Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA

The southeastern Piedmont of the USA was severely gullied during the early 20th century. A thick canopy established by reforestation in many areas now inhibits the identification or mapping of gullies by aerial photography or other conventional remote sensing methods. An Airborne Laser-Scanning (ALS or LiDAR) mapping mission flown for the U.S. Forest Service in April, 2004 acquired bare-Earth topographic data. This paper tests the ability of the ALS topographic data to identify headwater channels and gullies for two branching gully systems in forested areas and to extract gully morphologic information. Comparisons are made with field traverses using differential GPS and reference cross sections measured by leveling surveys. At the gully network scale, LiDAR data provide accurate maps – the best available – with robust detection of small gullies except where they are narrow or parallel and closely spaced. Errors in mapping channel location and network topological connectivity under forest canopy increase with attempts to identify smaller features such as large rills. The ability of LiDAR data to map gullies and channels in a forested landscape should improve channel-network maps and topological models. At the gully reach scale, attempts to use LiDAR data to extract gully cross-section morphologic information under forest canopy were less successful due to systematic underestimation of gully depths and overestimation of gully top widths. Limited morphologic accuracy of the data set at this scale may be due to low bare-Earth point densities, shadowing of gully bottoms, and filtering of topographic discontinuities during post-processing. The ALS data used in this study are not suitable for detailed morphometric analysis or subtle change detection to monitor gullies or develop sediment budgets. Data collection may be improved by orienting flights over gullies and with increased point densities through improved scanner technology or better filtering and software capabilities to differentiate between vegetation and ground surfaces.