{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:51:42Z","timestamp":1780732302842,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Oak Foundation"},{"name":"COmON Foundation"},{"name":"National Philanthropic Trust"},{"name":"DOB Ecology"},{"name":"Dutch Postcode Lottery"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Current mangrove mapping efforts, such as the Global Mangrove Watch (GMW), have focused on providing one-off or annual maps of mangrove forests, while such maps may be most useful for reporting regional, national and sub-national extent of mangrove forests, they may be of more limited use for the day-to-day management of mangroves and for supporting the Global Mangrove Alliance (GMA) goal of halting global mangrove loss. To this end, a prototype change mangrove loss alert system has been developed to identify mangrove losses on a monthly basis. Implemented on the Microsoft Planetary Computer, the Global Mangrove Watch v3.0 mangrove baseline extent map for 2018 was refined and used to define the mangrove extent mask under which potential losses would be identified. The study period was from 2018 to 2022 due to the availability of Sentinel-2 imagery used for the study. The mangrove loss alert system is based on optimised normalised difference vegetation index (NDVI) thresholds used to identify mangrove losses and a temporal scoring system to filter false positives. The mangrove loss alert system was found to have an estimated overall accuracy of 92.1%, with the alert commission and omission estimated to be 10.4% and 20.6%, respectively. Africa was selected for the mangrove loss alert system prototype, where significant losses were identified in the study period, with 90% of the mangrove loss alerts identified in Nigeria, Guinea-Bissau, Madagascar, Mozambique and Guinea. The primary drivers of these losses ranged from economic activities that dominated West Africa and Northern East Africa (mainly agricultural conversion and infrastructure development) to climatic in Southern East Africa (primarily storm frequency and intensity). The production of the monthly mangrove loss alerts for Africa will be continued as part of the wider Global Mangrove Watch project, and the spatial coverage is expected to be expanded to other regions over the coming months and years. The mangrove loss alerts will be published on the Global Mangrove Watch online portal and updated monthly.<\/jats:p>","DOI":"10.3390\/rs15082050","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T01:35:00Z","timestamp":1681349700000},"page":"2050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7435-0148","authenticated-orcid":false,"given":"Pete","family":"Bunting","sequence":"first","affiliation":[{"name":"Department Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4364-7547","authenticated-orcid":false,"given":"Lammert","family":"Hilarides","sequence":"additional","affiliation":[{"name":"Wetlands International, 6700 AL Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7896-502X","authenticated-orcid":false,"given":"Ake","family":"Rosenqvist","sequence":"additional","affiliation":[{"name":"solo Earth Observation (soloEO), Tokyo 104-0054, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3010-3302","authenticated-orcid":false,"given":"Richard M.","family":"Lucas","sequence":"additional","affiliation":[{"name":"Department Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edmond","family":"Kuto","sequence":"additional","affiliation":[{"name":"Wetlands International Eastern Africa, Nairobi PQ7X+5J7, Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yakhya","family":"Gueye","sequence":"additional","affiliation":[{"name":"Wetlands International West Africa, Dakar MGXW+333, Senegal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laye","family":"Ndiaye","sequence":"additional","affiliation":[{"name":"Wetlands International West Africa, Dakar MGXW+333, Senegal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119739","DOI":"10.1016\/j.foreco.2021.119739","article-title":"Now or later? Optimal timing of mangrove rehabilitation under climate change uncertainty","volume":"503","author":"Agaton","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1126\/science.abm9583","article-title":"High-resolution mapping of losses and gains of Earth\u2019s tidal wetlands","volume":"376","author":"Murray","year":"2022","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1038\/ngeo1123","article-title":"Mangroves among the most carbon-rich forests in the tropics","volume":"4","author":"Donato","year":"2011","journal-title":"Nat. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41561-018-0279-1","article-title":"Mangrove canopy height globally related to precipitation, temperature and cyclone frequency","volume":"12","author":"Simard","year":"2019","journal-title":"Nat. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.tree.2019.04.004","article-title":"The Role of Vegetated Coastal Wetlands for Marine Megafauna Conservation","volume":"34","author":"Sievers","year":"2019","journal-title":"Trends Ecol. Evol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107159","DOI":"10.1016\/j.ecss.2020.107159","article-title":"Fishers who rely on mangroves: Modelling and mapping the global intensity of mangrove-associated fisheries","volume":"248","author":"Ermgassen","year":"2021","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4404","DOI":"10.1038\/s41598-020-61136-6","article-title":"The Global Flood Protection Benefits of Mangroves","volume":"10","author":"Losada","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"15359","DOI":"10.1038\/s41598-021-94207-3","article-title":"Mangroves and coastal topography create economic \u201csafe havens\u201d from tropical storms","volume":"11","author":"Hochard","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103540","DOI":"10.1016\/j.marpol.2019.103540","article-title":"Global patterns in mangrove recreation and tourism","volume":"110","author":"Spalding","year":"2019","journal-title":"Mar. Policy"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Thomas, N., Lucas, R., Bunting, P., Hardy, A., Rosenqvist, A., and Simard, M. (2017). Distribution and drivers of global mangrove forest change, 1996\u20132010. