Creation of a high spatiotemporal resolution global database of continuous mangrove forest cover for the 21st Century (CGMFC-21) release_uw5ya7bl6bab5g4z5uxdzvw3lu

by Stuart Hamilton, Daniel Casey

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2014  

Abstract

The goal of this research is to provide high resolution local, regional, national and global estimates of annual mangrove forest area from 2000 through to 2012. To achieve this we synthesize the Global Forest Change database, the Terrestrial Ecosystems of the World database, and the Mangrove Forests of the World database to extract mangrove forest cover at high spatial and temporal resolutions. We then use the new database to monitor mangrove cover at the global, national and protected area scales. Countries showing relatively high amounts of mangrove loss include Myanmar, Malaysia, Cambodia, Indonesia, and Guatemala. Indonesia remains by far the largest mangrove-holding nation, containing between 26 percent and 29 percent of the global mangrove inventory with a deforestation rate of between 0.26 percent and 0.66 percent annually. Global mangrove deforestation continues but at a much reduced rate of between 0.16 percent and 0.39 percent annually. Southeast Asia is a region of concern with mangrove deforestation rates between 3.58 percent and 8.08 percent during the analysis period, this in a region containing half of the entire global mangrove forest inventory. The global mangrove deforestation pattern from 2000 to 2012 is one of decreasing rates of deforestation, with many nations essentially stable, with the exception of the largest mangrove-holding region of Southeast Asia. We provide a standardized global spatial dataset that monitors mangrove deforestation globally at high spatiotemporal resolutions, covering 99 percent of all mangrove forests. These data can be used to drive the mangrove research agenda particularly as it pertains to improved monitoring of mangrove carbon stocks and the establishment of baseline local mangrove forest inventories required for payment for ecosystem service initiatives.
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Date   2014-12-01
Version   v1
Language   en ?
arXiv  1412.0722v1
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