Relevant bibliographies by topics / Soil erosion Data processing

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Academic literature on the topic 'Soil erosion Data processing'

Author: Grafiati
Published: 11 December 2022
Last updated: 25 January 2023

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Journal articles on the topic "Soil erosion Data processing"

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Wahl, Tony L. "Methods for Analyzing Submerged Jet Erosion Test Data to Model Scour of Cohesive Soils." Transactions of the ASABE 64, no. 3 (2021): 785–99. http://dx.doi.org/10.13031/trans.14212.

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HighlightsFifty-two jet erosion tests performed on four cohesive soils were analyzed by nine different methods.Nonlinear methods performed well on some individual tests but fit inconsistently overall.Several alternate linear solution methods outperformed the widely used Blaisdell method.Simple linear regression of erosion rate versus applied shear stress provided the most consistent relationship between erosion rate and critical shear stress parameters.Abstract. The submerged jet erosion test (JET) is widely used in lab and field settings to quantify erodibility of cohesive soils and determine erosion rate coefficients and critical shear stress values. Test devices with different scales and configurations have been developed in recent years, along with several alternative methods for processing the collected data to determine parameters of linear and nonlinear soil erosion equations. To facilitate standardization, 52 JET experiments were conducted on four different cohesive soils compacted at optimum water content and 2% dry and wet of optimum. Each test was analyzed using nine different methods, four based on the linear excess stress equation (including the commonly used Blaisdell method) and five based on nonlinear erosion equations, including two using the recently popular Wilson model. Results were analyzed to determine the erosion equations and parameter-fitting methods that most effectively represent the observed erosion rates and are of greatest utility for soil erosion modeling and the ranking and classification of soils according to erodibility. Methods based on nonlinear erosion equations fit some data sets well, but they exhibited poor correlation between the erosion rate coefficient and the threshold shear stress parameter for initiating erosion, which is problematic for soil erodibility classification work. Linear methods that simultaneously optimized erosion equation parameters to best fit the total depth of scour or the elapsed time needed to reach specific depths of scour performed better than the Blaisdell method, which has been the informally accepted standard of practice since the late 1990s. However, they also exhibited weak correlation of the erosion rate and critical shear stress parameters. Simple linear regression of average scour rate versus average applied stress provided an effective method for representing the erosion rate versus applied stress curve and exhibited the strongest correlation of the erosion rate coefficient and critical shear stress parameters. Keywords: Cohesive soil, Critical shear stress, Erodibility, Erosion, Erosion laws, Erosion models, Jet erosion test, Shear strss, Soil moisture.
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Eltner, A., D. Schneider, and H. G. Maas. "INTEGRATED PROCESSING OF HIGH RESOLUTION TOPOGRAPHIC DATA FOR SOIL EROSION ASSESSMENT CONSIDERING DATA ACQUISITION SCHEMES AND SURFACE PROPERTIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 813–19. http://dx.doi.org/10.5194/isprs-archives-xli-b5-813-2016.

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Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.
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Eltner, A., D. Schneider, and H. G. Maas. "INTEGRATED PROCESSING OF HIGH RESOLUTION TOPOGRAPHIC DATA FOR SOIL EROSION ASSESSMENT CONSIDERING DATA ACQUISITION SCHEMES AND SURFACE PROPERTIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 813–19. http://dx.doi.org/10.5194/isprsarchives-xli-b5-813-2016.

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Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.
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Papaiordanidis, S., I. Z. Gitas, and T. Katagis. "Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE) cloud-based platform." Dokuchaev Soil Bulletin, no. 100 (January 3, 2020): 36–52. http://dx.doi.org/10.19047/0136-1694-2019-100-36-52.

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High-quality soils are an important resource affecting the quality of life of human societies, as well as terrestrial ecosystems in general. Thus, soil erosion and soil loss are a serious issue that should be managed, in order to conserve both artificial and natural ecosystems. Predicting soil erosion has been a challenge for many years. Traditional field measurements are accurate, but they cannot be applied to large areas easily because of their high cost in time and resources. The last decade, satellite remote sensing and predictive models have been widely used by scientists to predict soil erosion in large areas with cost-efficient methods and techniques. One of those techniques is the Revised Universal Soil Loss Equation (RUSLE). RUSLE uses satellite imagery, as well as precipitation and soil data from other sources to predict the soil erosion per hectare in tons, in a given instant of time. Data acquisition for these data-demanding methods has always been a problem, especially for scientists working with large and diverse datasets. Newly emerged online technologies like Google Earth Engine (GEE) have given access to petabytes of data on demand, alongside high processing power to process them. In this paper we investigated seasonal spatiotemporal changes of soil erosion with the use of RUSLE implemented within GEE, for Pindos mountain range in Greece. In addition, we estimated the correlation between the seasonal components of RUSLE (precipitation and vegetation) and mean RUSLE values.
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Lazovik, Hleb S., and Antonina A. Topaz. "Assessment of soil erosion hazard and its mapping using GIS technologies." Journal of the Belarusian State University. Geography and Geology, no. 2 (December 28, 2021): 18–31. http://dx.doi.org/10.33581/2521-6740-2021-2-18-31.

