Canyon erosion prediction tools could lead to better land management – ScienceDaily

Soil erosion is a major problem for agricultural production, affecting soil quality and causing pollutants to enter waterways. Of all the stages of soil erosion, gully erosion is the most severe stage of soil erosion, in which large channels are excavated across the field. Once grooves develop, they are challenged to manage them through tiling; They require a more comprehensive approach along the affected area.

University of Illinois researchers have developed a modeling framework that uses environmental remote sensing data to more accurately predict the erosion susceptibility of canyons. This predictive model allows landowners and conservation agencies to direct management resources to the most vulnerable areas.

“Erosion processes are complex to predict, because there are many factors influencing them, including farm activity, climate, precipitation, temperature, vegetation development, topography, and many other variables that always change over time. We wanted to incorporate more of these variables space and time in our model to reduce prediction uncertainty,” says Jorge Guzman, assistant professor in the Department of Agricultural and Biological Engineering (ABE) at U of I and co-author on the paper published in the journal Hydrology Journal: Regional Studies.

The researchers conducted the study in Jefferson County, Illinois, where 59% of the land is used for agricultural production, mainly corn and soybeans. The area is typical for producing row crops in the Midwest.

“We predict the geospatial location of canyon erosion based on high-resolution spatio-temporal data from satellite sensing,” says Jeongho Han, ABE doctoral student and lead author of the paper.

“We used a maximum entropy model, or MaxEnt, to predict areas with a high probability of canyon erosion. Typically, researchers have focused on constant variables such as soil, elevation, and slope, but we added temporal variables such as precipitation and vegetation because erosion is strongly influenced by crop growth, temperature, and rainfall intensity. “.

“For example, Illinois has a bimodal rain pattern, with heavy rainfall during the spring and fall. We need to account for the temporal variability of these factors.”

The addition of dynamic variables helped the researchers create a modeling framework that more accurately represents the complexity of the factors affecting erosion.

To corroborate their modeling results with actual canyon locations, Han and Guzman analyzed LiDAR data from the Illinois Geospatial Data Clearinghouse mapped at a two-meter spatial resolution, which provides airborne surface light detection for all of Illinois. By comparing images from two different years, they can identify changes in surface height that may indicate the formation of grooves. These specific locations were then filtered and processed to remove direct human interference such as mining, construction, and other activities, as well as to narrow the groove inference to LiDAR accuracy.

Overall, the researchers found that 7.4% of the farmland in the study area was at high risk of developing canyon erosion.

Of all the factors considered, slope, land use, maximum seasonal daily precipitation, and organic matter indicate the highest contribution in predicting the presence of canyons. The researchers also found that spatiotemporal changes in land cover and precipitation were critical in predicting canyon formation in agricultural areas.

Their approach can be applied across agricultural regions of the US Midwest that share similar land management and environmental variables.

“The main idea is if we know where gully is most likely to develop, we can start implementing land management practices,” says Guzman. “Many tools and programs are available to manage erosion and nutrients. The challenge is how to improve these efforts most effectively. Landowners, communities, policy makers and conservation agencies can use our tools to target programs and processes that direct resources to where they are needed most.”

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