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Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 46748

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Prof. Dr. Biswajeet Pradhan

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Chief Guest Editor

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems & Modelling, University of Technology Sydney, CB11.06.106, Building 11, 81 Broadway, Ultimo NSW 2007 (PO Box 123), Australia
Interests: remote sensing and image processing; GIS and complex modelling; soft computing techniques in natural hazards; environmental and natural resources applications
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Dr. Alireza Arabameri

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Guest Editor

Department of Geomorphology, Tarbiat Modares University, Tehran 36581-17994, Iran
Interests: geomorpohology; environmental management; natural hazard modelling using GIS

Special Issue Information

Dear Colleagues,

Gully erosion poses a significant threat to the environment worldwide, impacting soil and land functions. It is one of the most powerful agents of soil removal and erosion from highland regions to valley floors. Gully erosion is a significant source of sediment, and gully channels often comprise a very small area of the catchment. Gully initiation and growth is a normal phenomenon, but the alarming rate of these processes significantly impacts natural resources, agriculture practices, and environmental health as they promote soil and water degradation, disruption of the ecosystem, and intensification of hazards. Human pressure and activities (such as deforestation, unsuitable land use, and farming practices) have, however, increasingly intensified land degradation and particularly the risk of gully erosion. Through this view, the risks associated with gully erosion may be natural, human-induced, or both. Defining the location and rate of gully expansion for the purposes of generating inventory records and constant monitoring is essential. The main challenge is the establishment of an advanced strategy for continuous monitoring and mitigation of the issues for environmental protection.

The science of remote sensing has evolved in leaps and bounds in recent decades. High- and moderate-resolution remote sensing data, such as such as visible imaging, synthetic aperture radar (SAR), global navigation satellite system (GNSS), light detection and ranging (LiDAR), Quickbird, Worldview 3, LiDAR, SPOT 5, Google Earth Engine, etc., with the aid of geographic information system (GIS) tools, deliver state-of-the-art information for the detection of gully erosion and risk modelling processes. Advanced computing methods focused on state-of-the-art data processing, machine learning, deep learning (neural networks, developmental learning, artificial intelligence, automatic learning) may also be used for detailed investigations. Various models may be developed with a special emphasis on natural resources and environment to recognize and manage the gully erosion and effects.

In this Special Issue, we want to gather state-of-the-art research that directly explores how various types of remote sensing data coupled with deep learning and new machine learning algorithms are used in gully erosion studies to monitor, quantify, and model erosion.

The topics of interest include, but are not limited to:

Multi-temporal high resolution satellite images and gully erosion
New machine learning techniques in gully erosion modelling
Deep leaning techniques in gully erosion model.ing
New pixel-based image analysis
New object-based image analysis

Prof. Biswajeet Pradhan
Dr. Arabameri Alireza
Guest Editors

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Keywords

Gully erosion detection
Machine learning
Gully erosion monitoring
Gully erosion modelling
Gully erosion susceptibility

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Published Papers (7 papers)

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Research

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38 pages, 7142 KiB

Open AccessArticle

Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

by Indrajit Chowdhuri, Subodh Chandra Pal, Alireza Arabameri, Asish Saha, Rabin Chakrabortty, Thomas Blaschke, Biswajeet Pradhan and Shahab. S. Band

Remote Sens. 2020, 12(21), 3620; https://doi.org/10.3390/rs12213620 - 4 Nov 2020

Cited by 68 | Viewed by 4307

Abstract

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study. Full article

(This article belongs to the Special Issue Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling)

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35 pages, 9454 KiB

Open AccessArticle

Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility

by Paramita Roy, Subodh Chandra Pal, Alireza Arabameri, Rabin Chakrabortty, Biswajeet Pradhan, Indrajit Chowdhuri, Saro Lee and Dieu Tien Bui

Remote Sens. 2020, 12(20), 3284; https://doi.org/10.3390/rs12203284 - 10 Oct 2020

Cited by 45 | Viewed by 3726

Abstract

The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km² (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully. Full article

(This article belongs to the Special Issue Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling)

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31 pages, 9641 KiB

Open AccessArticle

Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

by Alireza Arabameri, Omid Asadi Nalivan, Subodh Chandra Pal, Rabin Chakrabortty, Asish Saha, Saro Lee, Biswajeet Pradhan and Dieu Tien Bui

Remote Sens. 2020, 12(17), 2833; https://doi.org/10.3390/rs12172833 - 1 Sep 2020

Cited by 72 | Viewed by 5518

Abstract

The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures. Full article

(This article belongs to the Special Issue Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling)

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15 pages, 4106 KiB

Open AccessArticle

Population Characteristics of Loess Gully System in the Loess Plateau of China

by Jiaming Na, Xin Yang, Guoan Tang, Weiqin Dang and Josef Strobl

Remote Sens. 2020, 12(16), 2639; https://doi.org/10.3390/rs12162639 - 15 Aug 2020

