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,

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 provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period. The drivers of mangrove loss were determined by examining land cover changes using a random forest machine learning technique that considered change from mangrove to wet soil, dry soil, and water at each loss pixel. A series of decision trees used several global-scale land-use datasets to identify the ultimate driver of the mangrove loss. Loss drivers include commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. Maps of loss extent per period, mangrove land cover changes, and loss drivers are provided for each of 39 mangrove holding nations.

The Global Mangrove Watch (GMW) was initiated as part of the JAXAKyoto & 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 Africaproject. 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 six epochs between 1996 and 2016 derived from JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2. Annual maps are planned from 2018 and onwards. Currently only the 2010 layer has been validated and is available for download. Validation of the other layers is ongoing and public release planned for early 2019.You may download the full raster dataset here: http://data.unep-wcmc.org/datasets/45

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.

This dataset provides estimates of mangrove extent for 2016, and mangrove change (gain or loss) from 2000 to 2016, in major river delta regions of eight countries: Bangladesh, Gabon, Jamaica, Mozambique, Peru, Senegal, Tanzania, and Vietnam. For mangrove extent, a combination of Landsat 8 OLI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) elevation data were used to create country-wide maps of mangrove landcover extent at a 30-m resolution. For mangrove change, the global mangrove map for 2000 (Giri et al., 2010) was used as the baseline. Normalized Difference Vegetation Indices (NDVI) were calculated for every cloud- and shadow-free pixel in the Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI collection and used to create an NDVI anomaly from 2000 to 2016. Areas of change (loss or gain) occurred at the extremes of the cumulative anomalies.

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.

The database was prepared using Landsat satellite data from the year 2000. More than 1,000 Landsat scenes obtained from the USGS Earth Resources Observation and Science Center (EROS) were classified using hybrid supervised and unsupervised digital image classification techniques. This database is the first, most comprehensive mangrove assessment of the world (Giri et al., 2011). Partial funding of this research was provided by NASA. The mangrove database is being used for identifying priority areas for mangrove conservation, studying the role of mangrove forests in saving lives and properties from natural disasters (e.g. tsunami), carbon accounting, and biodiversity conservation. The USGS EROS has been using the data to study the impact of sea level rise on mangrove ecosystems. The database serves as a baseline for mangrove monitoring. General Documentation

Mangroves are a group of tree and bush species that thrive in coastal intertidal zones, or the area between high and low tide. Because they prefer warm-water ecosystems, they are most commonly found at tropical and subtropical latitudes around the world. They are halophytes, or salt tolerant, and adapted to live in low-oxygen saline or brackish water. They are easily recognizable due to their tangle of prop roots, which keep their leafy branches above the water as the cycle of tides changes the water level.

Though more than 50—and possibly up to 110—species are considered mangroves, not all are from the genus Rhizophora. Some mangrove species are the result of convergent evolution, when unrelated species adapt to a harsh environment (such as a high-salt, low-oxygen intertidal zone) by taking on similar characteristics (such as a tangled prop root system). For this reason, extensive mangrove forests may contain only a handful of unique mangrove species—and even within a single forest, different types of mangroves each occupy distinct niches.

Mangroves are crucial to the health of coastal ecosystems. Their root systems protect the shoreline against erosion by reducing wave intensity and trapping sediments against the land. Mangrove forests are also effective at carbon sequestration, the process of removing carbon dioxide (a harmful greenhouse gas) from the atmosphere. Their labyrinth of roots is a popular hiding spot for fish and other marine life evading predators. They also shield coastal communities from tsunamis and storm surge.

Today, the largest mangrove forest in the world is the Sundarbans forest along the coast of Bangladesh and India, spanning 10,000 square kilometers (almost 4,000 square miles). In the past 50 years, though, it is estimated that up to 35 percent of the world's mangrove forests have been lost. This is largely due to shrimp farming, in which mangrove forests are cleared and replaced with artificial ponds for aquaculture practices, as well as other growing threats: unsustainable tourism activities; agricultural practices that cause mangrove removal or harmful runoff and pollution; and mangrove deforestation for coastal development or to gather charcoal and timber. These risks are exacerbated by climate change causing sea levels to rise, effectively drowning mangrove forests, and alterations to water chemistry, temperature, and other conditions, which put stress on the mangroves’ preferred growing conditions.

