{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:10:42Z","timestamp":1787065842455,"version":"build-2736575974"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T00:00:00Z","timestamp":1613779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Natural Resources of China","award":["NO.121133000000190020"],"award-info":[{"award-number":["NO.121133000000190020"]}]},{"name":"The National Key Research and Development Program of China","award":["NO.2016YFB0501403"],"award-info":[{"award-number":["NO.2016YFB0501403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Mangrove forests are important ecosystems in the coastal intertidal zone, but China\u2019s mangroves have experienced a large reduction in area from the 1950s, and the remaining mangrove forests are exhibiting increased fragmentation. A detailed mangrove dataset of China is crucial for mangrove ecosystem management and protection, but the fragmented mangrove patches are hardly mapped by medium resolution satellite imagery. To overcome these difficulties, we presented a fine-scale mangrove map for 2018 using the 2-meter resolution Gaofen-1 and Ziyuan-3 satellite imagery together with field data. We employed a hybrid method of object-based image analysis (OBIA), interpreter editing, and field surveying for mangrove mapping. The field survey route reached 9500 km, and 2650 patches were verified during the field work. Accuracy assessment by confusion matrix showed that the kappa coefficient reached 0.98, indicating a highly thematic accuracy of the mangrove dataset. Results showed the total area of mangrove forest in China for 2018 was 25,683.88 hectares, and approximately 91% of mangroves were found in the three provinces of Guangdong, Guangxi, and Hainan. About 64% of mangroves were distributed in or near the nature reserves established by national or local governments, which indicated that China\u2019s mangroves were well protected in recent years. The new fine-scale mangrove dataset was freely shared together with this paper, and it can be used by local authorities and research groups for mangrove management and ecological planning.<\/jats:p>","DOI":"10.3390\/ijgi10020092","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T21:15:01Z","timestamp":1613942101000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["A Fine-Scale Mangrove Map of China Derived from 2-Meter Resolution Satellite Observations and Field Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1772-0915","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"first","affiliation":[{"name":"Land Satellite Remote Sensing Application Center (LASAC), Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1163-8782","authenticated-orcid":false,"given":"Shanshan","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of Water Resources Security, College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"He","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center (LASAC), Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shucheng","family":"You","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center (LASAC), Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1643-8480","authenticated-orcid":false,"given":"Xiaomei","family":"Yang","sequence":"additional","affiliation":[{"name":"LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Gan","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center (LASAC), Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aixia","family":"Liu","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center (LASAC), Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,20]]},"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":"111223","DOI":"10.1016\/j.rse.2019.111223","article-title":"A review of remote sensing for mangrove forests: 1956\u20132018","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"189","article-title":"Multi-resolution segmentation for object-based classification and accuracy assessment of land use\/land cover classification using remotely sensed data","volume":"36","author":"Rahman","year":"2008","journal-title":"Photonirvachak J. Ind."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jenvman.2014.01.020","article-title":"Distribution and dynamics of mangrove forests of South Asia","volume":"148","author":"Giri","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Xia, J.S., Ha, N.T., Bui, D.T., Le, N.N., and Takeuchi, W. (2019). A review of remote sensing approaches for monitoring blue carbon ecosystems: Mangroves, seagrasses and salt marshes during 2010\u20132018. Sensors, 19.","DOI":"10.3390\/s19081933"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1890\/10-1510.1","article-title":"The value of estuarine and coastal ecosystem services","volume":"81","author":"Barbier","year":"2011","journal-title":"Ecol. Monogr."},{"key":"ref_7","unstructured":"FAO (2007). The World\u2019s Mangroves 1980\u20132005, FAO Forestry Paper."