Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquacul...

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Autores principales:	Yue Xu, Zhongwen Hu, Yinghui Zhang, Jingzhe Wang, Yumeng Yin, Guofeng Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-source remote sensing aquaculture mapping texture feature Google Earth Engine Pearl River Basin Science Q
Acceso en línea:	https://doaj.org/article/0fcc51db291c4fe99692e9a75003246a
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id	oai:doaj.org-article:0fcc51db291c4fe99692e9a75003246a
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spelling	oai:doaj.org-article:0fcc51db291c4fe99692e9a75003246a2021-11-11T18:53:50ZMapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China10.3390/rs132143202072-4292https://doaj.org/article/0fcc51db291c4fe99692e9a75003246a2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4320https://doaj.org/toc/2072-4292Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.Yue XuZhongwen HuYinghui ZhangJingzhe WangYumeng YinGuofeng WuMDPI AGarticlemulti-source remote sensingaquaculture mappingtexture featureGoogle Earth EnginePearl River BasinScienceQENRemote Sensing, Vol 13, Iss 4320, p 4320 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-source remote sensing aquaculture mapping texture feature Google Earth Engine Pearl River Basin Science Q
spellingShingle	multi-source remote sensing aquaculture mapping texture feature Google Earth Engine Pearl River Basin Science Q Yue Xu Zhongwen Hu Yinghui Zhang Jingzhe Wang Yumeng Yin Guofeng Wu Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
description	Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.
format	article
author	Yue Xu Zhongwen Hu Yinghui Zhang Jingzhe Wang Yumeng Yin Guofeng Wu
author_facet	Yue Xu Zhongwen Hu Yinghui Zhang Jingzhe Wang Yumeng Yin Guofeng Wu
author_sort	Yue Xu
title	Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
title_short	Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
title_full	Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
title_fullStr	Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
title_full_unstemmed	Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China
title_sort	mapping aquaculture areas with multi-source spectral and texture features: a case study in the pearl river basin (guangdong), china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0fcc51db291c4fe99692e9a75003246a
work_keys_str_mv	AT yuexu mappingaquacultureareaswithmultisourcespectralandtexturefeaturesacasestudyinthepearlriverbasinguangdongchina AT zhongwenhu mappingaquacultureareaswithmultisourcespectralandtexturefeaturesacasestudyinthepearlriverbasinguangdongchina AT yinghuizhang mappingaquacultureareaswithmultisourcespectralandtexturefeaturesacasestudyinthepearlriverbasinguangdongchina AT jingzhewang mappingaquacultureareaswithmultisourcespectralandtexturefeaturesacasestudyinthepearlriverbasinguangdongchina AT yumengyin mappingaquacultureareaswithmultisourcespectralandtexturefeaturesacasestudyinthepearlriverbasinguangdongchina AT guofengwu mappingaquacultureareaswithmultisourcespectralandtexturefeaturesacasestudyinthepearlriverbasinguangdongchina
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Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

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