As threats to mangroves become better understood, organizations around the world have been created and joined together to protect these important ecosystems. The data in this map layer is from the Global Mangrove Alliance, a collaboration between Conservation International, The International Union for the Conservation of Nature, The Nature Conservancy, Wetlands International, and the World Wildlife Fund. Supported by grants from the National Geographic Society, Explorers Ben Somerville and Margaret Owuor use research, education, and storytelling to protect mangrove forests near their homes in the Caribbean and southeastern Kenya. The map layer shows mangrove forest extent around the world in 2016.

About 40 percent of people live near the coast, and 90 percent of tropical storms form within 20 degrees of the Equator—the parts of the globe where mangrove forests flourish. You can use the Population Density and Longitudes and Latitudes layers to see how many people and mangroves live in these regions, and the Protected Areas layer to find the areas where special conservation plans exist to defend mangrove ecosystems.

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.

Mangroves are highly productive ecosystems that provide important ecosystem services, are strategic allies in carbon capture and storage, conserve different plant and wildlife species, are producers of aquatic species such as crabs and shrimp, and local communities have developed strong economic, cultural and identity ties. Despite their great ecological, economic, and social importance, mangroves are threatened by natural and anthropogenic factors, hence the importance of their constant monitoring. Remote sensing technology has demonstrated its ability to map changes in mangroves and technological advances allow faster application of mapping methodologies, optimizing costs and time. To facilitate the sustainable management of mangroves, an open tool based on remote sensing data and machine learning was developed on the Google Earth Engine platform (MANGLEE). MANGLEE was tested in the mangroves of Guayas, Ecuador. Mangrove cover maps were obtained for the years 2018, 2020 and 2022 as well as the mangrove change maps for the two periods 2018-2020 and 2020-2022. The maps generated in this service are also available on the APP MANGLEE: The methodology and the diffusion workshops are available at the following link. The source code can be found in GitHub MANGLEE: This publication is possible by the support of the people of the United States through the United States Agency for International Development .(USAID). The content of this publication is the responsibility of its authors and does not necessarily reflect the views of USAID or the Government of the United States of America. NOTE: See the updated version for all Ecuador in the following link: https://doi.org/10.7910/DVN/RDTUZC .

Irrecoverable carbon in ecosystems is defined by three criteria: (1) it can be influenced by direct and local human action (‘manageability’), (2) it is potentially vulnerable to loss during land-use conversion (‘vulnerability’) and (3) if lost, it could not be recovered within a specified timeframe (‘recoverability’). Here, we consider recoverability over 30 yr given the IPCC assessment that global emissions must reach net-zero by 2050 to limit global warming to <1.5 °C above pre-industrial levels.To create the irrecoverable carbon map, we:(1) Define relevant ecosystems that meet criteria 1, manageability.(2) Create a ‘total manageable carbon’ map for terrestrial and coastal ecosystems. This includes aboveground biomass carbon (AGC), belowground biomass carbon (BGC) and soil organic carbon (SOC) stocks. (3) Create a ‘vulnerable carbon’ map that considers the portion of biomass carbon (AGC + BGC) and SOC, respectively, that would be released in a typical land-use conversion. We used the most common drivers of recent destruction/loss in each major ecosystem.(4) Determine the amount of lost carbon that could be recovered within 30 yr following a conversion, assuming land abandonment and natural regeneration. Recoverability is based on biomass and SOC sequestration rates by ecosystem.(5) Subtract ‘recoverable carbon’ from the ‘vulnerable carbon’ map. The balance is the ‘irrecoverable carbon’ map.SourcesBunting, P., et al. The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10: 1669. doi:10.3390/rs1010669 Accessed: 6/13/2020 (2018).ESA. Annual Land Cover maps (1992-2018) 300m Available from: https://maps.elie.ucl.ac.be/CCI/viewer/ Accessed: 2/20/2020 (2018).FAO. Global Ecological Zones for FAO Forest Reporting: 2010 Update. 42 (2012).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 4302-4315, doi:10.1002/joc.5086 (2017).Harris, N. L., Goldman, E. D. & Gibbs, S. Spatial Database of Planted Trees (SDPT Version 1.0). Accessed: 5/16/2020 (World Resources Institute, 2019).ISRIC, SoilGrids250m 2.0 – WRB classes and probabilities, https://doi.org/10.17027/isric-soilgrids.c4dc161c-d62d-11ea-a1a3-292680b15169, Accessed: 9/22/2020. (2020).ISRIC, SoilGrids250m 2.0 - Soil organic stock (0-30cm, t/ha). https://doi.org/10.17027/isric-soilgrids.c4dc161c-d62d-11ea-a1a3-292680b15169, Accessed: 6/5/2020. (2020).McOwen, C., et al. A global map of saltmarshes (v. 6.0). Biodiversity Data Journal 5: e11764. DOI: https://bdj.pensoft.net/articles.php?id=11764 Accessed: 9/27/2018. (2018).Sanderman, J. et al. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environmental Research Letters 13, doi:10.1088/1748-9326/aabe1c (2018).Simard, M., et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12: 40–45. https://doi.org/10.1038/s41561- 018-0279-1 (2019).Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data https://doi.org/10.1038/s41597-020-0444-4 (2020).UNEP-WCMC, Short FT. Global Distribution of Seagrasses (version 6). Sixth update to the data layer used in Green and Short (2003), superseding version 5. Cambridge (UK): UN Environment World Conservation Monitoring Centre. (2018).Xu, J.R., Morris, P. J., Liu, J.G., & Holden, J. PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134-140 (2018).

