Soil erosion
Satellite(s)Sentinel-2, Landsat, Commercial VHR, LiDAR. | Monitoring elementLand surface reflectance, LiDAR backscatter. |
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Description techniqueSoil erosion has received comparatively limited attention from the remote sensing community when compared to environmental change processes impacting land cover (Luleva et al, 2012). To date most techniques have been centred on applying the existing Revised Universal Soil Loss Equation (RUSLE) or other models (Soil Erosion Model for Mediterranean regions - SEMMED) to assess erosion risk using satellite derived data to satisfy appropriate inputs (e.g. Jazouli et al, 2019) or traditional classification-based land cover change assessments attempting to directly delineate and quantify eroded area (e.g. Alatorre and Beguería, 2009). RUSLE considers a range of factors including rainfall runoff and soil erosivity, slope length and steepness and a land cover factor. Some factors may be estimated or modelled using satellite derived topographic and meteorological data, such as slope characteristics from LiDAR and precipitation for rainfall runoff erosivity (Ganasri and Ramesh 2016), but others may necessitate field measurement (Lu et al, 2004, Jazouli et al, 2019). The resulting value estimates soil loss per unit area in tons per ha. Classification based techniques have been shown to be effective, applying the same workflows and algorithms as the broader land use/land use change detection space (Sepuru and Dube, 2018). This technique has been extended by the combination of object based classification models with high resolution, commercial satellite imagery to allow finer delineation of erosion features (Shruthi et al, 2011). Several studies have attempting to assess indirect factors known to influence erosion, such as soil moisture, vegetation health, slope steepness and aspect (Luleva et al, 2012). This approach does present significant uncertainty, with the detection of factors such as soil moisture presenting significant challenges before being related to erosion. However, a number of studies have demonstrated the use of satellite imagery derived vegetation indices, such as NDVI (Normalized Difference Vegetation Index) and and SAVI (Soil Adjusted Vegetation Index) to extract erosion features at the landscape level (Sepuru and Dube, 2018). Taking advantage of the significant spectral difference between vegetated and bare, eroded land, this approach is straightforward to implement and can be applied to a range of imagery sources but best results will be achieved when near-infrared bands are available (Phinzi and Ngetar, 2017). | Accuracy / ResolutionVariable spatial and temporal resolution according to sensors. |
Case studyJazouli et al. (2019) combined RUSLE and land cover change detection techniques to assess weather and anthropogenically induced soil erosion within a topographically complex area of Morocco. Land cover change was assessed by classifying imagery captured by Sentinel-2 and Landsat 7 & 8 with via a Maximum Likelihood (ML) algorithm. The resulting land cover maps where compared to assess cover change dynamics, while also being used to satisfy the land cover term of the RUSLE equation. Interpolated rainfall data, the 30m ASTER DEM and a combination of soil samples existing geological maps formed the remainder of input data to the equation. The land cover classification performed well, averaging 85% accuracy across it classes, while soil loss estimates averaged 58, 66 and 142 tons per ha for each observation, with significant spatial variation apparent. Shruthi et al. (2011) present a novel approach delineating gully features from high resolution commercial satellite imagery (IKONOS and GeoEye-1) with object oriented image analysis. The study took place over two sites totaling around 1.8 km2, with a stereo pair of GeoEye scenes used to generate a high resolution DSM and DTM, along with a flow direction surface. These input were assessed using GLCM (Gray Level Co-occurrence Matrix) texture analysis to extract linear features and combined the IKONOS scene (processed to NDVI) and processed through the eCognition object oriented classification software to extract gully features. The results compared favorably to manual digitisation, offering a fine grained assessment of gullying at a catchment scale.
Example of Soil erosion toolbox: https://github.com/SoilWatch/soil-erosion-watch
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Applicability for NorthlandYes, several techniques would be applicable to Northland, with some caveats. The availability of open source multispectral data such as Sentinel-2 and Landsat (combined with careful cloud masking) should allow for catchment to regional scale landscape classification to extract and assess likely erosive features. Existing data would also help to estimate erosion volumes, e.g., precipitation data (NIWA), open source satellite derived data such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), topographic data (global scale DEMs provided by Shuttle Radar Topography Mission (SRTM), regional DEM/LiDAR capture) and land cover information (LCDB, LUM or developing a specific classification using satellite imagery for the project). Soil information will likely require local data, though some data is available via NZLRI. 1 LCDB: Land Cover Data Base (Manaaki Whenua, Landcare Research) Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive. | |
Publications referencesSepuru, T.K. and Dube, T., 2018. An appraisal on the progress of remote sensing applications in soil erosion mapping and monitoring. Remote Sensing Applications: Society and Environment, 9, pp.1-9. https://www.sciencedirect.com/science/article/pii/S2352938517301684 Luleva, M.I., Van Der Werff, H., Van Der Meer, F. and Jetten, V., 2012. Gaps and opportunities in the use of remote sensing for soil erosion assessment. Chemistry, 21(5), pp.748-764. Alatorre, L.C. and Beguería, S., 2009. Identification of eroded areas using remote sensing in a badlands landscape on marls in the central Spanish Pyrenees. Catena, 76(3), pp.182-190. El Jazouli, A., Barakat, A., Khellouk, R., Rais, J. and El Baghdadi, M., 2019. Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sensing Applications: Society and Environment, 13, pp.361-374. Lu, D., Li, G., Valladares, G.S. and Batistella, M., 2004. Mapping soil erosion risk in Rondonia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land degradation & development, 15(5), pp.499-512. Phinzi, K. and Ngetar, N.S., 2017. Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing. South African Journal of Geomatics, 6(1), pp.11-29. Shruthi, R.B., Kerle, N. and Jetten, V., 2011. Object-based gully feature extraction using high spatial resolution imagery. Geomorphology, 134(3-4), pp.260-268. Ganasri, B.P. and Ramesh, H., 2016. Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geoscience Frontiers, 7(6), pp.953-961. Kayet, N., Pathak, K., Chakrabarty, A. and Sahoo, S., 2018. Evaluation of soil loss estimation using the RUSLE model and SCS-CN method in hillslope mining areas. International Soil and Water Conservation Research, 6(1), pp.31-42. https://www.sciencedirect.com/science/article/pii/S2095633916301319 | |
Other comments or informationAncillary data used to complement satellite imagery: satellite derived DSMs / meteorological data + regional soil data | |