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

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.

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Straw mulch impact on soil properties and initial soil erosion processes in the maize field

10.5194/egusphere-egu21-2538 ◽

2021 ◽

Author(s):

Ivan Dugan ◽

Leon Josip Telak ◽

Iva Hrelja ◽

Ivica Kisić ◽

Igor Bogunović

Keyword(s):

Soil Erosion ◽

Soil Water ◽

Soil Loss ◽

Bare Soil ◽

Water Erosion ◽

Straw Mulch ◽

Erosion Processes ◽

Maize Field ◽

Sediment Loss ◽

Initial Soil

Straw mulch impact on soil properties and initial soil erosion processes in the maize fieldIvan Dugan*, Leon Josip Telak, Iva Hrelja, Ivica Kisic, Igor BogunovicUniversity of Zagreb, Faculty of Agriculture, Department of General Agronomy, Zagreb, Croatia(*correspondence to Ivan Dugan: [email protected])Soil erosion by water is the most important cause of land degradation. Previous studies reveal high soil loss in conventionally managed croplands, with recorded soil losses high as 30 t ha-1 under wide row cover crop like maize (Kisic et al., 2017; Bogunovic et al., 2018). Therefore, it is necessary to test environmentally-friendly soil conservation practices to mitigate soil erosion. This research aims to define the impacts of mulch and bare soil on soil water erosion in the maize (Zea mays&#160;L.) field in Blagorodovac, Croatia (45&#176;33&#8217;N; 17&#176;01&#8217;E; 132 m a.s.l.). For this research, two treatments on conventionally tilled silty clay loam Stagnosols were established, one was straw mulch (2 t ha-1), while other was bare soil. For purpose of research, ten rainfall simulations and ten sampling points were conducted per each treatment. Simulations were carried out with a rainfall simulator, simulating a rainfall at an intensity of 58 mm h-1, for 30 min, over 0.785 m2 plots, to determine runoff and sediment loss. Soil core samples and undisturbed samples were taken in the close vicinity of each plot. The results showed that straw mulch mitigated water runoff (by 192%), sediment loss (by 288%), and sediment concentration (by 560%) in addition to bare treatment. The bare treatment showed a 55% lower infiltration rate. Ponding time was higher (p < 0.05) on mulched plots (102 sec), compared to bare (35 sec), despite the fact that bulk density, water-stable aggregates, water holding capacity, and mean weight diameter did not show any difference (p > 0.05) between treatments. The study results indicate that straw mulch mitigates soil water erosion, because it immediately reduces runoff, and enhances infiltration. On the other side, soil water erosion on bare soil under simulated rainstorms could be high as 5.07 t ha-1, when extrapolated, reached as high as 5.07 t ha-1 in this study. The conventional tillage, without residue cover, was proven as unsustainable agro-technical practice in the study area.Key words: straw mulch, rainfall simulation, soil water erosionAcknowledgmentThis work was supported by Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO).LiteratureBogunovic, I., Pereira, P., Kisic, I., Sajko, K., Sraka, M. (2018). Tillage management impacts on soil compaction, erosion and crop yield in Stagnosols (Croatia). Catena, 160, 376-384.Kisic, I., Bogunovic, I., Birk&#225;s, M., Jurisic, A., Spalevic, V. (2017). The role of tillage and crops on a soil loss of an arable Stagnic Luvisol. Archives of Agronomy and Soil Science, 63(3), 403-413.

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COMPUTER ASSISTED MAPPING OF SOIL EROSION POTENTIAL

Canadian Journal of Soil Science ◽

10.4141/cjss85-045 ◽

1985 ◽

Vol 65 (3) ◽

pp. 411-418 ◽

Cited By ~ 3

Author(s):

