Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling

Author:

Ma Lei1ORCID,Hurtt George1,Tang Hao2,Lamb Rachel3,Lister Andrew4,Chini Louise5,Dubayah Ralph1,Armston John5,Campbell Elliott6,Duncanson Laura1,Healey Sean7,O'Neil‐Dunne Jarlath8,Ott Lesley9,Poulter Benjamin10ORCID,Shen Quan1ORCID

Affiliation:

1. Department of Geographical Sciences University of Maryland at College Park College Park Maryland USA

2. Department of Geography National University of Singapore Singapore

3. Geographical Sciences, Maryland Department of the Environment University of Maryland at College Park College Park Maryland USA

4. United States Department of Agriculture Forest Service Northern Research Station Newtown Square Pennsylvania USA

5. Geographical Sciences University of Maryland at College Park College Park Maryland USA

6. Maryland Department of Natural Resources Annapolis Maryland USA

7. USDA Forest Service Rocky Mountain Research Station Fort Collins Colorado USA

8. Rubenstein School of Environment and Natural Resources University of Vermont Burlington Vermont USA

9. NASA Goddard Space Flight Center, Global Modeling and Assimilation Office Greenbelt Maryland USA

10. NASA Goddard Space Flight Center Greenbelt Maryland USA

Abstract

AbstractForest 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.

Publisher

Wiley

Subject

General Environmental Science,Ecology,Environmental Chemistry,Global and Planetary Change

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