Remote Sensing of Mangroves and their Biomass

Mangroves provide many ecosystem services, including shoreline protection, habitat, and nutrient cycling. The need to mitigate climate change has focused attention on the ability of mangrove forests to sequester atmospheric carbon within biomass and store carbon for long periods within substrates. Traditional approaches for quantifying mangrove above-ground biomass relied upon destructive sampling and subsequent development of allometric equations. These equations related mangrove structural parameters, such as height and diameter at breast height, to their biomass and were used to estimate biomass by measuring mangrove forest structural parameters. Measuring mangrove structural parameters and developing allometric equations takes considerable effort and is limited by the degree and representativeness of fieldwork effort. Tree height (H) is a key parameter for accurately quantifying above-ground biomass (AGB) of trees, and increasing access to remotely sensed data that quantifies mangrove structural parameters may offer the ability to reduce the fieldwork effort required to quantify mangrove biomass and improve the accuracy. In particular, remotely sensed data, such as LiDAR, that allows the development of canopy height models, provides remarkably more data than can be gained from measurements of tree heights in the field. This thesis aimed to use LiDAR to accurately estimate mangrove above-ground biomass for a range of biogeographic and geomorphological settings.