This custom web map was generated from Wayback layers selected in the World Imagery Wayback app. Wayback imagery is a digital archive of the World Imagery basemap, enabling users to access different versions of World Imagery captured over the years. Each Wayback layer in this web map represents World Imagery as it existed on the date specified.

Irrecoverable carbon in ecosystems is defined by three criteria: (1) it can be influenced by direct and local human action (‘manageability’), (2) it is potentially vulnerable to loss during land-use conversion (‘vulnerability’) and (3) if lost, it could not be recovered within a specified timeframe (‘recoverability’). Here, we consider recoverability over 30 yr given the IPCC assessment that global emissions must reach net-zero by 2050 to limit global warming to <1.5 °C above pre-industrial levels.To create the irrecoverable carbon map, we:(1) Define relevant ecosystems that meet criteria 1, manageability.(2) Create a ‘total manageable carbon’ map for terrestrial and coastal ecosystems. This includes aboveground biomass carbon (AGC), belowground biomass carbon (BGC) and soil organic carbon (SOC) stocks. (3) Create a ‘vulnerable carbon’ map that considers the portion of biomass carbon (AGC + BGC) and SOC, respectively, that would be released in a typical land-use conversion. We used the most common drivers of recent destruction/loss in each major ecosystem.(4) Determine the amount of lost carbon that could be recovered within 30 yr following a conversion, assuming land abandonment and natural regeneration. Recoverability is based on biomass and SOC sequestration rates by ecosystem.(5) Subtract ‘recoverable carbon’ from the ‘vulnerable carbon’ map. The balance is the ‘irrecoverable carbon’ map.SourcesBunting, P., et al. The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10: 1669. doi:10.3390/rs1010669 Accessed: 6/13/2020 (2018).

ESA. Annual Land Cover maps (1992-2018) 300m Available from: https://maps.elie.ucl.ac.be/CCI/viewer/ Accessed: 2/20/2020 (2018).

FAO. Global Ecological Zones for FAO Forest Reporting: 2010 Update. 42 (2012).

Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 4302-4315, doi:10.1002/joc.5086 (2017).

Harris, N. L., Goldman, E. D. & Gibbs, S. Spatial Database of Planted Trees (SDPT Version 1.0). Accessed: 5/16/2020 (World Resources Institute, 2019).

ISRIC, SoilGrids250m 2.0 – WRB classes and probabilities, https://doi.org/10.17027/isric-soilgrids.c4dc161c-d62d-11ea-a1a3-292680b15169, Accessed: 9/22/2020. (2020).

ISRIC, SoilGrids250m 2.0 - Soil organic stock (0-30cm, t/ha). https://doi.org/10.17027/isric-soilgrids.c4dc161c-d62d-11ea-a1a3-292680b15169, Accessed: 6/5/2020. (2020).

McOwen, C., et al. A global map of saltmarshes (v. 6.0). Biodiversity Data Journal 5: e11764. DOI: https://bdj.pensoft.net/articles.php?id=11764 Accessed: 9/27/2018. (2018).

Sanderman, J. et al. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environmental Research Letters 13, doi:10.1088/1748-9326/aabe1c (2018).

UNEP-WCMC, Short FT. Global Distribution of Seagrasses (version 6). Sixth update to the data layer used in Green and Short (2003), superseding version 5. Cambridge (UK): UN Environment World Conservation Monitoring Centre. (2018).

