scholarly journals Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

2019 ◽  
Vol 11 (9) ◽  
pp. 1106 ◽  
Author(s):  
Dimitrios D. Alexakis ◽  
Evdokia Tapoglou ◽  
Anthi-Eirini K. Vozinaki ◽  
Ioannis K. Tsanis
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Erosion ◽  
Soil Properties ◽  
Satellite Remote Sensing ◽  
Soil Loss ◽  
Assessment Model ◽  
Landsat 8 ◽  
Field Spectroscopy ◽  
K Factor

Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.

scholarly journals A Remote Sensing Based Method to Detect Soil Erosion in Forests

2019 ◽  
Vol 11 (5) ◽  
pp. 513 ◽  
Author(s):  
Hanqiu Xu ◽  
Xiujuan Hu ◽  
Huade Guan ◽  
Bobo Zhang ◽  
Meiya Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Southern China ◽  
Red Soil ◽  
Forest Area ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Five Factors

Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.

scholarly journals Digital mapping of soil erodibility for water erosion in New South Wales, Australia

Soil Research ◽  
2018 ◽  
Vol 56 (2) ◽  
pp. 158 ◽  
Author(s):  
Xihua Yang ◽  
Jonathan Gray ◽  
Greg Chapman ◽  
Qinggaozi Zhu ◽  
Mitch Tulau ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Soil Properties ◽  
Soil Loss ◽  
New South ◽  
New South Wales ◽  
Field Measurements ◽  
Soil Erodibility ◽  
South Wales ◽  
Australian Continent ◽  
K Factor

Soil erodibility represents the soil’s response to rainfall and run-off erosivity and is related to soil properties such as organic matter content, texture, structure, permeability and aggregate stability. Soil erodibility is an important factor in soil erosion modelling, such as the Revised Universal Soil Loss Equation (RUSLE), in which it is represented by the soil erodibility factor (K-factor). However, determination of soil erodibility at larger spatial scales is often problematic because of the lack of spatial data on soil properties and field measurements for model validation. Recently, a major national project has resulted in the release of digital soil maps (DSMs) for a wide range of key soil properties over the entire Australian continent at approximately 90-m spatial resolution. In the present study we used the DSMs and New South Wales (NSW) Soil and Land Information System to map and validate soil erodibility for soil depths up to 100 cm. We assessed eight empirical methods or existing maps on erodibility estimation and produced a harmonised high-resolution soil erodibility map for the entire state of NSW with improvements based on studies in NSW. The modelled erodibility values were compared with those from field measurements at soil plots for NSW soils and revealed good agreement. The erodibility map shows similar patterns as that of the parent material lithology classes, but no obvious trend with any single soil property. Most of the modelled erodibility values range from 0.02 to 0.07 t ha h ha–1 MJ–1 mm–1 with a mean (± s.d.) of 0.035 ± 0.007 t ha h ha–1 MJ–1 mm–1. The validated K-factor map was further used along with other RUSLE factors to assess soil loss across NSW for preventing and managing soil erosion.

scholarly journals Application of RUSLE integrated with GIS and remote sensing techniques to assess soil erosion in Anambra State, South-Eastern Nigeria

2020 ◽  
Vol 11 (S1) ◽  
pp. 407-422 ◽  
Author(s):  
Fidelis Odedishemi Ajibade ◽  
Nathaniel Azubuike Nwogwu ◽  
Bashir Adelodun ◽  
Taofeeq Sholagberu Abdulkadir ◽  
Temitope Fausat Ajibade ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Landsat 8 ◽  
Gis And Remote Sensing ◽  
Anambra State ◽  
Remote Sensing Techniques ◽  
Soil Loss Equation

Abstract Soil erosion and mass movement processes spread across Anambra State in Nigeria, therefore making management and conservation techniques expensive and difficult in execution across the entire state. This study employed the Revised Universal Soil Loss Equation (RUSLE) model with the integration of geographic information system (GIS) and remote sensing techniques to assess the risk of soil erosion and hotspots in the area. Remotely sensed data such as Landsat 8 imagery, Shuttle Radar Topography Mission (SRTM) imagery, Era-Interim coupled with world soil database were used as digital data sources for land use map, digital elevation model, rainfall and soil data, respectively, to generate the Universal Soil Loss Equation (USLE) parameters. The results indicated vulnerability levels in low, medium and high cover areas of 4,143.62 (91%), 332.29 (7%) and 84.06 (2%) km2, respectively, with a total soil loss between 0 and 181.237 ton/ha/yr (metric ton per hectare per year). This study revealed that high rainfall erosivity, steep and long slopes, and low vegetation cover were the main factors promoting soil loss in the area. Thus, the amount of soil loss in Anambra State is expected to increase with climate change and anthropogenic activities.

scholarly journals Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)

2022 ◽  
Vol 14 (2) ◽  
pp. 348
Author(s):  
Yashon O. Ouma ◽  
Lone Lottering ◽  
Ryutaro Tateishi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Soil Degradation ◽  
Bare Soil ◽  
Landsat 8 ◽  
Arid Environments ◽  
Degradation Index ◽  
Erosivity Index ◽  
Loss Index ◽  
Very High

