Mangrove loss extent has been defined over the last two decades, but the underlying drivers of the losses have not been mapped or quantified at a resolution high enough to inform local level policy. Through mapping global mangrove loss drivers at 30-meter resolution, we enable conservation and restoration activities to account for past anthropogenic and natural threats in the region to insure long-term conservation success.
This dataset includes three global, 30-meter resolution layers:
Loss extent in three epochs: 2000-2005, 2005-2010, and 2010-2016
Land cover change in each loss pixel: mangrove to wet soil, dry soil, or water
Driver of loss in each loss pixel: commodities, settlement, non-productive conversion, erosion, and extreme weather events
Loss driver classes were derived from a series of decision trees based on a global Random Forest-based analysis of land cover change.
Commodities: Conversion of mangroves to agriculture or aquaculture areas
Settlement: Conversion of mangroves to urban areas
Non-Productive Conversion: Conversion of mangroves to unused land as a result of human influences (e.g. through clear-cutting or human-driven hydrologic disturbance)
Erosion: Physical removal of shoreline as a result of ocean processes (e.g. sea level rise, waves, storms)
Extreme Weather Events: Conversion of mangroves to unused land as a result of storm events (e.g. hurricanes, drought)
We found an overall map accuracy of 82% using recent best practices in area uncertainty evaluation. For more information on our accuracy assessment technique and results, please view the Supplementary Information of our manuscript.
We plan to update this dataset with contemporary loss extent and driver data within the next year. We also plan to expand the analysis to include the drivers of global mangrove gains to gain a better understanding of the net state of mangroves across the globe.
This dataset and all associated code fall under a Creative Commons Attribution 4.0 (CC BY 4.0) International License , and can be used for any purpose given the inclusion of a citation of the paper and acknowledgement of the data source.
We're in the process of preparing several starter scripts for dataset analysis within Google Earth Engine
Visualize data in Earth Engine
Export data from Earth Engine
Visit the Download page to view all dataset download options.
Manuscript citation:
Goldberg, L., Lagomasino, D., Thomas, N. and Fatoyinbo, T. (2020), Global declines in human‐driven mangrove loss. Glob Change Biol. Accepted Author Manuscript. doi:10.1111/gcb.15275
Visit mangrovescience.org and coastalstudiesinstitute.org for more information about related regional-to-global mangrove and coastal ecosystem analyses.
Please contact Liza Goldberg, the corresponding author of the study.