This dataset shows the global distribution of mangrove forests, derived from earth observation satellite imagery. The dataset was created using Global Land Survey (GLS) data and the Landsat archive. Approximately 1,000 Landsat scenes were interpreted using hybrid supervised and unsupervised digital image classification techniques. See Giri et al. (2011) for full details.

The Global Mangrove Watch (GMW) is a collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA).

The GMW aims to provide geospatial information about mangrove extent and changes to the Ramsar Convention, national wetland practitioners, decision makers and NGOs. It is part of the Ramsar Science and Technical Review Panel (STRP) work plan for 2016-2018 and a Pilot Project to the Ramsar Global Wetlands Observation System (GWOS), which is implemented under the GEO-Wetlands Initiative. The primary objective of the GMW has been to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation.

The GMW has generated a global baseline map of mangroves for 2010 using ALOS PALSAR and Landsat (optical) data, and changes from this baseline for seven epochs between 1996 and 2017 derived from JERS-1, ALOS and ALOS-2. Annual maps are planned from 2018 and onwards.

Citations: Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi: 10.3390/rs1010669. Other cited references: Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M. (2017). Distribution and drivers of global mangrove forest change,

Mangroves provide crucial biodiversity values to the ecosystem and the carbon dioxide sequestration capacity of mangrove forests helps mitigate global climate change. Given the diverse ecosystem and economic values, mapping how mangroves change over time is important for mangrove forest management and protection. The traditional mangrove mapping methodology using remote sensing requires a comprehensive understanding of mangrove ecology, remote sensing knowledge, and programming skills. The lack of specialists may stop the mapping of mangrove forests in many areas. In 2020, the Google Earth Engine Mangrove Mapping Methodology was introduced to accessibly map and monitor mangroves with random forest classifier and Landsat satellite imagery. This study applied Google Earth Engine Mangrove Mapping Methodology in Saloum Delta, Senegal for land cover classification with an overall accuracy of 96.45% in 2013 and 97.51% in 2023. Mangrove forests in Saloum Delta experienced heavy harvesting since 1950, and conservation projects have been conducted since 2004 by international organizations and the local government. The results suggest that 93.9% of mangrove forests remained unchanged, while 3.1% were lost and 2.9% saw an increase over the last decade. The results highlighted the mangrove loss areas that need more conservation attention and provided a valuable mangrove forest dynamic map to help the local communities in Saloum Delta with mangrove forest management. For the future development of Google Earth Engine Mangrove Mapping Methodology, the addition of more classifiers for land cover classification and higher resolution Sentinel satellite imagery should be considered. With a user-friendly interface and detailed guidance, Google Earth Engine Mangrove Mapping Methodology has a bright potential to help people with mangrove forest mapping for sustainable management globally in the future.

This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4 % (95th conf. int.: 86.2 - 88.6 %), although the accuracies of the individual gain and loss change classes were lower at 58.1 % (52.4 - 63.9 %) and 60.6 % (56.1 - 64.8 %), respectively. Sources of error included a mis-registration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km² (133,996 - 176,910) of mangroves were identified for 1996, with this decreasing by -5,245 km² (-13,587 - 3686) resulting in a total extent of 147,359 km² (127,925 - 168,895) in 2020, and representing an estimated loss of 3.4 % over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress towards conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide.

The paper which goes along with this dataset is available at the following reference:

Bunting, P.; Rosenqvist, A.; Hilarides, L.; Lucas, R.M.; Thomas, T.; Tadono, T.; Worthington, T.A.; Spalding, M.; Murray, N.J.; Rebelo, L-M. Global Mangrove Extent Change 1996 – 2020: Global Mangrove Watch Version 3.0. Remote Sensing. 2022

A vegetation type characterising a coastal swamp of brackish or saline water, in which specially adapted trees form a dense swamp forest

Data Dictionary for mangrove_poly: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-mangrove_poly.html

This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore islands, at 1:50,000 scale.

Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

Mangrove species dominance on Pohnpei island, Federated States of Micronesia was modeled with two geospatial model types: k-nearest neighbor (KNN) and random forest (RF) and a common set of predictors. Dominant mangroves were defined as species comprising the largest basal area per field plot. The KNN model produced one map, which shows all species' dominance locations in one raster layer. The KNN model results were the best based on field data and in field knowledge of the area. The KNN RStudio model and resulting map are shared here.

Model outputs were updated on Dec 20, 2017. This project used a machine learning data-driven model to predict the distribution of soil carbon under mangrove forests globally. Specifically this dataset contains: 1) a compilation of georeferenced and harmonized soil profile data under mangroves compiled from literature, reports and unpublished contributions 2) global mosaics of soil carbon stocks to 1m and 2m depths produced at 100 m resolution 3) tiled predictions of soil carbon stocks produced at 30 m resolution 4) shape file containing the tiling system 5) shape file containing country boundaries used for calculating national level statistics. For detailed methodologies, please see the scientific paper (https://doi.org/10.1088/1748-9326/aabe1c).

