2000—2020年三江源地区景观生态风险评价及驱动因素

doi:10.13866/j.azr.2024.11.11

摘要/Abstract

摘要：

三江源地区是重要的源头汇水区，其生态安全至关重要。探究三江源地区景观生态风险的时空变化及其驱动因素，对促进亚洲东南部的生态安全、水资源开发利用具有重要的意义。以三江源地区为研究区，基于5期土地利用数据，构建评价模型以分析其景观生态风险的时空分异特征和变化趋势，进而采用参数最优地理探测器对比分析其全局及局部区域景观生态风险空间分异的驱动因素。结果表明：（1） 2000—2020年研究区景观生态风险在空间上呈显著正相关，以中风险区和中低风险区为主，高风险区较少。（2） 2000—2020年研究区景观生态风险水平有所改善，研究区低风险和高风险分布向中低风险区、中风险区和中高风险区转化；澜沧江源区景观生态风险水平略高于长江源区和黄河源区。（3）三江源地区各土地类型的景观面积规模和景观完整性应是该区景观生态风险变化的关键因素，其景观生态风险空间分异是多种驱动因素共同作用的结果，其中归一化差分植被指数（NDVI）、高程、坡度为主要驱动因子；地势不同的地区，其因素的驱动作用会有较大差异。政府应严格保护与监测雪山冰川，采取适当措施抑制草地荒漠化，防范生态风险反弹，以保持生态系统的完整性，促使其生态功能的持续提升。

关键词: 景观生态风险, 驱动因素, 参数最优地理探测器, 三江源地区

Abstract:

The Three River Source Region is a crucial source and catchment area, and its ecological security is vital. Investigating the spatial and temporal variations of landscape ecological risks and their drivers in this region is essential for promoting ecological security and the sustainable development of water resources in Southeast Asia. In this study, we focused on the Three River Source Region and constructed an evaluation model using land use data from the fifth phase to analyze the spatial and temporal characteristics and trends of landscape ecological risk. We employed a parametric optimal geodetector to compare and analyze the driving factors behind the spatial variation of landscape ecological risk at both global and local scales. The results revealed the following: (1) From 2000 to 2020, the study area exhibited a significant positive correlation with landscape ecological risk, particularly within the medium-and medium-low-risk areas, with areas classified as high-risk. (2) From 2000 to 2020, the landscape ecological risk in the study area improved, with high-and low-risk areas transitioning into predominantly medium-low, medium-and medium-high-risk areas. The Lancang River source exhibited a slightly higher risk level than the Yangtze and Yellow River sources. (3) The area and fragmentation degree of landscape types were identified as key determinants of landscape ecological risk variations in the region, with spatial differentiation resulting from the combined influence of multiple driving factors. Among these, Normalized Difference Vegetation Index (NDVI), elevation, and slope were identified as the primary driving factors, with their impacts varying significantly across terrain. To ensure ecological integrity and sustain ecological functions, the government should prioritize the protection and monitoring of snow-capped glaciers, implement measures to curb grassland desertification, and prevent the resurgence of ecological risks.

Key words: landscape ecological risk, driving factors, Optimal Parameter Geographic Detector, Three River Source Region

王成武, 尧良杰, 汪宙峰, 张荞, 谢亮. 2000—2020年三江源地区景观生态风险评价及驱动因素[J]. 干旱区研究, 2024, 41(11): 1908-1920.

WANG Chengwu, YAO Liangjie, WANG Zhoufeng, ZHANG Qiao, XIE Liang. Landscape ecological risk assessment and driving factors analysis in the Three River Source Region from 2000 to 2020[J]. Arid Zone Research, 2024, 41(11): 1908-1920.

