Combining lidar/radar fusion, allometric scaling theory, and ecosystem modeling for improved estimation of forest biomass, structure and dynamics

Laura Duncanson, University of Maryland, lduncans@umd.edu (Presenter)
Ralph Dubayah, University of Maryland, dubayah@umd.edu

Accurately estimating aboveground biomass (AGBM) and carbon flux is critical to understanding and mitigating global climatic change. Lidar and radar technologies have been increasingly used for AGBM and canopy height estimation, however non-static forest properties such as age and growth rate are difficult to estimate using remote sensing alone. Ecosystem models are key tools for estimating carbon flux, however may require forest structural inputs that are currently unavailable. Allometric relationships provide a link between forest structure and dynamics, and thus can be applied to fill the gap between remote sensing data and the input requirements for ecosystem modeling. This research aims to link allometric scaling relationshipos with lidar/radar fusion to provide forest structure, age and other characteristics for improved ecosystem modeling of carbon stocks and fluxes. This research will increase our ability to address questions related to forest functionality and carbon dynamics at unprecedented scales.

Presentation Type:  Poster

Session:  Other   (Tue 11:30 AM)

Associated Project(s): 

• Dubayah, Ralph: Combining lidar/radar fusion, allometric scaling theory, and ecosystem modeling for improved estimation of forest biomass, structure and dynamics ...details

Poster Location ID: 173