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

doi:10.12082/dqxxkx.2019.190095

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (9): 1382-1391.doi: 10.12082/dqxxkx.2019.190095

• 专栏：青藏高原城镇化的生态环境影响数据挖掘 • 上一篇下一篇

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

陈炜^1,²,黄慧萍^1,^2,^*(),田亦陈¹,杜云艳^3,²

^1. 中国科学院遥感与数字地球研究所,北京 100101
^2. 中国科学院大学,北京 100049
^3. 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

收稿日期:2019-03-04 修回日期:2019-06-20 出版日期:2019-09-25 发布日期:2019-09-24
通讯作者: 黄慧萍 E-mail:hphuang@radi.ac.cn
作者简介:陈炜（1994-）,女,山东济宁人,硕士,主要从事城市多源数据融合和城市生态研究。E-mail: chenwei2016@radi.ac.cn
基金资助:
中国科学院战略性先导科技专项课题(XDA20040401)

Monitoring and Assessment of the Eco-Environment Quality in the Sanjiangyuan Region based on Google Earth Engine

CHEN Wei^1,²,HUANG Huiping^1,^2,^*(),TIAN Yichen¹,DU Yunyan^3,²

^1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China
^2. University of Chinese Academy of Sciences, Beijing 100049, China
^3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received:2019-03-04 Revised:2019-06-20 Online:2019-09-25 Published:2019-09-24
Contact: HUANG Huiping E-mail:hphuang@radi.ac.cn
Supported by:
The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20040401)

摘要/Abstract

摘要：

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。

关键词: 三江源地区, 生态环境质量, Google Earth Engine, 主成分分析, 遥感生态指数

Abstract:

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.

Key words: Sanjiangyuan region, ecological environment quality, Google Earth Engine, Principal Component Analysis, remote sensing ecological index

陈炜, 黄慧萍, 田亦陈, 杜云艳. 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[J]. 地球信息科学学报, 2019, 21(9): 1382-1391.DOI:10.12082/dqxxkx.2019.190095

CHEN Wei, HUANG Huiping, TIAN Yichen, DU Yunyan. Monitoring and Assessment of the Eco-Environment Quality in the Sanjiangyuan Region based on Google Earth Engine[J]. Journal of Geo-information Science, 2019, 21(9): 1382-1391.DOI:10.12082/dqxxkx.2019.190095

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		主成分
		1	2	3	4
1990年	NDVI	0.9685	-0.0443	-0.2177	0.1128
	Wet	0.0725	-0.2924	0.2178	0.9283
	NDBSI	-0.1897	0.6481	0.7361	0.0462
	LST	-0.1443	0.7018	-0.6027	0.3512
	特征值	0.0158	0.0100	0.0024	0.0003
	贡献率/%	55.2853	35.0531	8.4948	1.1667
2010年	NDVI	0.7608	0.3958	-0.4586	0.2329
	Wet	0.1241	-0.3349	0.3555	0.8637
	NDBSI	-0.0681	0.7910	0.6042	0.0679
	LST	-0.6333	0.3248	0.5462	0.4417
	特征值	0.0122	0.0063	0.0017	0.0002
	贡献率/%	59.8441	30.9822	8.1068	1.0688

生态等级	差	较差	中等	良	优
差	10 125	3382	1376	1220	43
较差	24 405	30 231	8588	2750	142
中等	12 212	37 899	39 759	10 038	159
良	2772	7401	43 252	49 578	595
优	264	186	4291	110 360	17 184

生态等级	差	较差	中等	良	优
差	29 210	15 843	4302	407	16
较差	15 124	39 797	22 531	1639	8
中等	1407	22 493	58 045	15 067	254
良	113	3571	32 395	120 795	17 072
优	3	57	191	5354	12 518

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

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