IGSNRR OpenIR
Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery
Wang, Jun1,2; Sui, Lichun1,2; Yang, Xiaomei3,4,5; Wang, Zhihua3,5; Liu, Yueming3,5; Kang, Junmei1,2; Lu, Chen3,5; Yang, Fengshuo3,5; Liu, Bin3,5
2019-03-01
Source PublicationSENSORS
ISSN1424-8220
Volume19Issue:5Pages:15
Corresponding AuthorWang, Zhihua(zhwang@lreis.ac.cn)
AbstractInformation, 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.
Keywordcoastal raft aquaculture remote sensing Landsat 8 OLI NDVI edge detection GEOBIA
DOI10.3390/s19051221
WOS KeywordVISUAL-ATTENTION ; SEGMENTATION ; DELINEATION ; CULTURE ; IMPACTS ; NDVI ; GIS
Indexed BySCI
Language英语
Funding ProjectCAS Earth Big Data Science Project of China[XDA19060303] ; National Science Foundation of China[41671436] ; National Science Foundation of China[41421001] ; Innovation Project of LREIS[O88RAA01YA]
Funding OrganizationCAS Earth Big Data Science Project of China ; National Science Foundation of China ; Innovation Project of LREIS
WOS Research AreaChemistry ; Electrochemistry ; Instruments & Instrumentation
WOS SubjectChemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS IDWOS:000462540400240
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/48676
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Zhihua
Affiliation1.Changan Univ, Geol Engn, Xian 710054, Shaanxi, Peoples R China
2.Changan Univ, Inst Surveying & Mapping, Xian 710054, Shaanxi, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Wang, Jun,Sui, Lichun,Yang, Xiaomei,et al. Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery[J]. SENSORS,2019,19(5):15.
APA Wang, Jun.,Sui, Lichun.,Yang, Xiaomei.,Wang, Zhihua.,Liu, Yueming.,...&Liu, Bin.(2019).Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery.SENSORS,19(5),15.
MLA Wang, Jun,et al."Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery".SENSORS 19.5(2019):15.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Jun]'s Articles
[Sui, Lichun]'s Articles
[Yang, Xiaomei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Jun]'s Articles
[Sui, Lichun]'s Articles
[Yang, Xiaomei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Jun]'s Articles
[Sui, Lichun]'s Articles
[Yang, Xiaomei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.