LIDAR Height Measures in Tropical and Coniferous Forests
Dar
A..
Roberts, Dept of Geography, UC Santa Barbara, dar@geog.ucsb.edu
(Presenting)
Matthew
L.
Clark, Dept of Geography and Global Studies, Cal State Sonoma, matthew.clark@sonoma.edu
Phil
E
Dennison, Dept of Geography, Univ of Utah, dennison@geog.utah.edu
Kerry
Q
Halligan, Dept of Geography, UC Santa Barbara, halligan@geog.ucsb.edu
Bothaina
Natour, Dept of Geography, Univ of Utah, bothaina.natour@geog.utah.edu
Geoffrey
G
Parker, Smithgsonian Environmental Research Center, parkerg@si.edu
Combined LIDAR and hyperspectral measures have the potential of improving our ability to estimate carbon stocks, through biomass-height relations and carbon fluxes, through improved maps of forest species and physiology. While strong relationships have been observed between LIDAR-derived heights and above ground biomass, significant questions remain, such as whether these relationships are global or site specific depending on management, disturbance and climate. Furthermore, it is unclear the extent to which hyperspectral data complement LIDAR by providing measures of health, physiology and species. To address these questions, we have begun a program evaluating LIDAR and hyperspectral data at six highly variable sites ranging from tropical rainforest (La Selva), western coniferous forest (Wind River and Sierra Nevada), central United States (Yellowstone) to east-coast broadleaf deciduous forest (Harvard Forest, SERC). In this poster we report upon initial analysis of LIDAR data at La Selva, Wind River and Yellowstone.
Two approaches were employed to generate a Digital Terrain Model (DTM) and calculate a Digital Canopy Model (DCM) from LIDAR. Both relied upon the concept of identifying bare earth between crowns, but relied on different means for determining appropriate window sizes and interpolating a DTM. LIDAR heights were evaluated for individual trees at all three sites and plots for two sites. In general, all sites showed a high correlation between LIDAR heights and measured heights that improved at plot scales. LIDAR tended to underestimate tree height, with errors increasing for more dense stands and for shorter trees. The most accurate tree heights were estimated at La Selva for pasture trees and for open stands at Yellowstone. Common error sources included rugged terrain, the inability to estimate an accurate DTM for dense stands and sparse LIDAR point density. Biomass showed a near-linear relationship to LIDAR-height at La Selva, but appears non-linear at Wind River.