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha−1 year−1, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha−1 year−1) and very high (3.053–5.854 ton ha−1 year−1) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better that the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

scholarly journals RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Landsat 8 ◽  
Land Resources ◽  
Rusle Model ◽  
Factor C ◽  
Significant Factors

Abstract Background: The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. The annual dramatic increment of the depletion of very important soil nutrients exposes the residents of this catchment to high expenses of money to use artificial fertilizers to increase the yield. This paper was conducted in Fincha Catchment where the soil is highly vulnerable to erosion, however, where such studies are not undertaken. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss, where such studies are not undertaken, by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Results: Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations (2010-2019) and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. Conclusion: Finally, it was concluded that having information about the spatial variability of soil loss severity map generated in the RUSLE model has a paramount role to alert land resources managers and all stakeholders in controlling the effects via the implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

scholarly journals Change Detection and Land Suitability Analysis for Extension of Potential Forest Areas in Indonesia Using Satellite Remote Sensing and GIS

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 398 ◽  
Author(s):  
Nety Nurda ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Satellite Remote Sensing ◽  
Land Suitability ◽  
Landsat 8 ◽  
Suitability Analysis ◽  
Land Suitability Analysis ◽  
Expert Opinions ◽  
Detection Analysis ◽  
The Government

The objective of this research was to detect changes in forest areas and, subsequently, the potential forest area that can be extended in the South Sumatra province of Indonesia, according to the Indonesian forest resilience classification zones. At first, multispectral satellite remote sensing datasets from Landsat 7 ETM+ and Landsat 8 OLI were classified into four classes, namely urban, vegetation, forest and waterbody to develop Land Use/Land Cover (LULC) maps for the year 2003 and 2018. Secondly, criteria, namely distance from rivers, distance from roads, elevation, LULC and settlements were selected and the reclassified maps were produced from each of the criteria for the land suitability analysis for forest extension. Thirdly, the Analytical Hierarchy Process (AHP) was incorporated to add expert opinions to prioritize the criteria referring to potential areas for forest extension. In the change detection analysis, Tourism Recreation Forest (TRF), Convertible Protection Forest (CPF) and Permanent Production Forest (PPF) forest zones had a decrease of 20%, 13% and 40% in area, respectively, in the forest class from 2003 to 2018. The Limited Production Forest (LPF) zone had large changes and decreased by 72% according to the LULC map. In the AHP method, the influential criteria had higher weights and ranked as settlements, elevation, distance from roads and distance from rivers. CPF, PPF and LPF have an opportunity for extension in the highly suitable classification (30%) and moderately suitable classification (41%) areas, to increase coverage of production forests. Wildlife Reserve Forests (WRFs) have potential for expansion in the highly suitable classification (30%) and moderately suitable classification (52%) areas, to keep biodiversity and ecosystems for wildlife resources. Nature Reserve Forests (NRFs) have an opportunity for extension in the highly suitable classification (39%) and moderately suitable classification (48%) areas, to keep the forests for nature and biodiversity. In case of TRF, there is limited scope to propose a further extension and is required to be managed with collaboration between the government and the community.

Alternative production systems' effects on the K-factor of the Revised Universal Soil Loss Equation

1997 ◽  
Vol 12 (2) ◽  
pp. 55-58 ◽  
Author(s):  
Kim L. Fleming ◽  
William L. Powers ◽  
Alice J. Jones ◽  
Glenn A. Helmers
Keyword(s):  
Organic Matter ◽  
Soil Loss ◽  
Production Systems ◽  
Sandy Loam ◽  
Organic Matter Content ◽  
Universal Soil Loss Equation ◽  
Soil Erodibility ◽  
Silty Clay ◽  
K Factor ◽  
Soil Loss Equation

AbstractThe soil erodibility factor (K) of the Revised Universal Soil Loss Equation is currently considered a constant for all soils in the same type, regardless of production practice. To examine the effect of alternative production systems on the K-factor we compared pairs of alternatively and conventionally farmed fields on a Judson silt loam (Fine-silty, mixed, mesic Cumulic Hapludolls), a Yutan silty clay loam (Fine-silty, mixed, mesic Mollic Hapludalf), and a Wann fine sandy loam (Coarse-loamy, mixed, mesic Fluvaquentic Haplustolls). Soil cores were taken from the surface 10 cm and analyzed for organic matter, permeability, structure, and texture. These data were used to estimate K-factors from a nomograph. All soils in the study had a fine granular structure. Organic matter content and permeability were significantly higher for the alternatively managed field at every location, except for no difference in permeability on the Judson soil. However, the K-factor was significantly lower for the alternative system on the Judson soil. Of all the parameters, texture has the greatest influence in determining K-factors within the nomograph, with soils higher in silt being more erodible than soils higher in sand or clay. Thus, the influences of alternative production systems affected the Judson soil to a greater degree than other textures because of its higher inherent susceptibility to erosion. This study shows that alternative production systems affect the K-factor of some soil types and can reduce soil erodibility, and therefore should be considered when developing conservation plans.

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