The accurate estimation of carbon stored in a tree is essential to accounting for the carbon emissions due to deforestation and degradation. Airborne LiDAR (Light Detection and Ranging) has been successful in estimating aboveground carbon density (ACD) by correlating airborne metrics, such as canopy height, to field-estimated biomass. This latter step is reliant on field allometry which is applied to forest inventory quantities, such as stem diameter and height, to predict the biomass of a given tree stem. Constructing such allometry is expensive, time consuming, and requires destructive sampling. Consequently, the sample sizes used to construct such allometry are often small, and the largest tree sampled is often much smaller than the largest in the forest population. The uncertainty resulting from these sampling errors can lead to severe biases when the allometry is applied to stems larger than those harvested to construct the allometry, which is then subsequently propagated to airborne ACD estimates.
Results/Conclusions
This study eliminates the “middleman” of field allometry by directly measuring, or harvesting, tree biomass within the extent of airborne LiDAR. This enables comparisons of field and airborne ACD estimates, and also enables creation of new airborne algorithms to estimate biomass at the scale of individual trees. A field campaign was conducted at Pompey Silica Mine 5km outside Kruger National Park, South Africa, in Mar–Aug 2010 to harvest and weigh tree mass. Since harvesting of trees is not possible within KNP, this was a unique opportunity to fell trees already scheduled to be cleared for mining operations. The area was first flown by the Carnegie Airborne Observatory in early May, prior to harvest, to enable correlation of LiDAR-measured tree height and crown diameter to harvested tree mass. Results include over 4,000 harvested stems and 13 species-specific biomass equations, including seven Kruger woody species previously without allometry. We found existing biomass stem allometry over-estimates ACD in the field, whereas airborne estimates based on harvest data avoid this bias while maintaining similar precision to field-based estimates. Lastly, a new airborne algorithm estimating biomass at the tree-level reduced error from tree canopies “leaning” into field plots but whose stems are outside plot boundaries. These advances pave the way to better understanding of savanna and forest carbon density at landscape and regional scales.