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The article presents a method for creating a territory erosion hazard integrated map using RUSLE integral model, Earth remote sensing data and GIS technologies. The studies carried out on this topic are presented, the analysis of which has shown a more active use of integral indicators of water-erosion processes in foreign scientific works. Urgency of updating methodology for studying erosion processes has been substantiated. Theoretical foundations of the application of integral models of soil erosion are given, the application of the RUSLE model is substantiated, and the optimal way of using this model is proposed. The research methodology has been developed, consisting of primary processing of remote sensing data, calculation of the factors of erosion development and creation of a territory erosion hazard integrated map. Based on the processing of aerial photography materials, a point cloud, a digital elevation model and an orthomosaic map of the study area were created. The results of the geoinformation analysis of the remote sensing data, which included calculation of the soil erodibility factor and the topographic factor, are presented. Based on the integral indicator of watererosion hazard, a complex map of the erosion hazard of the territory has been created. Main patterns of geographical distribution of the values of the integral indicator of the water-erosion hazard of the territory are revealed, devised methodology is assessed. It was found that the schematic map reflects the general pattern of water erosion processes: they are more active in places where more dissected relief is spread. Influence of the soil factor on the pattern of the schematic map is shown: the pattern in the territories occupied by sod-podzolic loamy soils qualitatively differs from the pattern on the lands where sod-podzolic sandy loam soils are widespread. Patterns on the schematic map of different parts of the developed linear forms of relief, formed by temporary streams, are described. It is shown that the proposed method can be used to assess the water-erosion hazard of the territory. The need to take into account a larger number of factors and to refine the assessment of existing ones is concluded.
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Oliveira, Bianca Souza de, Antonio Conceição Paranhos Filho, and Eliane Guaraldo. "Identification of erosive processes with free geotechnologies." Terr Plural 16 (September 2022): 1–17. http://dx.doi.org/10.5212/terraplural.v.16.2219806.023.

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Linear erosion is one of the types of water erosion that cause the most environmental problems due to the concentration of water flows that has great potential for land degradation. This work aims to identify areas of eroded soil that occur in the Paraíso River Watershed using free geotechnologies through the vectorization of erosion identified through the analysis of high spatial resolution satellite images freely available on the Google Earth platform. The results obtained point out that in the Paraíso River watershed most of the linear erosions are furrow-type features, the mildest form of this type of erosive process. A total of 463 erosion axes were identified, composed of furrows, ravines, and gullies. The temporal monitoring of images has elucidated the origin of the silting identified in a stretch of the Paraíso River near the MS-316 highway. Thus, the availability of high spatial resolution satellite images associated with the resources available for processing spatial data makes it possible to analyze extensive areas and identify erosive processes with greater agility, helping to identify the measures to be adopted to contain and/or recover the sites affected by this environmental problem.
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Bathrellos, G. D., H. D. Skilodimou, and K. G. Chousianitis. "SOIL EROSION ASSESMENT IN SOUTHERN EVIA ISLAND USING USLE AND GIS." Bulletin of the Geological Society of Greece 43, no. 3 (January 24, 2017): 1572. http://dx.doi.org/10.12681/bgsg.11331.

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In the present study the evaluation of soil erosion in Southern Evia Island was carried out. Data related with precipitation, morphology, land cover and lithology were collected. A spatial database was created and the further processing of the collected data was prepared using GIS. The Universal Soil Loss Equation (USLE) was used to predict the spatial distribution of the average annual rate of erosion. Five major factors were used to calculate the soil loss. These are rainfall erositivity (R), soil erodibility (K), slope length and steepness (LS), cropping management (C) and conservation supporting practice (P). Each factor is the numerical estimate of a specific condition that affects the severity of soil erosion. The obtained soil loss values were used to create the erosion risk map. The applied methodology provides a cost effected and rapid estimation of areas that are vulnerable to soil erosion and need immediate attention from soil conservation point of view. Moreover these results can be used to assist land use planning.
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SHTYKOV, Valery I., Andrey B. PONOMAREV, and Yury G. YANKO. "Discussing the calculation of erosion rates in the design of filtering structures in cohesive soils." Proceedings of Petersburg Transport University 2021, no. 2 (June 2021): 303–12. http://dx.doi.org/10.20295/1815-588x-2021-2-303-312.

