2000—2024年甘肃省土地生态脆弱性时空变化特征及模拟预测研究

doi:10.12118/j.issn.1000-6060.2025.580

Abstract

Abstract:

This study aims to investigate the spatiotemporal evolution and future trend of land ecological vulnerability in Gansu Province from 2000 to 2024. Toward this end, it constructs an evaluation system based on the sensitivity-resilience-pressure model. The study employs the entropy weight method, a geographic detector, and the Cellular Automata-Markov model for quantitative analyses and the simulation and prediction of conditions, respectively, for 2030 and 2035. The results indicated that land ecological vulnerability significantly decreased, ecosystem stability improved, and dominant vulnerability level shifted from high to light/moderate. Moreover, spatial patterns exhibited a gradient of high and low vulnerability in the northwest and southeast, respectively, with high- and low-value areas gradually shrinking and expanding, respectively. Annual evapotranspiration, normalized difference vegetation index, and sunshine hours were the dominant influencing factors and the interaction among them exerted the strongest effect, thereby indicating that climate and vegetation jointly regulated land ecosystem vulnerability. The simulation results predicted that, by 2035, the proportion of micro and mild vulnerability areas will reach 65.5%, extremely vulnerable areas will nearly disappear, and the overall ecological pattern will continue to improve. In summary, the land ecosystem in Gansu Province demonstrates a trend of continuous restoration and steady optimization. Ecological restoration policies and climate-adaptive governance jointly contribute to the gradual reformation of the regional ecological security pattern.

Key words: land ecological vulnerability, SRP model, geographical detector, CA-Markov model, Gansu Province

CAO Haoran, MENG Mei, LIU Hongguang. Spatiotemporal variation characteristics and simulation prediction of land ecological vulnerability in Gansu Province from 2000 to 2024[J].Arid Land Geography, 2026, 49(4): 841-855.

Figures/Tables 16

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数据名称	年份	数据来源及分辨率
DEM数据	2024	地理空间数据云（http://www.gscloud.cn）；90 m
气象数据	2000、2005、2010、2015、2020、2024	国家地球系统科学数据中心（http://www.geodata.cn），国家气象科学数据中心（http://www.data.cma.cn）；1 km
土壤数据	2023	HWSD2.0（https://www.fao.org/soils-portal/en）；1 km
遥感数据	2000、2005、2010、2015、2020、2024	美国地质调查局（USGS）官方数据平台（https://earth explorer.usgs.gov）；30 m
MODIS数据	2000、2005、2010、2015、2020、2024	美国国家航空航天局（NASA）地球数据检索系统（https://search.earth data.nasa.gov）；NDVI产品30 m，NPP产品250 m
地理信息数据	2024	资源环境科学与数据平台（https://www.resdc.cn）
社会经济数据	2000、2005、2010、2015、2020、2024	《甘肃统计年鉴（2001—2025）》《甘肃省农经统计报表（2000—2024）》

目标层	准则层	次准则层	影响因子层	属性
土地生态脆弱性评价	敏感度	地形因子（静态）	高程	正向
			坡度	定性
			地形起伏度	定性
			地表粗糙度	正向
		气象因子（动态）	年均气温	负向
		气象因子（动态）	干旱指数	正向
		环境因子（动态）	水土流失	正向
		环境因子（动态）	水源涵养能力	负向
		土壤因子（动态）	土壤沙化度	定性
			土壤盐渍化	定性
			土壤碳储量	负向
			土壤水分	负向
	恢复力	植被因子（动态）	NDVI	负向
		植被因子（动态）	NPP	负向
		多样性因子（动态）	生物丰度	负向
		水文因子（动态）	河流缓冲区	正向
	压力	社会因子（动态）	人口密度	正向
	压力	社会因子（动态）	人均GDP	正向

指标层	指标量化标准
指标层	0.00	0.25	0.50	0.75	1.00
坡向/(°)	157.5~202.5	112.5~157.5，202.5~247.5	67.5~112.5，247.5~292.5	22.5~67.5，292.5~337.5	0.0~22.5，337.5~360.0
坡度/(°)	0~5	5~10	10~15	15~25	>25
土壤沙化度/g·kg^-1	25	25~40	40~55	55~70	>70
土壤盐渍化/g·kg^-1	1	-	3	-	6