T. VOLD ◽

M. W. SONDHEIM ◽

N. K. NAGPAL

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Surface Soil ◽

Mineral Soil ◽

Weighted Average ◽

Bare Soil ◽

Computer Assisted ◽

Additional Information ◽

Potential Map ◽

Fraser Valley

Soil erosion potential maps and summary statistics can be produced from existing information with relative ease with the aid of computers. Soil maps are digitized and survey information is stored as attributes for each soil. Algorithms are then prepared which evaluate the appropriate data base attributes (e.g. texture, slope) for each interpretation. Forty surface soil erosion potential maps were produced for the Lower Fraser Valley which identify the most erosion-prone areas and indicate average potential soil losses to be expected under assumed conditions. The algorithm developed follows the universal soil loss equation. Differences across the landscape in the R, K, and S factors are taken into account whereas the L factor is considered as a constant equal to 1.0. Worst conditions of bare soil (no crop cover, i.e. C = 1.0) and no erosion control practices (i.e. P = 1.0) are assumed. The five surface soil erosion potential classes are determined by a weighted average annual soil loss value based both on the upper 20 cm of mineral soil and on the proportion of the various soils in the polygon. A unique polygon number shown on the erosion potential map provides a link to computer tables which give additional information for each individual soil within that polygon. Key words: Erosion, computer mapping, USLE

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

10.21203/rs.3.rs-250848/v1 ◽

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.

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A Remote Sensing Based Method to Detect Soil Erosion in Forests

Remote Sensing ◽

10.3390/rs11050513 ◽

2019 ◽

Vol 11 (5) ◽

pp. 513 ◽

Cited By ~ 4

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.

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Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments

Remote Sensing ◽

10.3390/rs12091466 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1466 ◽

Cited By ~ 1

Author(s):

Hitesh Supe ◽

Ram Avtar ◽

Deepak Singh ◽

Ankita Gupta ◽

Ali P. Yunus ◽

...

Keyword(s):

Land Surface ◽

Power Plants ◽

Remote Sensing Data ◽

Bare Soil ◽

Google Earth ◽

Landsat 8 ◽

Arid Environments ◽

Solar Panels ◽

Google Earth Engine ◽

Sand Deposition

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.

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Soil loss by water erosion in areas under maize and jack beans intercropped and monocultures

Ciência e Agrotecnologia ◽

10.1590/s1413-70542014000200004 ◽

2014 ◽

Vol 38 (2) ◽

pp. 129-139 ◽

Cited By ~ 2

Author(s):

Pedro Luiz Terra Lima ◽

Marx Leandro Naves Silva ◽

Nilton Curi ◽

John Quinton

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Soil Management ◽

Bare Soil ◽

Water Erosion ◽

Management Systems ◽

Water Runoff ◽

Jack Bean ◽

Favorable Conditions ◽

Soil Losses

Adequate soil management can create favorable conditions to reduce erosion and water runoff, consequently increase water soil recharge. Among management systems intercropping is highly used, especially for medium and small farmers. It is a system where two or more crops with different architectures and vegetative cycles are explored simultaneously at the same location. This research investigated the effects of maize intercropped with jack bean on soil losses due to water erosion, estimate C factor of Universal Soil Losses Equation (USLE) and how it can be affected by soil coverage. The results obtained also contribute to database generation, important to model and estimate soil erosion. Total soil loss by erosion caused by natural rain, at Lavras, Minas Gerais, Brazil, were: 4.20, 1.86, 1.38 and 1.14 Mg ha-1, respectively, for bare soil, maize, jack bean and the intercropping of both species, during evaluated period. Values of C factor of USLE were: 0.039, 0.054 and 0.077 Mg ha Mg-1 ha-1 for maize, jack bean and intercropping between both crops, respectively. Maize presented lower vegetation cover index, followed by jack beans and consortium of the studied species. Intercropping between species showed greater potential on soil erosion control, since its cultivation resulted in lower soil losses than single crops cultivation, and this aspect is really important for small and medium farmers in the studied region.

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GIS Based Universal Soil Erosion Estimation in District Chakwal Punjab, Pakistan

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol11.iss2.2020.443 ◽

2020 ◽

Vol 11 (2) ◽

pp. 30-36

Author(s):

