In This Space
Erosion Potential Mapping
Erosion potential, Nile Basin 2012 (Source: GeoVille/ESA)
This product provides information on soil erosion potential, based on indicators incorporating several types of geodata. This information is needed to identify areas with high erosion potential for the planning and prioritizing of watershed restoration activities.
To calculate the erosion potential a processing chain is used which utilizes an international standard method for erosion measurement, e.g. the Universal Soil Loss Equation (USLE), in a Geo-Information environment. Consequently, EO data can be used to support the erosion potential assessment.
The Revised Universal Soil Loss Equation (RUSLE) predicts the long term average annual rate of erosion on a slope based on rainfall pattern, soil type, topography, crop system and management practices. RUSLE only predicts the amount of soil loss that results from sheet or rill erosion on a single slope and does not account for additional soil losses that might occur from gully, wind or tillage erosion. The RUSLE erosion model was created for use in selected cropping and management systems, but is also applicable to non-agricultural conditions such as construction sites.
The service is mainly based on land cover (land cover change) characterisation supported by information on elevation and slope as well as soil type information and rainfall estimates.
This product delivers PDF maps or digital raster files that show:
Known restrictions / limitations
This product is normally derived from optical satellite data. If the mapping area is situated in the inner tropics persistent cloud coverage can complicate cloud-free data acquisitions. In these situations radar images can be used as a substitute.
When generating DEMs from stereo pairs, good quality imagery needs to be available with two or more images showing the same area from different directions. This can be a time consuming process. Lower resolution (2 – 30 m, e.g. WorldDEM or SRTM) DEM data is available ‘off the shelf’.
Lifecycle stage and demand
Development, Production & Decommissioning:
Geographic coverage and demand
Demand and coverage is global.
Input data sources
Optical: VHR1, VHR2, HR1, HR2, MR1, MR2
Radar: VHR1, VHR2, HR1, HR2, MR1, MR2
Spatial resolution and coverage
Spatial resolution: 10 m - 300 m pixel size
The resolution depends on the input data, which can differ significantly. The resulting product will be a merge of the input data resolution.
Minimum Mapping Unit (MMU)
n/a (the product is directly based on the input data; the smallest unit is one pixel)
Accuracy / constraints
Thematic accuracy: 80-90%
Spatial accuracy: Target is one pixel, but depends on reference data
Accuracy assessment approach & quality control measures
Stratified random points sampling approach utilizing VHR reference or other geospatial in-situ data. Statistical confusion matrix with user’s and producer’s accuracy as well as kappa statistics for erosion potential.
Frequency / timeliness
Observation frequency: The frequency is constrained by satellite revisit and acquisition timeframes, but also processing requirements. Depending on the requirements of the customer the best suitable satellite sensor has to be chosen considering spatial / spectral resolution as well as revisit frequency and timeliness.
Timeliness of deliverable: As a fixed model using a defined equation, the product can be generated within a day, depending on the size of the area of interest.
Freely available or commercially acquired depending on the sensor selected.
Delivery / output format
|Peer Reviewer:||Hatfield Consultants|
Maria Lemper; Jan Militzer
# of Pages:
Internal – Project consortium and science partners
External – ESA
This page has no comments.