A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management
Abstract
:1. Introduction
2. Soil Erosion Hazards
2.1. Modelling of Soil Erosion
2.1.1. Physics-Based Models
2.1.2. Empirical Models
- A—Average annual soil erosion rate in soil mass per unit area per year (t ha−1 year−1).
- R—Rainfall erosivity factor (MJ mm ha−1 h−1 yr−1),
- K—Soil erodibility factor (t ha h MJ−1 mm−1),
- LS—Slope length and steepness factor (dimensionless),
- C—Crop management factor (dimensionless)
- P—Land management factor (dimensionless).
2.1.3. Conceptual Models
2.2. Determinants of Water Erosion
2.2.1. Rainfall
2.2.2. Slope Length and Steepness
2.2.3. Soil Erodibility
2.2.4. Ground Cover
2.2.5. Conservation Practices
3. Advancement of Geo-Informatics Technology in Soil Erosion Research
Spatial and Temporal Detection and Predictions
Gully Erosion Mapping
4. Management Strategies
5. Challenges, Innovations and Future Directions
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Satellite | Temporal Resolution | Spatial Resolution |
---|---|---|
IKONOS | 24 h | 0.82 m panchromatic; 3.28 m multispectral, |
QuickBird | 3.5 days | 2.4 m spatial resolution and a panchromatic band at a 0.6 m |
Spot 5 | 26 days | 2.5 to 5 m in panchromatic mode and 10 m in multispectral mode |
Landsat 3–8, | 16 days | 15 m panchromatic 30 m multispectral |
MODIS | 1–2 days | 250 m at nadir, with five bands at 500 m, provides global coverage |
NOAA-AVHRR | twice per day | 1.1 km |
ASTER | 6 days at the equator | 60 km |
Sentinel-2A | 5 days | 10–60 m |
Vegetation Index | Equation Formula | Reference |
---|---|---|
NDVI | [123] | |
SAVI | where L = correction factor between 0 and 1 | [119] |
EVI | G = 2.5; C1 = 6; C2 = 7.5; L = 1 | [123] |
s = the soil line slope a = the soil line intercept X = an adjustment factor that is set to minimize soil noise | [120] | |
MSAVI | [122] | |
TSAVI | (2) | [120] |
Soil Erosion Model | Remote Sensing-Based Methods | Data Sources | Study Area | Reference |
---|---|---|---|---|
RUSLE | Normalized Difference Vegetation Index (NDVI) for vegetation cover | Multi-source remotely sensed data (MODIS, Gaofen (GF)-1) | China | [141] |
RUSLE | Land use/land cover classes generation | Indian remote sensing (IRS) satellite 1D-LISS-3 image | India | [50] |
Soil plots and RUSLE | Digital erosion Model (DEM) Rainfall depths and intensity Ground cover | Light Detection and Ranging (LiDAR) and Shuttle Radar Topography Mission (SRTM)- radar rain-field data in NetCDF, Landsat-8 imagery Rapideye, Aerial photograph | Australia | [76] |
USLE | Land use/cover classification for C- factor generation using Supervised classification | SPOT 5 and Landsat ETM+ | Malaysia | [1] |
RUSLE and Land- use change | Object-based image classification for land-use land cover change detection | SPOT-5 | Malaysia | [142] |
Field plot method | Normalized Difference Vegetation Index (NDVI) for vegetation cover | ALOS data | Sri Lanka | [84] |
Analytical hierarchy process (AHP) | NDVI Different land use/land cover classification | IRS-P6 LISS III | China | [14] |
Biophysical factors derivation | Vegetation cover, DEM and topographic variables (slope, stream erosivity-SPI, topographic wetness index TWI) | Landsat TM images ASTER DEM | South Africa | [92] |
USLE | Land transfer model (LTM) | Spot 5 | Malaysia | [13] |
RUSLE and Sediment yield | Normalized Difference Vegetation Index (NDVI) for C-factor generation | Landsat images (TM) | Iran | [143] |
Gully erosion detection | Normalized Difference Vegetation Index (NDVI) for vegetation cover | Unmanned Aerial Vehicles (UAVs) | Morocco | [144] |
Model Type | Country | Attributes | Techniques | Reference |
---|---|---|---|---|
Knowledge-based model | Iran | Elevation, slope degree, slope–length (LS), slope aspect, plan curvature, lithology, distance from the river, drainage density, distance from the road, use/land cover, topography wetness index (TWI), stream power index (SPI), land normalized difference vegetation index (NDVI), | Analytic Hierarchy Process (AHP) | [150] |
Statistical Models | Iran | Soil texture, lithology, altitude, slope angle, slope aspect, plan curvature, land use, topographic wetness index (TWI), drainage density and distance from rivers | Certainty Factor (CF), a bivariate statistical model; | [163] |
Machine learning model | India | Soil type, altitude, Slope gradient, slope aspect, plan curvature, land use, slope length (LS), drainage density, topographical wetness index (TWI), distance from the river and road, distance from the lineament, | Flexible discriminant analysis (FDA), Random forest (RF), Multivariate additive regression splines (Jin et al.) and Support Vector Machine (SVM). | [157] |
Machine learning model | Iran | Elevation, slope degree, slope aspect, plan curvature, profile curvature, catchment area, stream power index, topographic position index, topographic wetness index, land use and normalized difference vegetation index | Generalized linear model, boosted regression tree (BRT), multivariate adaptive regression spline and artificial neural network (ANN). | [164] |
Machine learning model | Australia | Digital elevation model, Annual precipitation, Geology, Temperature, land-use, soil characteristics, distance to the river and so on | Random forest | [165] |
Knowledge-based model | China | Topographic factors, Vegetation cover and land use | Remote sensing techniques with visual interpretations | [161] |
Knowledge-based model | Morocco | Slop, Specific catchment area, Flow direction, stream power index, Sediment transport capacity index, NDVI | Object-based image analysis | [166] |
Hybrid method | Iran | Elevation, slope, soil type, lithology, plan curvature, stream power index (SPI), topographic wetness index (TWI), distance to road, distance to stream, drainage density, land use/land cover and rainfall | Weighted regression (GWR), Certainty factor (CF) and Random forest (RF) | [167] |
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Senanayake, S.; Pradhan, B.; Huete, A.; Brennan, J. A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management. Remote Sens. 2020, 12, 4063. https://doi.org/10.3390/rs12244063
Senanayake S, Pradhan B, Huete A, Brennan J. A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management. Remote Sensing. 2020; 12(24):4063. https://doi.org/10.3390/rs12244063
Chicago/Turabian StyleSenanayake, Sumudu, Biswajeet Pradhan, Alfredo Huete, and Jane Brennan. 2020. "A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management" Remote Sensing 12, no. 24: 4063. https://doi.org/10.3390/rs12244063
APA StyleSenanayake, S., Pradhan, B., Huete, A., & Brennan, J. (2020). A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management. Remote Sensing, 12(24), 4063. https://doi.org/10.3390/rs12244063