Integration of Lidar Remote Sensing from Multiple Platforms to Assess Vegetation Biophysical Parameters

Abstract

This research concentrates on using multiple platforms of lidar remote sensing for assessing vegetation biophysical parameters. Airborne and spaceborne light detection and ranging (lidar) (i.e., ICESat) remote sensing can characterize the three-dimensional structure of vegetation and therefore can provide useful information for assessing forest and rangeland woody plant biomass. The objectives of this research are 1) developing robust methods using airborne lidar and multispectral data to generate a local woody plant biomass map in northern Texas, 2) investigating the accuracy of existing global forest canopy height maps using airborne lidar data in multiple ecoregions in the southern United States, and 3) upscaling local forest aboveground biomass estimates to regional scale in an ecoregion. This research integrates statistical methods and remote sensing techniques to develop the procedure for building the regional forest aboveground biomass map. First, this research results in an approach for employing both airborne lidar and multispectral data with statistical methods to create a local scale woody plant aboveground biomass map in northern Texas. Then, the validation and calibration of the global forest canopy height map (GCHM) are used throughout rangelands and forests in the southern United States. A calibrated global forest canopy height map (cGCHM) serves as a primary data source for upscaling the forest aboveground biomass map from the local- to regional-scale in the South Central Plains ecoregion. In summary, the research utilized lidar data which was collected from multiple platforms to estimate aboveground biomass at multiple scales.