Constraining Forest Ecosystem Dynamics by integrating Remote Sensing data with a State-of-the-Art Terrestrial Biosphere Model
Alexander
S
Antonarakis, Harvard University, aantonarakis@oeb.harvard.edu
(Presenting)
Paul
R
Moorcroft, Harvard University, paul_moorcroft@harvard.edu
(Presenting)
Insights into the dynamics of vegetation and above-ground biomass within terrestrial ecosystems have previously come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and Synthetic Aperture Radar are promising remote sensing-based techniques for estimating forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived above-ground biomass can be used to constrain the dynamics of the ED2 terrestrial ecosystem model. Four year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest inventory data and output from a long-term potential vegetation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential vegetation initialized-simulation. This resulted in improved predictions of ecosystem scale carbon fluxes and structural dynamics compared to predictions from the potential vegetation simulation. Further improvements of structural and carbon fluxes metrics also depend on obtaining reliable estimates of forest composition and accurate representation of the fine-scale vertical and horizontal structure of plant canopies.
Presentation Type: Poster
Poster Session: Ecosystems Science
NASA TE Funded Awards Represented:
Saatchi, Sassan
Detecting Changes of Forest Biomass from Fusion of Radar and Lidar: Developing DESDynl measurement requirements