Estimation of Tropical Forest Structure Using the Full Waveform Lidar from ICESat
Michael
Palace, Complex System Research Center, UNH, Morse Hall, 8 College Road, Durham, NH 03824, michael.palace@unh.edu
(Presenting)
Maria
Hunter, Complex System Research Center, UNH, Morse Hall, 8 College Road, Durham, NH 03824, maria.hunter@unh.edu
Stephen
Hagen, Applied GeoSolutions, LLC, 87 Packers Falls Road, Durham, NH 03824, steve.hagen@agsemail.com
Mark
Ducey, Department of Natural Resources, UNH, Rudman Hall, 46 College Road, Durham, NH 03824, mjducey@cisunix.unh.edu
Bobby
Braswell, Atmospheric and Environmental Research, Inc., 131 Hartwell Avenue, Lexington, MA 02421, bbraswel@aer.com
Michael
Keller, NEON, 5340 Airport Boulevard,, Boulder, CO 80301, mkeller@neoninc.org
The Amazon basin contains the world’s largest continuous tropical forest constituting 40% of the remaining area for this ecotype and is made up of heterogeneous canopies and forest communities with unique assemblages of tree species, complex vegetation dynamics and history, and high biodiversity. Forest structural components include canopy geometry and tree architecture, size distributions of trees, and are closely linked with ecosystem functioning. The dynamic processes of growth and disturbance are reflected in the structural components of forest. Large footprint lidar has been used to estimate biomass in tropical and temperate forests, primarily through the correlation with field measured height, basal area, and plot biomass estimates. However, in tall-stature forests height loses much of its correlation with basal area, so the height-biomass curve becomes asymptotic and is associated with greater error at large biomass values. Use of lidar in such an analysis also does not include estimations of other stand level structural properties.
We used full lidar waveforms from ICESat GLAS to estimate forest stand structure. We developed a 3D canopy model that uses trunk or crown diameter distributions and allometric equations of associated crown depth and canopy height to generate a synthetic canopy. Using geometric series of tree size distributions, we generated thousands of synthetic vegetation profiles. These synthesized forest canopy profiles were rapidly and efficiently compared with lidar waveforms and matches identified using least squared difference. Using GLAS lidar waveforms, we identified patterns of forest structure across Amazonia. . Landscape level estimates of q-values derived from lidar estimates are similar to estimates of q-values from field based data from a 400 ha area in Tapajos National Forest, approximately q=1.7, with a range of 1.69 to 1.82 per 100 ha plot. Estimates comparing field data collected in areas associated specifically with a GLAS footprint were found to be similar.
Presentation Type: Poster
Poster Session: Ecosystems Science
NASA TE Funded Awards Represented:
Palace, Michael
Scaling Forest Biometric Properties Derived from High Resolution Imagery to the Amazon Basin using Moderate Resolution Spectral Reflectance Data