Xu, J.R., Morris, P. J., Liu, J.G., & Holden, J. PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134-140 (2018).

OverviewThis carbon removals layer is part of the forest carbon flux model described in Harris et al. (2021). This paper introduces a geospatial monitoring framework for estimating global forest carbon fluxes which can assist a variety of actors and organizations with tracking greenhouse gas fluxes from forests and in decreasing emissions or increasing removals by forests. Forest carbon removals from the atmosphere (sequestration) by forest sinks represent the cumulative carbon captured (megagrams CO2/ha) by the growth of established and newly regrowing forests during the model period between 2001-2023. Removals include accumulation of carbon in both aboveground and belowground live tree biomass. Following IPCC Tier 1 assumptions for forests remaining forests, removals by dead wood, litter, and soil carbon pools are assumed to be zero. In each pixel, carbon removals are calculated following IPCC Guidelines for national greenhouse gas inventories where forests existed in 2000 or were established between 2000 and 2020 according to Potapov et al. 2022. Atmospheric carbon removed in each pixel is based on maps of forest type (e.g., mangrove, plantation), ecozone (e.g., humid Neotropics), forest age (e.g., primary, old secondary), and number of years of carbon removal. This layer reflects the cumulative removals during the model period (2001-2023) and must be divided by 23 to obtain an annual average during the model duration; removal rates cannot be assigned to individual years of the model. All input layers were resampled to a common resolution of 0.00025 x 0.00025 degrees each to match Hansen et al. (2013). Each year, the tree cover loss, drivers of tree cover loss, and burned area are updated. In 2023 and 2024, a few model input data sets and constants were changed as well, as described below. Please refer to this blog post for more information. The source of the ratio between belowground biomass carbon and aboveground biomass carbon. Previously used one global constant; now uses map from Huang et al. 2021 The years of tree cover gain. Previously used 2000-2012; now uses 2000-2020 from Potapov et al. 2022. The source of fire data. Previously used MODIS burned area; now uses tree cover loss from fires from Tyukavina et al. 2022. The source of peat maps. New tropical data sets have been included and the data set above 40 degrees north has been changed. Global warming potential (GWP) constants for CH4 and N2O. Previously used GWPs from IPCC Fifth Assessment Report; now uses GWPs from IPCC Sixth Assessment Report. Removal factors for older (>20 years) secondary temperate forests and their associated uncertainties. Previously used removal factors published in Table 4.9 of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; now uses corrected removal factors and uncertainties from the 4th Corrigenda to the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Planted tree extent and removal factors. Previously used Spatial Database of Planted Trees (SDPT) Version 1.0; now uses SDPT Version 2.0 and associated removal factors. Removals are available for download in two different area units over the model duration: 1) megagrams of CO2 removed/ha, and 2) megagrams of CO2 removed/pixel. The first is appropriate for visualizing (mapping) removals because it represents the density of removals per hectare. The second is appropriate for calculating the removals in an area of interest (AOI) because the values of the pixels in the AOI can be summed to obtain the total removals for that area. The values in the latter were calculated by adjusting the removals per hectare by the size of each pixel, which varies by latitude. When estimating removals occurring over a defined number of years between 2001 and 2023 to compare to emissions, divide total carbon removals by the model duration and then multiply by the number of years in the period of interest. Both datasets only include pixels within forests, as defined in the methods of Harris et al. (2021) and updated with tree cover gain through 2020. Related Open Data Portal layers: Forest Carbon Emissions, Net Forest Carbon FluxGoogle Earth Engine asset and visualization scriptResolution: 30 x 30mGeographic Coverage: GlobalFrequency of Updates: AnnualDate of Content: 2001-2023CautionsData are the product of modeling and thus have an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. Values are applicable to forest areas (canopy cover >30 percent and >5 m height or areas with tree cover gain). See Harris et al. (2021) for further information on the forest definition used in the analysis. Carbon removals reflect the total removals over the model period of 2001-2023, not an annual time series from which a trend can be derived. Thus, values must be divided by 23 to calculate average annual removals. Uncertainty is higher in gross removals than emissions, particularly driven by uncertainty in removal factors. Carbon removals reflect a gross estimate, i.e., carbon emissions from previous or subsequent loss of tree cover are not included. Instead, gross carbon emissions are accounted for in the companion forest carbon emissions layer. Removals data contain temporal inconsistencies because tree cover gain represents a cumulative total from 2000-2020, rather than annual gains as estimated through 2023. Forest carbon removals reflect those occurring only within forest ecosystems and do not reflect carbon stock increases in the harvested wood products (HWP) pool. Large jumps in removals along some boundaries are due to the use of ecozone-specific removal factors. The changes in removals occur at ecozone boundaries, where different removal factors are applied on each side. This dataset has been updated since its original publication. See Overview for more information.