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Objective: To develop a method for calculating erosion rates in the place of contact of filtering structures with cohesive soils. Methods: Previously at the VNIIG n. a. B. E. Vedeneev, hydrodynamic and strength criteria of contact suffusion were established concerning hydraulic structures. However, there were no calculated dependencies for determining the critical Reynolds number and, accordingly, the critical velocity for coarse-grained materials. Through special processing of experimental data on filtration in non-cavity drain fillers of various grades, it was possible to integrate numerous curves into a single one, making it possible to isolate the transient regime boundaries and obtain a formula for the critical Reynolds number. This made it possible to calculate the critical rate. As a result, a formula was obtained for calculating the erosion rate. Results: The calculated dependencies made it possible to determine the critical rate for the filler material and the erosion rate at the contact boundary between the filtering structure and the soil, based on the initial data of the coarse-grained material from which the filtering structures are made, and the characteristics of the soils in which they are built. Practical importance: The proposed calculation method made it possible to: 1) establish whether the manifestations of erosion by the filtration flow are possible in the place of contact of the filtering structure with the soil; 2) develop measures to eliminate erosion. Geotextile material can be laid along the border of the contact of the filtering structure with the soil, or a finer material can be used as a filler in the filtering structure, the actual filtration flow rates in which will be less than the erosional rates. In this case, the erosion rates in the place of contact with the finer material will increase.
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Dahal, Roshan. "Soil Erosion Estimation Using RUSLE Modeling and Geospatial Tool: Case Study of Kathmandu District, Nepal." Forestry: Journal of Institute of Forestry, Nepal 17 (December 23, 2020): 118–34. http://dx.doi.org/10.3126/forestry.v17i0.33627.

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Revised Universal Soil Loss Equation (RUSLE) model is applied in this study to evaluate the risk of erosion in Kathmandu district. The calculation of erosion requires certain data from various sources available in different formats and scales. Geographic Information System (GIS) was used which allowed considerable time savings in the processing of spatial data, screening the effects of each factor affecting soil erosion. Among various erosion factors, topography, rainfall, soil properties, and soil conservation practices were used for the study. Average soil loss was calculated by multiplying these factors. Final results of soil erosion rates were separated into six classes based on erosion severity, in which 2.18% of land (> 80Mg ha-1yr-1), followed by 2.85% of land (40-80 Mg ha-1yr-1), 5.56% of land (20-40 Mg ha-1yr-1), 8.73% of land (10-20 Mg ha-1yr-1), 10.53% of land (5-10 Mg ha-1yr-1) and 70.14% of land (0-5 Mg ha-1yr-1), falls under very severe, severe, very high, moderate and low severity zone respectively. Area having high slope length (LS) factor has high erosion rate. In Dakshinkali, Nagarjun and Budanilkantha area, there is high erosion rate. From the result, spatial distribution of soil erosion across Kathmandu district, can be applied for management and controlling the erosion.
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Csáfordi, Péter, Andrea Pődör, Jan Bug, and Zoltán Gribovsyki. "Soil Erosion Analysis in a Small Forested Catchment Supported by ArcGIS Model Builder." Acta Silvatica et Lignaria Hungarica 8, no. 1 (December 1, 2012): 39–56. http://dx.doi.org/10.2478/v10303-012-0004-5.

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Abstract - To implement the analysis of soil erosion with the USLE in a GIS environment, a new workflow has been developed with the ArcGIS Model Builder. The aim of this four-part framework is to accelerate data processing and to ensure comparability of soil erosion risk maps. The first submodel generates the stream network with connected catchments, computes slope conditions and the LS factor in USLE based on the DEM. The second submodel integrates stream lines, roads, catchment boundaries, land cover, land use, and soil maps. This combined dataset is the basis for the preparation of other USLE-factors. The third submodel estimates soil loss, and creates zonal statistics of soil erosion. The fourth submodel classifies soil loss into categories enabling the comparison of modelled and observed soil erosion. The framework was applied in a small forested catchment in Hungary. Although there is significant deviation between the erosion of different land covers, the predicted specific soil loss does not increase above the tolerance limit in any area unit. The predicted surface soil erosion in forest subcompartments mostly depends on the slope conditions.
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Dissertations / Theses on the topic "Soil erosion Data processing"

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Segarra, Eduardo. "A dynamic analysis of the crop productivity impacts of soil erosion: an application to the Piedmont area of Virginia." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/51930.

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This study was born out of the desire to analyze the complex soil management problem faced by individual economic agents as well as society. The focus of this study, however, was on the theoretical formulation and estimation of partial equilibrium dynamic economic models directed toward optimizing the private use of the soil resource. In particular, four empirical representative farm models were formulated. Solutions to the four representative farm models showed that sizable reductions in topsoil loss, which contributes to non-point source pollution, and aggravates the crop productivity impacts of soil erosion, can be accomplished by adopting alternative support practices. Because of the change in support practices, reductions in the present value of net returns are expected, but this decrease in return was found to be minimal when compared to reductions in topsoil loss. Policy implications as well as several policy recommendations stemming from those results, with respect to soil conservation, are outlined and analyzed.
Ph. D.
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林旭明 and Yuk-ming Lam. "Automation in soil testing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209774.

