HR: 0800h
AN: H31C-05    [Abstracts]
TI: Investigating the Capability of High Resolution ALSM to Provide Accurate Watershed Delineation and Stream Network Data
AU: * Sedighi, A
EM: alised@ufl.edu
AF: University of Florida, 570 Weil Hall, Gainesville, FL 32611, United States
AU: Slatton, K C
EM: slatton@ece.ufl.edu
AF: University of Florida, 570 Weil Hall, Gainesville, FL 32611, United States
AU: Hatfield, K
EM: khatf@ce.ufl.edu
AF: University of Florida, 570 Weil Hall, Gainesville, FL 32611, United States
AB: The development of geographic information systems (GIS) and digital elevation models (DEMs) has provided an opportunity to describe the pathways of water movement in a watershed. Adequate DEM resolution is of high importance in stream network detection. Local, state, and federal agencies have relied on US Geological Survey 1:24,000 scale topographic maps for information on stream networks for planning, management, and regulatory programs related to streams. DEM creation techniques that avoid map contours as the source of digital heights can improve watershed delineation and stream network data quality. Airborne Laser Swath Mapping (ALSM) technology (also referred to as LIDAR) provides DEMs of fine resolution and high accuracy. However, there are shortcomings in using both low resolution and high resolution DEMs. The focus of this work will be in the unique aspects of using ALSM data in watershed delineation and stream network mapping, in comparison to the other sources of DEM. In particular the reliability of both input data and output results of stream network using different resolutions will be evaluated. In this study, stream location resulting from high-resolution ALSM and low- resolution NED are compared to ground truth locations of the stream in Hogtown Creek Watershed, located in Gainesville, Florida. This study shows that ALSM-derived models are more successful at delineating streams and at locating them in their topographically correct position as compared to lower resolution DEMs. However, high resolution ALSM data produce artifacts that can affect the flow of water as predicted by stream network algorithms. Methods for overcoming the challenges with regard to ALSM data in stream network detection are presented.
DE: 1850 Overland flow
DE: 1855 Remote sensing (1640)
DE: 1879 Watershed
SC: Hydrology [H]
MN: 2007 Joint Assembly