High Resolution Ecosystem Structure, Biomass and Blue Carbon stocks in Mangrove Ecosystems- Methods and Applications of Lidar, radar Interferometry and High Resolution imagery

Mangrove forests and other Blue Carbon ecosystems are amongst some of the most carbon dense ecosystems. Because of the carbon density in these regions, losses and gains of these ecosystems may contribute a significant global fraction of carbon emission despite only representing 3% of the global forest cover. One of the main challenges to implementing carbon mitigation projects is not only measuring carbon efficiently, effectively, and safely, but also working with local communities to help address these issues. Access to mangrove forests, in particular, can be extremely difficult as a result of the terrain and has hindered the establishment of sufficient field plots needed to accurately measure carbon on the scale necessary to relate remotely sensed measurements and field measurements within specific protocols required for REDD and other carbon trading mechanisms. In this presentation, we will review the recent results from collaborative efforts with local in-country and international partners to develop methodologies, and produce the initial remote sensing products that are necessary to implement a Monitoring, Reporting, and Verification (MRV) system for Blue Carbon ecosystems in parts of Africa and South-East Asia. Specifically, we will show how new remote sensing techniques, such as radar interferometry and stereo-photogrammetry may be incorporated into coastal blue carbon monitoring efforts. The aim of our study is to provide an openly-available framework to generate land cover, change, and 3D map products, using statistically representative samples of forest types, and above and belowground biomass. Our ultimate goals are to develop carbon storage, emissions, and sequestration estimates for mangroves and other coastal wetlands by integrating satellite/airborne imagery and in situ measurements. By incorporating these datasets we anticipate being able to estimate mangrove blue carbon storage with an error under 20% at the project-level and permit the evaluation of UNFCCC mechanisms for the mitigation of carbon emissions from coastal ecosystems.