Characterizing Forest Structure for
Assessments of Carbon Cycling and Biodiversity: An Integrated Approach Using
Lidar Remote Sensing, Field Studies, and Ecosystem Modeling
Ralph Dubayah,
Michelle Hofton, Scott Goetz, J. Bryan Blair (GSFC), David Clark (UMSL) George
Hurtt (UNH), Robert Waide (UNM)
Forests are the focus of intense research in global environmental change. The effects of natural and anthropogenic forest structural changes and dynamics on carbon cycling and biotic diversity are of particular interest. One of the major sources of error in estimates of land surface carbon and other biogeochemical fluxes arises from uncertainty in prescribing initial forest carbon stocks. Lidar remote sensing has emerged as a proven technology for capturing spatial and vertical forest structure. In particular, recent studies using airborne scanning lidar as part of the Vegetation Canopy Lidar (VCL) program have validated its ability to retrieve many aspects of forest structure, including canopy height, canopy closure, canopy height (foliar) profile, aboveground biomass and carbon, basal area, and bulk crown density, among others.
The purpose of this collaborative effort is to link lidar remote sensing of
forest structure with field studies and ecosystem modeling across a range
of environmental gradients to improve land surface carbon predictions, and
to explore the effects of this structure on species richness and distributions.
In particular we seek to answer the following two methodological questions:
(1) How can changes in forest carbon stocks and associated fluxes be observed over time using a combined lidar remote sensing/modeling approach? (2) How can lidar remote sensing be used to discover and characterize forest spatial and vertical structure relevant to species distribution and richness?
Our activities fall into three categories: (1) the production of a time series of forest structure from lidar and other remote sensing data; (2) the modeling of carbon stocks and fluxes using an ecosystem model as initialized with lidar data; and (3) application of the derived and modeled products for assessments of biodiversity.
Example publication: Dubayah, R., R. Knox, M. Hofton, J.B. Blair, and J. Drake. 2000. Land surface characterization using lidar remote sensing. In M. Hill and R. Aspinall (eds.) Spatial Information for Land Use Management. Singapore: International Publishers Direct.