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1080\/01431161.2012.717181","article-title":"Comparison of Landsat image classification methods for detecting mangrove forests in Sundarbans","volume":"34","author":"Rahman","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Giri, C. (2016). Observation and monitoring of mangrove forests using remote sensing: Opportunities and challenges. Remote Sens., 8.","DOI":"10.3390\/rs8090783"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Spalding, M. (2010). World Atlas of Mangroves, Routledge.","DOI":"10.4324\/9781849776608"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1111\/geb.12449","article-title":"Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21)","volume":"25","author":"Hamilton","year":"2016","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Buitre, M.J.C., Zhang, H.S., and Lin, H. (2019). The mangrove forests change and impacts from tropical cyclones in the Philippines using time series satellite imagery. Remote Sens., 11.","DOI":"10.3390\/rs11060688"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, D.Z., Wan, B., Qiu, P.H., Su, Y.J., Guo, Q.H., and Wu, X.C. (2018). Artificial mangrove species mapping using pleiades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10020294"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111185","DOI":"10.1016\/j.rse.2019.05.004","article-title":"Mapping the multi-decadal mangrove dynamics of the Australian coastline","volume":"238","author":"Lymburner","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ecss.2015.07.047","article-title":"Investigating the decline of ecosystem services in a production mangrove forest using Landsat and object-based image analysis","volume":"164","author":"Aziz","year":"2015","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.isprsjprs.2020.10.001","article-title":"10-m-resolution mangrove maps of China derived from multi-source and multi-temporal satellite observations","volume":"169","author":"Zhao","year":"2020","journal-title":"ISPRS J. Photogramm."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106776","DOI":"10.1016\/j.ecss.2020.106776","article-title":"March of the mangroves: Drivers of encroachment into southern temperate saltmarsh","volume":"240","author":"Whitt","year":"2020","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xia, J.S., Yokoya, N., and Pham, T.D. (2020). Probabilistic mangrove species mapping with multiple-source remote-sensing datasets using label distribution learning in Xuan Thuy National Park, Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12223834"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zheng, Y.H., and Takeuchi, W. (2020). Quantitative assessment and driving force analysis of mangrove forest changes in China from 1985 to 2018 by integrating optical and radar imagery. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090513"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s12665-018-7373-y","article-title":"Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region","volume":"77","author":"Pham","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bunting, P., Rosenqvist, A., Lucas, R.M., Rebelo, L.M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., and Finlayson, C.M. (2018). The global mangrove watch\u2014A new 2010 global baseline of mangrove extent. Remote Sens., 10.","DOI":"10.3390\/rs10101669"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Thomas, N., Bunting, P., Lucas, R., Hardy, A., Rosenqvist, A., and Fatoyinbo, T. (2018). Mapping mangrove extent and change: A globally applicable approach. Remote Sens., 10.","DOI":"10.3390\/rs10091466"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1093\/jpe\/rtp009","article-title":"Recent progresses in mangrove conservation, restoration and research in China","volume":"2","author":"Chen","year":"2009","journal-title":"J. Plant Ecol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.ecoleng.2008.01.007","article-title":"Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan","volume":"35","author":"Lee","year":"2009","journal-title":"Ecol. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11355-010-0126-z","article-title":"Wetland changes and mangrove restoration planning in Shenzhen Bay, Southern China","volume":"7","author":"Ren","year":"2011","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"891","DOI":"10.3390\/rs5020891","article-title":"Relationship between hyperspectral measurements and mangrove leaf nitrogen concentrations","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11673","DOI":"10.3390\/rs61211673","article-title":"Separating mangrove species and conditions using laboratory hyperspectral data: A case study of a degraded mangrove forest of the Mexican Pacific","volume":"6","author":"Zhang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, H.S., Lin, H., and Fang, C.Y. (2016). Textural-spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8010024"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.ecss.2018.04.021","article-title":"Assessing changes of mangrove forest in a coastal region of southeast China using multi-temporal satellite images","volume":"207","author":"Wang","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lu, C.Y., Liu, J.F., Jia, M.M., Liu, M.Y., Man, W.D., Fu, W.W., Zhong, L.X., Lin, X.Q., Su, Y., and Gao, Y.B. (2018). Dynamic analysis of mangrove forests based on an optimal segmentation scale model and multi-seasonal images