图/表 15

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图2

表1

景观格局指数计算及意义"

指数名称	计算公式	参数意义
景观生态风险指数（ERI_x）	$E R I x = ∑ i = 1 N S x i / S x × R i$	ERI_x为第x个评价单元的景观生态风险指数；S_x和S_xi分别为评价单元x的总面积和其中第i类景观的面积；R_i为第i类景观的损失度指数；N为评价单元中景观类型的数量
景观损失度（R_i）	$R i = W i × V i$	W_i为景观干扰度指数；V_i为景观脆弱度指数
景观脆弱度（V_i）	借鉴已有研究成果^[30-31]，结合三江源生态地理区位和主体生态特征，对9类景观类型的景观脆弱度赋值，其依次是高覆盖度草地1、中覆盖度草地2、低覆盖度草地3、林地4、水域5、湿地6、雪地7、耕地8、荒地9，经归一化处理后依次为0.02、0.04、0.07、0.09、0.11、0.13、0.16、0.18、0.20	景观脆弱度衡量不同景观抵抗外界干扰的能力
景观干扰度（W_i）	$W i = x E i + y D i + z F i$	x、y、z分别为3类指数的权重，且权重之和为1。结合已有研究和三江源地区特征^[31-32]，x、y、z分别赋值为0.5、0.3、0.2
景观破碎度（E_i）	$E i = n i / A i$	n_i为景观i的斑块数；A_i为景观i的面积。破碎度表征景观被分割的破碎程度，反映景观空间结构的复杂性，在一定程度上反映了景观的受干扰程度
景观分离度（D_i）	$D i = A 2 A i × n i / A i$	A为景观总面积；A_i为景观i的面积；n_i为景观i的斑块数。表征某一景观类型中不同斑块分布的离散程度
景观分维数（F_i）	$F i = 2 l n p i 4 / l n A i$	p_i为景观i的周长；A_i为景观i的面积。表征斑块形状的复杂程度

表1

表2

图3

图4

表3

图5

表4

图6

表5

表6

图7

表7

表8

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风险等级	ERI值区间
低风险区	0.010~0.038
中低风险区	0.038~0.055
中风险区	0.055~0.074
中高风险区	0.074~0.099
高风险区	0.099~0.155

类型	2000年		2005年		2010年		2015年		2020年
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/%
低风险区	59243.1	18.54	53793.6	16.83	41183.7	12.89	48140.4	15.07	41852.7	13.10
中低风险区	111140.4	34.78	135208.5	42.31	129369.6	40.49	126420.3	39.56	123225.2	38.56
中风险区	83715.2	26.20	77350.5	24.21	78732.1	24.64	87060.9	27.25	93922.2	29.39
中高风险区	50384.5	15.77	43305.6	13.55	52432.6	16.41	47276.9	14.80	56865.3	17.80
高风险区	15066.5	4.72	9891.4	3.10	17831.6	5.58	10651.1	3.33	3684.2	1.15

类型	2000—2005年		2005—2010年		2010—2015年		2015—2020年		2000—2020年
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/%
下降	129775.7	40.61	13868.2	4.34	100721.6	31.52	45867.8	14.35	85611.0	26.79
稳定	101413.7	31.74	190564.2	59.64	174994.2	54.76	219596.6	68.72	137913.8	43.16
上升	88360.2	27.65	115117.2	36.02	43833.8	13.72	54085.1	16.93	96024.8	30.05

地类	高程	坡度	气温	降雨	NDVI	土壤可蚀性	邻道路距离	人类活动强度
解释强度（q值）	0.15	0.16	0.10	0.08	0.32	0.12	0.07	0.04
贡献率/%	14.02	15.37	9.71	7.20	30.95	11.87	6.79	4.09

驱动因素	高程	坡度	气温	降雨	NDVI	土壤可蚀性	邻道路距离	人类活动强度
高程	0.15
坡度	0.34	0.16
气温	0.22	0.25	0.10
降雨	0.25	0.22	0.15	0.08
NDVI	0.38	0.45	0.38	0.36	0.32
土壤可蚀性	0.22	0.28	0.23	0.22	0.35	0.12
邻道路距离	0.24	0.31	0.17	0.14	0.34	0.20	0.07
人类活动强度	0.19	0.25	0.16	0.14	0.34	0.16	0.11	0.04