Retrieving forest biomass through integration of CASI and LiDAR data

Abstract

To increase understanding of forest carbon cycles and stocks, estimates of total and component (e.g. leaf, branch and trunk) biomass at a range of scales are desirable. Focusing on mixed species forests in central south‐east Queensland, two different approaches to the retrieval of biomass from small footprint Light Detection and Ranging (LiDAR) and Compact Airborne Spectrographic Imager (CASI) hyperspectral data were developed and compared. In the first, stems were located using a LiDAR crown openness index, and each was associated with crowns delineated and identified to species using CASI data. The component biomass for individual trees was then estimated using LiDAR‐derived height and stem diameter as input to species‐specific allometric equations. When summed to give total above‐ground biomass (AGB) and aggregated to the plot level, these estimates showed a reasonable correspondence with ground (plot‐based) estimates (r ² = 0.56, RSE = 25.3 Mg ha⁻¹, n = 21) given the complex forest being assessed. In the second approach, a Jackknife linear regression utilizing six LiDAR strata heights and crown cover at the plot‐scale produced more robust estimates of AGB that showed a closer correspondence with plot‐scale ground data (r ² = 0.90, RSE = 11.8 Mg ha⁻¹, n = 31). AGB aggregated from the tree‐level and Jackknife regression plot‐based AGB estimates (for 270 plots—each of 0.25 ha) compared well for more mature homogeneous and open forests. However, at the tree level, AGB was overestimated in taller forests dominated by trees with large spreading crowns, and underestimated AGB where an understorey with a high density of stems occurred. The study demonstrated options for quantifying component biomass and AGB through integration of LiDAR and CASI data but highlighted the requirement for methods that give improved estimation of tree density (by size class distributions) and species occurrence in complex forests.

Acknowledgements

The authors would like to thank the Australian Research Council, Queensland Department of Natural Resources and Mines (QDNR&M), Queensland Herbarium, Bureau of Rural Sciences (BRS), Australian Greenhouse Office, Cooperative Research Centre (CRC) for Greenhouse Accounting, and Queensland Department of Primary Industries (QDPI) Tropical Beef Centre. Ursula Benz and Definiens AG are especially thanked for providing the facilities and software for Peter Bunting, who was funded by an EU AMPER Training Network Scholarship and by the Institute of Geography and Earth Sciences and Computing Science Department, University of Wales Aberystwyth. Ball Aims are thanked for acquiring the CASI data.