Scaling Up Forest Ecosystem Carbon Budget from Stand to Landscape: Impacts of Forest Structures

University Of North Carolina At Chapel Hill, Chapel Hill NC

Investigators

Abstract

The overall goal of this project is to improve our understanding of the impacts of the multi-dimensional forest structures on scaling up forest ecosystem carbon cycle from stand to landscape through the integrative use of remote sensing, ecological models and ground observations. Though funding is requested for UNC Chapel Hill only, this is a collaborative research between the Department of Geography at UNC Chapel Hill and the School of Environment at Duke University. The expertise and research facilities from the two campuses are complementary. The proposed research will expands the scope and depth of the existing projects in Duke Forest. The project will take the necessary steps to transform the understanding of mass and energy exchange between forest ecosystems and the atmosphere at Duke Forest to a regional understanding. The project will use the AmeriFlux and FACE sites in the Duke Forest as the anchor points for scaling up through the integrative use of remote sensing and ecosystem models. Currently the global FLUXNET has over 200-flux towers world wide, and the up-scaling strategy investigated in this project will provide in-depth understanding of how to scale up from FLUXNET measurements to improve our understanding in global carbon cycle. The project proposes to use multisensor remotely sensed data to map multi-dimensional forest structures, including tree size and density, stand ages, leaf area index, and subpixel tree cover. The remotely sensed data include high-resolution (=1m) digital orthophoto quads and space-born images from Ikonos/QuickBird, medium resolution (30 m) Landsat images, and coarse-resolution (250m) MODIS/MISR images. The project will develop algorithms to use information from spatial, spectral/temporal and directional domains of remotely sensed data. The project can substantially enhance the use of remote sensing to extract detailed spatial vegetation information. A series of well-established ecological models will be used in the project, each of which will take forest structure at the appropriate scale to simulate terrestrial ecosystem carbon cycle. In addition to quantifying the errors caused by omitting forest structures in simulating carbon cycle, the project will lead to major improvements to the these models.