Open-Surface Water Bodies Dynamics Analysis in the Tarim River Basin (North-Western China), Based on Google Earth Engine Cloud Platform

被引:21
作者
Chen, Jiahao [1 ,2 ]
Kang, Tingting [1 ,2 ]
Yang, Shuai [1 ,2 ]
Bu, Jingyi [1 ,2 ]
Cao, Kexin [1 ,2 ]
Gao, Yanchun [1 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
关键词
open-surface water bodies; Landsat image; Google Earth Engine; Tarim River Basin; climate change; TIME-SERIES; EXTENT DYNAMICS; LAND-COVER; INDEX NDWI; DECADES; TM; DELINEATION; VARIABILITY; PERFORMANCE; EXTRACTION;
D O I
10.3390/w12102822
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The Tarim River Basin (TRB), located in an arid region, is facing the challenge of increasing water pressure and uncertain impacts of climate change. Many water body identification methods have achieved good results in different application scenarios, but only a few for arid areas. An arid region water detection rule (ARWDR) was proposed by combining vegetation index and water index. Taking computing advantages of the Google Earth Engine (GEE) cloud platform, 56,284 Landsat 5/7/8 optical images in the TRB were used to detect open-surface water bodies and generated a 30-m annual water frequency map from 1992 to 2019. The interannual changes and trends of the water body area were analyzed and the impacts of climatic and anthropogenic drivers on open-surface water body area dynamics were examined. The results show that: (1) ARWDR is suitable for long-term and large-scale water body identification, especially suitable for arid areas lacking vegetation. (2) The permanent water area was 2093.63 km(2) and the seasonal water area was 44,242.80 km(2), accounting for 4.52% and 95.48% of the total open-surface water area of he TRB, respectively. (3) From 1992 to 2019, the permanent and seasonal water bodies of the TRB all showed an increasing trend, with obvious spatial heterogeneity. (4) Among the effects of human activities and climate change, precipitation has the largest impact on the water area, which can explain 65.3% of the change of water body area. Our findings provide valuable information for the entire TRB's open-surface water resources planning and management.
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页数:27
相关论文
共 86 条
[1]  
Ablekim A., 2016, GEOGR RES, V35, DOI [10.11821/dlyj201611006, DOI 10.11821/DLYJ201611006]
[2]   Climate variability and forecasting surface water recovery from acidification: Modelling drought-induced sulphate release from wetlands [J].
Aherne, J. ;
Larssen, T. ;
Cosby, B. J. ;
Dillon, P. J. .
SCIENCE OF THE TOTAL ENVIRONMENT, 2006, 365 (1-3) :186-199
[3]  
Al-Khudhairy DHA, 2002, PHOTOGRAMM ENG REM S, V68, P809
[4]  
Arkin A., 2012, RESOUR ENV YANGTZE B, V21, P624
[5]   PERSIANN-CDR Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies [J].
Ashouri, Hamed ;
Hsu, Kuo-Lin ;
Sorooshian, Soroosh ;
Braithwaite, Dan K. ;
Knapp, Kenneth R. ;
Cecil, L. Dewayne ;
Nelson, Brian R. ;
Prat, Olivier P. .
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2015, 96 (01) :69-+
[6]  
Aung E.M.M., 2020, P 3 INT C INF COMM S
[7]   Tracking palustrine water seasonal and annual variability in agricultural wetland landscapes using Landsat from 1997 to 2005 [J].
Beeri, Ofer ;
Phillips, Rebecca L. .
GLOBAL CHANGE BIOLOGY, 2007, 13 (04) :897-912
[8]  
Cao R., 2008, SCI SURV MAPP, V33, DOI [10.3771/j.issn.1009-2307.2008.02.054, DOI 10.3771/J.ISSN.1009-2307.2008.02.054]
[9]   Quantifying Surface Water Dynamics at 30 Meter Spatial Resolution in the North American High Northern Latitudes 1991-2011 [J].
Carroll, Mark ;
Wooten, Margaret ;
DiMiceli, Charlene ;
Sohlberg, Robert ;
Kelly, Maureen .
REMOTE SENSING, 2016, 8 (08)
[10]   Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000-2015) [J].
Che, Xianghong ;
Feng, Min ;
Sexton, Joe ;
Channan, Saurabh ;
Sun, Qing ;
Ying, Qing ;
Liu, Jiping ;
Wang, Yong .
REMOTE SENSING, 2019, 11 (11)