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生态学杂志 ›› 2023, Vol. 42 ›› Issue (10): 2545-2554.doi: 10.13292/j.1000-4890.202310.034

• 技术与方法 • 上一篇    下一篇

基于GEE的黄河上游生态环境质量动态监测与评价: 以龙羊峡至积石峡段为例

周喆1,胡夏嵩1,刘昌义1*,付江涛2,赵吉美3,邢光延3,何伟鹏1


  

  1. 1青海大学地质工程系, 西宁 810016; 2青海大学农林科学院, 西宁 810016; 3青海大学农牧学院, 西宁 810016)

  • 出版日期:2023-10-10 发布日期:2023-10-08

Dynamic monitoring and evaluation of eco-environmental quality in the upper reaches of the Yellow River based on GEE: A case study of the section from Longyang Gorge to Jishi Gorge.

ZHOU Zhe1, HU Xiasong1, LIU Changyi1*, FU Jiangtao2, ZHAO Jimei3, XING Guangyan3, HE Weipeng1#br#

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  1. (1Department of Geological Engineering, Qinghai University, Xining 810016, China; 2Qinghai Academy of Agriculture and Forestry Sciences, Xining 810016, China; 3College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China).

  • Online:2023-10-10 Published:2023-10-08

摘要: 为了解黄河上游龙羊峡至积石峡段生态环境质量的动态变化,本研究基于GEE(Google Earth Engine)遥感大数据平台,以Landsat TM/OLI遥感影像作为数据源,构建遥感生态指数(remote sensing ecological index,RSEI),评价黄河上游龙羊峡至积石峡段流域2000—2020年期间生态环境质量变化特征并探讨其影响因子。结果表明:2000—2020年龙羊峡至积石峡段流域生态环境质量呈现先恶化后改善的变化趋势;2000—2010年区内生态环境质量处于轻微退化和中等退化状态,而2010—2020年区内生态环境质量处于轻微改善和中等改善状态;地理探测器显示,区内2000—2020年单因子解释力由大至小依次为干度、绿度、湿度、热度,其中绿度和湿度之间的双因子相互作用在2000—2020年期间对区内生态环境质量的影响程度相对最大。本研究结果可为黄河上游开展生态环境治理、植被恢复和生态监测,以及实现有效保护黄河上游生态环境等,提供理论支撑和实际指导。


关键词: 遥感生态指数, Google Earth Engine, 主成分分析, 地理探测器

Abstract: This study aimed to clarify the temporal variations of eco-environmental quality of the section from Longyang Gorge to Jishi Gorge in the upper reaches of the Yellow River. Based on remote sensing big data platform of Google Earth Engine (GEE) and Landsat TM/OLI remote sensing images, we constructed a remote sensing ecological index (RSEI) model, evaluated the variations of eco-environmental quality of the section from Longyang Gorge to Jishi Gorge during 2000-2020, and analyzed influencing factors. The results showed that the eco-environmental quality was deteriorated first and then improved during 2000-2020. The eco-environmental quality in the region was slightly or moderately degraded during 2000-2010, while it was slightly or moderately improved during 2010-2020. The explanatory power of dryness, greenness, humidity, and heat followed a descending order during 2000-2020. The double-factor interaction between greenness and humidity had the strongest influence on eco-environmental quality during 2000-2010. Our results can provide theoretical support and practical guidance for eco-environment management, vegetation restoration, and ecological monitoring, as well as effective protection of the eco-environment in the upper reaches of the Yellow River.


Key words: remote sensing-based ecological index (RSEI), Google Earth Engine, principal component analysis, geographical detector.