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黄河中游不同地貌分区景观格局脆弱性及其驱动力
引用本文:何梦真,张乐涛,魏仪媛,郑泽豪,王清源. 黄河中游不同地貌分区景观格局脆弱性及其驱动力[J]. 环境科学, 2024, 45(6): 3363-3374
作者姓名:何梦真  张乐涛  魏仪媛  郑泽豪  王清源
作者单位:河南大学地理与环境学院环境与规划国家级实验教学示范中心, 开封 475004
基金项目:河南省自然科学基金项目(232300421245);国家自然科学基金项目(41807066,42371223)
摘    要:黄河中游生态环境脆弱,科学评估流域景观格局脆弱性是因地制宜开展该区生态环境建设的基础.以黄河中游1990~2018年5期土地利用数据为基础,采用景观脆弱度指数,分析景观格局脆弱性的时空演变,并结合最优参数地理探测器方法探究不同自然地貌分区景观格局脆弱性的影响因素.结果表明:①1990~2018年,耕地(面积占比为36.96 %~39.97 %)是黄河中游的优势景观,耕地(减少10185.00 km2)和建设用地(增加7678.46 km2)面积变化最大.②1990~2018年,景观格局以较低和中度脆弱区为主,面积占比为70 %~80 %,较高和高脆弱区集中在黄土丘陵沟壑区,低脆弱区集中在河谷平原区和土石山区,脆弱度呈先降后升的变化趋势;1990~2000年和2000~2005年,脆弱性等级变化以“脆弱度减少”为主,2005~2010年和2010~2018年,以“脆弱度增加”为主.③年降水和NDVI是影响景观格局脆弱性的主要驱动因素,不同自然地貌分区的影响因素则各有不同:黄土丘陵沟壑区和土石山区的主导因子分别为年降水和DEM(自然因素),黄土高塬沟壑区、河谷平原区和沙地沙漠区的主导因子分别为人口密度、土地利用程度和距道路的距离(人为因素).任意两个影响因子的交互结果均表现为双因子增强或非线性增强.风险探测显示,各自然地貌分区的景观格局脆弱度高值区分别分布于对应主导因素的不同取值范围.因此,在黄河中游的生态治理实践中,应针对不同自然地貌的脆弱性特点,实施因地制宜的治理策略,进一步提升流域的生态治理水平.

关 键 词:景观格局脆弱性  时空变化  驱动力  最优参数地理探测器  不同地貌分区  黄河中游
收稿时间:2023-07-12
修稿时间:2023-08-29

Landscape Pattern Vulnerability and Its Driving Forces in Different Geomorphological Divisions in the Middle Yellow River
HE Meng-zhen,ZHANG Le-tao,WEI Yi-yuan,ZHENG Ze-hao,WANG Qing-yuan. Landscape Pattern Vulnerability and Its Driving Forces in Different Geomorphological Divisions in the Middle Yellow River[J]. Chinese Journal of Environmental Science, 2024, 45(6): 3363-3374
Authors:HE Meng-zhen  ZHANG Le-tao  WEI Yi-yuan  ZHENG Ze-hao  WANG Qing-yuan
Affiliation:National Demonstration Center for Environmental and Planning, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
Abstract:The ecological environment of the middle Yellow River is highly vulnerable. Conducting a scientific assessment of landscape pattern vulnerability holds great significance, as it serves as the basis for the rational construction of the ecological environment in this area. Based on five periods of land use data from the middle Yellow River from 1990 to 2018, the landscape pattern vulnerability index was employed to analyze the spatio-temporal evolution of the landscape pattern vulnerability. Furthermore, the influencing factors for landscape pattern vulnerability in different natural geomorphological divisions were explored using an optimal parameters-based geographical detector model. The results showed that:① From 1990 to 2018, cultivated land (which accounted for 36.96 % to 39.97 % of the area) remained the predominant landscape in the middle Yellow River. Among all landscape types, cultivated land and construction land exhibited the most significant changes. The area of cultivated land decreased by 10 185.00 km2, whereas the area of construction land increased by 7 678.46 km2. ② From 1990 to 2018, the landscape pattern was dominated by low and medium vulnerability and accounted for 70 %-80 % of the total area. The high and higher vulnerability areas were concentrated in the loess hilly and gully region, whereas the lower vulnerability area was concentrated in the valley plain and the earth-rock mountain regions. During this period, landscape pattern vulnerability underwent an incipient decrease, followed by a subsequent increase. From 1990 to 2000 and from 2000 to 2005, the changes in the level of landscape pattern vulnerability were dominated by a "reduction in the degree of vulnerability". However, from 2005 to 2010 and from 2010 to 2018, it was mainly an "increase in the degree of vulnerability". ③ Annual precipitation and NDVI were the main factors influencing the vulnerability of landscape patterns, whereas the influencing factors varied across different natural geomorphological divisions:the loess hilly and gully region and the earth-rock mountain region were dominated by natural factors, with annual precipitation and DEM being the dominant factors, respectively; the loess plateau tableland-gully region, valley plain region, and sandy land and desert region were dominated by human factors, with population density, degree of land use, and distance from roads being the dominant factors, respectively. The interaction results of any two influencing factors were manifested as two-factor enhancement or nonlinear enhancement. Risk detection revealed that high vulnerability areas of landscape patterns in different natural geomorphological divisions were distributed over distinct ranges of their corresponding dominant factors. Therefore, in the practices of ecological management in the middle Yellow River, appropriate management strategies should be implemented based on the vulnerability characteristics of different natural landforms, to further improve the ecological management level of the watershed.
Keywords:landscape pattern vulnerability  spatial-temporal variations  driving force  optimal parameters-based geographical detector  different geomorphological divisions  the middle Yellow River
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