Remote Sensing Derived Estimates of Forest Structure and Composition

Paul R. Moorcroft, Harvard University, paul_moorcroft@harvard.edu (Presenter)
Alexander Antonarakis, Harvard University, aantonarakis@oeb.harvard.edu

Consistent measurements of forest structure and composition are vital for assessing the current state of terrestrial ecosystems and their resulting carbon, water and energy fluxes. Until recently, insights into forest composition and structure have come almost exclusively from ground-based inventories that are limited in their spatial extent. More recently however, remote sensing measurements have been used to estimate particular forest metrics, such as Lidar-derived estimates of canopy height and Radar-derived estimates of above-ground biomass. In this study, we examined methods for obtaining a more comprehensive estimation of forest structure and composition using a combination of Lidar and hyperspectral (imaging spectrometer) remote sensing data. A preliminary evaluation of these methods, in which we used LVIS Lidar data in combination with AVIRIS hyperspectral data to estimate ecosystem structure and composition at Harvard Forest, yields estimates of forest structure and composition that closely match those obtained from ground-based forest inventories. This remote-sensing based methodology offers promising avenue for obtaining accurate, spatially- comprehensive estimates of vegetation structure and composition that can be used to both initialize and evaluate terrestrial biosphere models.

Presentation Type:  Poster

Session:  Coupled Processes at Land-Atmosphere-Ocean Interfaces   (Mon 4:00 PM)

Associated Project(s): 

• Saatchi, Sassan: Requirements for Spaceborne Fusion of Lidar and Radar Measurements of Forest 3-D Structure and Above-ground Biomass ...details

Poster Location ID: 57