This project evaluated ecosystem damage and recovery by developing a time series of habitat maps for the Rookery Bay National Estuarine Research Reserve. Habitat maps were created based on WorldView-2 and Landsat-8 satellite imagery from 2010-2018 using an automated technique and validated with a field campaign. Landsat images were mapped using the Support Vector Machine machine learning method...

The Global Mangrove Watch (GMW) was initiated as part of the JAXA Kyoto & Carbon Initiative in 2011. It is led by Aberystwyth University and solo Earth Observation, in collaboration with Wetlands International, the International Water Management Institute and the UN Environment World Conservation Monitoring Centre (U.K.). The African part is supported by DOB Ecology through the Mangrove Capital Africa project. The GMW aims to provide geospatial information about mangrove extent and changes to the Ramsar Convention, national wetland practitioners, decision makers and NGOs. It is part of the Ramsar Science and Technical Review Panel (STRP) work plan for 2016-2018 and a Pilot Project to the Ramsar Global Wetlands Observation System (GWOS), which is implemented under the GEO-Wetlands Initiative. The primary objective of the GMW has been to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation.

The GMW has generated a global baseline map of mangroves for 2010 using ALOS PALSAR and Landsat (optical) data, and changes from this baseline for epochs between 1996 and 2020 derived from JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2. Annual maps are planned from 2018 and onwards.

Mangrove Extent maps for the Klang Islands, MalaysiaSatellite derived maps of mangrove extent in the Klang Islands, Malaysia for use within a GIS.Using the 2010 Global Mangrove Watch (GMW) map downloaded from https://data.unep-wcmc.org/datasets/45 as a baseline, a refined local area product was generated by manually editing the GMW polygons in QGIS to match satellite data from 2010.This refined map was then used as a baseline and manually edited to reflect changes when comparing to satellite data for a given year. The output is a map of mangrove extent for each year.Due to the difficulty in obtaining cloud free satellite data for the entire area multiple scenes were used for many years.Data are supplied as polygons in ESRI shapefile format with a separate shapefile for each year.

The Global Mangrove Forests Distribution, 2000 data set is a compilation of the extent of mangroves forests from the Global Land Survey and the Landsat archive with hybrid supervised and unsupervised digital image classification techniques. The data are available at 30-m spatial resolution. The total area of mangroves in the year 2000 was estimated at 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world.

To improve scientific understanding of the extent and distribution of mangrove forests of the world the status and distribution of global mangroves were mapped using recently available Global Land Survey (GLS) data and the Landsat archive.The project interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Results were validated using existing GIS data and the published literature to map ‘true mangroves’.The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude.The remaining area of mangrove forest in the world is less than previously thought; the estimate provided in this study is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. This data set presents the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created

This is an update of maps produced by Sanderman et al (2018). The improvements to the 3D spatial prediction include:

• new updated global mangrove coverage map (contact Thomas Worthington),

• new ALOS-based DEM of the world AW3D30 v18.04,

• new radar ALOS-based PALSAR radar images of the world,

• additional SOC points (ca 550) published in Rovai et al. (2018) used in model training (see gpkg file).

To open map in QGIS or similar, drag and drop the "mangroves_dSOC_0_100cm_30m.vrt" file. You can than add also the gpkg file contain the training points. A preview (WMS) of the predictions is available here.

Production steps (ensemble predictions using SuperLearner) are explained in detail at:

• R code: https://github.com/whrc/Mangrove-Soil-Carbon/ (see "R_code/GMW_mangroves_SOC_30m.R")
• Tutorial: "Predictive Soil Mapping with R"

Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <elandis@TNC.ORG>. Contact IUCN / University of Cambridge: Thomas Worthington <taw52@cam.ac.uk>.

Mangrove species dominance on Pohnpei island, Federated States of Micronesia was modeled with two geospatial model types: k-nearest neighbor (KNN) and random forest (RF) and a common set of predictors. Dominant mangroves were defined as species comprising the largest basal area per field plot. The RF model predicted species dominance for each species separately, resulting in 8 maps (one for each species). The maps of Rhizophora stylosa and R. mucronata dominance were combined because these species were difficult to tell apart in field identification (resulting in the 7 maps presented here). The KNN model produced one map, which shows all species' dominance locations in one raster layer. The KNN model results were the best based on field data and in field knowledge of the area but the RF results are still shared here.

