Developing a High Resolution RUSLE Model in QGIS

Explore the soil's composition, formation, and biodiversity, including weathering, pedosphere functions, horizons, texture classes, and key degradation risks that affect ecosystem services.

Explore soil erosion, including wind and water erosion, their causes, and major types. Learn how landscape conditions and rainfall accelerate soil loss and degradation.

Explore soil erosion prevention measures, including planting ground cover with herbs, wildflowers, and creepers, mulching, contour farming, windbreakers, and no-till farming to reduce wind and water erosion on steep slopes.

Explore a range of soil erosion models, including Russell, SWAT, Eurosam, Erosion 3D, and ASL, spanning empirical to physical approaches.

Explore the russell model concept, an upgraded equation estimating annual soil loss with r, k, l, s, c, and p factors, enhanced by remote sensing and gis for large-scale assessment.

Explore the ls factor in the Russell model, a dimensionless multiplier of slope length and steepness, with m and s exponents, capping, and options like saga and UCA.

Explore the C-factor, its link to land cover and erosion protection, and three derivation methods—field-based, land-use/land-cover, and NDVI-based—for high-resolution erosion mapping.

Examine the rainfall erosivity R factor and its calculation with the corrected 17.02 Fournier coefficient, and apply the modified Fournier index for regional erosion risk in QGIS.

Explore the p factor and conservation practices like contour farming and terracing, and learn slope-based methods and Werner's equation for p values.

Explore the k factor and r factor, soil erodibility measures that link soil texture, organic matter, and permeability to erosion risk, illustrated with equations and real-world soil types.

Learn to download ALOS PALSAR high-resolution DEM for free through Earthdata's ECF data search. Import shapefiles, define your area of interest, adjust start dates, and mosaic the selected tiles.

Open QGIS, select the study area, and export geotiff rasters as saga binary grid (.sdat) for use in SAGA, preparing image tiles for RLS factor analysis.

Compute the LS factor for a high-resolution RUSLE model in QGIS using the SAGA toolkit. Load data and run a one-step LS factor tool with fill sinks and flow-related pre-processing.

Mosaic LS factor rasters in QGIS, clip by the study-area shapefile, review histograms, classify with single-band pseudo color, and cap maxima with a raster calculator.

Create a c factor map in Google Earth Engine by uploading a shapefile, selecting Uzbekistan as study area, and applying Sentinel-2 surface reflectance median composite for 2024.

Compute indices such as NDVI, NDWI, and NDBI and add them as bands to the median image to improve land use land cover classification with a random forest.

Add elevation and slope bands from the alos TSM digital elevation model, mosaic and rename to elevation, then derive slope for the final multi-band input used in random forest classification.

Collect ground control points for eight land cover classes by sampling dispersed points and assigning class values to build a reference dataset for high-resolution RUSLE modeling in QGIS.

Collect and merge ground control points across land cover classes into a feature collection, then split data for training and validation using a 75/25 percentile split on a random column.

Implement k-fold cross validation for a high-resolution RUSLE model in Google Earth Engine, producing multiple classification accuracies and reporting mean accuracy.

Train a final land use classification using a multi-band input with ground control points and a 50-tree random forest, then export a geotiff map showing results and accuracy.

Assign c-factor values to each land use class in a rusle model in qgis. Clip the land cover map, build a raster attribute table, and generate a class-specific c-factor map.

The lecture introduces the GloREDa global rainfall erosivity database, created by the European Soil Data Center, aggregating data from about 4000 stations in 65 countries with monthly R-factor maps.

Clip the global rainfall erosivity raster to your study area, convert to points, reproject to utm zone 42n, interpolate with idw, and clip result for a ten-meter high-resolution RUSLE map.

Compute the r factor for a high-resolution RUSLE model in Google Earth Engine by correcting unit conversions, aggregating daily to monthly precipitation, and testing logarithmic formulations.

Resample the r-factor map from gee in qgis to ten-meter resolution, clip to the study area, and reproject to utm zone 42n using cubic convolution.

Learn how to derive the p factor for a high resolution RUSLE model using GIS and remote sensing, employing Google Earth Pro to identify conservation practices in the study area.

Derive p factor values by slope and conservation practices such as contour farming and drip irrigation to assign a final p factor map using Google Earth Pro.

Explore mapping and discovering conservation practice methods in agricultural landscapes using Google Earth Pro, including strip cropping, agroforestry, grass strips, contour farming, terraces, and GIS shapefiles.

Merge and mosaic elevation models, clip to the study area, and convert to slope in percent. Reproject and resample to ten-meter resolution for a RUSLE model in QGIS, considering p-factor.

Reclassify a continuous slope raster into seven discrete classes in QGIS using raster calculator, linking classes to conservation practices such as contour farming and strip cropping for a RUSLE model.

Learn to build a rusle model in qgis by converting kmz to kml, reprojecting land-use and slope rasters to utm 42, and rasterizing polygons to ten-meter cropland versus non-cropland layers.

Combine practice rasters with cropland and non-cropland layers in QGIS to build p factor map for a RUSLE model, using align rasters and raster calculator to classify into conservation classes.

In this final step, the lecturer uses raster calculator in QGIS to compute the p-factor map by combining cropland with practices and slope classes, incorporating strip cropping and contour farming.

Use the FAO UNESCO digital soil map of the world to create a k factor map in QGIS; download the shapefile and assign CRS 4326.

Compute the k-factor for dominant soils in Excel using Williams formula, drawing from a global FAO/UNESCO soil database, and apply a QGIS workflow to clip the study area.

Reproject the k factor attribute to utm 42 n, then rasterize the vector to raster to produce a geotiff with three classes using a palette.

Learn to access global soil erodibility k factor maps from ESDAC, compare KSAT and texture-based models, and execute a QGIS workflow to interpolate and clip for a high-resolution RUSLE model.

Create four rusle maps by combining two r factors and two k factors in QGIS, using raster calculator and statistics to compare results.

Learn to correct extreme values in Russell rasters with the r quantile percentile tool in QGIS, test 98th and 99th percentiles, and cap outliers at 4000 using raster calculator.

Learn to create and present high-quality map outputs in QGIS from final results, including map layout, legends, raster classifications, and export of JPEG images for articles and dissertations.

Improve the RUSLE model accuracy in QGIS using advanced lhs, Morse r-factor, and dual k factors with refined p/c factors and higher-resolution dem and land cover.