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

干旱区地理 ›› 2026, Vol. 49 ›› Issue (9): 1826-1839.doi: 10.12118/j.issn.1000-6060.2025.703 cstr: 32274.14.ALG2025703

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

毕文焌¹^,²(), 闫庆武¹^,³(), 赵甫心³, 移明昊³, 吴子豪¹^,³

¹ 江苏国土资源智库中国矿业大学研究基地，江苏徐州 221116
² 中国矿业大学环境与测绘学院，江苏徐州 221116
³ 中国矿业大学公共管理学院，江苏徐州 221116

收稿日期:2025-10-30 修回日期:2025-11-21 出版日期:2026-09-25 发布日期:2026-09-07
通讯作者: 闫庆武（1975-），男，博士，教授，主要从事土地生态监测研究. E-mail: yanqingwu@cumt.edu.cn
作者简介:毕文焌（2002-），男，硕士研究生，主要从事土地生态监测研究. E-mail: ts24160142p31@cumt.edu.cn
基金资助:
第三次新疆综合科学考察项目(2022xjkk1004);国家自然科学基金(42101459);国家自然科学基金(42201447)

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

BI Wenjun¹^,²(), YAN Qingwu¹^,³(), ZHAO Fuxin³, YI Minghao³, WU Zihao¹^,³

¹ Research Base of Jiangsu Land Resource Think Tank in China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
² School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
³ School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China

Received:2025-10-30 Revised:2025-11-21 Published:2026-09-25 Online:2026-09-07

摘要/Abstract

摘要：

景观生态风险是指区域景观结构的改变对生态过程造成的潜在不良影响，深入研究其时空分异特征及驱动因素，可以为国土空间优化提供决策支撑。以天山中段为研究对象，采用景观生态风险评价模型研究2005—2023年景观生态风险的时空变化格局，并基于极限梯度提升-沙普利加性解释（XGBoost-SHAP）方法，探究了景观生态风险驱动因素及其交互作用。结果表明：（1）时间维度上，2005—2023年天山中段景观生态风险总体平稳，景观生态风险指数（ERI）微降。期间，66.12%区域风险等级稳定，33.88%区域有升降转换，局部敏感性与整体稳态并存。（2）空间格局上，天山中段景观生态风险呈“北低南高”两极化格局，以天山主脊为界近似“人”字形分隔。高风险区集中于天山南部塔克拉玛干沙漠地带，低风险区集中于北部的伊犁河谷地带，景观组成对风险空间分异影响显著。（3）驱动因素上，单因素分析中自然地理因素主导天山中段景观生态风险格局，其中归一化植被指数（NDVI）与气温贡献突出。交互因素分析中高景观生态风险区域受“低NDVI与较高气温”和“低GDP与高人口密度”耦合驱动，这种多重阈值非线性互馈机制是干旱区生态风险管理需重点关注的内容。

关键词: 景观生态风险, XGBoost-SHAP, 时空演变, 驱动因素, 天山中段

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

毕文焌, 闫庆武, 赵甫心, 移明昊, 吴子豪. 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[J]. 干旱区地理, 2026, 49(9): 1826-1839.

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.

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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

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

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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