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

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 are nature-based solutions for coastal protection however their ability to attenuate waves and stabilise and accrete sediment varies with their species-specific frontal area. Hydrodynamic models are typically used to predict and assess the protection afforded by mangroves, modelling them via a drag coefficient or as rigid cylinders on increasingly larger spatial scales. However, the results can significantly differ from reality without information of the distribution of mangrove species and/or genera. Data identifying the spatial distribution of the frontal species and/or genera of mangroves exposed to waves and tides can provide information that can be used in hydrodynamic models to more accurately forecast the protection benefit provided by mangroves. Globally, frontal species/genera were identified from existing mangrove zonation diagrams and assigned to each of the Marine Ecoregions of the World (MEOW) to create the first global map of the distribution of frontal mangrove species/genera. This dataset aims to improve the accuracy of hydrodynamic models predicting the coastal protection provided by mangroves. Data may be of interest to researchers in coastal engineering, marine science, wetland ecology and blue carbon.

This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.

Black mangrove (Avicennia germinans (L.) L.) has historically occurred along the Louisiana coast in saline wetland habitats, but its distribution has been sparse. Mangroves are tropical to semi-tropical species and their distribution is limited by freezing temperatures. Black mangrove distribution and abundance has increased and decreased in the coastal zone of Louisiana according to freeze frequency and duration and concomitant freeze damage and dieback or lack thereof. In 2009, a fixed wing aircraft was used to conduct an aerial cruise census of the entire coastal area of Louisiana to document and map the total distribution of mangroves in the state.

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.

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).

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

By combining simple noniterative superpixel segmentation and the object-oriented random forest method, an SNIC-RF algorithm for mangrove remote sensing extraction was developed on the GEE cloud platform, and annual mangrove distribution maps of the GBG from 2000 to 2023 were generated.

These range maps were overlain on ecoregion maps and species totals calculated for each. These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at https://nature.org/atlas. Data derived from: Spalding, M. D., M. Kainuma, L. Collins. Forthcoming. World Mangrove Atlas. London: Earthscan, with International Society for Mangrove Ecosystems, Food and Agriculture Organization of the United Nations, UNEP-WCMC, The Nature Conservancy, United Nations Scientific and Cultural Organisation, United Nations University.

Mangroves are a vital component of coastal ecosystems, providing essential ecological services such as seawater purification, carbon storage, and biodiversity support. However, mangrove ecosystems have long been disturbed by human activities and natural disasters, making accurate monitoring and long-term tracking of mangrove changes particularly important. Existing large-scale mangrove mapping products typically have spatial resolutions of 10 meters or 30 meters. While these studies have made significant progress in advancing mangrove monitoring and conservation, they still face challenges, including inaccurate boundary identification, difficulty in extracting internal details, and issues with detecting small patches. To address these challenges, this dataset employs a novel Semi-automatic Sub-meter Mapping Method (SSMM). The method enhances the spectral separability between mangroves and other land cover types by selecting nine key features from Sentinel-2 and Google Earth imagery. Additionally, an innovative automated sample collection method was developed to ensure the acquisition of representative and sufficient samples. Finally, the dataset was generated using a random forest classifier, resulting in the 2020 Large-scale Sub-meter Mangrove Map (LSMM). This dataset reflects the spatial distribution of mangroves in China's coastal areas in 2020 and provides a new benchmark for large-scale or long-term mangrove monitoring.

The Sample can drive classification algorithms, thus is a prerequisite for accurate classification. Coastal areas are located in the transitional zone between land and sea, requiring more samples to describe diverse land covers. However, there are scarce studies sharing their sample datasets, leading to a repeat of the time-consuming and laborious sampling procedure. To alleviate the problem, we share a sample set with a total of 16,444 sample points derived from a study of mapping mangroves of China. The sample set contains a total of 10 categories, which are described as follows. 1) The mangroves refer to “true mangroves” (excluding the associate mangrove species). In sampling mangroves, we used the data from the China Mangrove Conservation Network (CMCN, http://www.china-mangrove.org/), a non-governmental organization aiming to promote mangrove ecosystems. The CMCN provides an interactive map that can be annotated by volunteers with text or photos to record mangrove status at a location. Although the locations were shifted due to coordinate system differences and positioning errors, mangroves could be found around the mangrove locations depicted by the CMCN’s map on Google Earth images. There is a total of 1887 mangrove samples. 2) The cropland is dominated by paddy rice. We collected a total 1383 points according to its neat arrangement based on Google Earth images. 3) Coastal forests neighboring mangroves are mostly salt-tolerant, such as Cocos nucifera Linn., Hibiscus tiliaceus Linn., and Cerbera manghas Linn. We collected a total 1158 samples according to their distance to the shoreline based on Google Earth images. 4) Terrestrial forests are forests far from the shoreline, and are intolerant to salt. By visual inspection on Google Earth, we sampled 1269 points based on their appearances and distances to the shoreline. 5) For the grass category, we collected 1282 samples by visual judgement on Google Earth. 6) Saltmarsh, dominated by Spartina alterniflora, covering large areas of tidal flats in China. We collected 2065 samples according to Google Earth images. 7) The tidal flats category was represented by 1517 samples, which were sampled using the most recent global tidal flat map for 2014–2016 and were visually corrected. 8) The “sand or rock” category refers to sandy and pebble beaches or rocky coasts exposed to air, which are not habitats of mangroves. We collected 1622 samples on Google Earth based on visual inspection. 9) For the permanent water category, samples were first randomly sampled from a threshold result of NDWI (> 0.2), and then were visually corrected. A total of 2056 samples were obtained. 10) As to the artificial impervious surfaces category, we randomly sampled from a threshold result corresponding to normal difference built-up index (NDBI) (> 0.1), and corrected them based on Google Earth. The artificial impervious surface category was represented by 2205 samples. This sample dataset covers the low-altitude coastal area of five Provinces (Hainan, Guangdong, Fujian, Zhejiang, and Taiwan), one Autonomous region (Guangxi), and two Special Administrative Regions (Macau and Hong Kong) (see “study_area.shp” in the zip for details). It can be used to train models for coastal land cover classification, and to evaluate classification results. In addition to mangroves, it can also be used in identifying tidal flats, mapping salt marsh, extracting water bodies, and other related applications.Compared with the V1 version, we added a validation dataset for mangrove maps (Mangrove map validation dataset.rar), and thus can evaluate mangrove maps under the same dataset, which benefit the comparison of different mangrove maps. The validation dataset contains 10 shp files, in which each shp file contains 600 mangrove samples (cls_new field = 1) and 600 non-mangrove samples (cls_new field = 0).Compared with the V2 version, we added two classes of forest near water and grass near water, in addition to suppress the prevalent misclassified patches due to the spectral similarity between mangroves and those classes.

