Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine

Land-use classification is fundamental for environmental and water resource evaluation in coastal plain areas. However, comprehensive remote sensing image-based land-use analysis is challenged by the lack of massive remote sensing images and the massive computing power of large-scale server systems....

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yinghui Zhao, Ru An, Naixue Xiong, Dongyang Ou, Congfeng Jiang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	land use coastal plain spatial-temporal change Google Earth Engine Agriculture S
Acceso en línea:	https://doaj.org/article/a6a66d1f7bea49c9ab599e20edb14ed6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a6a66d1f7bea49c9ab599e20edb14ed6
record_format	dspace
spelling	oai:doaj.org-article:a6a66d1f7bea49c9ab599e20edb14ed62021-11-25T18:09:17ZSpatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine10.3390/land101111492073-445Xhttps://doaj.org/article/a6a66d1f7bea49c9ab599e20edb14ed62021-10-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1149https://doaj.org/toc/2073-445XLand-use classification is fundamental for environmental and water resource evaluation in coastal plain areas. However, comprehensive remote sensing image-based land-use analysis is challenged by the lack of massive remote sensing images and the massive computing power of large-scale server systems. In this paper, the spatial-temporal land-use change characteristics of the Hangzhou Bay area coastal plain are investigated on the Google Earth Engine platform. The proposed model uses a random forest algorithm to assist the land-use classification. The dataset is selected from the year 2009 to 2020 and classified with an average classification accuracy of 89% and Kappa coefficient of 88%. The results show that the land use in the selected region is affected by urbanization, the balance of cultivated land occupation and compensation, construction of economic development zone, and other activities. The investigation also shows that in the past 12 years, land use has changed rapidly, and each land-use type maintains the dynamic balance of occupation and compensation. Although the overall land-use distribution is stable, the information entropy fluctuates at a high level, with an average value of 1.15, and the multi-year average value of equilibrium is as high as 0.83. The driving force of land-use change is analyzed and accounted as demographics and human population dynamics, social-economic development, urbanization, and coupling effects of the above-mentioned factors.Yinghui ZhaoRu AnNaixue XiongDongyang OuCongfeng JiangMDPI AGarticleland usecoastal plainspatial-temporal changeGoogle Earth EngineAgricultureSENLand, Vol 10, Iss 1149, p 1149 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	land use coastal plain spatial-temporal change Google Earth Engine Agriculture S
spellingShingle	land use coastal plain spatial-temporal change Google Earth Engine Agriculture S Yinghui Zhao Ru An Naixue Xiong Dongyang Ou Congfeng Jiang Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine
description	Land-use classification is fundamental for environmental and water resource evaluation in coastal plain areas. However, comprehensive remote sensing image-based land-use analysis is challenged by the lack of massive remote sensing images and the massive computing power of large-scale server systems. In this paper, the spatial-temporal land-use change characteristics of the Hangzhou Bay area coastal plain are investigated on the Google Earth Engine platform. The proposed model uses a random forest algorithm to assist the land-use classification. The dataset is selected from the year 2009 to 2020 and classified with an average classification accuracy of 89% and Kappa coefficient of 88%. The results show that the land use in the selected region is affected by urbanization, the balance of cultivated land occupation and compensation, construction of economic development zone, and other activities. The investigation also shows that in the past 12 years, land use has changed rapidly, and each land-use type maintains the dynamic balance of occupation and compensation. Although the overall land-use distribution is stable, the information entropy fluctuates at a high level, with an average value of 1.15, and the multi-year average value of equilibrium is as high as 0.83. The driving force of land-use change is analyzed and accounted as demographics and human population dynamics, social-economic development, urbanization, and coupling effects of the above-mentioned factors.
format	article
author	Yinghui Zhao Ru An Naixue Xiong Dongyang Ou Congfeng Jiang
author_facet	Yinghui Zhao Ru An Naixue Xiong Dongyang Ou Congfeng Jiang
author_sort	Yinghui Zhao
title	Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine
title_short	Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine
title_full	Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine
title_fullStr	Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine
title_full_unstemmed	Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine
title_sort	spatio-temporal land-use/land-cover change dynamics in coastal plains in hangzhou bay area, china from 2009 to 2020 using google earth engine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a6a66d1f7bea49c9ab599e20edb14ed6
work_keys_str_mv	AT yinghuizhao spatiotemporallanduselandcoverchangedynamicsincoastalplainsinhangzhoubayareachinafrom2009to2020usinggoogleearthengine AT ruan spatiotemporallanduselandcoverchangedynamicsincoastalplainsinhangzhoubayareachinafrom2009to2020usinggoogleearthengine AT naixuexiong spatiotemporallanduselandcoverchangedynamicsincoastalplainsinhangzhoubayareachinafrom2009to2020usinggoogleearthengine AT dongyangou spatiotemporallanduselandcoverchangedynamicsincoastalplainsinhangzhoubayareachinafrom2009to2020usinggoogleearthengine AT congfengjiang spatiotemporallanduselandcoverchangedynamicsincoastalplainsinhangzhoubayareachinafrom2009to2020usinggoogleearthengine
_version_	1718411576617205760

Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine

Ejemplares similares