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White, Susan Mary. "Sediment yield estimation from limited data sets : a Philippines case study." Thesis, University of Exeter, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332300.

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Marr, Paul Gerard. "Approximating soil loss calculations with satellite data and multivariate regression analyses." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc798418/.

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Digital satellite remote sensing and Geographic Information Systems (GIS) have been used effectively to determine the Universal Soil Loss Equation (USLE) output for a number of North Texas watersheds. This method involves determining the values of each of the USLE factors and using these factors as information layers within the GIS.
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Ghassemi, Ali. "Nonparametric geostatistical estimation of soil physical properties." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63904.

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Skurupey, James. "A sensitivity analysis of uncertainty in the spatial resolution of the underlying data used for estimating soil erosion susceptibility in New Zealand." Thesis, University of Canterbury. School of Forestry, 2013. http://hdl.handle.net/10092/7827.

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This study investigates the effect of changes in map scale on the error in the development of areal map units and their associated erosion severity measurements of New Zealand’s (NZ) Land Use Capability (LUC) surveying system. A map scale of 1:50,000 was used in the underlying data (i.e., a LUC survey) of an Erosion Susceptibility Classification (ESC) system, which was developed by Bloomberg and others (2011) of the University of Canterbury for the Ministry for the Environment’s (MFE) 2010 proposed National Environmental Standard for Plantation Forestry. The ESC was intended for local erosion management decisions, yet most literature would classify the map scale of 1:50,000 as more appropriate for regional management issues. Thus, this study will test two finer 1:10,000 scale datasets against the current 1:50,000 national LUC areal map units and their erosion severity measurements of the underlying data for the ESC system, to quantify the level of agreement. This study first attempted to identify a unique discriminating parameter of high erosion severity. A case study was conducted in the Sherry River catchment, located in the Tasman District of the South Island, NZ. The Sherry River Case Study had two aims; the first was to investigate the correlation between the Melton ratio and LUC erosion severity. This was accomplished by calculating the Melton ratio, a tested morphometric factor that describes basin (watershed) ruggedness, using Irvine’s (2011) Geographic Information Systems (GIS) debris-flow model. The product of this GIS debris-flow model, a calculated Melton ratio ≥ 0.50 with the areal extent outlined by a River Environment Classification (REC) order one polygon, were designated the areas of interest (AOIs). The Melton ratio was then tested against LUC erosion severity using the Spearman’s Ranked Correlation Coefficient, within the designated AOIs. A field investigation was conducted to verify debris-flow in GIS identified AOIs. Only five of the thirteen AOIs identified showed evidence of debris-flow. Two were un-checked due to accessibility and the others had a high degree of fluvial activity, which indicates a high probability that surface evidence of alluvial erosion deposition was erased. Nominal association between the two measurements of erosion (Melton ratio and LUC erosion severity) was found at the map scales of 1:50,000 or 1:10,000. Therefore the Melton ratio was not recommended as an independent parameter of erosion severity. The second aim of the Sherry River Catchment study was to assess the sensitivity of empirically generalised LUC areal map units and their erosion severity measurements to spatial resolution, that is, what is the effect of agreement between the smallest measurable value when looking at LUC map units and their erosion severity measurements recorded at two different map scales. A hard classification accuracy assessment was chosen to accomplish this objective. An accuracy assessment is a statistical model, which provides a probability of error (uncertainty), in essence a goodness-of-fit measurement, and quantified the agreement between a sample and reference dataset. This was accomplished by the calculation of an Overall accuracy (i.e., overall thematic agreement), Producer’s accuracy, and a User’s accuracy analytical statistics. The Producer’s accuracy refers to the probability that an area of sampled erosion severity category in the sample map is classified as such according to the reference map, while the user’s accuracy refers to the probability that a point labelled as a certain erosion severity in the sample map has that severity rating in reality (i.e., according to the reference map). An accuracy assessment also includes a second goodness-of-fit test, the Kappa statistic (K ̂), which measures the agreement between the sample and references map as well as chance agreement. An accuracy assessment of the AOIs within the Sherry Catchment Study area using an 85% significance criterion was conducted. This accuracy assessment investigated a sample LUC survey measured at the map scale of 1:10,000, as compared to the referenced underlying data of the ESC (1:50,000 map scale). Overall accuracy was marginal (69%) with equally marginal levels of Producer’s and User’s accuracy. The Kappa statistic showed a marginal level of significance according to Landis and Koch (1977) (K ̂ = 44%). The disagreement seen between the two LUC surveys, which were empirically developed using different map scales, provides evidence of high spatial resolution sensitivity, when comparing areal map units and erosion severity measurements. To further investigate evidence of spatial resolution sensitivity in LUC surveying, a second case study was conducted using a LUC survey across a broad geographical area of the Manawatu-Wanganui Region of the North Island, NZ. A sample dataset from the LUC survey, empirically generalised at 1:10,000 map scale by the Horizons Regional Council, was compared to the referenced underlying data of the ESC. There was a moderately-strong consistency found between the assessors of each LUC survey using Spearman’s Ranked Correlation Coefficient. This provides evidence of limited surveyor bias, as each map was made using empirical judgment. The accuracy assessment’s overall agreement was 63% and as for the previous case study, had equally low Producer’s and User’s accuracy levels. The Kappa statistic for this case study was K ̂= 46%, a moderate chance agreement. This evidence, along with the evidence provided by the Sherry River Catchment Case study, suggested that the MFE’s ESC system is sensitive to changes in map scale and that any decision based on it will have different results when its underlying data is produced at different spatial resolutions. It is therefore recommended that MFE reassess the map scales and resolutions of its underlying data, given that the ESC’s purpose is for local level environmental management, before imposing the system as a regulatory requirement in the National Environmental Standards for Plantation Forestry.
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Yang, Wenwei, and 楊文衛. "Development and application of automatic monitoring system for standard penetration test in site investigation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36811919.

