Soil Compaction and Erosion

Science Center Objects

Extensive off-highway vehicle (OHV) use on desert lands can directly and indirectly lead to human health problems and impact soil, vegetation, and wildlife habitat. Soil pulverization and loosening caused by OHVs contribute to dust hazards, and to respiratory illnesses and diseases (e.g., valley fever) in adjacent, downwind communities.  Repeated soil compaction by OHVs can also degrade natural resources through soil erosion, altered watershed hydrology, habitat fragmentation, and direct mortality of plants and animals. Alterations of soil conditions and hydrological function in turn influence water availability to native vegetation through decreased infiltration rates and increased runoff flow and OHV tracks can facilitate non-native seed production and germination by disturbing and upturning the soil and by creating surface ruts that collect and store available moisture.


To help land managers identify areas susceptible to soil erosion from anthropogenic activities, we developed a series of erosion potential models based on factors from the Universal Soil Loss Equation (USLE). To better express the vulnerability of soils to human disturbances, we refined two factors whose categorical and spatial representations limit the application of the USLE for non-agricultural landscapes: the C-factor (vegetation cover) and the P-factor (support practice/management). A soil compaction index (P-factor) was calculated as the difference in saturated hydrologic conductivity (Ks) between disturbed and undisturbed soils, which was then scaled up to maps of vehicle disturbances digitized from aerial photography. The C-factor was improved upon with Landsat TM vegetation index data, which were better correlated with estimated ground cover (r2 = 0.77) than C-factor data based on land cover maps (r2 = 0.06). The erosion potential models capture spatial patterns of both fine- and broad-scale factors contributing to natural and anthropogenic erosion and are at a scale appropriate for adaptive management and restoration of arid environments.

Photo of off-highway vehicle tracks

Figure 1.4.1. Example of off-highway vehicles (OHV) tracks in a remote part of the Sonoran Desert.

(Credit: Miguel Villarreal, USGS. Public domain.)

Photo of field crew

Figure 1.4.2. USGS field crew collecting validation information for remote sensing products.

(Credit: Miguel Villarreal, USGS. Roy Petrakis' image used with permission)


An image depiction how remote sensing is used to sense soil erosion

Figure 1.4.3. Information from satellite imagery helped to differentiate areas vulnerable to soil erosion from off-highway vehicle use. For this study we developed a “P-factor” by scaling ground measurements of soil compaction to vehicle disturbances mapped from aerial imagery. We also used NDVI to better represent the spatial complexity of vegetation cover, which helps to reduce soil erosion. This figure shows four Erosion Potential Models based on different input factors: A) land cover model with no P-factor, B) Landsat NDVI model with no P-factor, C) land cover model with P-factor, and D) Landsat NDVI model with P-factor. The models that include NDVI and P-factor data can be used by managers to better identify areas sensitive to erosion from anthropogenic land uses.

(Public domain.)

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