Spatio-temporal evolution of ecological environment quality in the Three Gorges Reservoir Area: from the perspective of synergistic effects of natural and human activities

IF 3 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Zhonghao Chen, Tao Ming
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引用次数: 0

Abstract

The Three Gorges Reservoir Area is an important human activity area in the hinterland of China. There are many cities and major water conservancy projects in the area, and it is also an ecologically fragile area, which is highly sensitive to the complex terrain and the impact of the Three Gorges reservoir. As the key focus of the national regional development policy, the region involves a number of regional development strategic plans, such as the great protection of the Yangtze River, the construction of the Yangtze River economic belt, the rise strategy of central China, the conversion of farmland to forests, and resettlement. Therefore, its ecological environment quality and protection measures are highly valued by the state. However, the previous related research institutes still have the following shortcomings: First, the time series data time resolution is low. Secondly, the research methods And methods are not close enough to the regional geographical And human characteristics, which reduce the adaptability of the research results to a certain extent. In view of the above shortcomings, this study uses time series harmonic analysis algorithm to optimize image data based on Landsat remote Sensing images from 2000 to 2020 and Google Earth engine platform. Based on the traditional RSEI, the normalized water index and soil regulation vegetation index were added to build an improved remote sensing ecological index SRSEI for TGRA, which can effectively improve the ability of water identification and monitoring of sparse vegetation areas. Sen slope estimation and Mann–Kendall test were used to Analyze the spatio-temporal variation trend of SRSEI, And the driving mechanism of land use, elevation, climate, social economy, and other factors was explored by using geographic detector model. Get more reliable evaluation results and impact factors. The results show that the average SRSEI of TGRA from 2000 to 2020 is 0.529, And the overall ecological environment quality is at the upper middle level, And the area with slight improvement after 2010 accounts for more than 50%, showing a good trend as a whole. Single factor driving Analysis showed that land use And elevation were the dominant factors affecting the quality of ecological environment; The interaction showed that the synergistic effect of land use and other factors further magnified its impact on ecological quality. In conclusion, human activities may be the main reason for the change of ecological environment quality in the Three Gorges Reservoir Area in the past 20 years. This study can provide a scientific basis for the formulation and implementation of ecological and environmental protection policies in TGRA.

三峡库区生态环境质量时空演变——基于自然与人类活动协同效应的视角
三峡库区是中国腹地重要的人类活动区域。区内城市和重大水利工程较多,同时也是生态脆弱区,对复杂地形和三峡水库影响高度敏感。该区域作为国家区域发展政策的重点,涉及到长江大保护、长江经济带建设、中部崛起战略、退耕还林、移民安置等一系列区域发展战略规划。因此,其生态环境质量和保护措施受到国家的高度重视。但是,以往的相关研究机构仍然存在以下不足:一是时间序列数据时间分辨率低。其次,研究方法和方法不够贴近区域地理和人文特征,在一定程度上降低了研究结果的适应性。针对上述不足,本研究基于2000 - 2020年Landsat遥感影像和谷歌地球引擎平台,采用时间序列谐波分析算法对影像数据进行优化。在传统RSEI的基础上,加入归一化水分指数和土壤调节植被指数,构建改进的TGRA遥感生态指数SRSEI,可有效提高稀疏植被区水分识别和监测能力。采用Sen slope估算和Mann-Kendall检验分析了SRSEI的时空变化趋势,并利用地理探测器模型探讨了土地利用、高程、气候、社会经济等因素对SRSEI的驱动机制。获得更可靠的评价结果和影响因子。结果表明:2000—2020年,该区平均SRSEI为0.529,整体生态环境质量处于中等偏上水平,2010年以后略有改善的地区占比超过50%,整体呈现良好趋势。单因素驱动分析表明,土地利用和高程是影响生态环境质量的主导因素;交互作用表明,土地利用与其他因素的协同效应进一步放大了其对生态质量的影响。总之,人类活动可能是近20年来三峡库区生态环境质量变化的主要原因。本研究可为青藏高原生态环境保护政策的制定与实施提供科学依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Monitoring and Assessment
Environmental Monitoring and Assessment 环境科学-环境科学
CiteScore
4.70
自引率
6.70%
发文量
1000
审稿时长
7.3 months
期刊介绍: Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.
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