, Volume 168, Issue 4, pp 1147–1160

A universal airborne LiDAR approach for tropical forest carbon mapping


    • Department of Global EcologyCarnegie Institution for Science
  • Joseph Mascaro
    • Department of Global EcologyCarnegie Institution for Science
    • Smithsonian Tropical Research Institute
  • Helene C. Muller-Landau
    • Smithsonian Tropical Research Institute
  • Ghislain Vieilledent
    • CIRAD, UR105 Forest Ecosystem Goods and Services
    • DRP Forêt et Biodiversité
  • Romuald Vaudry
    • GoodPlanet Foundation
  • Maminiaina Rasamoelina
    • World Wide Fund for Nature
  • Jefferson S. Hall
    • Smithsonian Tropical Research Institute
  • Michiel van Breugel
    • Smithsonian Tropical Research Institute
Ecosystem ecology - Original Paper

DOI: 10.1007/s00442-011-2165-z

Cite this article as:
Asner, G.P., Mascaro, J., Muller-Landau, H.C. et al. Oecologia (2012) 168: 1147. doi:10.1007/s00442-011-2165-z


Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height–diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r2 = 0.80, RMSE = 27.6 Mg C ha−1). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy.


BiomassCarbon emissionsForest carbonLight detection and rangingRain forestREDDRemote sensing

Supplementary material

442_2011_2165_MOESM1_ESM.docx (147 kb)
Supplementary material 1 (DOCX 146 kb)

Copyright information

© Springer-Verlag 2011