基于地理探测器的泾河流域景观生态风险及驱动因素

doi:10.13866/j.azr.2025.08.12

干旱区研究 ›› 2025, Vol. 42 ›› Issue (8): 1488-1500.doi: 10.13866/j.azr.2025.08.12 cstr: 32277.14.AZR.20250812

基于地理探测器的泾河流域景观生态风险及驱动因素

刘凤莲¹^,²(), 罗芹芹¹^,³, 杨博文¹, 陈洪敏¹, 高梓燚⁴()

1.云南财经大学国土资源与持续发展研究所，云南昆明 650221
2.云南财经大学云南省服务计算重点实验室，云南昆明 650221
3.云南财经大学财政与公共管理学院，云南昆明 650221
4.云南财经大学国际工商学院，云南昆明 650221

收稿日期:2025-04-02 修回日期:2025-06-15 出版日期:2025-08-15 发布日期:2025-11-24
通讯作者: 高梓燚. E-mail: gaozy@ynufe.edu.cn
作者简介:刘凤莲（1981-），副教授，主要研究方向为土地利用与可持续发展. E-mail: zz2105@ynufe.edu.cn
基金资助:
国家乡村振兴局委托项目(80026091881);云南省服务计算重点实验室开放课题(YNSC24305);云南省教育厅科学研究基金项目(2025Y0762);云南省教育厅科学研究基金项目(2024J0634);云南省教育厅科学研究基金项目(2024J0657);云南财经大学引进人才项目(2024D50);云南财经大学科学研究基金青年项目(2023B11)

Landscape ecological risk and driving factors in the Jinghe River Basin based on geodetector analysis

LIU Fenglian¹^,²(), LUO Qinqin¹^,³, YANG Bowen¹, CHEN Hongmin¹, GAO Ziyi⁴()

1. Institute of Land & Resources and Sustainable Development, Yunnan University of Finance and Economics, Kunming 650221, Yunnan, China
2. Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming 650221, Yunnan, China
3. School of Finance and Public Administration, Yunnan University of Finance and Economics, Kunming 650221, Yunnan, China
4. International Business School, Yunnan University of Finance and Economics, Kunming 650221, Yunnan, China

Received:2025-04-02 Revised:2025-06-15 Published:2025-08-15 Online:2025-11-24

摘要/Abstract

摘要： 泾河流域作为我国西北部生态屏障的重要组成部分，其生态安全至关重要。基于土地利用数据，结合景观格局指数构建景观生态风险评价模型，评估2000—2020年泾河流域景观生态风险时空演变动态特征，并运用地理探测器识别影响流域景观生态风险的关键驱动因子。结果表明：（1）耕地和草地为流域主要景观类型，两者相互流转较频繁，建设用地呈持续扩张趋势，2000—2020年共增加394.5 km²。（2） 2000—2020年泾河流域的景观生态风险类型以中风险为主，面积占比在41%以上；高风险和中高风险区二者面积占流域面积的1/10左右，沿泾河河网分布，集中分布在地势平坦的中部和东南部；低风险区集中分布在人为干扰较少的东部和西南部。（3） 2000—2020年流域80%以上区域风险等级较稳定，整体上流域景观生态风险降低。（4）年均气温、GDP密度和高程是影响流域景观生态风险的主要驱动因子；年均气温、年均降水量同其他影响因素交互作用对流域景观生态风险影响显著。

关键词: 景观生态风险, 时空演变, 地理探测器, 驱动因子, 泾河流域

Abstract:

