Integration of GLAS Height Product and MODIS Observations for Biomass Mapping and Validation in Central Africa

Alessandro Baccini, The Woods Hole Research Center, abaccini@whrc.org (Presenting)
Scott J Goetz, The Woods Hole Research Center, sgoetz@whrc.org
Mindy Sun, The Woods Hole Research Center, msun@whrc.org
Nadine Laporte, The Woods Hole Research Center, nlaporte@whrc.org

Strategies to reduce emissions from deforestation and forest degradation (REDD) require not just information on land use and land cover change, but also the amount of carbon that is stored as above-ground biomass (AGB) in the areas subject to change or disturbance. Observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used in combination with a large data set of field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000 - 2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show the model explained 82% of the variance in AGB, with root mean square error (RMSE) of 50.5 Mg/ha for a range of biomass between 0 and

454 Mg/ha. Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate that the model successfully stratified the landscape across the full range of biomass classes. The results also showed a strong positive correlation between the GLAS height of median energy and predicted AGB.