摘要
为准确、及时地获取长江上游生态环境质量的变化趋势以及演变格局,基于Google Earth Engine(GEE)平台的MODIS系列遥感数据,通过计算长江上游流域生长季5—10月的绿度(NDVI)、湿度(WET)、热度(LST)及干度(NDSI)4个指标,采用主成分分析法(PCA)构建出遥感生态环境指数RSEI,并对长江上游流域生态环境进行评价。结果表明:(1) 4个指标在第1主成分(PC1)上的平均贡献率为71%,表明依据这4个指标在长江上游流域构建RSEI是可行的;(2)长江上游流域RSEI整体呈现显著增加趋势(p<0.05),增加速率为1.1×10^(-3)/a,具体可细分为两个阶段,分别是快速增长期(2000—2010年),其速率为5.9×10^(-3)/a,以及增速放缓期(2010—2020年),其速率为3.9×10^(-3)/a;(3)长江上游流域生态环境质量以优和良为主,且表现为南部比北部好及东部比西部好的空间分布格局;(4) 2000—2010年流域生态环境质量改善明显,改善面积占34.7%,2010—2020年改善面积与退化面积接近,两者仅差1.3%左右;(5) 2000—2020年长江上游流域RSEI变化趋势为正的像元面积占长江上游流域总面积的64.7%,其中显著退化的区域主要集中在嘉陵江下游地区;(6)生态环境质量主导自然影响因子在2000年为热度>绿度>湿度>干度;2010年依次为绿度>热度>湿度>干度;2020年依次为绿度>湿度>热度>干度。综上,利用GEE平台能快速、准确地监测长江上游流域生态环境质量,近21年来流域生态环境质量整体呈改善态势,但针对嘉陵江下游生态退化现象需要引起政府重视,尽快提出一系列生态修复措施,防止其生态环境进一步恶化。
In order to accurately and timely obtain the change trend and evolution pattern of ecological environment quality in the upper reaches of the Yangtze River, based on the MODIS series remote sensing data of the Google Earth Engine(GEE) platform, the greenness(NDVI), humidity(WET), heat(LST) and dryness(NDSI) of the upper Yangtze River basin during the growing season from May to October. Using principal component analysis(PCA) to construct a remote sensing ecological environment index RSEI to evaluate the ecological environment of the upper reaches of the Yangtze River. The results show that:(1) average contribution rate of the four indicators on the first principal component(PC1) is 71%, indicating that it is feasible to build RSEI in the upper Yangtze River basin based on these four indicators;(2) overall RSEI in the upper Yangtze River is a significant increase trend(p<0.05), and the increase rate is 1.1×10^(-3)/a, which can be divided into two stages, namely the rapid growth period(2000—2010), with a rate of 5.9×10^(-3)/a, and the slowdown period(2010—2020), the rate is 3.9×10^(-3)/a;(3) ecological environment quality of the upper reaches of the Yangtze River is mainly and good, and the performance is better in the south than in the northeastern part has a better spatial distribution pattern than the western part;(4) from 2000 to 2010, the quality of the ecological environment in the watershed improved significantly, with an improved area accounting for 34.7. From 2010 to 2020, the improved area was close to the degraded area, and the difference between the two was only about 1.3%;(5) from 2000 to 2020, the area of pixels with a positive RSEI change trend in the upper Yangtze River accounted for 64.7% of the total area of the upper Yangtze River basin, and the areas with significant degradation mainly concentrated in the lower reaches of the Jialing River;(6) environment quality dominated natural influences factors in 2000 were heat>greenness>humidity>dryness;2010, the greenness>heat>humidity>dryness;in 2020, the greenness>humidity>heat>dryness. In summary, the use of the GEE platform can quickly and accurately monitor the ecological environment quality of the upper reaches of the Yangtze River. In the past 21 years, the ecological environment quality of the basin shown an overall improvement trend. However, the ecological degradation phenomenon in the lower reaches of the Jialing River needs to be paid attention by the government, and a series of ecological restoration measures are proposed as soon as possible prevent further deterioration of its ecological environment.
作者
章程焱
杨少康
董晓华
赵程铭
薄会娟
刘冀
ZHANG Chengyan;YANG Shaokang;DONG Xiaohua;ZHAO Chengming;BAO Huijuan;LIU Ji(College of Hydraulic and Environmental Engineering,China Three Gorges University,Yichang,Hubei 443002,China;Engineering Research Center of Eco-environment in Three Gorges Reservoir Region,of Education,Yichang,Hubei 443002,China;Hubei Collaborative Innovation Center for Water Resources Security,Wuhan 430072,China)
出处
《水土保持研究》
CSCD
北大核心
2023年第1期356-363,共8页
Research of Soil and Water Conservation
基金
欧洲空间局、中国国家遥感中心项目(58516)
中国电建集团华东勘测设计研究院有限公司项目(DJ-ZDZX-2016-02-09)。
关键词
遥感生态环境指数
长江上游流域
时空特征
Google
Earth
Engine
地理探测器
remote sensing ecological environment index
the upper reaches of the Yangtze River Basin
spatial and temporal characteristics
Google Earth Engine
geographic detector