Modelling soil erosion risk for pipelines using remote sensed data

Winning, Herbert Keith; Hann, M. J.

doi:10.1016/j.biosystemseng.2014.08.020

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Landsburg and Cannon (1995) stated that wind erosion potential increased on pipeline areas if revegetation was not successful, particularly in soils with clayey surfaces. Additionally, Winning and Hann (2014) note that erosion potential also increased near rivers and in areas of high seismic activity. Schindelbeck and van Es (2012) found evidence of significant reduction in aggregate stability in all land types studied (agricultural areas, wetlands, and fallow lands) following pipeline installation, resulting in an average of 32% reduction in aggregate stability following construction activities.…”

Section: Resultsmentioning

confidence: 99%

Pipeline installation effects on soils and plants: A review and quantitative synthesis

Brehm

Culman

2022

Agrosystems Geosci & Env

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Oil and natural gas pipelines are essential to the transport of energy materials, but construction of these pipelines commonly causes disturbance to ecosystems. Due to variability in pipeline installation practices and environments, drawing consensus about how pipeline installations typically impact ecosystems is challenging. Here, we performed a systematic literature review to compile studies that have evaluated impacts of pipeline installation on soil and plant properties. We found 34 studies reporting pipeline impacts on agricultural and natural ecosystems from eight countries. We quantified and synthesized the magnitude of responses and found that the majority of studies found pipeline installation resulted in soil degradation via increased compaction and soil mixing, paired with decreased aggregate stability and soil carbon (C) relative to adjacent, undisturbed areas. Averaged across all studies, aggregate stability decreased 44.8%, water infiltration was reduced 85.6%, and compaction via penetration resistance increased 40.9% over pipeline areas relative to nondisturbed adjacent areas. This soil degradation led to general declines in plant productivity, with 15 out of 25 studies documenting declines in crop yields (6.2-45.6%) and six out of nine studies reporting decreased biomass from natural ecosystems (1.7-56.8%). We conclude from our quantitative synthesis that pipeline installation typically results in degraded soil and vegetation resources, and this can persist for many years following installation.

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Section: Resultsmentioning

confidence: 99%

Pipeline installation effects on soils and plants: A review and quantitative synthesis

Brehm

Culman

2022

Agrosystems Geosci & Env

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“…Therefore this model helps to increase the prediction capability and accuracy of remote sensing and GIS based analysis. In (Winning and Hann, 2014;) is applied the RUSLE, remote sensing, and GIS to the mapping of soil erosion risk in Brazilian Amazonia as the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). In this research is that remote sensing by Landsat 7 satellite and GIS estimation and its spatial distribution feasible with reasonable costs and better accuracy in larger areas.…”

Section: A=r * K * L * S * C * Pmentioning

confidence: 99%

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Filchev

Kolev

2021

Earth and Environmental Sciences Library

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During past decades a marked manifestation of widespread erosion phenomena was studied worldwide. Global conservation community has launched campaigns at local, regional and continental level in developing countries for preservation of soil resources in order not only to stop or mitigate human impact on nature but also to improve life in rural areas introducing new approaches for soil cultivation. After the adoption of Sustainable Development Goals of UNs and launching several world initiatives such as the Land Degradation Neutrality (LDN) the world came to realise the very importance of the soil resources on which the biosphere relies on for its existence. The main goal of the chapter is to review of different types and structures erosion models as well as its some applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as: Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in USA and Modèle d'Evaluation Spatiale de l'ALéa Erosion des Sols (MESALES) model. These and more models are being discussed in present work alongside more experimental models and methods for assessing soil erosion risk such as Artificial Intelligence (AI), Machine and Deep Learning etc. At the end of this work a prospectus for the future development of soil erosion risk assessment is drawn.

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“…Hyperspectral information provides procedures such as collinear indicator factors, which is, autonomous from the independent variable, and these images also has a disadvantage. To solve this problem, dimensionality reduction technique is the standard procedure [12,13]. Partial Least Squares Regression (PLSR) [14,15], for instance, extends the information into a low-dimensional space shaped by a lot of symmetrical factors with an intention to increase the covariance among indicators and target variable(s).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

Bharathi¹,

Srinivasan²,

Chamundeeswari³

et al. 2023

Computer Systems Science and Engineering

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Now a days, Remote Sensing (RS) techniques are used for earth observation and for detection of soil types with high accuracy and better reliability. This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil's minerals and its characteristics. There are a few challenges that is present in soil classification using image enhancement such as, locating and plotting soil boundaries, slopes, hazardous areas, drainage condition, land use, vegetation etc. There are some traditional approaches which involves few drawbacks such as, manual involvement which results in inaccuracy due to human interference, time consuming, inconsistent prediction etc. To overcome these draw backs and to improve the predictive analysis of soil characteristics, we propose a Hybrid Deep Learning improved BAT optimization algorithm (HDIB) for soil classification using remote sensing hyperspectral features. In HDIB, we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral (HS) image. Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology. Then, a recurring Deep Learning (DL) Neural Network (NN) is used for classifying the HS images, considering the datasets of Pavia University, Salinas and Tamil Nadu Hill Scene, which in turn improves the reliability of classification. Finally, the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron (SLP), Convolutional Neural Networks (CNN) and Deep Metric Learning (DML) and it shows an improved classification accuracy of 99.87%, 98.34% and 99.9% for Tamil Nadu Hills dataset, Pavia University and Salinas scene datasets respectively.

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Modelling soil erosion risk for pipelines using remote sensed data

Cited by 8 publications

References 15 publications

Pipeline installation effects on soils and plants: A review and quantitative synthesis

Pipeline installation effects on soils and plants: A review and quantitative synthesis

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

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