脆弱类型	脆弱等级	分级范围	生态效果
微度脆弱	Ⅰ	<0.40	土地生态系统结构完整，生态服务功能价值显著，稳定性良好，抗扰动能力与恢复潜力强，生态环境风险水平低
轻度脆弱	Ⅱ	0.40~0.50	土地生态系统结构相对完整，生态服务功能价值可观，稳定性较好，具备一定抗扰动能力与较强恢复潜力，存在潜在生态风险
中度脆弱	Ⅲ	0.50~0.58	土地生态系统处于临界状态，结构完整性受挑战，生态服务功能有限；稳定性差，扰动缓冲能力接近阈值，敏感性显著；自然恢复缓慢低效，已显现生态退化迹象
重度脆弱	Ⅳ	0.58~0.65	土地生态系统结构缺陷明显，生态服务功能显著退化；稳定性严重不足，对外界压力高度敏感；自我调节与恢复机制受损，多重生态风险显现，系统整体失衡
极度脆弱	Ⅴ	>0.65	土地生态系统结构高度脆弱，生态服务功能严重退化；处于高度不稳定状态，对高强度外界压力极端敏感；自然恢复能力基本丧失，区域生态异常广泛存在，系统濒临崩溃

类别	影响因子	数据来源
社会经济因子	人为影响综合指数（X₁）	中国科学院资源环境科学数据中心
	耕地面积比重（X₂）	《甘肃省统计年鉴》
	人口增长率（X₃）	《甘肃省统计年鉴》
	城市化率（X₄）	《甘肃省统计年鉴》
自然因素	NDVI（X₅）	中国科学院资源环境科学数据中心
	地形起伏度（X₆）	地理空间数据云DEM数据
	平均日照时数（X₇）	国家地球系统科学数据中心
	年蒸散发量（X₈）	国家地球系统科学数据中心
	土壤类型（X₉）	国家青藏高原科学数据中心，并结合FAO1990的土壤分类
景观因素	生态用地比例（X₁₀）	中国科学院资源环境科学数据中心；第三次全国国土调查成果分析报告

Spatiotemporal variation characteristics and simulation prediction of land ecological vulnerability in Gansu Province from 2000 to 2024

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脆弱类型	2000年		2005年		2010年		2015年		2020年		2024年
脆弱类型	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
微度脆弱	70441.67	16.54	92745.38	21.78	112913.85	26.52	119256.13	28.01	139340.04	32.72	155618.58	36.55
轻度脆弱	67143.68	15.77	70357.10	16.52	73845.36	17.34	69976.57	16.43	104520.89	24.55	110207.80	25.88
中度脆弱	62915.49	14.78	69257.77	16.27	95366.85	22.40	99933.30	23.47	137437.36	32.28	132046.41	31.01
重度脆弱	148663.20	34.91	168641.41	39.61	138557.83	32.54	132109.84	31.03	44396.01	10.43	27863.78	6.54
极度脆弱	76635.97	18.00	24798.34	5.82	5116.11	1.20	4524.16	1.06	105.70	0.02	63.42	0.01

影响因子	解释力（q值）
影响因子	2000年	2005年	2010年	2015年	2020年	2024年
X₁	0.378	0.277	0.274	0.278	0.295	0.310
X₂	0.082	0.080	0.101	0.156	0.125	0.130
X₃	0.122	0.163	0.183	0.218	0.215	0.210
X₄	0.030	0.129	0.160	0.123	0.024	0.035
X₅	0.417	0.514	0.589	0.557	0.586	0.600
X₆	0.007	0.029	0.124	0.022	0.027	0.030
X₇	0.589	0.517	0.456	0.467	0.555	0.560
X₈	0.453	0.594	0.451	0.418	0.650	0.670
X₉	0.031	0.023	0.026	0.117	0.013	0.020
X₁₀	0.224	0.255	0.276	0.240	0.219	0.225

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脆弱类型	2024年		2030年		2035年
脆弱类型	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
微度脆弱	159360.53	37.43	164962.88	38.74	163144.76	38.31
轻度脆弱	108854.78	25.56	117564.86	27.61	115831.30	27.20
中度脆弱	63824.55	14.99	123484.33	29.00	125936.68	29.58
重度脆弱	93591.01	21.98	19766.79	4.64	20866.12	4.90
极度脆弱	169.13	0.04	21.14	0.01	21.14	0.01