The ecological security of the Jinghe River Basin, a crucial component of the ecological barrier in northwest China, is of paramount importance. This study constructed a landscape ecological risk assessment model using land use data and landscape pattern indices to evaluate the dynamic characteristics of landscape ecological risk evolution in the Jinghe River Basin. Key driving factors affecting this risk were identified through geodetector analysis. The results revealed the following: (1) Cultivated land and grassland are the predominant landscape types in the basin, with frequent mutual transfer between the two. In addition, the construction land has shown a continuous expansion trend, increasing by a total of 394.5 km² during the study period. (2) Medium-risk areas account for over 41% of the landscape ecological risk types in the Jinghe River Basin. High- and medium-high-risk areas are primarily located along the Jinghe River network, particularly concentrated in the middle and southeastern flat terrain, together constituting about one-tenth of the basin area. Low-risk areas are predominantly found in the eastern and southwestern parts of the basin, where human interference is minimal. (3) Throughout the study period, more than 80% of the basin’s areas maintained stable risk levels, contributing to a reduction in the overall landscape ecological risk. (4) The main driving factors influencing landscape ecological risk include average annual temperature, GDP density, and elevation. The interaction between average annual temperature, average annual precipitation, and other influencing factors has significant effects on landscape ecological risk.

Key words: landscape ecological risk, space-time evolution, geographical detector, driving factor, Jinghe River Basin

刘凤莲, 罗芹芹, 杨博文, 陈洪敏, 高梓燚. 基于地理探测器的泾河流域景观生态风险及驱动因素[J]. 干旱区研究, 2025, 42(8): 1488-1500.

LIU Fenglian, LUO Qinqin, YANG Bowen, CHEN Hongmin, GAO Ziyi. Landscape ecological risk and driving factors in the Jinghe River Basin based on geodetector analysis[J]. Arid Zone Research, 2025, 42(8): 1488-1500.

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名称	公式	含义
景观破碎度指数（P_i）	${P}_{i}=\frac{{n}_{i}}{{A}_{i}}$	指景观被分割成不连续、破碎化斑块的程度，是衡量区域景观空间复杂性的重要指标
景观分离度指数（K_i）	${K}_{i}=\frac{1}{2}\times \frac{A}{{A}_{i}}\sqrt{\frac{{n}_{i}}{{A}_{i}}}$	指同一类型景观的不同斑块之间在空间上的分离程度，用于反映景观在空间分布上的分散状态
景观优势度指数（T_i）	${T}_{i}=\frac{{S}_{i}+{U}_{i}}{4}+\frac{{L}_{i}}{2}$	用于测度整体景观结构中某一景观类型支配其他景观类型的程度，反映景观在生态、美学和社会层面上的优势度
景观干扰度指数（G_i）	${G}_{i}=a{P}_{i}+b{K}_{i}+c{T}_{i}$	用于表示景观生态环境受外界因素活动影响的程度
景观脆弱度指数（W_i）	建设用地赋值为1，林地为2，草地为3，耕地为4，水域为5，未利用地为6，进行归一化处理	指用于衡量景观系统对外界干扰的敏感性和恢复能力
景观损失度指数（X_i）	X_i=G_i×W_i	指景观生态系统在受到外界因素影响后受损状况和恢复能力的强弱

变化趋势	2000—2010年		2010—2020年		2000—2020年
变化趋势	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
稳定	3.83×10⁴	82.35	4.13×10⁴	88.71	3.74×10⁴	80.24
升高	924.81	1.99	2681.24	5.74	1725.17	3.71
降低	7022.51	15.07	2080.93	4.47	6888.20	14.78
显著升高	172.30	0.37	398.68	0.86	396.27	0.85
显著降低	101.91	0.22	100.56	0.22	196.07	0.42

影响因素	因子/序号	贡献度/%
影响因素	因子/序号	2000年	2005年	2010年	2015年	2020年
自然因素	高程（X1）	17.23	22.09	22.31	19.86	20.66
	NDVI（X2）	6.56	7.06	5.61	6.37	2.37
	年均降水量（X3）	5.54	5.98	7.90	8.03	13.34
	年均气温（X4）	19.56	24.57	25.53	23.03	24.48
社会经济因素	人口密度（X5）	1.60	5.08	5.71	5.33	7.65
	夜间灯光（X6）	19.19	8.31	6.46	5.29	5.99
	GDP密度（X7）	21.50	18.27	18.70	18.54	18.44
	人为干扰度（X8）	8.82	8.64	7.78	13.55	7.07

基于地理探测器的泾河流域景观生态风险及驱动因素

Landscape ecological risk and driving factors in the Jinghe River Basin based on geodetector analysis

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