{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:08:21Z","timestamp":1770908901704,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area","award":["SZU51029202010"],"award-info":[{"award-number":["SZU51029202010"]}]},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, China","award":["SZU51029202010"],"award-info":[{"award-number":["SZU51029202010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wide-scale automatic monitoring based on the Normalized Difference Water Index (NDWI) and Mask Region-based Convolutional Neural Network (Mask R-CNN) with remote sensing images is of great significance for the management of aquaculture areas. However, different spatial resolutions brought different cost and model performance. To find more suitable image spatial resolutions for automatic monitoring offshore aquaculture areas, seven different resolution remote sensing images in the Sandu\u2019ao area of China, from 2 m, 4 m, to 50 m, were compared. Results showed that the remote sensing images with a resolution of 15 m and above can achieve the corresponding recognition effect when no financial issues were considered, with the F1 score of over 0.75. By establishing a cost-effectiveness evaluation formula that comprehensively considers image price and recognition effect, the best image resolution in different scenes can be found, thus providing the most appropriate data scheme for the automatic monitoring of offshore aquaculture areas.<\/jats:p>","DOI":"10.3390\/rs14133079","type":"journal-article","created":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:07:02Z","timestamp":1656374822000},"page":"3079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["The Assessment of More Suitable Image Spatial Resolutions for Offshore Aquaculture Areas Automatic Monitoring Based on Coupled NDWI and Mask R-CNN"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5517-9261","authenticated-orcid":false,"given":"Yonggui","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Yangtze Catchment Environmental Aquatic Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yaxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Yangtze Catchment Environmental Aquatic Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[{"name":"United Center for Eco-Environment in Yangtze River Economic Belt, Chinese Academy of Environmental Planning, Beijing 100012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4839-7724","authenticated-orcid":false,"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area 883 & Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Hui","family":"Bai","sequence":"additional","affiliation":[{"name":"United Center for Eco-Environment in Yangtze River Economic Belt, Chinese Academy of Environmental Planning, Beijing 100012, China"}]},{"given":"Bo","family":"Wu","sequence":"additional","affiliation":[{"name":"United Center for Eco-Environment in Yangtze River Economic Belt, Chinese Academy of Environmental Planning, Beijing 100012, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"GEOXAIR (Fujian) Technology Co., Ltd., Fuzhou 350003, China"}]},{"given":"Shouwei","family":"Li","sequence":"additional","affiliation":[{"name":"GEOXAIR (Fujian) Technology Co., Ltd., Fuzhou 350003, China"}]},{"given":"Tianyu","family":"Zheng","sequence":"additional","affiliation":[{"name":"GEOXAIR (Fujian) Technology Co., Ltd., Fuzhou 350003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"ref_1","unstructured":"FAO (2020). The State of World Fisheries and Aquaculture. Sustainability in Action, FAO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.2307\/2403474","article-title":"The Enrichment of a Mesotrophic Lake by Carbon, Phosphorus and Nitrogen from the Cage Aquaculture of Rainbow Trout, Salmo gairdneri","volume":"19","author":"Penczak","year":"1982","journal-title":"J. Appl. Ecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1080\/08920753.2013.822284","article-title":"A Race for Marine Space: Science, Values, and Aquaculture Planning in New Zealand","volume":"41","author":"Mcginnis","year":"2013","journal-title":"Coast. Manag."},{"key":"ref_4","first-page":"102118","article-title":"Satellite-based monitoring and statistics for raft and cage aquaculture in China\u2019s offshore waters","volume":"91","author":"Liu","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-005-9063-y","article-title":"Environmental impact of the marine aquaculture in G\u00fcll\u00fck Bay, Turkey","volume":"123","author":"Demirak","year":"2006","journal-title":"Environ. Monit. Assess."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fu, Y., Ye, Z., Deng, J., Zheng, X., and Wang, K. (2019). Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11141678"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, Y., Hu, Z., Zhang, Y., Wang, J., Yin, Y., and Wu, G. (2021). Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China. Remote Sens., 13.","DOI":"10.3390\/rs13214320"},{"key":"ref_8","first-page":"92","article-title":"Information extraction of floating raft aquaculture based on GF-1","volume":"45","author":"Chu","year":"2020","journal-title":"Sci. Surv. Mapp."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1007\/s00343-019-8265-z","article-title":"Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model","volume":"37","author":"Liu","year":"2019","journal-title":"J. Oceanol. Limnol."},{"key":"ref_10","first-page":"9","article-title":"The Analysis on Spatial-temporal Evolution of Beach Cultivation and Its Policy Driving in Xiamen in Recent Two Decades","volume":"9","author":"Lin","year":"2007","journal-title":"Geo-Inf. Sci."},{"key":"ref_11","first-page":"87","article-title":"The identification of Porphyra culture area by remote sensing and spatial distribution change and driving factors analysis","volume":"42","author":"Lu","year":"2018","journal-title":"Mar. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, C., Chen, J., and Wang, F. (2022). Shape-Constrained Method of Remote Sensing Monitoring of Marine Raft Aquaculture Areas on Multitemporal Synthetic Sentinel-1 Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14051249"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1109\/JSTARS.2019.2910786","article-title":"Marine Floating Raft Aquaculture Detection of GF-3 PolSAR Images Based on Collective Multikernel Fuzzy Clustering","volume":"12","author":"Fan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/LGRS.2017.2648641","article-title":"Weighted Fusion-Based Representation Classifiers for Marine Floating Raft Detection of SAR Images","volume":"14","author":"Geng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hu, Y., Fan, J., and Wang, J. (2017, January 16\u201319). Target recognition of floating raft aquaculture in SAR image based on statistical region merging. Proceedings of the 2017 Seventh International Conference on Information Science and Technology (ICIST), Da Nang, Vietnam.","DOI":"10.1109\/ICIST.2017.7926798"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, C., Ji, Y., Chen, J., Deng, Y., Chen, J., and Jie, Y. (2020). Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images. Remote Sens., 12.","DOI":"10.3390\/rs12244182"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ottinger, M., Bachofer, F., Huth, J., and Kuenzer, C. (2022). Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series. Remote Sens., 14.","DOI":"10.3390\/rs14010153"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cui, B., Fei, D., Shao, G., Lu, Y., and Chu, J. (2019). Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure. Remote Sens., 11.","DOI":"10.3390\/rs11172053"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.5194\/essd-13-1829-2021","article-title":"A new satellite-derived dataset for marine aquaculture areas in the China\u2019s coastal region","volume":"13","author":"Fu","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liang, C., Cheng, B., Xiao, B., He, C., Liu, X., Jia, N., and Chen, J. (2021). Semi-\/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images\u2014Taking the Fujian Coastal Area (Mainly Sanduo) as an Example. Remote Sens., 13.","DOI":"10.3390\/rs13061083"},{"key":"ref_21","unstructured":"Fujian Development and Reform Commission (2020). Layout and Construction Planning of Fishing Ports in Fujian Province, Fujian Development and Reform Commission."},{"key":"ref_22","first-page":"76","article-title":"Analysis of Variation Trend of Water Quality Based on Time Series in Sansha Bay","volume":"39","author":"Wang","year":"2017","journal-title":"Environ. Impact Assess."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context, Springer International Publishing.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and Silva, E.A.B.D. (2020, January 1\u20133). A Survey on Performance Metrics for Object-Detection Algorithms. Proceedings of the 2020 Interna-tional Conference on Systems, Signals and Image Processing (IWSSIP), Rio de Janeiro, Brazil.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_25","first-page":"136","article-title":"Cost-benefit Model and Its Application of Reclaimed Water Project Based on Perspective of Stakeholders","volume":"39","author":"Zhang","year":"2021","journal-title":"Water Resour. Power"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3079\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:56Z","timestamp":1760139536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,27]]},"references-count":25,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133079"],"URL":"https:\/\/doi.org\/10.3390\/rs14133079","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,27]]}}}