title: Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas.
abstract: Tree biomass is an integrated measure of net growth and is critical for understanding, monitoring, and modeling ecosystem functions. Despite the importance of accurately measuring tree biomass, several fundamental barriers preclude direct measurement at large spatial scales, including the facts that trees must be felled to be weighed and that even modestly sized trees are challenging to maneuver once felled. Allometric methods allow for estimation of tree mass using structural characteristics, such as trunk diameter. Savanna trees present additional challenges, including limited available allometry and a prevalence of multiple stems per individual. Here we collected airborne lidar data over a semiarid savanna adjacent to the Kruger National Park, South Africa, and then harvested and weighed woody plant biomass at the plot scale to provide a standard against which field and airborne estimation methods could be compared. For an existing airborne lidar method, we found that half of the total error was due to averaging canopy height at the plot scale. This error was eliminated by instead measuring maximum height and crown area of individual trees from lidar data using an object-based method to identify individual tree crowns and estimate their biomass. The best object-based model approached the accuracy of field allometry at both the tree and plot levels, and it more than doubled the accuracy compared to existing airborne methods (17% vs. 44% deviation from harvested biomass). Allometric error accounted for less than one-third of the total residual error in airborne biomass estimates at the plot scale when using allometry with low bias. Airborne methods also gave more accurate predictions at the plot level than did field methods based on diameter-only allometry. These results provide a novel comparison of field and airborne biomass estimates using harvested plots and advance the role of lidar remote sensing in savanna ecosystems.
authors: Department of Global Ecology, Carnegie Institution for Science, Stanford, California 94305, USA. colganm@stanford.edu
authors: Matthew S
authors: MS
authors: Colgan
authors: Gregory P
authors: GP
authors: Asner
authors: Tony
authors: T
authors: Swemmer
mesh_terms: D018533:Biomass; D017753:Ecosystem; D004784:Environmental Monitoring; D016468:Forestry / Q000379:methods*; D008954:Models, Biological; D013019:South Africa; D014197:Trees / Q000502:physiology*