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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6427152 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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