Identifying critical agricultural areas with three-meter LiDAR elevation data for precision conservation

Abstract

Determining which portions of agricultural landscapes are major sources of pollution within a watershed is time consuming and labor intensive. Small critical areas of the landscape contribute disproportionate amounts of sediment and phosphorus to nearby waterways. Critical areas are defined here as areas of accumulated overland runoff that are hydrologically connected to surface waters. With advancements in light detection and ranging (LiDAR) technologies, landscape topography can be represented with highly accurate terrain data. The objective of this study is to determine the effectiveness of using LiDAR–based terrain attributes to identify fine-scale critical areas in selected Minnesota watersheds and to analyze cost efficiency of this type of analysis. The LiDAR digital elevation model data were acquired for two south central Minnesota watersheds, and the terrain attributes slope, flow accumulation, and stream power index were calculated with a 3 m (9.8 ft) spatial resolution. Field surveys were conducted in these watersheds along the riparian corridor to identify side inlets and active gullies that contribute to surface water quality degradation. Terrain attributes were able to identify 80% of field-verified gullies in the study watersheds. Furthermore, an even higher percentage of gullies with a high sediment delivery potential were identified using terrain attributes. Gully size was ranked during field surveys, and 31 of the 32 largest gullies ranked in the field were successfully identified with LiDAR–based terrain attributes. In contrast, only 7 of these gullies could be identified using 30 m (98 ft) digital elevation model terrain attributes. The LiDAR approach for identifying critical source areas using terrain attributes has a large potential for cost savings relative to time-consuming field surveys. With an ever-increasing availability of LiDAR data, terrain analysis may prove very useful in the future for targeting best management practices to critical areas for reductions in nonpoint source pollution.