Soil erosion assessment using earth observation data in a trans-boundary river basin

Singh, Sudhir Kumar

doi:10.1007/s11069-021-04571-6

Cited by 52 publications

(13 citation statements)

References 70 publications

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“…The ever‐emerging remote sensing and geographic information system (GIS)‐based topographic analysis with a digital elevation model (DEM) come in handy for quick surveying and erosion estimation; with machine learning techniques they have been found to be the most efficient way to study gullies at local, regional, and global scale (Yang et al, 2019). Starting with the universal soil loss equation (USLE), revised‐USLE (RUSLE), brightness index, and more recent deep learning models, these approaches employing multiple remotely sensed data have been widely implemented to map gully erosion, analyze causative factors and control gully erosion, assess susceptibility to gulling and quantifying erosion‐fill volume changes with precision (Kou et al, 2020; Kumar & Singh, 2021; Pal et al, 2021; Valentin et al, 2005; Vieira et al, 2021). Very recently, Kumar et al (2021) have proposed a novel approach to assess future soil erosion under different climate projection scenarios.…”

Section: Introductionmentioning

confidence: 99%

Towards evaluating gully erosion volume and erosion rates in the Chambal badlands, Central India

et al. 2022

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High-resolution multi-temporal digital elevation model (DEM) are key to accurate mapping of gully erosion volume change studies. Owing to the lack of multi-temporal DEM at a high spatial resolution, gully development rate, and gully erosion-fill volume change estimates in the Indian badlands are poorly studied. Our study explored the use of multi-temporal TerraSAR-X add-on for digital elevation measurement (TanDEM-X) derived elevation models to quantify the erosion volume and gully susceptibility mapping in the Chambal badlands, Central India. The average volume of gully erosion based on the DEM subtraction method in the study area was found to be 135 Â 10 5 m 3 , and the estimated annual rate of soil erosion was $284 t hr À1 yr À1 . Using machine learning models, we trained these data for gully erosion susceptibilities and volume prediction for a larger study region; and validated the results with independent samples. The accuracy of the model in terms of area under the receiver operating curve (AUC) values has reached 0.85 for training and 0.87 for validation, indicating satisfactory model performance. After validation, the best fit model was implemented onto a testing site (no multi-temporal DEM available) in order to predict erosion zones and erosion volume estimation. The model predicted that about 40% of the area is highly affected by gully erosion, with the maximum gullying process in the north-Central and lowest in the southwest parts of the testing area. The research framework presented in this study can be useful in estimating the erosion rate in the badlands of the Chambal Valley and can be used effectively in ravine reclamation projects.

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Section: Introductionmentioning

confidence: 99%

Towards evaluating gully erosion volume and erosion rates in the Chambal badlands, Central India

et al. 2022

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“…The RUSLE is a popular model applied in several studies to assess the rill and interrill soil erosion (Kulimushi et al 2021;Kumar and Singh 2021).…”

Section: Soil Loss Estimationmentioning

confidence: 99%

“…Soil erosion is the leading cause of land degradation worldwide (Ebabu 2019;Kumar and Singh 2021). It alone contributes approximately 46% of the land degradation worldwide (Biggelaar et al 2003), and is responsible for the 15-30 billion tons of the annual sediment transported by the world's rivers into the ocean (Thomas et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of soil erosion and sediment yield spatio-temporal pattern during 1990–2019

Kulimushi

Maniragaba

Choudhari

et al. 2021

Geomatics, Natural Hazards and Risk

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Land use/land cover (LULC) change is an important measure to monitor the land degradation. This study focused on the Sebeya watershed located in the western province of Rwanda to evaluate the soil erosion and sediment yield spatio-temporal pattern from 1990 to 2019 occurred due to LULC change. Multi-temporal LULC maps of 1990-2019 and RUSLE model were used to achieve the objectives. The sediment yield was computed as a product of sediment delivery ratio (SDR) and the gross soil erosion. From the period 1990-2019, each year forestland decreased by 2.6%/yr, whereas the grassland, cropland, and settlement increased by 23.6%/yr, 14.5%/yr, and 81.6%/yr respectively. As a result, the mean soil erosion for 1990, 2000, 2010, and 2019 was 14.7, 28.3, 34, and 34.7 t ha À1 yr À1 whereas the total sediment yield was 3254.15, 5029.8, 6814.56, and 9074.04 t yr À1 respectively. Cropland was an exception to the other LULC classes because it has generated soil erosion in very high severity > 50 t ha À1 yr À1 , while this severity class showed an upward trend. The study demonstrated that LULC change has a significant impact on soil erosion and sediment yield and suggests proper land-use planning and encourages afforestation to arrest soil loss.

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“…In recent years, with the development of Remote Sensing and Geographic Information System (GIS), many digital elevation model (DEM)-derived geomorphic indexes have been developed, and they are now widely used for quantitatively describing the shape and dimension of the Earth's landforms and analyzing the geomorphic evolution and the surface process (Mahmood and Gloaguen, 2012;Liu et al, 2019;Radaideh and Mosar, 2019;Ayaz and Dhali, 2020;Kumar and Singh, 2021). Among them, the drainage density (D d ), defined as the ratio between the cumulative length of channels and the area of the drained basin (Horton, 1932), is one of the many physiographic properties of basins considered as an index of surface processes and the basic parameter to control and influence the hydrological characteristics of any basin (Horton, 1945).…”

Section: Introductionmentioning

confidence: 99%

Drainage Density and Its Controlling Factors on the Eastern Margin of the Qinghai–Tibet Plateau

et al. 2022

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The drainage density (Dd) is an important index to show fluvial geomorphology. The study on Dd is helpful to understand the evolution of the whole hydrological and geomorphic process. Based on the Shuttle Radar Topography Mission 90-m digital elevation model, the drainage network of basins along the eastern margin of the Qinghai–Tibet Plateau is extracted using a terrain morphology-based method in ArcGIS 10.3, and Dd is calculated. The spatial characteristics of Dd are analyzed, and the relationship between Dd and its influencing factors, e.g., the topography, precipitation, and vegetation coverage, is explored. Our results show that terrains with a plan curvature ≥3 can represent the channels in the study area. Dd ranges from 2.5 to 0.1 km/km2, increases first, and then decreases from north to south on the eastern margin of the Qinghai–Tibet Plateau. Dd decreases with increasing average slope and average local relief. On the low-relief planation surfaces, Dd increases with increasing altitude, while on the rugged mountainous above planation surfaces, Dd decreases rapidly with increasing altitude. Dd first increased and then decreased with increasing mean annual precipitation (MAP) and normalized difference vegetation index (NDVI), and Dd reaches a maximum in the West Qinling Mountains with a semi-arid environment, indicating that Dd in different climatic regions of the eastern margin of the Qinghai–Tibet Plateau was mainly controlled by precipitation and vegetation.

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Soil erosion assessment using earth observation data in a trans-boundary river basin

Cited by 52 publications

References 70 publications

Towards evaluating gully erosion volume and erosion rates in the Chambal badlands, Central India

Towards evaluating gully erosion volume and erosion rates in the Chambal badlands, Central India

Evaluation of soil erosion and sediment yield spatio-temporal pattern during 1990–2019

Drainage Density and Its Controlling Factors on the Eastern Margin of the Qinghai–Tibet Plateau

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