The study of erosional risk mapping in terrestrial and fluvial environments is critical in academia to help build predictive models and preventative strategies to alleviate issues associated with land cover change. Engineers and geomorphologists rely on this information to understand how environmental and anthropogenic forces are impacting erosional rates and the risks associated with them (Chen et al. 2010).
There is currently a gap in knowledge, as no study has yet to construct a SPI gulley network that overlays a continuous erosion rate map to assess soil degradation while using aerial imagery to assess the accuracy of the model’s predictions, this study will be the first case of precise sub-watershed soil erosion and SPI mapping throughout Ontario’s agricultural network. When compared to literature, this study will bridge the gap between erosion mapping and SPI mapping to assess the validity and accuracy of such analysis on a fine scale. This study will test the effectiveness of using fine scale LiDAR DEM data to accurately identify in-field gullies that supply credible soil erosion metrics to the farmers of Ontario, and aims to formally consolidate a proper methodology, and work flow, that clearly states which software and inputs are effective at outputting erosion rate maps and stream power indices.
1. To identify factors important to calculating potential soil loss accessible for a catchment scale assessment.
2. To use ArcGIS to effectively compute potential erosion for the studied watershed based off the RUSLE on a cell-by cell basis.
3. To apply SPI tools to generated predicted soil loss maps to identify areas of increased gullying risk from erosion.
4. To evaluate the accuracy of SPI to predict gully location using aerial photographs between the studied watershed to identify any preference between high and low gradient systems.