降水量	国家地球系统科学数据中心（https://www.geodata.cn）
	气温	国家地球系统科学数据中心（https://www.geodata.cn）
社会经济因素	人口密度	美国能源部橡树岭国家实验室（https://landscan.ornl.gov）
社会经济因素	GDP	中国科学院资源环境科学数据中心（https://www.resdc.cn）

相关指数	计算公式	变量含义
景观损失度（R_i）	R_i=V_i×D_i	V_i为第i类景观的景观脆弱度；D_i为第i类景观的景观干扰度
景观脆弱度（V_i）	归一化处理	根据前人研究^[34]，将研究区域景观脆弱性分成6个级别，并对各类景观脆弱性数据归一化，得出对应的脆弱度指数
景观干扰度（D_i）	D_i=a×C_i+b×N_i+c×F_i	a、b和c分别为C_i、N_i和F_i的权重，且a+b+c=1，根据相关研究^[18]对这3个指标分别赋值为0.5、0.3和0.2
景观破碎度（C_i）	${\mathit{C}}_{\mathit{i}}=\frac{{\mathit{n}}_{\mathit{i}}}{{\mathit{A}}_{\mathit{i}}}$	n_i为第i类景观的斑块数量；A_i为第i类景观的总面积（km²）
景观分离度（N_i）	${\mathit{N}}_{\mathit{i}}=\frac{1}{2}\times \frac{\mathit{A}}{{\mathit{A}}_{\mathit{i}}}\sqrt{\frac{{\mathit{n}}_{\mathit{i}}}{{\mathit{A}}_{\mathit{i}}}}$	A为景观总面积（km²）
景观分维数（F_i）	${\mathit{F}}_{\mathit{i}}=\frac{2\mathrm{l}\mathrm{n}\left({\mathit{P}}_{\mathit{i}}/4\right)}{\mathrm{l}\mathrm{n}{\mathit{A}}_{\mathit{i}}}$	P_i为第i类景观的周长（km）

景观生态风险类型	2005年		2010年		2015年		2020年		2023年
景观生态风险类型	面积/10⁴ km²	占比/%	面积/10⁴ km²	占比/%	面积/10⁴ km²	占比/%	面积/10⁴ km²	占比/%	面积/10⁴ km²	占比/%
低风险	13.34	23.52	13.29	23.43	13.38	23.59	13.33	23.50	13.19	23.26
中低风险	10.07	17.75	11.12	19.60	11.16	19.68	10.98	19.36	11.13	19.62
中风险	7.13	12.57	6.13	10.81	6.00	10.58	6.21	10.95	6.32	11.14
中高风险	6.50	11.46	6.34	11.18	6.18	10.89	6.11	10.77	6.21	10.95
高风险	19.68	34.70	19.84	34.98	20.00	35.26	20.09	35.42	19.87	35.03

Spatiotemporal evolution and driving factors of landscape ecological risk in the middle section of the Tianshan Mountains based on the XGBoost-SHAP method

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