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Liao, Tianfei. "Post processing of cone penetration data for assessing seismic ground hazards, with application to the New Madrid seismic zone." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-05042005-133640/.

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Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2006.
Mayne, Paul W., Committee Chair ; Goldsman, David, Committee Member ; Lai, James, Committee Member ; Rix, Glenn J., Committee Member ; Santamarina, J. Carlos, Committee Member.
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Bussi, Gianbattista. "Implementation of a distributed sediment model in different data availability scenarios." Doctoral thesis, Editorial Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/36534.

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Soil erosion by water can cause agricultural soil losses, desertification, water pollution, reservoir sedimentation, local excess of erosion (such as bridge scour) or deposition, etc. For this reason, the assessment of soil erosion and sediment transport is a key component of integrated catchment management. One of the most useful and up-to-date tools available to catchment managers for soil erosion and sediment transport assessment is distributed modelling. During the last few decades, many sedimentological distributed models were developed and applied for a wide range of climates and basins. Their main advantage is that they allow spatial interpolation or extrapolation of their results. Nevertheless, their use is still limited by some constraints. One of the most relevant limitations to the use of such models is the lack of recorded sediment transport data to be used for model calibration and validation. It is widely recognised that both sediment discharge series and soil erosion measurements are only available in a few and small- to medium-size experimental catchments. The aim of this dissertation is to investigate the possibility of using reservoir sedimentation data as a source of proxy information for sedimentological model calibration and validation. In order to carry out this task, a distributed sedimentological model called TETIS was tested in set of catchments with different sediment data availability. First of all, the TETIS model, developed over the last years by the research group of hydrological and environmental modelling of the Technical University of Valencia, is described, especially focusing on the new features developed within this dissertation (sedimentological sub-model automatic calibration algorithm, small pond sediment retention module, etc.). Then, the model is applied to three catchments with different sediment data availability. The first case-study is the Goodwin Creek catchment (Mississippi, US), an experimental catchment with high sediment transport data availability. The model performance is evaluated, and some considerations are made on the estimation of the sediment volume deposited into the drainage network at the beginning of a rainstorm. The second case-study is the Rambla del Poyo catchment (Valencia, Spain), a medium size semi-arid catchment draining to a coastal lagoon with severe sedimentation problems. The TETIS sedimentological sub-model is calibrated and validated using check-dam sedimentation volumes as an estimator of the total sediment transport. A detailed description of the alluvial stratigraphy infilling a check dam that drains a 12.9 km2 sub-catchment was used as indirect information of sediment yield data. A further application was also developed in this catchment in order to investigate the possibility of calibrating and validating both the hydrological and the sediment sub-models by using reservoir sedimentation volumes and employing neither water nor sediment discharge direct records. The third case-study is the Ésera River catchment (Huesca, Spain), a 1,500 km2 Pyrenean catchment drained by a large reservoir. The depositional history of the reservoir was reconstructed and used for sediment sub-model implementation. The model results were compared with gauged suspended sediment data in order to verify model robustness. The results of this dissertation indicate that TETIS model is a robust tool which provides a reliable reconstruction of the catchment sediment cycle. Its implementation is subject to data availability, both for parameter estimation and for model calibration and validation. Nevertheless, this dissertation proved that sediment records can be replaced by reservoir sedimentation volumes with satisfactory results, taking into account reservoir trap efficiency and sediment dry bulk density. Two modelling approaches were proposed for sediment model implementation, depending on the data availability. These methodologies proved to be consistent and provided a correct estimation of the sediment transport. Nevertheless, further research is needed to address model limitations and to reduce model results uncertainty
Bussi, G. (2014). Implementation of a distributed sediment model in different data availability scenarios [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/36534
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馮可達 and Ho-tat Fung. "Soil property determination through a knowledge-based system with emphasis on undrained shear strength." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31236868.

