基于Google Earth Engine平台的三江源地区生态环境质量动态监测与分析
Monitoring and Assessment of the Eco-Environment Quality in the Sanjiangyuan Region based on Google Earth Engine
查看参考文献23篇
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文摘
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三江源是中国陆地生态系统最脆弱和敏感的区域之一,一旦遭到破坏则不可逆转。受三江源地区多云等不利气象条件的影响,很难获取大范围尺度上季相一致的、无云的Landsat遥感影像。本文利用Google Earth Engine平台,对1990-2015年的相同季节的3766景Landsat影像进行像元级融合并重构最小云量影像集,借助GEE的并行云端计算,快速得到了能够反映生态环境质量的遥感生态指数(RSEI),对三江源地区的生态环境质量进行了评价与监测。三江源时空变化与差异分析表明:1990-2000年生态环境质量呈快速下降状态,RSEI平均值从0.588下降到了0.505,生态环境质量变化以轻度恶化为主;2000-2015年生态环境质量下降速度变缓,并于2015年呈现变好态势,生态环境质量变化以不变为主,且轻度恶化面积大幅减少;该地区的生态环境状况呈现出空间分异,自西向东,生态状况变差。基于GEE平台在三江源地区的实验结果表明,GEE可以作为大区域范围的生态环境质量评价与监测的计算平台。 |
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其他语种文摘
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The Sanjiangyuanregion is known in China forits fragile and sensitive terrestrial ecosystem. Degradation of its ecosystem is often irreversible, to which remote sensing based monitoring has much to offer. However, due to the cloudy and other adverse meteorological conditions there, obtaining cloudless Landsat imagery of the same season in the large region remains a big challenge. In this study, based on the Google Earth Engine platform (GEE), an image composite method was applied and 3766 tiles of Landsat TM historical images were employed to generate the same seasonal clear imagery with the lowest cloud possible composited at the pixel level. With the help of parallel cloud computing ability in GEE, a remote sensing ecological index (RSEI) was calculated directly and efficiently in GEE to reflect regional eco-environment quality. Specifically, we monitored and assessed the eco-environment quality of the Sanjiangyuan region in Qinghai Province during 1990-2015. Results show that: (1) During 1990-2000, the eco-environment quality dropped quickly (RSEI average value dropped quickly from 0.588 to 0.505), and his region suffered from mainly mild degradation; (2) During 2000-2015, degradation of the eco-environment quality slowed down toward stabilization and the eco-environment quality showed upgrade tide from 2015, and the areas of mild degradation significantly decreased. Moreover, the eco-environment quality in this region showed a spatial gradient of west-to-east degradation. Our findings provide comprehensive information for improving the eco-environment of the Sanjiangyuan region, and demonstrate the potential of using GEE for monitoring and assessing eco-environment quality at large scales. |
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来源
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地球信息科学学报
,2019,21(9):1382-1391 【核心库】
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DOI
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10.12082/dqxxkx.2019.190095
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关键词
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三江源地区
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生态环境质量
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Google Earth Engine
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主成分分析
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遥感生态指数
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地址
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1.
中国科学院遥感与数字地球研究所, 北京, 100101
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中国科学院大学, 北京, 100049
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中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京, 100101
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1560-8999 |
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学科
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测绘学;环境质量评价与环境监测 |
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基金
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中国科学院战略性先导科技专项
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文献收藏号
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CSCD:6584259
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参考文献 共
23
共2页
|
|
1.
樊江文. 1988-2005年三江源草地产草量变化动态分析.
草地学报,2010,18(1):5-10
|
被引
40
次
|
|
|
|
|
2.
张永勇. 三江源区径流演变及其对气候变化的响应.
地理学报,2012,67(1):71-82
|
被引
29
次
|
|
|
|
|
3.
唐红玉. 三江源区植被变化及其对气候变化的响应.
气候变化研究进展,2006,2(4):177-180
|
被引
29
次
|
|
|
|
|
4.
李林. 三江源地区气候变化及其对生态环境的影响.
气象,2004,30(8):18-22
|
被引
35
次
|
|
|
|
|
5.
朱霞. 三江源区“黑土滩”型退化草地时空变化.
草业科学,2014,31(9):1628-1636
|
被引
9
次
|
|
|
|
|
6.
任继周. 江河源区草地生态建设构想.
草业学报,2005,14(2):1-8
|
被引
78
次
|
|
|
|
|
7.
Liu J. Grassland degradation in the "Three-River Headwaters" region, Qinghai Province.
Journal of Geographical Sciences,2008,18(3):259-273
|
被引
10
次
|
|
|
|
|
8.
孙庆龄. 2000-2013年三江源植被NDVI变化趋势及影响因素分析.
地球信息科学学报,2016,18(12):1707-1716
|
被引
27
次
|
|
|
|
|
9.
曹西凤. MODIS遥感产品在三江源地区草产量估测中的应用.
国土资源遥感,2018,30(4):115-124
|
被引
2
次
|
|
|
|
|
10.
宋瑞玲. 基于MODIS-EVI评估三江源高寒草地的保护成效.
生物多样性,2018,26(2):149-157
|
被引
12
次
|
|
|
|
|
11.
徐新良. 三江源生态工程实施以来草地恢复态势及现状分析.
地球信息科学学报,2017,19(1):50-58
|
被引
31
次
|
|
|
|
|
12.
邵全琴. 基于目标的三江源生态保护和建设一期工程生态成效评估及政策建议.
中国科学院院刊,2017,32(1):35-44
|
被引
29
次
|
|
|
|
|
13.
徐燕. 初论我国生态环境质量评价研究进展.
干旱区地理(汉文版),2003,26(2):166-172
|
被引
37
次
|
|
|
|
|
14.
王士远. 长白山自然保护区生态环境质量的遥感评价.
地理科学进展,2016,35(10):1269-1278
|
被引
55
次
|
|
|
|
|
15.
徐涵秋. 区域生态环境变化的遥感评价指数.
中国环境科学,2013,33(5):889-897
|
被引
241
次
|
|
|
|
|
16.
张滔. 基于Google Earth Engine的京津冀2001~2015年植被覆盖变化与城镇扩张研究.
遥感技术与应用,2018,33(4):593-599
|
被引
24
次
|
|
|
|
|
17.
Mateo-Garcia G. Multitemporal cloud masking in the Google Earth Engine.
Remote Sensing,2018,10(7):1079
|
被引
6
次
|
|
|
|
|
18.
Tsai Y. Mapping vegetation and land use types in fanjingshan national nature reserve using google earth engine.
Remote Sensing,2018,10(6):927
|
被引
4
次
|
|
|
|
|
19.
Zurqani H A. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine.
International Journal of Applied Earth Observation and Geoinformation,2018,69:175-185
|
被引
10
次
|
|
|
|
|
20.
Liu X. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform.
Remote sensing of environment,2018,209:227-239
|
被引
37
次
|
|
|
|
|
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