Saima Siddiqui ◽

Mirza Wajid Ali Safi ◽

Aqil Tariq ◽

Naveed Ur Rehman ◽

Syed Waseem Haider

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Management Practices ◽

Rainwater Harvesting ◽

Landsat 8 ◽

River Channels ◽

Slope Length ◽

Raster Calculator ◽

Aster Gdem ◽

Steep Slopes

Soil erosion is a serious environmental problem faced by district Chakwal. Unpredictable short term and high intensity rainfall, improper cultivation and deforestation have accelerated the soil erosion in the district. The agricultural productivity of the study area can be enhanced by understanding, estimating and controlling the root causes of soil erosion. This study was undertaken to estimate and spatially represent the rate of average annual soil erosion in Chakwal using GIS/RS techniques. The soil erosion was estimated using Universal Soil Loss Equation (USLE) model. To find out parameters of USLE, ASTER GDEM of 30 m resolution was used to estimate slope length and elevation of the study area. Landsat 8 satellite imagery of year 2019, was used to prepare land use map using supervised classification. Soil map with texture and geomorphology was used to identify soils of study area and rainfall data of last 7 years was also studied. Finally, the soil loss has been computed using raster calculator of ArcGIS 10.2 software. The average annual soil loss was predicted up to 268,619 tons/acre/year, of which maximum soil erosion was occurring near the steep slopes and river channels. It is necessary to adapt sustainable land management practices to reduce the risk of further soil erosion, by adopting rainwater harvesting and choosing right crops for suitable soil types.

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Status, processes, and drivers of soil degradation in the Mediterranean region

10.5194/egusphere-egu21-10093 ◽

2021 ◽

Author(s):

Carla S. S. Ferreira ◽

Samaneh Seifollahi-Aghmiuni ◽

Georgia Destouni ◽

Marijana Solomun ◽

Navid Ghajarnia ◽

...

Keyword(s):

Climate Change ◽

Organic Matter ◽

Soil Organic Matter ◽

Soil Erosion ◽

Soil Degradation ◽

Mediterranean Region ◽

High Sensitivity ◽

Degradation Processes ◽

The Mediterranean ◽

Very High

Soil supports life on Earth and provides several goods and services of essence for human wellbeing. Over the last century, however, intensified human activities and unsustainable management practices, along with ongoing climate change, have been degrading soils&#8217; natural capital, pushing it towards possible critical limits for its ability to provide essential ecosystem services. Soil degradation is characterized by negative changes in soil health status that may lead to partial or total loss of productivity and overall capacity to support human societies, e.g., against increasing climate risks. Such degradation leads to environmental, social and economic losses, which may in turn trigger land abandonment and desertification. In particular, the Mediterranean region has been identified as one of the most vulnerable and severely affected European regions by soil degradation, where the actual extent and context of the problem is not yet well understood. This study provides an overview of current knowledge about the status of soil degradation and its main drivers and processes in the European Mediterranean region, based on comprehensive literature review. In the Mediterranean region, 34% of the land area is subject to &#8216;very high sensitivity&#8217; or &#8216;high sensitivity&#8217; to desertification, and risk of desertification applies to over more than 65% of the territory of some countries, such as Spain and Cyprus (IPCC, 2019). The major degradation processes are: (i) soil erosion, due to very high erosion rates (>2 t/ha); (ii) loss of soil organic matter, due to high mineralization rates while the region is already characterized by low or very low soil organic matter (<2%); and (iii) soil and water salinisation, due to groundwater abstraction and sea water intrusion. However, additional physical, chemical and biological degradation processes, such as soil sealing and compaction, contamination, and loss of biodiversity, are also of great concern. Some of the degradation processes, such as soil erosion, have been extensively investigated and their spatial extent is relatively well described. Other processes, however, such as soil biodiversity, are poorly investigated and have limited data availability. In general, a lack of systematic inventories of soil degradation status limits the overall knowledge base and impairs understanding of the spatial and temporal dimensions of the problem. In terms of drivers, Mediterranean soil degradation has mainly been driven by increasing population, particularly in coastal areas, and its concentration in urban areas (and consequent abandonment of rural areas), as well as by land-use changes and intensification of socio-economic activities (e.g. agriculture and tourism). Additionally, climate change, with increasing extent and severity of extreme events (droughts, floods, wildfires), may also be a key degradation driver in this region. Improved information on soil degradation status (including spatio-temporal extent and severity) and enhanced knowledge of degradation drivers, processes and socio-economic, ecological, and biodiversity impacts are needed to better support regional soil management, policy, and decision making. Science and evidence based improvements of soil resource governance and management can enhance soil resilience to regional and global changes, and support the region to achieve related Sustainable Development Goals and the Land Degradation Neutrality targets.

