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从地理空间分异角度探究遥感生态指数变化的驱动因素:以中国渭河流域为例。

Exploring the Driving Factors of Remote Sensing Ecological Index Changes from the Perspective of Geospatial Differentiation: A Case Study of the Weihe River Basin, China.

机构信息

College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.

Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China.

出版信息

Int J Environ Res Public Health. 2022 Sep 1;19(17):10930. doi: 10.3390/ijerph191710930.

DOI:10.3390/ijerph191710930
PMID:36078638
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9518415/
Abstract

Using the Google Earth Engine (GEE) platform, Moderate-resolution image spectroradiometer (MODIS) data of the Weihe River Basin from 2001 to 2021 were acquired, four ecological indicators, namely, greenness, wetness, heat, and dryness, were extracted, and the remote sensing ecological index (RSEI) was constructed through principal component analysis. In addition, the geographic detectors and a multi-scale geographic weighted regression model (MGWR) were used to identify the main driving factors of RSEI changes and capture the differences in spatial changes from different perspectives using multiple indicators. The results show that (1) the quality of the eco-environment in the Weihe River basin improved as a whole from 2001 to 2021, and the RSEI increased from 0.376 to 0.414. In terms of the RSEI grade, the medium RSEI and high RSEI areas increased significantly and the growth rate increased significantly, reaching 26.42% and 27.70%, respectively. (2) Spatially, the quality of the eco-environment in the Weihe River Basin exhibited a spatial distribution pattern that was high in the south and low in the north, among which the quality of the eco-environment in the Weihe River Basin in northern Shaanxi and northwestern Ningxia and Gansu was relatively low. In addition, during the study period, the RSEI of the Qinling Mountains in the southern part of the Weihe River Basin and the Jinghe River and Luohe River areas improved significantly. The urban area on the Guanzhong Plain in the Weihe River Basin experienced rapid economic growth, and urban expansion led to a significant decrease in the quality of the eco-environment. (3) The eco-environment quality in the Weihe River Basin is the result of the interaction of natural, anthropogenic, and landscape pattern factors. All of the interactions between the influencing factors had a stronger influence than those of the individual factors. There were significant differences between the individual drivers and the spatial variation in RSEI, suggesting that different factors dominate the variation in RSEI in different regions, and zonal management is crucial to achieving sustainable management of RSEI. The study shows that to improve the eco-environment quality of the Weihe River Basin, it is necessary to further strengthen ecological protection projects, reasonably allocate landscape elements, and strengthen the resistance and resilience of the ecosystem.

摘要

利用 Google Earth Engine(GEE)平台,获取了渭河流域 2001 年至 2021 年的中分辨率成像光谱仪(MODIS)数据,提取了四个生态指标,即绿色度、湿度、热度和干度,通过主成分分析构建了遥感生态指数(RSEI)。此外,采用地理探测器和多尺度地理加权回归模型(MGWR),识别了 RSEI 变化的主要驱动因素,并通过多指标从不同角度捕捉空间变化的差异。结果表明:(1)2001 年至 2021 年,渭河流域生态环境质量整体呈改善趋势,RSEI 由 0.376 增加到 0.414。就 RSEI 等级而言,中、高 RSEI 区面积显著增加,增长率显著提高,分别达到 26.42%和 27.70%。(2)空间上,渭河流域生态环境质量呈南高北低的空间分布格局,其中渭河流域陕北和宁甘北部地区生态环境质量相对较低。此外,研究期间,渭河流域南部秦岭地区和泾河、洛河流域 RSEI 改善明显。渭河流域关中平原城区经济快速增长,城区扩张导致生态环境质量显著下降。(3)渭河流域生态环境质量是自然、人为和景观格局因素相互作用的结果。各影响因素的相互作用均强于单一因素的影响。各驱动因素与 RSEI 的空间变化存在显著差异,表明不同区域 RSEI 的变化受不同因素的主导,分区管理对于实现 RSEI 的可持续管理至关重要。研究表明,要改善渭河流域生态环境质量,需要进一步加强生态保护工程,合理配置景观要素,增强生态系统的抵抗力和恢复力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/6dc0f20df21a/ijerph-19-10930-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/8ef47533ee5f/ijerph-19-10930-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/efd3f8aff352/ijerph-19-10930-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/fd980814eaf7/ijerph-19-10930-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/13aa087d209d/ijerph-19-10930-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/b948077cfe5b/ijerph-19-10930-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/7fd0d0f15300/ijerph-19-10930-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/6dc0f20df21a/ijerph-19-10930-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/8ef47533ee5f/ijerph-19-10930-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/efd3f8aff352/ijerph-19-10930-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/fd980814eaf7/ijerph-19-10930-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/13aa087d209d/ijerph-19-10930-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/b948077cfe5b/ijerph-19-10930-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/7fd0d0f15300/ijerph-19-10930-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a274/9518415/6dc0f20df21a/ijerph-19-10930-g007.jpg

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