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0179302"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1146\/annurev-environ-101718-033302","article-title":"The State of the World\u2019s Mangrove Forests: Past, Present, and Future","volume":"44","author":"Friess","year":"2019","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_12","unstructured":"Wilkie, M.L., and Fortuna, S. (2003). Status and Trends in Mangrove Area Extent Worldwide, The Food and Agriculture Organization (FAO). Available online: http:\/\/www.fao.org\/3\/j1533e\/J1533E02.htm."},{"key":"ref_13","unstructured":"Wilkie, M.L., and Fortuna, S. (2007). Forest Resources Assessment (FRA) 2005 Thematic Study on Mangroves, Forest Resources Division, Food and Agriculture Organization of the United Nations. Technical Report."},{"key":"ref_14","unstructured":"(2022, December 12). Global Mangrove Alliance. Available online: https:\/\/www.mangrovealliance.org."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R.M., and Thomas, N. (2022). Global Mangrove Watch: Updated 2010 Mangrove Forest Extent (v2.5). Remote Sens., 14.","DOI":"10.3390\/rs14041034"},{"key":"ref_17","unstructured":"Bunting, P., Rosenqvist, A., Lucas, R., Rebelo, L.M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., and Finlayson, M. (2019). Global Mangrove Watch (1996\u20132016) Version 2.0, Zenodo."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bunting, P., Rosenqvist, A., Lucas, R., Rebelo, L.M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., and Finlayson, C. (2018). The Global Mangrove Watch\u2014A New 2010 Global Baseline of Mangrove Extent. Remote Sens., 10.","DOI":"10.3390\/rs10101669"},{"key":"ref_19","unstructured":"(2022, December 12). Global Mangrove Watch Portal. Available online: https:\/\/globalmangrovewatch.org."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.oneear.2020.08.003","article-title":"Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally","volume":"3","author":"Worthington","year":"2020","journal-title":"One Earth"},{"key":"ref_21","unstructured":"Camberlin, P. (2018). Oxford Research Encyclopedia of Climate Science, Oxford University Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1111\/j.1365-2486.2010.02307.x","article-title":"Challenges in using land use and land cover data for global change studies","volume":"17","author":"Verburg","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Awty-Carroll, K., Bunting, P., Hardy, A., and Bell, G. (2021). Evaluation of the Continuous Monitoring of Land Disturbance Algorithm for Large-Scale Mangrove Classification. Remote Sens., 13.","DOI":"10.3390\/rs13193978"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1080\/0143116021000021189","article-title":"Post-classification change detection with data from different sensors: Some accuracy considerations","volume":"24","author":"Serra","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.006","article-title":"A critical synthesis of remotely sensed optical image change detection techniques","volume":"160","author":"Tewkesbury","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111116","DOI":"10.1016\/j.rse.2019.03.009","article-title":"Continuous monitoring of land disturbance based on Landsat time series","volume":"238","author":"Zhu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Awty-Carroll, K., Bunting, P., Hardy, A., and Bell, G. (2019). Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region. Remote Sens., 11.","DOI":"10.3390\/rs11232833"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"034008","DOI":"10.1088\/1748-9326\/11\/3\/034008","article-title":"Humid tropical forest disturbance alerts using Landsat data","volume":"11","author":"Hansen","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"024005","DOI":"10.1088\/1748-9326\/abd0a8","article-title":"Forest disturbance alerts for the Congo Basin using Sentinel-1","volume":"16","author":"Reiche","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112643","DOI":"10.1016\/j.rse.2021.112643","article-title":"Refined algorithm for forest early warning system with ALOS-2\/PALSAR-2 ScanSAR data in tropical forest regions","volume":"265","author":"Watanabe","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"1","article-title":"Alerts of forest disturbance from MODIS imagery","volume":"33","author":"Hammer","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Achard, F., and Hansen, M.C. (2016). Global Forest Monitoring from Earth Observation, Taylor & Francis. Chapter 9.","DOI":"10.1201\/b13040"},{"key":"ref_37","unstructured":"(2022, December 12). Microsoft Planetary Computer. Available online: https:\/\/planetarycomputer.microsoft.com."},{"key":"ref_38","unstructured":"McFarland, M., Emanuele, R., Morris, D., and Augspurger, T. (2022). Microsoft Open Source: Microsoft\/PlanetaryComputer: October 2022 (2022.10.28), Zenodo."