Poor management practices paired with intense rainfall can catalyze intensive sediment erosion. In an agricultural setting, sediment erosion is the main driver for soil degradation due to organic matter losses, decreased moisture retention, reduced soil quality, increased nutrient runoff and pollution (Blanco & Lal, 2010). Quantifying soil erosion rates is extremely useful for agricultural purposes, more specifically, in-field gullies are a good representation of the sediment pathways flowing out of the agricultural system. Gullies can be responsible for crop yield losses and increased workload (Valentin, Poesen, & Yong, 2005). Being able to remotely identify in-field gullies can direct farmers toward the areas that would benefit from erosion limiting measures, such as improved drainage or vegetated field boundaries (Blanco & Lal, 2010). Supplying farmers with soil erosion information of their fields allows them to assess the state of their soil and formulate management practices.  

Ontario’s Ministry of Agriculture, Food, and Rural Affairs (OMAFRA) is in the early stages of addressing this problem and supplying this information to its agricultural community (Saurette & McKague, 2019). A previous study by Galzki et al. (2011) performed a similar analysis, using fine resolution LiDAR data, a 1m DEM was derived using the LP360 ArcGIS extension from QCoherent Software. The DEM was resampled to a 3m resolution to reduce processing times while preserving adequate terrain definition required for the scope of the study (Galzki et al., 2011). The D8 method of slope calculation offered by ArcGIS was used to calculate slope and identify gully flow paths (Galzki et al., 2011). It was found that their methods were able to accurately identify 96% of gullies in the field, and the findings suggest that this study should consider using a high-resolution DEM (<5m) to maintain important terrain characteristics while limiting processing times (Galzki et al., 2011).  

Another analysis conducted by McNair & Miskowiak (2016), mapped agricultural field erosion variability and SPI network, similar to this study. However, their study area considered a whole watershed as opposed to this study, which will consider the sub-watershed at a smaller extent (McNair & Miskowiak, 2016). The results of their study were able to identify areas of high to low erosion vulnerability due to field variability and SPI but were unable to accurately identify gullies in individual farm fields due to small-scale datasets and processing constraints (McNair & Miskowiak, 2016). 

Many other studies have conducted similar analysis, focusing on the influences different land covers, such as forest, grassland, and fallow land, have on erosion during storm events and flash flooding (Abdu, Battey, & Mohammed, 2017). These other studies are useful to evaluate the effectiveness and compatibility of other software for the scope of this study. A study by Abdu et al. (2017) utilized the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) GDEM (Global Digital Elevation Model) to output credible erosional pathways to an accuracy of +/- 12m, but this study will require greater precision. However, the utilization of ArcGIS to process their DEM data and to apply the Universal Soil Loss Equation (USLE) in a timely and effective manner gives insight towards the capacities of ESRI software in conducting a similar analysis on a smaller, more precise, extent.