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Books on the topic "Soil erosion Data processing"

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A. P. J. de Roo. Modelling surface runoff and soil erosion in catchments using geographical information systems: Validity and applicability of the 'ANSWERS' model in two catchments in the loess area of South Limburg (The Netherlands) and one in Devon (UK). [Amsterdam]: Koninklijk Nederlands Aardrijkskundig Genootschap, 1993.

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Programme zur Erfassung von Landschaftsdaten, eine Bodenerosionsgleichung und ein Modell der Kaltluftentstehung =: Programmes for the collection of landdscape data, a soil erosion equation and a model showing how cold air arises. Heidelberg: Im Selbstverlag des Geographischen Institutes der Universität Heidelberg, 1986.

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Herbert, Hoover. Targeting erosion control: Basebook -- methods and data, a report from a national project. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service, Agricultural Research Service, 1985.

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Experimental soil mechanics. Upper Saddle River, N.J: Prentice Hall, 1997.

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F, Trant Douglas, ed. Estimating agricultural soil erosion looses from census of agriculture crop coverage data. Ottawa: Statistics Canada, 1989.

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Branch, Statistics Canada Analytical Studies. Estimating agricultural soil erosion losses from census of agriculture crop coverage data. Ottawa, Ont: Statistics Canada, 1989.

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Peters, Matthew P. Integrating fine-scale soil data into species distribution models: Preparing soil survey geographic (SSURGO) data from multiple counties. Newtown Square, PA: U.S. Dept. of Agriculture, Forest Service, Northern Research Station, 2013.

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Sutton, John D. Farm-level effects of soil conservation and commodity policy alternatives: Model and data documentation. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, Natural Resource Economics Division, 1986.

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Sutton, John D. Farm-level effects of soil conservation and commodity policy alternatives: Model and data documentation. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, Natural Resource Economics Division, 1986.

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Sutton, John D. Farm-level effects of soil conservation and commodity policy alternatives: Model and data documentation. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, Natural Resource Economics Division, 1986.

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Book chapters on the topic "Soil erosion Data processing"

1

Baade, Jussi, and Seppo Rekolainen. "Existing Soil Erosion Data Sets." In Soil Erosion in Europe, 717–28. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470859202.ch51.

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Evans, R. "Field Data and Erosion Models." In Modelling Soil Erosion by Water, 313–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58913-3_23.

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Patil, Rupesh Jayaram. "Study Area and Data Collection." In Spatial Techniques for Soil Erosion Estimation, 29–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74286-1_3.

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Quinton, John N., and Roy P. C. Morgan. "EUROSEM: An Evaluation with Single Event Data from the C5 Watershed, Oklahoma, USA." In Modelling Soil Erosion by Water, 65–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58913-3_7.

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Grossman, R. B., and C. R. Berdanier. "Erosion Tolerance for Cropland: Application of the Soil Survey Data Base." In Determinants of Soil Loss Tolerance, 113–30. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/asaspecpub45.c10.

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De Roo, A. P. J., and P. A. Burrough. "Using GIS for Processing data from the Survey and Monitoring of Pollution." In Soil Monitoring, 329–44. Basel: Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-7542-4_25.

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Kraushaar, Sabine. "Sediment Fingerprinting: A Revised Approach for Data Correction and Evaluation." In Soil Erosion and Sediment Flux in Northern Jordan, 91–121. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31888-2_5.

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Aiello, Antonello, Maria Adamo, and Filomena Canora. "Modelling Spatially–Distributed Soil Erosion through Remotely–Sensed Data and GIS." In Computational Science and Its Applications – ICCSA 2014, 372–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09147-1_27.

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Zhang, Jinxin, Hui Li, Xiufang Zhang, Hua Yu, Min Wang, Yazhi Shen, Miaohua Jiang, and Fuquan Zhang. "A Sensitivity Analysis Method of Soil Erosion Based on Land Use Type." In Advances in Intelligent Data Analysis and Applications, 125–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5036-9_14.

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Balashov, D. B. "Soil Identification Using Data Processing of Penetration Experiments." In Material Identification Using Mixed Numerical Experimental Methods, 236. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-1471-1_25.

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Conference papers on the topic "Soil erosion Data processing"

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Ghaly, Ashraf. "Processing LiDAR data to visualize soil erosion and analyze slope stability." In the 2nd International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1999320.1999375.

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Li, Jiacun, Wenji Zhao, and Xiaohui Zhang. "The Application of Remote Sensing Data to Assess Soil Erosion." In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5629726.

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Dong, Tingting, Zengxiang Zhang, and Lijun Zuo. "Quantitative research on soil erosion based on BP artificial neural network." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Yongji Wang, Jun Li, Bangjun Lei, and Jingyu Yang. SPIE, 2007. http://dx.doi.org/10.1117/12.748770.

