Reducing Uncertainties in Satellite-derived Forest Aboveground Biomass Estimates using a High Resolution Forest Cover Map: A Case Study over California

Sangram Ganguly, NASA ARC BAERI, sangramganguly@gmail.com
Saikat Basu, Louisiana State University, sbasu8@tigers.lsu.edu (Presenter)
Ramakrishna Nemani, NASA ARC, rama.nemani@nasa.gov
Cristina Milesi, NASA ARC, cristina.milesi-1@nasa.gov (Presenter)
Supratik Mukhopadhyay, Louisiana State University, supratik@csc.lsu.edu
Andrew Michaelis, NASA ARC UCMB, amac@hyperplane.org
Petr Votava, NASA ARC UCMB, petr.votava-1@nasa.gov
Sassan Saatchi, NASA JPL, sasan.s.saatchi@jpl.nasa.gov

Several studies to date have provided an extensive knowledge base for estimating forest aboveground biomass (AGB) and recent advances in space-based modeling of the 3-D canopy structure, combined with canopy reflectance measured by passive optical sensors and radar backscatter, are providing improved satellite-derived AGB density mapping for large scale carbon monitoring applications. A key limitation in forest AGB estimation from remote sensing, however, is the large uncertainty in forest cover estimates from the coarse-to-medium resolution satellite-derived land cover maps (present resolution is limited to 30-m of the USGS NLCD Program). As part of our CMS Phase II activities, we have demonstrated the use of Landsat-based estimates of Leaf Area Index and ICESat Geoscience Laser Altimeter System (GLAS) derived canopy heights for estimating AGB at a 30-m spatial resolution, which compare relatively well with inventory based plot level estimates. However, uncertainties in forest cover estimates at the Landsat scale result in high uncertainties for AGB estimation, predominantly in heterogeneous forest and urban landscapes. We have successfully tested an approach using a machine learning algorithm and High-Performance-Computing with NAIP air-borne imagery data for mapping tree cover at 1-m over California. In a comparison with high resolution LiDAR data available over selected regions in the two states, we found our results to be promising both in terms of accuracy as well as our ability to scale nationally. In this project, we propose to estimate forest cover for the continental US at spatial resolution of 1-m in support of reducing uncertainties in the AGB estimation. The generated 1-m forest cover map will be aggregated to the Landsat spatial grid to demonstrate differences in AGB estimates (pixel-level AGB density, total AGB at aggregated scales like ecoregions and counties) when using a native 30-m forest cover map versus a 30-m map derived from a higher resolution dataset. The process will also be complemented with a LiDAR derived AGB estimate at the 30-m scale to aid in true validation. The proposed work will substantially contribute to filling the gaps in ongoing NASA CMS research and help quantifying the errors and uncertainties in NASA CMS products.

Associated Project(s): 

• Saatchi (CMS 2011)