B042-04
Stereo and LiDAR observations of young North American boreal forest regrowth

Wednesday, 9 December 2020: 17:42
Virtual
Christopher S R Neigh1, Paul Montesano2,3, Joseph O Sexton4, William Wagner2,5, Margaret Wooten2,5, Min Feng6, Panshi Wang7, Saurabh Channan7, Nuno Carvalhais8, Leonardo Calle9 and Benjamin Poulter10, (1)NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, United States, (2)Science Systems and Applications, Inc., Lanham, MD, United States, (3)Biospheric Sciences Laboratory, Code 618, NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)terraPulse, Inc., Gaithersburg, MD, United States, (5)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (6)terraPulse, Inc., Washington, United States, (7)terraPulse, Inc., North Potomac, MD, United States, (8)Max Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, Jena, Germany, (9)Montana State University, Bozeman, United States, (10)NASA GSFC, Biospheric Science, Greenbelt, MD, United States
Recorded Presentation
Abstract:
We are studying the age and regrowth of young boreal forests to improve understanding of Arctic/Boreal terrestrial ecosystems that may be approaching a potential tipping point of C release. At continental scales, climate change is altering vegetation productivity and C sequestration. Currently a need exists to understand environmental constraints on site-scale canopy structure and to predict impacts of environmental change on vegetation cover and C-stock/flux. Dynamic Global Vegetation Models (DGVMs) thus far have found large increases in productivity and C-flux. However, turnover rates in these models have a large source of divergence between them.

Site Index (SI) is a parameter widely used in forestry to describe the potential height-growth of trees in a particular location or 'site' at a given age. SI knowledge will reduce uncertainty of live C turnover into soil C pools by constraining woody accumulation rates. We have successfully estimated boreal forest SI by pairing Landsat estimates of forest age with Land, Vegetation, and Ice Sensor (LVIS) LiDAR and WorldView stereo estimates of forest height. We will present approaches for estimating SI from these data and recent results from select locations in North America.

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