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Kulemin, Gennady P., Andrei A. Kurekin, Vladimir V. Lukin, and Alexander A. Zelensky. "Soil moisture and erosion degree estimation from multichannel microwave remote sensing data." In Satellite Remote Sensing II, edited by Edwin T. Engman, Gerard Guyot, and Carlo M. Marino. SPIE, 1995. http://dx.doi.org/10.1117/12.227178.

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Binner, Richard, Ulrike Homberg, Steffen Prohaska, Ute Kalbe, and Karl-Josef Witt. "Identification of Descriptive Parameters of the Soil Pore Structure Using Experiments and CT Data." In International Conference on Scour and Erosion (ICSE-5) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41147(392)38.

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Panagos, Panos, Christos Karydas, Pasqualle Borrelli, Cristiano Ballabio, and Katrin Meusburger. "Advances in soil erosion modelling through remote sensing data availability at European scale." In Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014), edited by Diofantos G. Hadjimitsis, Kyriacos Themistocleous, Silas Michaelides, and Giorgos Papadavid. SPIE, 2014. http://dx.doi.org/10.1117/12.2066383.

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Chashchin, A. N., N. M. Mudrykh, and I. A. Samofalova. "PREDICTION OF EROSION SOIL LOSSES ACCORDING TO UAV AND SRTM DATA (PERM KRAI)." In РОЛЬ АГРАРНОЙ НАУКИ В ОБЕСПЕЧЕНИИ ПРОДОВОЛЬСТВЕННОЙ БЕЗОПАСНОСТИ СИБИРИ. Красноярск: Федеральное государственное бюджетное научное учреждение «Федеральный исследовательский центр «Красноярский научный центр Сибирского отделения Российской академии наук», 2022. http://dx.doi.org/10.52686/9785604525029_137.

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Saputri, Shelly Yeni, Sudaryatno, and Noorhadi Rahardjo. "Estimation Soil Erosion Using MUSLE Method and TRMM Data in Mongo Watershed, Purworejo." In 2020 6th International Conference on Science and Technology (ICST). IEEE, 2020. http://dx.doi.org/10.1109/icst50505.2020.9732871.

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Zhu, Yuanfeng, and Runyuan Kuang. "Quantitative assessment of soil erosion in Shanchonghe watershed supported by RS and GIS." In Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jinwen Tian and Jie Ma. SPIE, 2013. http://dx.doi.org/10.1117/12.2031361.

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Zhu, Lian-Qi, and Wen-Bo Zhu. "Application of RS and GIS in soil erosion load evaluation: case study in Xingzigou Basin, Shaanxi Province." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837797.

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Reports on the topic "Soil erosion Data processing"

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Hazard, John W., Jeralyn Snellgrove, and J. Michael Geist. Processing data from soil assessment surveys with the computer program SOILS. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station, 1985. http://dx.doi.org/10.2737/pnw-gtr-179.

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C. Harrington, R. Kelly, and K.T. Ebert. VARIATION IN EROSION/DEPOSITION RATES OVER THE LAST FIFTTY YEARS ON ALLUVIAL FAN SURFACES OF L. PLEISTOCENE-MID HOLOCENE AGE, ESTIMATIONS USING 137CS SOIL PROFILE DATA, AMARGOSA VALLEY, NEVADA. Office of Scientific and Technical Information (OSTI), August 2005. http://dx.doi.org/10.2172/884944.

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Harris, Kathleen, and Travis Dahl. Technical assessment of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers : HEC-RAS BSTEM analysis of the Atchafalaya River. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45174.

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This report documents the bank erosion modeling performed under Task 6 (HEC-RAS Sediment Modeling) of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers System Technical Assessment. The objectives of the bank erosion modeling effort were to compare the relative impact various flow scenarios might have on bank retreat on a stretch of the Atchafalaya River between Simmesport, LA, and the Whiskey Bay Pilot Channel. The effort included compilation of field and soil boring data, selection of bank retreat sites, creation of representative soil profiles for the reach, calibration of soil parameters to measured retreat rates, and modeling bank retreat and volume of material eroded under various flow scenarios. This modeling effort was intended for scenario comparison and should not be used as a prediction of exact rates of bank erosion. The study found that varying the amount of flow entering the Atchafalaya River from the Mississippi River could increase dramatically or significantly reduce the extent of bank erosion, relative to the current management scenario.
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Agassi, Menahem, Michael J. Singer, Eyal Ben-Dor, Naftaly Goldshleger, Donald Rundquist, Dan Blumberg, and Yoram Benyamini. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7586479.bard.

