2023-05-04T13:11:58.897415

2023-05-04T13:11:58.897417

100

Global

global

Global Mangrove Watch, 2018. 'Global Mangrove Extent (v2.0).' www.globalmangrovewatch.org. Accessed through Global Forest Watch on [date]. www.globalforestwatch.org.

Global Mangrove Extent

Global Mangrove Watch, v2.0 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](https://www.mdpi.com/2072-4292/10/10/1669). Remote Sensing, 2018, 10, 1669; doi:10.3390/rs10101669

[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)

English

This data set was generated by [Aberystwyth University](https://www.aber.ac.uk/en/dges/) and [soloEO](http://www.soloeo.com/) within the framework of the [Global Mangrove Watch (GMW) project](http://www.eorc.jaxa.jp/ALOS/en/kyoto/mangrovewatch.htm), which is a part of the Japan Aerospace Exploration Agency's (JAXA) Kyoto & Carbon Initiative and the Mangrove Capital Africa Programme coordinated by [Wetlands International](http://www.wetlands.org/) and financed by [DOB Ecology](http://dobecology.nl/). The map (v2.0) depicts the global extent of mangrove forests for the year 2010, derived by Random Forest Classification of a combination of L-band radar (ALOS PALSAR) and optical (Landsat-5, -7) satellite data. All satellite data and software used to derive the GMW mangrove maps are available in the public domain. Approximately 15,000 Landsat scenes and 1,500 ALOS PALSAR (1 x 1 degree) mosaic tiles were used to create optical and radar image composites covering the coastlines along the tropical and sub-tropical coastlines in the Americas, Africa, Asia and Oceania. The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can 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 38 million points using the mangrove masks (for the year 2000) of [Giri et al. (2011)](http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2010.00584.x/abstract) and [Spalding et al. (2010)](https://www.routledge.com/World-Atlas-of-Mangroves/Spalding-Kainuma-Collins/p/book/9781844076574) and the water occurrence layer defined by [Pekel et al. (2017)](https://www.nature.com/articles/nature20584). The data set is available for download at [http://data.unep-wcmc.org/datasets/45](http://data.unep-wcmc.org/datasets/45)

Global coverage of mangroves for select years from 1996 to 2016

The Landsat-7 scan-line error affects the classification in certain areas, resulting in striping artefacts in the data. Classification accuracy was assessed with over 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. Users should be aware that it is a global-scale dataset, generated with a single methodology applied over all regions, and as such, the accuracy of the map may vary between locations. Factors such as satellite data availability (due to clouds, cloud shadows and Landsat-7 scan-line error), mangrove species composition and level of degradation all influence the local accuracy. The mangrove seaward border is generally also more accurately defined than the landward side, where distinction between mangrove and certain terrestrial vegetation species can be unclear. Areas known to be missing in this version (v2.0) of the dataset: Bermuda (U.K.); Europa Island (France); Fiji, east of longitude 180°0E; Guam and Saipan (U.S.); Kiribati; Maldives; Peru, south of latitude S4°0; and Wallis and Futuna Islands (France).

Land Cover

Most robust global mangrove layer available

www.globalmangrovewatch.org.

bdea831e-c20e-4c7c-adb2-964cc45e6715