A validated and accurate method for quantifying and extrapolating mangrove above‐ground biomass using lidar data

Publication Name

Remote Sensing


LiDAR data and derived canopy height models can provide useful information about mangrove tree heights that assist with quantifying mangrove above‐ground biomass. This study presents a validated method for quantifying mangrove heights using LiDAR data and calibrating this against plot‐based estimates of above‐ground biomass. This approach was initially validated for the mangroves of Darwin Harbour, in Northern Australia, which are structurally complex and have high species diversity. Established relationships were then extrapolated to the nearby West Alligator River, which provided the opportunity to quantify biomass at a remote location where intensive fieldwork was limited. Relationships between LiDAR‐derived mangrove heights and mean tree height per plot were highly robust for Ceriops tagal, Rhizophora stylosa and Sonneratia alba (r2 = 0.84–0.94, RMSE = 0.03– 0.91 m; RMSE% = 0.07%–11.27%), and validated well against an independent dataset. Additionally, relationships between the derived canopy height model and field‐based estimates of above‐ground biomass were also robust and validated (r2 = 0.73–0.90, RMSE = 141.4 kg–1098.58 kg, RMSE% of 22.94– 39.31%). Species‐specific estimates of tree density per plot were applied in order to align biomass of individual trees with the resolution of the canopy height model. The total above‐ground biomass at Darwin Harbour was estimated at 120 t ha−1 and comparisons with prior estimates of mangrove above-ground biomass confirmed the accuracy of this assessment. To establish whether accurate and validated relationships could be extrapolated elsewhere, the established relationships were applied to a LiDAR‐derived canopy height model at nearby West Alligator River. Above‐ground biomass derived from extrapolated relationships was estimated at 206 t ha−1, which compared well with prior biomass estimates, confirming that this approach can be extrapolated to remote locations, providing the mangrove forests are biogeographically similar. The validated method presented in this study can be used for reporting mangrove carbon storage under national obligations, and is useful for quantifying carbon within various markets.

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Article Number


Funding Sponsor

University of Wollongong



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