{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:32:31Z","timestamp":1722385951391},"reference-count":27,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hainan Province Science and Technology Special Fund","award":["ATIC-2023010004-06"]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["323MS111"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"With the intensification of global climate change, there is an increasing emphasis on protecting natural resources. Mangrove forests, critical to tropical and subtropical intertidal ecosystems, have garnered considerable attention in recent years for their strong carbon sink capacity, rich species diversity, and abundant natural resources. This study utilizes the 2020 global mangrove vector data as a baseline to construct a reasonable buffer zone by calculating the increase in mangrove crown width. The Google Earth Engine (GEE) platform and its Sentinel-2 data from 2022 are employed to acquire synthetic images across all regions using the mosaic algorithm. Then, mangrove forests are extracted using the Otsu algorithm, and a map depicting the global spatial distribution of mangrove forests in 2022 is obtained. The average overall accuracy of the extracted mangrove forests in this study reaches 92.4%, and it is determined that the global mangrove forest area expanded by 4920.6 km2 between 2020 and 2022, This study provides crucial data support for the global monitoring of mangrove changes and holds significant importance for protecting and restoring mangrove ecosystems.<\/jats:p>","DOI":"10.3390\/rs16152723","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T15:43:44Z","timestamp":1721922224000},"page":"2723","source":"Crossref","is-referenced-by-count":0,"title":["Extraction of 10 m Resolution Global Mangrove in 2022"],"prefix":"10.3390","volume":"16","author":[{"given":"Xiangyu","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Hainan Aerospace Technology Innovation Center, Wenchang 571339, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5915-2661","authenticated-orcid":false,"given":"Jingjuan","family":"Liao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Guozhuang","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Hainan Aerospace Technology Innovation Center, Wenchang 571339, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5880-7507","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6377-1094","authenticated-orcid":false,"given":"Bowei","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/j.1466-8238.2010.00584.x","article-title":"Status and Distribution of Mangrove Forests of the World Using Earth Observation Satellite Data","volume":"20","author":"Giri","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s00114-001-0283-x","article-title":"Relevance of Mangroves for the Production and Deposition of Organic Matter along Tropical Continental Margins","volume":"89","author":"Jennerjahn","year":"2002","journal-title":"Naturwissenschaften"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1038\/s41558-018-0090-4","article-title":"Global Carbon Stocks and Potential Emissions Due to Mangrove Deforestation from 2000 to 2012","volume":"8","author":"Hamilton","year":"2018","journal-title":"Nat. Clim. Change"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s13157-010-0082-2","article-title":"Assessing Distribution Patterns, Extent, and Current Condition of Northwest Mexico Mangroves","volume":"30","year":"2010","journal-title":"Wetlands"},{"key":"ref_5","first-page":"9781119312994","article-title":"Mangrove Blue Carbon in the Face of Deforestation, Climate Change, and Restoration","volume":"3","author":"Friess","year":"2020","journal-title":"Annu. Plant Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1038\/387253a0","article-title":"The Value of the World\u2019s Ecosystem Services and Natural Capital","volume":"387","author":"Costanza","year":"1997","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1016\/j.scib.2023.05.004","article-title":"Mapping Global Distribution of Mangrove Forests at 10-m Resolution","volume":"68","author":"Jia","year":"2023","journal-title":"Sci. Bull."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R.M., Thomas, N., Tadono, T., Worthington, T.A., Spalding, M., Murray, N.J., and Rebelo, L.-M. (2022). Global Mangrove Extent Change 1996\u20132020: Global Mangrove Watch Version 3.0. Remote Sens., 14.","DOI":"10.3390\/rs14153657"},{"key":"ref_9","unstructured":"Liao, J. (2022). 2000\u20132020 Global 30m Mangrove Spatial Distribution Products (GMF30_2000-2020), International Research Center for Sustainable Development Big Data."