Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 5/25/2011
Publication Date: 10/26/2011
Citation: Momm, H.G., Bingner, R.L., Wells, R.R., Dabney, S.M. 2011. Methods for gully characterization in agricultural croplands using ground-based light detection and ranging. In: Sediment Transport - Flow and Morphological Processes, Faruk Bhuiyan (Ed.), ISBN: 978-953-307-374-3, InTech, p.101-124. Interpretive Summary: Ephemeral and classical gullies are important sources of sediment in agricultural croplands. Understanding the physical processes involved in gully formation and evolution, require detailed topographic representation of the terrain at different scales (individual plot, field, and watershed scales). The dynamic nature of gullies adds an extra layer of complexity in monitoring such features as the gully evolves due to changing conditions, such as precipitation events, vegetation growth, and conservation/management practices. The use of ground-based Light Detection and Ranging (LiDAR) systems can generate multi-temporal detailed topographic information at plot and field scales. The presence of vegetation, standing residues, and shadows (regions with no data) represent a multifaceted task that often cannot be addressed by standard off-the-shelf commercial geospatial software packages. In this study, a set of new and enhanced tools to pre-process and morphologically characterize gullies have been developed. For pre-processing LiDAR generated data, the tools proposed include: an assessment of the location accuracy between scans; point sampling density and distribution investigation; outlier removal; and, surface reconstruction. Quantitative morphological characterization of the gully can be performed using a cross-sectional analysis. Semi-automated tools were developed to: generate cross-sections throughout the length of the gully; compute cross-sectional area; and, extract cross-section geometric parameters are described. A study case is included to illustrate some the techniques previously described. These methods are critical in characterizing the evolution of gullies as they erode so effective conservation practices can be implemented to reduce the impact on the environment and maintain the productivity of agricultural fields.
Technical Abstract: Gullies constitute an important source of sediment from agricultural fields. In order to properly understand gully formation and evolution over time, as well as, sediment yield, detailed topographic representations of agricultural fields are required. New technologies such as ground-based Light Detection and Ranging (LiDAR) systems can improve the generation of the required topographic representation. The challenge resides in the post-processing of the generated data sets due to their large size combined with the presence of vegetation, standing crop residues, and regions with no data due to shadowing. The post-processing of the generated data set to obtain a true representation of the terrain represents a multifaceted task that often cannot be addressed by standard off-the-shelf commercial geospatial software packages. In this study, new and enhanced technology was developed to pre-process and morphologically characterize gullies from data sets generated using ground-based LiDAR systems. For processing of the LiDAR point clouds, the techniques developed include accuracy assessment of scans, point sampling density and distribution investigation, outlier removal, and surface reconstruction. Quantitative morphological characterization of the gully’s channel is performed by cross-sectional analysis. Semi-automated methods to generate sets of cross-sections, compute cross-sectional area, and to extract cross-section geometric parameters are described. A study case is included to illustrate some the techniques previously described. This technology provides a significant advancement needed to characterize the evolution of gullies as they erode or to implement conservation practices to reduce the impact of resulting pollutant loads on the environment.