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Effects of Long-Term Crop-Livestock-Forestry Systems on Soil Erosion and Water Infiltration in a Brazilian Cerrado Site

Sustainability ◽

10.3390/su11195339 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5339 ◽

Cited By ~ 5

Author(s):

Jullian Souza Sone ◽

Paulo T. Sanches de Oliveira ◽

Pedro A. Pereira Zamboni ◽

Nelson O. Motta Vieira ◽

Glauber Altrão Carvalho ◽

...

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Agricultural Land ◽

Bare Soil ◽

Water Infiltration ◽

Land Uses ◽

Brazilian Cerrado ◽

No Till ◽

One Year

Integrating agricultural land uses is a suitable alternative for fostering economic development and improving food security. However, the effects of long-term integrated systems on soil erosion and water infiltration are still poorly understood. Here, we investigate the influence of different agricultural land uses on soil erosion and water infiltration in an Oxisol site located in the Brazilian Cerrado region. The experimental area consisted of continuous grazing under variable stocking rates with regular fertilization (CG-RF), continuous cropping under no-till (CC-NT) and no-till with 4-year subsoiling (CC-SS), rotation of one year cropping and three years livestock in the livestock phase (C1-L3), rotation of four years cropping and four years livestock in the cropping phase (CL-4C) and in the livestock phase (CL-4L), and integrated crop-livestock-forestry in the cropping phase (CLF-C) and in the livestock phase (CLF-L). To evaluate water infiltration and soil loss, we used a rainfall simulator with a constant rainfall intensity of 74.9 ± 3.6 mm h−1 in plots of 0.7 m2. We carried out 72 rainfall simulations comprising four repetitions in each treatment under vegetation and bare soil. Stable infiltration rate (SIR) ranged from 45.9 to 74.8 mm h−1 and 19.4 to 70.8 mm h−1 under vegetation covers and bare soil, respectively. Our findings indicated that SIR values under CLF-C were 60% greater than under CG-RF. We also found that soil loss rates under CLF-C were 50% smaller than under CG-RF. The crop–livestock rotation period that presented better results of SIR and soil loss was one year of cropping and three years of livestock (C1-L3). Overall, we noted that SIR and soil loss values under CLF-C are similar to the Cerrado native vegetation. Therefore, our study reveals the opportunity to increase agricultural production, improve food supply, and reduce soil erosion with adequate soil and agricultural management.

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Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach

Applied Sciences ◽

10.3390/app11094154 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4154

Author(s):

Siniša Polovina ◽

Boris Radić ◽

Ratko Ristić ◽

Jovan Kovačević ◽

Vukašin Milčanović ◽

...

Keyword(s):

Soil Erosion ◽

Land Cover ◽

Urban Areas ◽

Soil Loss ◽

Soil Degradation ◽

Machine Learning Techniques ◽

Slight Reduction ◽

Master Plan ◽

Time Periods ◽

The Impact

Soil erosion is a global problem that negatively affects the quality of the environment, the availability of natural resources, as well as the safety of inhabitants. Soil erosion threatens the functioning of urban areas, which was the reason for choosing the territory of the Master Plan of Belgrade (Serbia) as the research area. The calculation of soil erosion loss was analyzed using the G2 erosion model. The model belongs to a group of empirical models and is based on the synthesis of the equation from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM). The estimation of soil degradation was analyzed in two time periods (2001 and 2019), which represent the time boundaries of the management of the Master Plan of Belgrade. The novel approach used in this research is based on using the land cover inventory as a dynamic indicator of the urbanization process. Land cover was identified using remote sensing, machine learning techniques, and the random forest algorithm applied to multispectral satellite images of the Landsat mission in combination with spectral indices. Climatic parameters were analyzed on the basis of data from meteorological stations (first scenario, i.e., 2001), as well as on simulations of changes based on climate scenario RCP8.5 (representative concentration pathways) concerning the current condition of the land cover (second scenario). A comparative analysis of the two time periods identified a slight reduction in total soil loss. For the first period, the average soil loss value is 4.11 t·ha−1·y−1. The analysis of the second period revealed an average value of 3.63 t·ha−1·y−1. However, the increase in non-porous surfaces has led to a change in the focus of soil degradation. Increased average soil loss as one of the catalysts of torrential flood frequencies registered on natural and semi-natural areas were 43.29% and 16.14%, respectively. These results are a significant contribution to the study of soil erosion in urban conditions under the impact of climate change.

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