Seychelles Ecosystem Services: Seagrass and Mangrove Blue CarbonMangroves and seagrasses represent rich sources of blue carbon, that is carbon stored and sequestered by coastal and marine ecosystems. In mangroves, carbon is stored and sequestered in living aboveground biomass and in the soil.Model OutputsMangrove carbon: Estimates of mangrove carbon have been calculated for Seychelles using the global mangrove map developed by Global Mangrove Watch (GMW) 2016. Unfortunately, the GMW misses several key mangrove areas in the Seychelles, most notably in the Aldabra group. While these errors are being amended in newer versions of the global mangrove map, the GMW base-map is relatively low resolution while the mangrove layer created for the Seychelles MSP (Klaus 2015) provides higher resolution and an estimate of 30.7 km2 of total mangroves. Existing estimates of mangrove carbon for the Seychelles from the global extents are thus major undestimates. To improve estimates of carbon stored in Seychelles’ mangroves, we used the Global Mangrove Watch models of aboveground biomass (AGB) (derived from Simard et al. 2019) and soil organic carbon (SOC) (derived from Sanderman et al. 2018) and applied them to the locally-derived mangrove map layer (Klaus 2015). For areas of the local-scale layer that overlapped with the global carbon estimates, we used zonal statistics to find the mean AGB and SOC values (expressed in MgC per ha) per mangrove polygon. We then multiplied this value by the area (in ha) for each polygon to get the total values AGB and SOC values per polygon. For local-scale mangrove polygons that did not overlap with the global carbon estimates, we used a spatial join to assign the nearest AGB and SOC values to each polygon, and converted the values from MgC per ha by multiplying the value by the polygon area to obtain total AGB and SOC. To convert AGB to aboveground carbon (AGC), we used a conversion factor of 0.451 (Simard et al. 2019); AGC and SOC values were summed to get total carbon values. Seagrass carbon: As no known global or local-scale estimates of seagrass carbon exist for Seychelles, we provide an estimate based on maps of seagrass derived for the MSP (Klaus 2015). These maps assign a density class (high, medium, low) to each mangrove polygon. To estimate the above and belowground biomass for each seagrass polygon, we used aboveground and belowground dry weight biomass estimates per unit area (m2) for low, medium, an high density seagrass from Mallombasi et al. (2020). These biomass values were then converted to carbon using a conversion factor of 0.35 (from Fourqueran et al. 2012) and then converted to total carbon by multiplying by the area of the seagrass polygon. Model Output Datasets Seagrass Blue Carbon Dataset name: Seychelles_Seagrass_Blue_Carbon.shp Dataset type: ESRI File Geodatabase, polygon feature class Values: Estimated seagrass blue carbon (summed by polygon) Field ValuesAGgDWm2 Above-ground dry weight biomass estimates per unit area (meters squared) for low, medium and high density seagrassBGDWm2 Below-ground dry weight biomass estimates per unit area (meters squared) for low, medium and high density seagrassAGMgCha AGgDWm2 converted to aboveground carbon (MgC) per unit area (hectare). Biomass converted to carbon using 0.35 carbon conc from Fourqueran et al (2012)BGMgCha BGgDWm2 converted to aboveground carbon (MgC) per unit area (hectare). Biomass converted to carbon using 0.35 carbon concentration from Fourqueran et al (2012)socMgCha Mean soil organic carbon (Mg) per unit area (hectare) TotMgCha AGC + BGC + SOC per unit area (hectares)TotMgC TotMgCha Mean soil organic carbon (Mg) per unit area (hectare) multiplied by the area estimate of each unique polygon (MgCha * ha)TotTgC TotMgC converted to teragrams (TgC) Mangrove Blue Carbon Dataset name: Seychelles_Mangrove_Blue_Carbon.shp Dataset type: ESRI File Geodatabase, polygon feature class Values: Estimated mangrove blue carbon (summed by polygon) Field ValuesAGC_ton Aboveground carbon, metric tonnesSOC_ton Soil organic carbon, metric tonnesTotal_ton AGC_ton + SOC_tonHa Unique polygon areal estimate in hectares References: Fourqurean, J. W., Duarte, C. M., Kennedy, H., Marbà, N., Holmer, M., Mateo, M. A., ... & Serrano, O. (2012). Seagrass ecosystems as a globally significant carbon stock. Nature geoscience, 5(7), 505-509. Klaus, R. (2015). Strengthening Seychelles ’ protected area system through NGO management modalities. Mallombasi, A., Mashoreng, S., & La Nafie, Y. A. (2020). The relationship between seagrass Thalassia hemprichii percentage cover and their biomass. Jurnal Ilmu Kelautan SPERMONDE, 6(1), 7-10. Palacios, M. M., Waryszak, P., de Paula Costa, M. D., Wartman, M., Ebrahim, A., & Macreadie, P. I. (2021). Literature Review: Blue Carbon research in the Tropical Western Indian Ocean.Simard, M., Fatoyinbo, T., Smetanka, C., Rivera-Monroy, V. H., CASTANEDA, E., Thomas, N., & Van der Stocken, T. (2019). Global Mangrove Distribution, Aboveground Biomass, and Canopy Height. ORNL DAAC. Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi: 10.3390/rs1010669.

Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M. (2017). Distribution and drivers of global mangrove forest change, 1996-2010. PLOS ONE 12: e0179302. doi: 10.1371/journal.pone.0179302

Polygons represent mangrove areas in Kenya.This is a shape file of mangrove cover of Kenya coast derived from Landsat 7. Mangroves forests cover map was created through a supervised digital image classification techniques. The data are available at 30-m spatial resolution. A cover 430.60 square kilometer was realized for Kenya coastline.

This dataset characterizes canopy heights of mangrove-forested wetlands globally for 2015 at 12-m resolution. Estimates of maximum canopy height (height of the tallest tree) were derived from the German Space Agency's TanDEM-X data that produced global digital surface models. Also provided are Lidar estimates of canopy height based on the GEDI instrument, which were used for training and validation of the TanDEM-X estimates of forest height. The coverage of these data follows Global Mangrove Watch's mangrove extent maps. These spatially explicit maps of mangrove canopy height can be used to assess local-scale geophysical and environmental conditions that may regulate forest structure and carbon cycle dynamics. Maps revealed a wide range of canopy heights, including maximum values (>60 m) that surpass maximum heights of other forest types. Maps are provided in cloud optimized GeoTIFF format, and mangrove heights for individual GEDI tiles are compiled in a comma separated values (CSV) files.

The Global Mangrove Watch (GMW) data layers were developed in a collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA).

The aim was to provide geospatial information about mangrove extent and changes to the Ramsar Convention, national wetland practitioners, decision makers and NGOs. It is part of the Ramsar Science and Technical Review Panel (STRP) work plan for 2016-2018 and a Pilot Project to the Ramsar Global Wetlands Observation System (GWOS), which is implemented under the GEO-Wetlands Initiative. The primary objective has been to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation.

The GMW has generated a global baseline map of mangroves for 2010 using ALOS PALSAR and Landsat (optical) data, and changes from this baseline for seven epochs between 1996 and 2016 derived from JERS-1, ALOS and ALOS-2.

Mangrove area data for the Mekong countries are extracted from global mangrove area data by Bunting, P et al. This data used the L-band Synthetic Aperture Radar (SAR) global dataset from the Japan Aerospace Exploration Agency (JAXA) from 1996 to 2020. The study used a follow-up approach. map-image approach for change detection where the base map (GMW version 2.5) is updated using contextual mangrove change thresholds and masks. This approach was applied between all date-image pairs producing 10 maps per epoch, summed up to generate a global mangrove time series. The obtained mangrove coverage maps have an estimated accuracy of 87.4% (95th conf. int.: 86.2 - 88.6%), although the accuracy of the layers is variable and individual losses were lower at 58.1% (52.4 - 63.9 %) and 60.6 %. (56.1 - 64.8%).

Mangroves are unique plant species that support livelihoods and provide critical ecosystem services. Globally, mangroves have faced significant threats from deforestation, causing rapid decline; however, recent restoration efforts have made a positive impact. Effective restoration requires regular monitoring and evaluation to ensure its long-term success. This study aims to support mangrove restoration efforts in the Bay of Assassins, Madagascar by evaluating the utility of remote sensing technologies for short-term monitoring. Since 2015, restoration efforts in the area have achieved notable success making continued monitoring essential for sustainability. Blue Ventures’ Google Earth Mangrove Mapping Methodology (GEM), a cloud computing tool hosted on Google Earth Engine, along with Sentinel-2 satellite imagery, were leveraged to examine mangroves from 2019 to 2024. The objective was to assess and quantify mangrove growth and health dynamics, identifying trends in gain and loss over the five-year period. Previous GEM applications exclusively used Landsat imagery with each pixel representing 30 by 30 meters of ground area. This study aimed to build on this work by using the higher level of detail provided by Sentinel-2’s 10-meter resolution. The findings demonstrate that Sentinel-2 imagery, and GEM effectively captured changes in mangrove extent and health, revealing a net increase in total mangrove cover from 2019 to 2024. Mangrove health assessments using spectral indices indicated that many restored areas improved in health, while some exhibited stagnation or decline, suggesting the need for further investigation into site-specific conditions. With an average accuracy of 96% across all mangrove maps, this study successfully demonstrated the utility of the GEM tool in performing high-resolution analysis and assessing mangrove growth