Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the...

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Autores principales:	Wang Ruimeng, Pan Li, Niu Wenhui, Li Rumeng, Zhao Xiaoyang, Bian Xiqing, Yu Chong, Xia Haoming, Chen Taizheng
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	landsat imagery google earth engine water body extraction spatiotemporal change xiaolangdi reservoir Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/1d5a5daa2dcc4d70a1b962b5a5c4afc5
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spelling	oai:doaj.org-article:1d5a5daa2dcc4d70a1b962b5a5c4afc52021-12-05T14:10:49ZMonitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine2391-544710.1515/geo-2020-0305https://doaj.org/article/1d5a5daa2dcc4d70a1b962b5a5c4afc52021-10-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0305https://doaj.org/toc/2391-5447Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.Wang RuimengPan LiNiu WenhuiLi RumengZhao XiaoyangBian XiqingYu ChongXia HaomingChen TaizhengDe Gruyterarticlelandsat imagerygoogle earth enginewater body extractionspatiotemporal changexiaolangdi reservoirGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 1290-1302 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landsat imagery google earth engine water body extraction spatiotemporal change xiaolangdi reservoir Geology QE1-996.5
spellingShingle	landsat imagery google earth engine water body extraction spatiotemporal change xiaolangdi reservoir Geology QE1-996.5 Wang Ruimeng Pan Li Niu Wenhui Li Rumeng Zhao Xiaoyang Bian Xiqing Yu Chong Xia Haoming Chen Taizheng Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
description	Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.
format	article
author	Wang Ruimeng Pan Li Niu Wenhui Li Rumeng Zhao Xiaoyang Bian Xiqing Yu Chong Xia Haoming Chen Taizheng
author_facet	Wang Ruimeng Pan Li Niu Wenhui Li Rumeng Zhao Xiaoyang Bian Xiqing Yu Chong Xia Haoming Chen Taizheng
author_sort	Wang Ruimeng
title	Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
title_short	Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
title_full	Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
title_fullStr	Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
title_full_unstemmed	Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
title_sort	monitoring the spatiotemporal dynamics of surface water body of the xiaolangdi reservoir using landsat-5/7/8 imagery and google earth engine
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/1d5a5daa2dcc4d70a1b962b5a5c4afc5
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Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

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