Other Land Use (OLU)    |    Wetlands

Mangroves


This dataset shows the global extent of mangroves for eleven annual epochs between 1996 and 2020, derived using a combination of optical and radar satellite data.


The dataset is supplied by Global Mangrove Watch, a collaboration between Aberystwyth University, solo Earth Observation, Wetlands International, The Nature Conservancy, and the Japan Aerospace Exploration Agency (JAXA).

Dataset Description

The Global Mangrove Watch (GMW) dataset provides estimates of the global extent and changes of mangrove forests for eleven annual epochs between 1996 and 2020. Annual updates from 2021 and onwards are foreseen as is filling temporal gaps in the historical annual time series. 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 of the map 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 mangrove maps were derived in two steps:

  • Generation of a baseline map of global mangrove extent for the year 2010, and,
  • Detection of changes between the 2010 baseline and each of the other years, respectively.
The 2010 baseline map was derived through a Random Forest classification of a combination of radar and optical satellite data. The classification was confined within a mangrove habitat mask, which defines regions where mangrove ecosystems might be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation and distance from ocean water. Training for the habitat mask and classification of the 2010 mangrove mask was based on randomly sampling some 38 million points using historical mangrove maps (for the year 2000) of Giri et al. (2011) and Spalding et al. (2010) and the water occurrence dataset by Pekel et al. (2017).

The classification accuracy of the 2010 baseline dataset was assessed with approximately 53,800 randomly sampled points across 20 randomly selected regions. Overall accuracy was 95.25%, while User’s and Producer’s accuracies for the mangrove class were estimated at 97.5% and 94.0%, respectively.

The maps for the other years were derived by detection and classification of losses (with a decrease in L-band radar backscatter) and gains (where backscatter increased) between the 2010 ALOS PALSAR data and JERS-1 SAR (1996), ALOS PALSAR (2007, 2008, 2009) and ALOS-2 PALSAR-2 (2015, 2016) data. The change pixels for each year in question were then added or removed from the 2010 baseline raster mask, which was buffered to allow detection of mangrove gains also immediately outside of the mask. Using this approach, new yearly extent maps were produced.

Uncertainty and Accuracy

Classification accuracies for the changes were assessed with over 45,000 points, with an overall accuracy of 75.0%. The User’s accuracies for the loss, gain and no-change classes respectively were estimated at 66.5%, 73.1% and 83.5%. The corresponding Producer’s accuracies for the three classes were estimated as 87.5%, 73.0% and 69.0%, respectively.

Support on data usage is available to potential users.

Technical Characteristics

Spatial resolution: 25m

Geographical coverage: Global (all countries with mangroves)

Temporal coverage:
1996, 2007, 2008, 2009, 2010 (baseline year)
Annual data 2015-2020
(2017-2020 data scheduled for release in August 2021)

Update frequency: Annual from 2021

Format: GIS shapefile (.shp), geocoded raster (Geotiff), numerical statistics (.csv)

Data Policy: Public open (Creative Commons CC BY 4.0)


Associated Guidance and User Manual

Global Mangrove Watch Platform: www.globalmangrovewatch.org

Web address for dataset download:
UNEP-WCMC (vector data)
JAXA (raster data from Q4/2021)


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

Prof. Richard Lucas
GMW scientific lead
Institute of Geography and Earth Sciences
Aberystwyth University
SY23 3DB Wales, UK
Email: richard.lucas@aber.ac.uk