},{"key":"ref_39","unstructured":"STAC (2022, November 14). STAC: SpatioTemporal Asset Catalogs. Available online: https:\/\/stacspec.org."},{"key":"ref_40","unstructured":"(2023, March 09). Project Jupyter. Available online: https:\/\/jupyter.org."},{"key":"ref_41","unstructured":"(2023, March 09). Kubernetes. Available online: https:\/\/kubernetes.io\/."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rocklin, M. (2015, January 6\u201312). Dask: Parallel Computation with Blocked algorithms and Task Scheduling. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-7b98e3ed-013"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hoyer, S., and Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw., 5.","DOI":"10.5334\/jors.148"},{"key":"ref_44","unstructured":"(2022, December 18). Open Data Cube STAC API. Available online: https:\/\/github.com\/opendatacube\/odc-stac."},{"key":"ref_45","first-page":"1042704","article-title":"Sen2Cor for Sentinel-2","volume":"10427","author":"Pflug","year":"2017","journal-title":"Image Signal Process. Remote Sens. XXIII"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2120616","article-title":"Flattening Gamma: Radiometric Terrain Correction for SAR Imagery","volume":"49","author":"Small","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New global forest\/non-forest maps from ALOS PALSAR data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"(2022, December 18). PALSAR-2 Mosaic Description v2.1.2. Available online: https:\/\/www.eorc.jaxa.jp\/ALOS\/en\/dataset\/pdf\/DatasetDescription_PALSAR2_Mosaic_ver212.pdf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2020.06.001","article-title":"Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping","volume":"166","author":"Baloloy","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.ecolmodel.2007.03.041","article-title":"Rectangular and hexagonal grids used for observation, experiment and simulation in ecology","volume":"206","author":"Birch","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_51","unstructured":"(2022, December 18). Norway\u2019s International Climate and Forests Initiative Satellite Data Program. Available online: https:\/\/www.planet.com\/nicfi\/."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/cbdv.200900203","article-title":"Upstream Petroleum Degradation of Mangroves and Intertidal Shores: The Niger Delta Experience","volume":"7","author":"Osuji","year":"2010","journal-title":"Chem. Biodivers."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"12876","DOI":"10.1038\/s41598-018-30904-w","article-title":"Global long-term observations of coastal erosion and accretion","volume":"8","author":"Mentaschi","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106078","DOI":"10.1016\/j.ecoleng.2020.106078","article-title":"Managing erosion of mangrove-mud coasts with permeable dams\u2013lessons learned","volume":"158","author":"Winterwerp","year":"2020","journal-title":"Ecol. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"10416","DOI":"10.1002\/ece3.4485","article-title":"The extent of mangrove change and potential for recovery following severe Tropical Cyclone Yasi, Hinchinbrook Island, Queensland, Australia","volume":"8","author":"Asbridge","year":"2018","journal-title":"Ecol. Evol."},{"key":"ref_56","first-page":"213","article-title":"Tropical cyclones and the organization of mangrove forests: A review","volume":"125","author":"Krauss","year":"2019","journal-title":"Ann. Bot."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ocecoaman.2014.09.025","article-title":"Decline of mangroves\u2014A threat of heavy metal poisoning in Asia","volume":"102","author":"Sandilyan","year":"2014","journal-title":"Ocean Coast. Manag."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1111\/nph.12605","article-title":"How mangrove forests adjust to rising sea level","volume":"202","author":"Krauss","year":"2014","journal-title":"New Phytol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"eabo6602","DOI":"10.1126\/sciadv.abo6602","article-title":"The lunar nodal cycle controls mangrove canopy cover on the Australian continent","volume":"8","author":"Saintilan","year":"2022","journal-title":"Sci. Adv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Charrua, A.B., Padmanaban, R., Cabral, P., Bandeira, S., and Romeiras, M.M. (2021). Impacts of the Tropical Cyclone Idai in Mozambique: A Multi-Temporal Landsat Satellite Imagery Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13020201"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1038\/ncomms2986","article-title":"Human deforestation outweighs future climate change impacts of sedimentation on coral reefs","volume":"4","author":"Maina","year":"2013","journal-title":"Nat. Commun."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11273-014-9370-6","article-title":"An approach to monitoring mangrove extents through time-series comparison of JERS-1 SAR and ALOS PALSAR data","volume":"23","author":"Thomas","year":"2014","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Rosen, P.A., and Kumar, R. (2021, January 7\u201314). NASA-ISRO SAR (NISAR) Mission Status. Proceedings of the 2021 IEEE Radar Conference (RadarConf21), Atlanta, GA, USA.","DOI":"10.1109\/RadarConf2147009.2021.9455211"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2050\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:15:05Z","timestamp":1760123705000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,12]]},"references-count":63,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082050"],"URL":"https:\/\/doi.org\/10.3390\/rs15082050","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,12]]}}}