Dataset Description
The Global Mangrove Watch (GMW) dataset currently provides estimates of the global extent and changes of mangrove forests for every year between 1985 and 2025. Geospatial (map) data and numerical area estimates are available, from which national or regional area statistics can be extracted. Mangrove losses and gains between the respective years are provided as separate data layers.
Usage
Users should keep in mind that the GMW dataset is a global-scale dataset, generated with a single methodology applied over all regions. As such, the accuracy may vary between locations and with scale. The dataset can therefore be expected to be most useful for countries that do not have their own mangrove monitoring systems. With the caveats in mind, the GMW dataset can be used as activity data proxy by countries considering inclusion of Other Land Uses (Wetlands) in their Nationally Determined Contributions (NDCs) for reporting to the UNFCCC. The dataset can furthermore support national reporting on Sustainable Development Goals, specifically Task 6.6 (Protect and restore water-related ecosystems) Indicator 6.6.1 (Change in the extent of water-related ecosystems over time). To this end, GMW has been selected by the United Nations Environment Programme (UNEP) as the official 'default' mangrove dataset for SDG 6.6.1 reporting for countries that lack their own information about mangrove resources required for improved management, protection and restoration.
Methodology
The Global Mangrove Watch v4.1 time-series layers were generated using the full Landsat and Sentinel-2 time-series and available JAXA L-band SAR data (JERS-1 SAR, ALOS PALSAR, ALOS-2 PALSAR-2).
The methodology is built on knowledge gained from developing the previous GMW layers (GMW v2.0, GMW v3.0, and GMW v4.0). Building on the GMW v4.0 10 m baseline for defining the reference data, the GMW v4.1 time-series used multiple baseline years (1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025), with classifications created using an XGBoost classifier and composites of Landsat and Sentinel-2 imagery. Areas of missing data due to the availability of Landsat imagery were copied through from the nearest year. The baselines were visually checked, and for 1985, 1990, and 1995, areas of poor quality were copied using the base quality and the earliest available layer. Subsequently, these baselines were filtered into a time-series using a Markov chain with probabilities defined for the transitions.
To create the annual time-series, every Landsat and Sentinel-2 scene with less than 70% cloud cover was classified using 130 random forest classifiers in Google Earth Engine and summarised on an annual basis with the number of valid pixel observations and the number of times the pixel was classified as mangroves. These classifications were combined with thresholded JAXA L-band SAR data and the baseline classifications using a likelihood ratio methodology to define a probability of a pixel being mangroves for each year in the timeseries. The subsequent time-series was then filtered using Bayesian updating and Markov chains, with the output probabilities thresholded to define the final mangrove extent maps. Throughout, extensive visual quality assurance (QA) checks were integrated, and the layers were updated based on the feedback.
Uncertainty and Accuracy
The accuracy assessment for the mangrove extent estimated a global F1-Score of 0.9220 - 0.9330, with an omission error of 5.65% - 7.13% and a commission error of 7.10% - 9.13%. The accuracy assessment was based on 32,569 reference points distributed across 120 geographic sites covering the full range of years within the analysis. The accuracy statistics therefore represent the average for the product and will vary locally in both time and geography.
It should be noted that the time-series are dependent on the availability of satellite imagery, and before around 2000, the availability of imagery can be significantly reduced in certain regions. This problem increases the further back in time you go. To provide full coverage on an annual basis throughout the timeseries, this has required some infilling of data from other years, particularly in the 1990s and 1980s.
Product coverage, JAXA / Global Mangrove Watch
Technical Characteristics
Spatial resolution: 30m
Geographical coverage: Global (all countries with mangroves)
Temporal coverage:1985-2025Annual data 2015-2020.
Update frequency: Annual. Next release scheduled for mid-2027.
Format: GIS shapefile (.shp), geocoded raster (Geotiff), numerical statistics (.xslx)
Data Policy: Public open (Creative Commons CC BY 4.0)
Associated Guidance and User Manual
Global Mangrove Watch Platform: www.globalmangrovewatch.org
Data download:
JAXA (raster data)
Zenodo Data Repository (all v4.1 datasets)
Points of contact for queries
Dr. Ake Rosenqvist
GMW and K&C Science Coordinator
solo Earth Observation (soloEO)
Tokyo 104-0054, Japan
Email: ake.rosenqvist@soloEO.com
Dr. Pete Bunting
GMW technical lead
Institute of Geography and Earth Sciences
Aberystwyth University
SY23 3DB Wales, UK
Email: pfb@aber.ac.uk
