PARENT SESSION
Oral Session #71: Remote Sensing and GIS.
Presiding: J.D. Allan
Wednesday, August 7. 1:00 PM to 4:45 PM. Palo Verde Room, Radisson.
Using lidar and a radiative transfer model to derive forest canopy measurements.
Peterson, Birgit*,1, Ni-Meister, Wenge2, Blair, J. Bryan2, Hofton, Michelle1, Knox, Robert2, Hyde, Peter1, Dubayah, Ralph1, 1 University of Maryland, College Park, MD2 Goddard Space Flight Center, Greenbelt, MD
ABSTRACT- In the past, obtaining reliable measurements of key forest canopy metrics has been difficult, even after the advent of remote sensing technologies. Fortunately, next-generation lidar systems are proving to be useful tools for deriving critical canopy measurements, such as height, structure and biomass. Most studies have relied on empirical comparisons between lidar-derived and field-sampled measurements and the results of these studies have shown that lidar remote sensing instruments can successfully measure forest canopy characteristics. However, physically-based remote sensing models are still necessary to better understand and interpret the interactions of the laser energy with the forest canopy and to derive additional, as well as more accurate, canopy measurements from the lidar signal. In this study the Geometric Optical and Radiative Transfer (GORT) model is used to simulate lidar waveforms based on field data. Previous research has shown that GORT can model lidar returns from canopies with clumped multiple layers and multiple species. For this study, GORT was used to model waveforms over the Sierra National Forest in California. Field data input into GORT are a representative sample of the different vegetation types found in the forest. The modeled waveforms were then validated against actual lidar data collected by the Laser Vegetation Imaging Sensor (LVIS) which mapped the area in October 1999. By modeling lidar waveforms based on the physical principles of radiative transfer, GORT fills a missing link between the remotely sensed and actual canopy structure. The results of this study also demonstrate how the GORT model can be inverted to predict stand structure from lidar and other remote sensing data.
KEY WORDS: lidar, canopy structure