HR: 0800h
AN: B11D-0776    [Abstracts]
TI: Quantifying Biomass and Bare Earth Changes from the Hayman Fire Using Multi-temporal Lidar
AU: * Stoker, J M
EM: jstoker@usgs.gov
AF: Science Applications International Corporation, Contractor to USGS EROS 47914 252nd St, Sioux Falls, SD 57198,
AU: Kaufmann, M R
EM: mkaufmann@fs.fed.us
AF: USFS Rocky Mountain Research Station *Retired, 240 W Prospect Rd, Fort Collins, CO 80526,
AU: Greenlee, S K
EM: sgreenlee@usgs.gov
AF: USGS Earth Resources Observation and Science, 47914 252nd St, Sioux Falls, SD 57198,
AB: Small-footprint multiple-return lidar data collected in the Cheesman Lake property prior to the 2002 Hayman fire in Colorado provided an excellent opportunity to evaluate Lidar as a tool to predict and analyze fire effects on both soil erosion and overstory structure. Re-measuring this area and applying change detection techniques allowed for analyses at a high level of detail. Our primary objectives focused on the use of change detection techniques using multi-temporal lidar data to: (1) evaluate the effectiveness of change detection to identify and quantify areas of erosion or deposition caused by post-fire rain events and rehab activities; (2) identify and quantify areas of biomass loss or forest structure change due to the Hayman fire; and (3) examine effects of pre-fire fuels and vegetation structure derived from lidar data on patterns of burn severity. While we were successful in identifying areas where changes occurred, the original error bounds on the variation in actual elevations made it difficult, if not misleading to quantify volumes of material changed on a per pixel basis. In order to minimize these variations in the two datasets, we investigated several correction and co-registration methodologies. The lessons learned from this project highlight the need for a high level of flight planning and understanding of errors in a lidar dataset in order to correctly estimate and report quantities of vertical change. Directly measuring vertical change using only lidar without ancillary information can provide errors that could make quantifications confusing, especially in areas with steep slopes.
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting