For agricultural production, soil erosion is a major issue since it affects soil quality and lets pollutants into streams. The most severe stage of soil erosion, known as gully erosion, results in the creation of wide pathways through the field. Once gullies form, they are difficult to control with tiling and call for a more thorough approach along the damaged region.
Using remote sensing environmental data, researchers at the University of Illinois developed a modeling framework that more precisely predicts gully erosion susceptibility.Landowners and conservation organizations can focus management resources on the most vulnerable locations according to our predictive model.
“Because there are so many elements at play, such as farmer activity, climate, rainfall, temperature, vegetation development, topography, and many other variables that are constantly changing over time, erosion processes are difficult to forecast.” In order to reduce the uncertainty of the prediction, we wanted to include more of these spatial and temporal variations in our model. According to Jorge Guzman, co-author of the article that was published in the Journal of Hydrology: Regional Studies and research assistant professor in the Department of Agricultural and Biological Engineering (ABE) at the University of Illinois,
In Jefferson County, Illinois, where 59% of the land is used for agricultural production, principally maize and soybeans, the researchers performed their study. The area produces row crops in a manner that is typical of the Midwest.
Jeongho Han, a PhD student in ABE and the paper’s lead author, explains, “We predict the geographical location of gully erosion based on high-resolution spatial and temporal data from satellite sensing.”
“The maximum entropy model, often known as MaxEnt, was employed to identify regions with a high likelihood of gully erosion.” “Because crop growth, temperature, and rainfall intensity all have a large impact on erosion, we introduced temporal variables like precipitation and vegetation where academics have typically focused on static variables like soil, elevation, and slope,” Han says.
“For instance, the rain in Illinois is bimodal, falling more frequently in the spring and fall. “We must take into account these factors’ temporal fluctuation.”
The researchers were able to develop a modeling framework that more accurately depicts the variety of elements that cause erosion by including dynamic variables.
Han and Guzman examined LiDAR data from the Illinois Geospatial Data Clearinghouse, which offers airborne surface light detection for all of Illinois, and mapped it at a 2-meter spatial resolution to verify their modeling findings with actual gully sites. They were able to identify differences in surface elevation that might point to the creation of gullies by contrasting photos from two separate years. These places were then filtered and processed to exclude activities directly related to humans, such as mining, building, and other endeavors, and to limit the gully inference to the LiDAR’s accuracy.
Overall, the researchers discovered a 7.4% higher risk of gully erosion on the study area’s agricultural land.
The slope, land use, seasonal daily maximum precipitation, and organic matter showed the highest impact in predicting the presence of gullies among all the parameters evaluated. The researchers also discovered that spatial and temporal variations in precipitation and land cover were significant indicators of gully formation in agricultural areas.
Their method can be used in any agricultural region of the Midwest of the United States that has comparable environmental and land management factors.
According to Guzman, the key concept is that by identifying the areas where gullies are more likely to form, land management strategies may be put into place. “For the management of nutrient loads and erosion, numerous tools and strategies are available.” The difficult part is determining how to more successfully optimize these efforts. “Our technologies can be used by landowners, communities, governments, and conservation organizations to focus programs and processes and allocate resources where they are most needed.”
