Soil erosion spatial modeling with RUSLE in ArcGIS software

Explore soil as a mixture of organic matter, minerals, air, water, and organisms, and see how climate, relief, and parent material drive weathering, soil formation, and biodiversity, along with degradation.

Examine soil erosion, including wind and water processes, and causes such as overgrazing and unsustainable cultivation. Identify major erosion types, from sheet and gully to mass movement.

Plant vegetation as ground cover, including trees, grass, creepers, and wildflowers, to bind soil and prevent wind and water erosion, complemented by mulching and contour farming on high slopes.

Explore soil erosion models across empirical, conceptual, semi-empirical, and physical deterministic approaches, with SWAT highlighted as a widely used tool for hydrology, sediment, and erosion predictions.

Explore the rusle model concept in ArcGIS, detailing how five factors—r, k, ls, c, and p—combine with rainfall, soil, dem, and Landsat data to estimate annual soil loss per pixel.

Explore the R factor theory in soil erosion modeling, detailing monthly to annual rainfall calculations and country-specific simplifications, using Gloria and CRU precipitation datasets.

Download the latest CRU precipitation data from the University of East Anglia and R factors from the JRC to support erosion modeling with RUSLE in ArcGIS.

Explore the global rainfall erosivity map from ESDAC in ArcGIS to drive soil erosion spatial modeling with RUSLE, including data access, format, and 827-meter resolution.

Learn to work with CRU netCDF precipitation data in ArcGIS, extract study area annual precipitation for 2020, reproject to UTM, and prepare data for R factor calculation in RUSLE.

Reproject monthly rainfall data in ArcGIS using Model Builder to convert 12 months of precipitation to UTM zone 43 for accurate soil erosion modeling with rusle.

Compute the monthly R factor for soil erosion using CRU data in ArcGIS with a raster calculator in Model Builder, then sum months to obtain the annual R factor.

Derive the R factor for RUSLE in ArcGIS using the EUJRE global rainfall aggressivity map, reproject to global and UTM, and compare with CRU precipitation data to guide factor choice.

In ArcGIS, compute the R factor for a 30-meter pixel resolution using the raster calculator by dividing hectare values by 11, producing a smaller, localized R factor map.

The k factor theory quantifies soil erodibility and soil loss rate by linking soil texture, organic matter, structure, and permeability in Williams 1995’s advanced empirical formula, using FAO data.

Calculate k factor for dominant soils from FAO data using an Excel model in ArcGIS, utilizing topsoil clay percent and organic carbon from the global soil map for study area.

Create a key factor data layer and enter K factor values for each soil type in the UTM study area. Save edits and apply the K factor symbology in ArcGIS.

Convert key factor polygon shapefile to a raster in ArcGIS using polygon to raster, preserving the key factor attribute and setting the cell size to 30 meters with five classes.

Learn to compute the k factor from a soil database in ArcGIS by joining tables, unifying fields, extracting study area, and creating a raster k factor map from K1 values.

Explore ls factor theory for slope length and steepness in soil erosion, using a digital elevation model to compute flow direction, flow accumulation, and the unified ls equation in ArcGIS.

Download the free digital elevation model for the study area, derive slope and slope length, and mosaic the eight granules to obtain flow accumulation data for RUSLE in ArcGIS.

Prepare the digital elevation model, apply field operation smoothing, and compute slope, flow accumulation, and flow direction using model builder for a study-area erosion workflow.

learn to compute flow accumulation in ArcGIS from flow direction images, build a model with Model Builder, mosaic eight outputs, and export at 30-meter resolution.

Learn how to create a single mosaic from multiple raster images, compute slope in degrees, and convert the slope to radians for a precise RUSLE analysis in ArcGIS.

Compute the LS factor map for soil erosion using map algebra and raster calculator on slope in radians with a 30-m cell size, integrating flow accumulation.

Cap LS factor values above 1000 in ArcGIS using raster calculator conditional to treat them as errors, producing a final 1000-ceiling map for RUSLE modeling, saved as TIFF.

Compute the LS factor in SAGA GIS using terrain analysis tools, generating factor maps from digital elevation models, and compare SAGA results with Rxjs outputs for reliability.

Learn how the C factor, a key reduction factor linking cropping, management practices, and land use, controls erosion rates in RUSLE modeling, with region-specific equations and land-cover classifications.

Build a c-factor map for soil erosion modeling using global and european c-factor equations, and process Landsat 2020 imagery to mosaic cloud-free layers.

Combine four bands into a single composite image in ArcGIS by stacking bands, assigning band order, and saving after excluding dark no-data pixels.

learn how to mosaic four Landsat images into a single raster in ArcGIS, ensuring proper band alignment by spectral reflectors, setting output location and pixel type, and handling no-data areas.

Develop an ndvi map in ArcGIS by combining red and near-infrared bands, configuring true color display, and saving the mosaic to analyze vegetation patterns in the study area.

The lecture demonstrates creating a C factor map in ArcGIS by applying multiple C factor formulas, normalizing to 0–1, comparing results, and finalizing the raster.

Explore P factor theory in soil erosion modeling with ArcGIS and RUSLE, detailing conservation practices (terracing, strip cropping, contour farming, and tree planting), slope effects, B factors, and equation-based adjustments.

Learn to compute the P factor in ArcGIS for soil erosion using slope and land cover, applying conservation practices to protect water and wind erosion in agricultural areas.

Convert digital elevation model slopes to percent rise, reclassify into discrete classes, and combine with land cover classification to create a multi-class map for soil erosion modeling in ArcGIS.

Assign p factor values to each class in the attribute table by editing the factor field, linking slope and land-cover classes, and visualizing the resulting p factor map.

Extract P factor values into a separate continuous raster using a lookup in the reclass tool, then reproject to a 30 by 30 meter grid.

Compute two final RUSLE models in ArcGIS by multiplying rainfall, slope, land cover, and conservation factors, using CRU and EC data, then classify and compare results.

Resample the soil erosion raster from 30 m to 100 m in ArcGIS, scaling values proportionally to the larger pixel, then reclassify into 10 classes and calculate area per class.

This final lecture guides you through creating and presenting final soil erosion maps from RUSLE in arcgis, adjusting layouts, legends, symbology, scale bars, and exporting high-quality images.

Apply the RUSLE model using the same R and P factors but with the K factor from Saga GIS and the soil database, and map it in ArcGIS.