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

This dataset includes a regional map of mangrove extent for Myanmar, Thailand, and Cambodia for the period of 1972-1977. The map was developed from Landsat 1-2 MSS Collection 1 Tier 2 imagery. Mangrove extent was generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2 at the nominal Landsat scale of 30 m. This map of mangrove extent served as a baseline to analyze changes in mangrove distribution in Southeast Asia from 1970s through 2020. Southeast Asia is home to some of the planet's most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia for the early 21st century, data prior to the mid-1990s are limited due to the scarcity of Earth Observation (EO) data of sufficient quality and the historical limitations to publicly available EO data. The data are provided in Cloud optimized GeoTIFF format at 60-m resolution. In addition, a shapefile outlines the region of analysis.

Mangroves are woody, salt-tolerant plants that occur along mudflats, estuary banks, and coastlines in tropical and subtropical environments around the world. They are able to thrive under harsh environmental conditions, and their extensive root systems provide the structure to support highly diverse and productive coastal ecosystems.While over 70 species of mangrove are recognized globally (Spaulding et al., 1997, ISBN: 9784906584031), only four are found in the along the coastal regions of the U.S. Gulf and Mexico: red mangrove (Rhizophora mangle), white mangrove (Laguncularia racemosa), black mangrove (Avicennia germinans), and button mangrove (Conocarpus erectus). The predominant climactic factor restricting the geographic range of mangroves is believed to be freezing winter temperatures; thus, northward range expansion may be an indicator of a warming climate (Montagna et al., 2011; Giri et al., 2011).Data:Florida Mangroves (Florida Fish and Wildlife Conservation Commission)Texas Mangroves (NCEI; .zip)Mexico Mangroves (Gobierno de México)Metadata:Florida MangrovesTexas MangrovesMexico MangrovesThis is a component of the Gulf Data Atlas (V1.0) for the Biotic topic area.

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...

Historical (1978) and contemporary (2019) data on the distribution of mangrove alliances in Fiji. Lineage: Neil Sims digitised paper maps from A mangrove management plan for Fiji Phase 1 and Phase 2 (Watling 1985, 1987) describing mangrove distribution in 1978. Brent Murray (visiting post doc U Winnipeg) analysed the data using Sentinel satellite imagery and created new layers. Published in Murray, B.A., Sims, N. & Storie, J. Mapping mangrove alliances using historical data in Fiji. J Coast Conserv 26, 47 (2022). https://doi.org/10.1007/s11852-022-00887-y

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.

In response to the growing concerns about mangrove deforestation, recent studies have used various remote sensing technology like satellite imagery to measure the mangrove extent. In this work, we investigated the mangrove distribution in Northwestern Madagascar by using fine spatial imagery with a pixel size as small as 3m and compared it with the result of the traditional method based on relatively coarser Landsat data. Mangroves are an essential biodiverse ecosystem found along tropical and subtropical intertidal beaches, providing critical goods and services to coastal communities, and supporting diverse organisms. However, anthropogenic activities have caused the loss of mangroves in Madagascar, necessitating a new mapping approach utilizing the fine spatial resolution map from Planet data to create a map with advanced detail. The quantitative result central to this work is the new multi-date map of the Tsimipaika- Ampasindava-Ambaro Bays (TAB) from 2020 to 2022, which provides advanced detail and direct comparison with the shift in local mangrove species. The classification maps are based on Random Forest and Maximum Likelihood algorithms, and all of them have an overall accuracy of over 85%. The dynamics of mangrove forests from 2020 to 2022 are quantified, with a 12.6% loss in closed-canopy mangroves, and a 24.1% loss in open-canopy mangroves I am overestimated. Limitations regarding the classification model are also found in this study, including the overestimation of open canopy mangroves caused by the shadow and the seamline in the base map. This result shows the potential of using fine-resolution satellite imagery in supervised land cover classification, and the corresponding challenges raised by the smaller pixel size.