Using NASA Remote Sensing Data to Reduce Uncertainty of Land-use Transitions in Global Carbon-Climate Models

Louise Chini, University of Maryland, lchini@umd.edu (Presenter)
Justin Fisk, University of Maryland, fisk@umd.edu
Matthew Hansen, University of Maryland College Park, mhansen@umd.edu
George Hurtt, University of Maryland, gchurtt@umd.edu
Peter Potapov, University of Maryland, College Park, peter.potapov@hermes.geog.umd.edu

Recently, many Earth System Models (ESMs) have incorporated new gridded products of land-use and land-use change that have been harmonized to ensure a continuous transition from the historical to the future data in a consistent format for all models. However, the land-use transitions in these Land-Use Harmonization (LUH) data products are estimates, constrained with data where available, and with modeling assumptions, and the remaining challenge is to quantify, and reduce, the uncertainty in these products. At the same time, satellite remote sensing of the terrestrial biosphere has also evolved. Global-scale land cover extent and change monitoring is now possible given systematically acquired earth observation data sets, advanced characterization algorithms and data intensive computing capabilities. This provides us with a unique opportunity to use NASA satellite remote sensing products as an added constraint in the LUH process and to generate new gridded maps of land-use transitions for use in coupled carbon-climate simulations that are based on, and consistent with, observations of actual forest cover change. In this presentation we compare maps of forest extent and change from NASA remote sensing data and the LUH products. We then identify the geospatial locations of the biggest discrepancies between these products and the underlying modeling processes that contribute to these discrepancies. Our study shows that the representation of both shifting cultivation and wood harvesting are key sources of spatial uncertainty in the LUH forest extent and change maps. Our path forward involves new modifications to the LUH representation of these processes to not only further improve fidelity with the remote sensing data, but also to improve the historical reconstructions and future projections of forest extent and change in the LUH datasets.

Presentation Type:  Poster

Session:  Theme 4: Human influence on global ecosystems   (Mon 4:30 PM)

Associated Project(s): 

• Chini, Louise: Using NASA Remote Sensing Data to Reduce Uncertainty of Land-Use Transitions in Global Carbon-Climate Models ...details

Poster Location ID: 38