Code 614.4 Branch seminar

University of Minnesota, Dept. of Forest Resource
The development of light detection and ranging (LiDAR) sensors for sub-orbital aircraft and satellite platforms has provided terrestrial remote sensing scientists with an unprecedented opportunity to derive height, biomass and 3D structural attributes of stratified plant communities. A powerful extension of this technology is the fusion of LiDAR and spectral data to characterize the structure, composition, and functional attributes of terrestrial vegetation. In this seminar, I will discuss methods and algorithms that have been developed for LiDAR and multispectral data to improve land cover mapping and estimates of standing biomass, leaf area, and fraction of photosynthetically active radiation absorbed by plant canopies (fPAR). LiDAR and Quickbird data were collected at a NASA Earth Observing System (EOS) land validation core site in the Great Lakes Region of North America, and derived variables were used as inputs in light- and carbon-use efficiency algorithms for the purpose of evaluating uncertainties due to wetlands detection and land cover generalizations at resolutions comparable to Landsat, AWiFS, and MODIS sensors. In addition, LiDAR data collected during leaf-on and leaf-off periods were used to characterize the vertical distribution of vegetation types and foliage in mixed forests containing evergreen needleleaf and deciduous broadleaf species. Inversion of these data with a physically-based radiative transfer model (e.g., Sun et al., 2000) has the potential to provide quantifiable information on canopy strata and spatiotemporal variations. I will conclude this seminar by proposing a study that would combine LiDAR-derived estimates of canopy structure and forest inventory and analysis (FIA) data to develop more sophisticated algorithms of plant production and resource utilization.