},{"key":"ref_10","first-page":"35346","article-title":"Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM","volume":"5","author":"Alsaaideh","year":"2013","journal-title":"J. Geogr. Inf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.5194\/isprs-archives-XLI-B6-259-2016","article-title":"Synergy of Optical and SAR Data for Mapping and Monitoring Mangroves","volume":"41","author":"Monzon","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"555","DOI":"10.5194\/isprs-archives-XLI-B8-555-2016","article-title":"Automatic Extraction of Mangrove Vegetation from Optical Satellite Data","volume":"41","author":"Agrawal","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xia, Q., Qin, C.-Z., Li, H., Huang, C., and Su, F.-Z. (2018). Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091343"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, C.-C., Hsu, T.-W., Wen, H.-L., and Wang, K.-H. (2019). Mapping Pure Mangrove Patches in Small Corridors and Sandbanks Using Airborne Hyperspectral Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050592"},{"key":"ref_15","first-page":"102918","article-title":"Tracking Annual Dynamics of Mangrove Forests in Mangrove National Nature Reserves of China Based on Time Series Sentinel-2 Imagery during 2016\u20132020","volume":"112","author":"Zhang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_16","first-page":"5373","article-title":"Mangrove Ecosystem Segmentation from Drone Images Using Otsu Method","volume":"2301","author":"Pratiwia","year":"2021","journal-title":"J. Elektron. Ilmu Komput. Udayana p-ISSN"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, K., Cao, J., Peng, L., and Wen, X. (2022). Annual Change Analysis of Mangrove Forests in China during 1986\u20132021 Based on Google Earth Engine. Forests, 13.","DOI":"10.3390\/f13091489"},{"key":"ref_18","first-page":"348","article-title":"Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine","volume":"26","author":"Cheng","year":"2022","journal-title":"J. Remote Sens."},{"key":"ref_19","unstructured":"Wen, X. (2021). Extra Spatial Distribution Information Estimation of Aboveground Biomass Mangrove Forest Based on Multi-Source Remote Sensing Data Change. Jilin Univ., 1\u201378. (In Chinese)."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isprsjprs.2022.09.011","article-title":"Decision Surface Optimization in Mapping Exotic Mangrove Species (Sonneratia Apetala) across Latitudinal Coastal Areas of China","volume":"193","author":"Zhao","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","first-page":"1","article-title":"Spatio\u2013Temporal Variations in Mangrove Forests in the Shankou Mangrove Nature Reserve Based on the GEE Cloud Platform and Landsat Data","volume":"35","author":"Min","year":"2023","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_22","first-page":"239456","article-title":"Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with Airborne LiDAR Data","volume":"2","author":"Galvincio","year":"2016","journal-title":"Int. J. Adv. Eng. Manag. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lagomasino, D., Fatoyinbo, T., Lee, S., Feliciano, E., Trettin, C., and Simard, M. (2016). A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space. Remote Sens., 8.","DOI":"10.3390\/rs8040327"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1016\/j.patrec.2011.01.021","article-title":"Characteristic Analysis of Otsu Threshold and Its Applications","volume":"32","author":"Xu","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1109\/HIS.2009.74","article-title":"Otsu Method and K-Means","volume":"Volume 1","author":"Liu","year":"2009","journal-title":"Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5844","DOI":"10.1111\/gcb.15275","article-title":"Global Declines in Human-driven Mangrove Loss","volume":"26","author":"Goldberg","year":"2020","journal-title":"Glob. Change Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12","DOI":"10.13057\/biodiv\/d150103","article-title":"Distribution of Mangrove Species Reported as Rare in Andaman and Nicobar Islands with Their Taxonomical Notes","volume":"15","author":"Ragavan","year":"2014","journal-title":"Biodiversitas J. Biol. Divers."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/15\/2723\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T13:15:26Z","timestamp":1722345326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/15\/2723"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,25]]},"references-count":27,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16152723"],"URL":"https:\/\/doi.org\/10.3390\/rs16152723","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,7,25]]}}}