Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling
Lei Ma, University of Maryland, lma6@umd.edu (Presenter)
George Hurtt, University of Maryland, gchurtt@umd.edu
Hao Tang, University of Maryland, htang@umd.edu
Rachel L Lamb, Maryland Department of Environment (DEP), rachel.lamb@maryland.gov
Andrew J. Lister, USDA Forest Service, andrew.lister@usda.gov
Louise Parsons Chini, University of Maryland, lchini@umd.edu
Ralph Dubayah, University of Maryland, dubayah@umd.edu
John Armston, University of Maryland, armston@umd.edu
Elliott Campbell, Maryland Department of Natural Resources, elliott.campbell@maryland.gov
Laura Duncanson, University of Maryland, lduncans@umd.edu
Sean P Healey, USDA Forest Service, sean.healey@usda.gov
Jarlath O'Neil-Dunne, University of Vermont, jarlath.oneil-dunne@uvm.edu
Lesley Ott, NASA GSFC GMAO, lesley.e.ott@nasa.gov
Benjamin Poulter, NASA GSFC, benjamin.poulter@nasa.gov
Quan Shen, University of Maryland, qshen@umd.edu
Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing- based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process- based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process- based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.
Poster: Poster_Ma__102_35.pdf
Associated Project(s):
Poster Location ID: 3-19
Presentation Type: Poster
Session: Poster Session 3
Session Date: Thu (May 11) 3:00-5:00 PM
CCE Program: TE