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The objective of this one-year project was to show whether a significant correlation can be established between the decreasing infiltration rate of the soil, during simulated rainstorm, and a following increase in the reflectance of the crusting soil. The project was supposed to be conducted under laboratory conditions, using at least three types of soils from each country. The general goal of this work was to develop a method for measuring the soil infiltration rate in-situ, solely from the reflectance readings, using a spectrometer. Loss of rain and irrigation water from cultivated fields is a matter of great concern, especially in arid, semi-arid regions, e.g. much of Israel and vast area in US, where water is a limiting factor for crop production. A major reason for runoff of rain and overhead irrigation water is the structural crust that is generated over a bare soils surface during rainfall or overhead irrigation events and reduces its infiltration rate (IR), considerably. IR data is essential for predicting the amount of percolating rainwater and runoff. Available information on in situ infiltration rate and crust strength is necessary for the farmers to consider: when it is necessary to cultivate for breaking the soil crust, crust strength and seedlings emergence, precision farming, etc. To date, soil IR is measured in the laboratory and in small-scale field plots, using rainfall simulators. This method is tedious and consumes considerable resources. Therefore, an available, non-destructive-in situ methods for soil IR and soil crusting levels evaluations, are essential for the verification of infiltration and runoff models and the evaluation of the amount of available water in the soil. In this research, soil samples from the US and Israel were subjected to simulated rainstorms of increasing levels of cumulative energies, during which IR (crusting levels) were measured. The soils from the US were studied simultaneously in the US and in Israel in order to compare the effect of the methodology on the results. The soil surface reflectance was remotely measured, using laboratory and portable spectrometers in the VIS-NIR and SWIR spectral region (0.4-2.5mm). A correlation coefficient spectra in which the wavelength, consisting of the higher correlation, was selected to hold the highest linear correlation between the spectroscopy and the infiltration rate. There does not appear to be a single wavelength that will be best for all soils. The results with the six soils in both countries indeed showed that there is a significant correlation between the infiltration rate of crusted soils and their reflectance values. Regarding the wavelength with the highest correlation for each soil, it is likely that either a combined analysis with more then one wavelength or several "best" wavelengths will be found that will provide useful data on soil surface condition and infiltration rate. The product of this work will serve as a model for predicting infiltration rate and crusting levels solely from the reflectance readings. Developing the aforementioned methodologies will allow increased utilization of rain and irrigation water, reduced runoff, floods and soil erosion hazards, reduced seedlings emergence problems and increased plants stand and yields.
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Ziegler, Nancy, Nicholas Webb, Adrian Chappell, and Sandra LeGrand. Scale invariance of albedo-based wind friction velocity. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40499.

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Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity (u*) derived from traditional methods (wind speed profiles) with those derived from the albedo model at two separate scales using bare soil patch (via net radiometers) and landscape (via MODIS 500 m) datasets. Results show that profile-derived estimates of u* are highly variable in anisotropic surface roughness due to changes in wind direction and fetch. Wind speed profiles poorly estimate soil surface (bed) wind friction velocities necessary for aeolian sediment transport research and modeling. Albedo-based estimates of u* at both scales have small variability because the estimate is integrated over a defined, fixed area and resolves the partition of wind momentum be-tween roughness elements and the soil surface. We demonstrate that the wind tunnel-based calibration of albedo for predicting wind friction velocities at the soil surface (us*) is applicable across scales. The albedo-based approach enables consistent and reliable drag partition correction across scales for model and field estimates of us* necessary for wind erosion and dust emission modeling.
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Edwards, Lulu, Charles Weiss, J. Newman, Fred Nichols, L. Coffing, and Quint Mason. Corrosion and performance of dust palliatives : laboratory and field studies. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42125.

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This report details laboratory and field experiments on BioPreferred® dust suppressants to assess performance and corrosion characteristics. Numerous bio-based dust suppressant products are marketed, but little data are available to assess performance for dust abatement and corrosion of common metals. A laboratory study used an air impingement device and the Portable In-Situ Wind ERosion Laboratory (PI-SWERL) to simulate wind speeds similar to those in field conditions for rotary wing aircraft. Laboratory corrosion studies used metal coupons imbedded in soil treated with dust palliative. Field trials were conducted using ground vehicle traffic to minimize cost and lower safety concerns while increasing surface wear from repetitive traffic. These studies clearly show that bio-based products demonstrate low corrosion potential with similar dust abatement performance to synthetic-based agents.
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Бабець, Євген Костянтинович, Ірина Петрівна Антонік, Ірина Євгенівна Мельникова, and Антон Всеволодович Петрухін. nfluence of Mining and Concentration Works Activity on Land Resources. Petroșani, 2019. http://dx.doi.org/10.31812/123456789/3120.

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The research provides assessment of current and longer-term consequences of iron ore open pit mining for land resources of adjacent areas. There are applied methods of analysis of fund materials; comparison of topographic sheets and special maps, visual observation, soil testing, laboratory analyses and statistic processing of data obtained. It is revealed that facilities of iron ore mining and concentration waste accumulation (dumps and tailing ponds) are destructive factors for the local lithosphere, dust chemical contamination being the basic one. The steps aimed at reducing negative impacts of technogenic objects of the mining and raw material complex on the environment are under study.
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