Cited by 20 | Viewed by 4247

Abstract

Gullies in the Loess Plateau of China vary in developmental stages and morphologic sizes. In this case study, in Linjialian watershed in the loess hilly region, we introduced some perspectives from population ecology to explore the population characteristics of the loess gully system. Different types of gullies were extracted based on the digital elevation model and imagery data. Population analysis was then carried out from three aspects, namely, quantity, structure, and distribution. Results showed that in terms of the quantity, hillslope ephemeral gullies (187 numbers/km² in number density) and bank gullies (8.3 km/km² in length density) are the most active gullies in this area with an exponential growth trend, and the hillslope ephemeral gully is the dominant type. Along with age structure analysis, the pyramid-shaped age structure indicated that the gully system is at its early or middle stages of development. The spatial distribution of hillslope ephemeral gullies has a clear aspect asymmetry pattern, and the bank gully distribution is symmetrical. A hierarchical structure (hillslope ephemeral gully–bank gully–valley gully in upslope–shoulder line–bottom area) in an elevation distribution is presented. These preliminary results are helpful for further understanding the organized, systematic development, and evolution of the gully system. Full article

(This article belongs to the Special Issue Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling)

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30 pages, 7383 KiB

Open AccessArticle

Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility

by Alireza Arabameri, Omid Asadi Nalivan, Sunil Saha, Jagabandhu Roy, Biswajeet Pradhan, John P. Tiefenbacher and Phuong Thao Thi Ngo

Remote Sens. 2020, 12(11), 1890; https://doi.org/10.3390/rs12111890 - 11 Jun 2020

Cited by 51 | Viewed by 6023

Abstract

Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy is GLM, MaxEnt, SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, GLM-ANN-SVM-MaxEnt, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM and MaxEnt-ANN-SVM. The resulting gully erosion susceptibility models (GESMs) are efficient and powerful and could be used to improve soil and water conservation and management. Full article

(This article belongs to the Special Issue Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling)

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18 pages, 3594 KiB

Open AccessArticle

Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka

by Sumudu Senanayake, Biswajeet Pradhan, Alfredo Huete and Jane Brennan

Remote Sens. 2020, 12(9), 1483; https://doi.org/10.3390/rs12091483 - 7 May 2020

Cited by 64 | Viewed by 7661

Abstract

This study aims to identify the vulnerable landscape areas using landslide frequency ratio and land-use change associated soil erosion hazard by employing geo-informatics techniques and the revised universal soil loss equation (RUSLE) model. Required datasets were collected from multiple sources, such as multi-temporal Landsat images, soil data, rainfall data, land-use land-cover (LULC) maps, topographic maps, and details of the past landslide incidents. Landsat satellite images from 2000, 2010, and 2019 were used to assess the land-use change. Geospatial input data on rainfall, soil type, terrain characteristics, and land cover were employed for soil erosion hazard classification and mapping. Landscape vulnerability was examined on the basis of land-use change, erosion hazard class, and landslide frequency ratio. Then the erodible hazard areas were identified and prioritized at the scale of river distribution zones. The image analysis of Sabaragamuwa Province in Sri Lanka from 2000 to 2019 indicates a significant increase in cropping areas (17.96%) and urban areas (3.07%), whereas less dense forest and dense forest coverage are significantly reduced (14.18% and 6.46%, respectively). The average annual soil erosion rate increased from 14.56 to 15.53 t/ha/year from year 2000 to 2019. The highest landslide frequency ratios are found in the less dense forest area and cropping area, and were identified as more prone to future landslides. The river distribution zones Athtanagalu Oya (A-2), Kalani River-south (A-3), and Kalani River- north (A-9), were identified as immediate priority areas for soil conservation. Full article

(This article belongs to the Special Issue Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling)

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Review

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25 pages, 2756 KiB

Open AccessReview

A Review on Assessing and Mapping Soil Erosion Hazard Using Geo-Informatics Technology for Farming System Management

by Sumudu Senanayake, Biswajeet Pradhan, Alfredo Huete and Jane Brennan

Remote Sens. 2020, 12(24), 4063; https://doi.org/10.3390/rs12244063 - 11 Dec 2020

Cited by 42 | Viewed by 13630

Abstract

Soil erosion is a severe threat to food production systems globally. Food production in farming systems decreases with increasing soil erosion hazards. This review article focuses on geo-informatics applications for identifying, assessing and predicting erosion hazards for sustainable farming system development. Several researchers have used a variety of quantitative and qualitative methods with erosion models, integrating geo-informatics techniques for spatial interpretations to address soil erosion and land degradation issues. The review identified different geo-informatics methods of erosion hazard assessment and highlighted some research gaps that can provide a basis to develop appropriate novel methodologies for future studies. It was found that rainfall variation and land-use changes significantly contribute to soil erosion hazards. There is a need for more research on the spatial and temporal pattern of water erosion with rainfall variation, innovative techniques and strategies for landscape evaluation to improve the environmental conditions in a sustainable manner. Examining water erosion and predicting erosion hazards for future climate scenarios could also be approached with emerging algorithms in geo-informatics and spatiotemporal analysis at higher spatial resolutions. Further, geo-informatics can be applied with real-time data for continuous monitoring and evaluation of erosion hazards to risk reduction and prevent the damages in farming systems. Full article

(This article belongs to the Special Issue Monitoring and Modelling of Gully Erosion Using Remote Sensing Data and Spatial Modelling)

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