Mapping
Canopy Structure and Surface Topography Using Space-Based Lidar
Ralph Dubayah, Michelle Hofton, J. Bryan Blair
(GSFC), Robert Knox (GSFC), Scott Luthcke (GSFC), David Clark (UMSL), John
Weishampel (UCF)
A
collaborative effort between UMD, Goddard, the University of Central Florida,
and the University of Missouri Saint Louis is investigating the use of
space-based lidar remote sensing to produce global data sets of canopy
height, vertical canopy structure and sub-canopy topography. Such data
can be used for landcover characterization for terrestrial ecosystem modeling,
monitoring and prediction, and climate modeling and prediction, as well
as provide a global reference data set of topographic spot heights and
transects.
As
a prelude to the UMD/NASA VCL mission, we conducted calibration and validation
field experiments in a variety of biomes, from the Giant Sequoias groves
of the Sierra Nevada to the dense, tropical forests of Central America.
Calibration and validation studies include the validation of lidar retrievals
of canopy structure and topography, investigations to improve accuracy,
and radiative transfer studies. Science application activities include
habitat characterization, fire fuels modeling, carbon mapping and modeling,
energy balance studies, and surface change detection.
Highlights
include one of the most accurate retrievals of forest structure and carbon
in high biomass tropical areas yet achieved by remote sensing, estimation
of landscape successional state, and detailed sub-canopy topographic mappings.
Taken as a group, these results strongly validate the concepts behind space-based
lidar remote sensing and illustrate the remarkable power of lidar for land
surface characterization.
Example publication: Ralph
Dubayah, J. Bryan Blair, Jack Bufton, David Clark, J. JaJa, Robert Knox,
Scott Luthcke, Steve Prince, and John Weishampel. The Vegetation Canopy
Lidar Mission.In: Land Satellite
Information in the Next Decade II: Sources and Applications, pp100-112.
American Society for Photogrammetry and Remote Sensing, Bethesda, MD. 1997.