The Murray Global Tidal Wetland Change Dataset contains maps of the global extent of tidal wetlands and their change. The maps were developed from a three stage classification that sought to (i) estimate the global distribution of tidal wetlands (defined as either tidal marsh, tidal flat or mangrove ecosystems), (ii) detect their change over the study period, and (iii) estimate the ecosystem type and timing of tidal wetland change events. The dataset was produced by combining observations from 1,166,385 satellite images acquired by Landsat 5 to 8 with environmental data of variables known to influence the distributions of each ecosystem type, including temperature, slope, and elevation. The image contains bands for a tidal wetland extent product (random forest probability of tidal wetland occurrence) for the start and end time-steps of the study period and a tidal wetland change product over the full study period (loss and gain of tidal wetlands). Please see the usage notes on the project website. A full description of the methods, validation, and limitations of the data produced by this software is available in the associated scientific paper. See also UQ/murray/Intertidal/v1_1/global_intertidal for global maps of the distribution of tidal flat ecosystems.

This layer shows the extent of mangrove cover of Pate Kiunga seascape in 2005. The cover map was developed by CORDIO EA through Landsat Image classification under the International Centre for Research in Agroforestry (ICRAF)-led project funded by the Inter-Governmental Authority on Development (IGAD). The project aimed to link the use and benefit of these natural resources to the local people, as well as better understand the drivers and pressures of biodiversity loss to improve manag...

Sea level rise is an ever-looming climate hazard facing the coastal communities of the United States. Communities big and small need easy-to-use tools for climate resilience planning and mitigation strategies. This layer shows coastal census tracts that are at risk from sea level rise and also are capable of growing mangrove and have a high percentage of undeveloped area. Mangroves are proved to stabilize shoreline while also reducing the damage of high energy wind and storm swells during severe storm events. This layer should be used to identify and prioritize where to plant mangrove. © 2024 Adobe Stock. All rights reserved.This layer shows the results of a composite index built with the following attributes: Percent of the Tract Area Predicted to be Below Sea Level by 2050 (%)Length of Mangrove Habitat (meters)Percent of the Tract Area that is Undeveloped (%) - Expressed as a percentage of the tract with landcover classified as anything other than “built-up”.These attribute links take you to the original data sources. Preprocessing was needed to prepare many of these inputs for inclusion in our index. The links are provided for reference only.This layer is one of six in a series developed to support local climate resilience planning. Intended as planning tools for policy makers, climate resilience planners, and community members, these layers highlight areas of the community that are most likely to benefit from the resilience intervention it supports. Each layer focuses on one specific sea level rise intervention that is intended to help mitigate against the climate hazard.For more information about how Esri enriched the census tracts with exposure, demographic, and environmental data to create composite indices called intervention indices, please read this technical reference.Each intervention index will help with planning and prioritizing mitigations against a suite of climate change hazards. A climate resilience intervention index can be used to rank census tracts in a particular area as benefitting more or less from that particular intervention.

Each intervention index in this set is envisioned to help with planning and prioritizing mitigations against sea level rise.Layers in the sea level rise hazard series include,Where Would Planting Mangrove Trees Mitigate Coastal Flooding?Where Would Planting Marsh Grass Mitigate Coastal Flooding?Where Would a Buddy Program Improve Coastal Flooding Preparedness?Where Would Coastal Flooding Awareness Increase Resilience?Low-income Households At Risk From Sea Level RiseDisadvantaged Communities At Risk From Sea Level RiseDid you know you can build your own climate resilience index or use ours and customize it? The Customize a climate resilience index Tutorial provides more information on the index and also walks you through steps for taking our index and customizing it to your needs so you can create intervention maps better suited to your location and sourced from your own higher resolution data. For more information about how Esri enriched the census tracts with exposure, demographic, and environmental data to create composite indices called intervention indices, please read this [TODO: Add URL to white paper].This feature layer was created from the Climate Resilience Planning Census Tracts hosted feature layer view and is one of 18 similar intervention layers, all of which can be found in ArcGIS Living Atlas of the World.