in Quanzhou Bay, China. Remote Sens., 10.","DOI":"10.3390\/rs10122020"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ecss.2013.03.023","article-title":"Change and fragmentation trends of Zhanjiang mangrove forests in southern China using multi-temporal Landsat imagery (1977\u20132010)","volume":"130","author":"Li","year":"2013","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_32","first-page":"88","article-title":"Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics","volume":"73","author":"Hu","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_33","first-page":"535","article-title":"Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China","volume":"73","author":"Jia","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s13157-013-0449-2","article-title":"Mapping China\u2019s mangroves based on an object-oriented classification of Landsat imagery","volume":"34","author":"Jia","year":"2014","journal-title":"Wetlands"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2017.07.011","article-title":"A mangrove forest map of China in 2015: Analysis of time series Landsat 7\/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform","volume":"131","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abdel-Hamid, A., Dubovyk, O., Abou El-Magd, I., and Menz, G. (2018). Mapping mangroves extents on the red sea coastline in Egypt using polarimetric SAR and high resolution optical remote sensing data. Sustainability, 10.","DOI":"10.3390\/su10030646"},{"key":"ref_37","first-page":"1","article-title":"Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image","volume":"10790","author":"Kamal","year":"2018","journal-title":"Earth Resour. Environ. Remote Sens. GIS Appl. IX"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Yokoya, N., Bui, D.T., Yoshino, K., and Friess, D.A. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sens., 11.","DOI":"10.3390\/rs11030230"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pereira, F.R.D., Kampel, M., Soares, M.L.G., Estrada, G.C.D., Bentz, C., and Vincent, G. (2018). Reducing uncertainty in mapping of mangrove aboveground biomass using airborne discrete return lidar data. Remote Sens., 10.","DOI":"10.3390\/rs10040637"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3470","DOI":"10.1029\/2018JG004501","article-title":"Hurricane-induced changes in the Everglades National Park Mangrove Forest: Landsat observations between 1985 and 2017","volume":"123","author":"Han","year":"2018","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"927","DOI":"10.14358\/PERS.81.12.927","article-title":"Geometric accuracy analysis model of the ZiYuan-3 satellite without GCPs","volume":"81","author":"Tang","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1080\/10095020.2014.917454","article-title":"Development, application, and prospects for Chinese land observation satellites","volume":"17","author":"Xu","year":"2014","journal-title":"Geo Spat. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cao, J.J., Leng, W.C., Liu, K., Liu, L., He, Z., and Zhu, Y.H. (2018). Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6064","DOI":"10.3390\/rs6076064","article-title":"Mangrove species identification: Comparing WorldView-2 with aerial photographs","volume":"6","author":"Heenkenda","year":"2014","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.rse.2014.04.019","article-title":"Exploring the effects of biophysical parameters on the spatial pattern of rare cold damage to mangrove forests","volume":"150","author":"Liu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, T., You, S., Yang, X., and Hu, S. (2020). Mangroves Map of China 2018 (MC2018) Derived from 2-Meter Resolution Satellite Observations and Field Data, Science Data Bank.","DOI":"10.3390\/ijgi10020092"},{"key":"ref_47","first-page":"406","article-title":"Remote sensing monitoring and analysis of the changes of mangrove resource in China in the past 20 years","volume":"31","author":"Wu","year":"2013","journal-title":"Adv. Mar. Sci."},{"key":"ref_48","first-page":"435","article-title":"Area, distribution and species composition of mangroves in China","volume":"12","author":"Liao","year":"2014","journal-title":"Wetl. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Mishra, R.K. (2012, January 22\u201327). A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351607"},{"key":"ref_50","unstructured":"Wang, W.Q., and Wang, M. (2007). Mangroves in China, Science Press."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"94044","DOI":"10.1088\/1748-9326\/ab9be3","article-title":"Mapping global urban boundaries from the global artificial impervious area (GAIA) data","volume":"15","author":"Li","year":"2020","journal-title":"Environ. Res. Lett."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/2\/92\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:26:44Z","timestamp":1760160404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/2\/92"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,20]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["ijgi10020092"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10020092","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,20]]}}}