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Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analy...

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Autores principales: Wang, Jun, Sui, Lichun, Yang, Xiaomei, Wang, Zhihua, Liu, Yueming, Kang, Junmei, Lu, Chen, Yang, Fengshuo, Liu, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427152/
https://www.ncbi.nlm.nih.gov/pubmed/30862001
http://dx.doi.org/10.3390/s19051221
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author Wang, Jun
Sui, Lichun
Yang, Xiaomei
Wang, Zhihua
Liu, Yueming
Kang, Junmei
Lu, Chen
Yang, Fengshuo
Liu, Bin
author_facet Wang, Jun
Sui, Lichun
Yang, Xiaomei
Wang, Zhihua
Liu, Yueming
Kang, Junmei
Lu, Chen
Yang, Fengshuo
Liu, Bin
author_sort Wang, Jun
collection PubMed
description Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.
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spelling pubmed-64271522019-04-15 Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery Wang, Jun Sui, Lichun Yang, Xiaomei Wang, Zhihua Liu, Yueming Kang, Junmei Lu, Chen Yang, Fengshuo Liu, Bin Sensors (Basel) Article Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale. MDPI 2019-03-11 /pmc/articles/PMC6427152/ /pubmed/30862001 http://dx.doi.org/10.3390/s19051221 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jun
Sui, Lichun
Yang, Xiaomei
Wang, Zhihua
Liu, Yueming
Kang, Junmei
Lu, Chen
Yang, Fengshuo
Liu, Bin
Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery
title Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery
title_full Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery
title_fullStr Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery
title_full_unstemmed Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery
title_short Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery
title_sort extracting coastal raft aquaculture data from landsat 8 oli imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427152/
https://www.ncbi.nlm.nih.gov/pubmed/30862001
http://dx.doi.org/10.3390/s19051221
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