Article
Green & Sustainable Science & Technology
Shishant Gupta, Chandra Shekhar Prasad Ojha, Vijay P. Singh, Adebayo J. Adeloye, Sanjay K. Jain
Summary: This study estimated soil loss in the Sutlej catchment using remote sensing and GIS techniques combined with the Revised Universal Soil Loss Equation (RUSLE) and prioritized areas for action. The research concluded that extensive control treatment is necessary for soil and water conservation.
Article
Engineering, Multidisciplinary
Ahmed M. Helmi
Summary: Soil erosion is a global challenge that causes environmental and economic problems. The Universal Soil Loss Equation (USLE) and its updates are widely used for assessing soil erosion. The application of USLE is difficult due to the variability of data accuracy and resolution across different regions. This study proposes a methodology that uses remote sensing, ArcGIS, and synthetic storm distribution to collect and process the necessary data for USLE. The results show good agreement between calculated and measured sediment release in two catchments in the Sinai Peninsula, Egypt.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Green & Sustainable Science & Technology
Devendra Kumar, Arvind Dhaloiya, Ajeet Singh Nain, Mahendra Paal Sharma, Amandeep Singh
Summary: Soil erosion is a major concern at the watershed scale, with the study combining RUSLE modeling with remote sensing and GIS techniques to predict soil erosion in Nainital district, India. The results showed that the majority of the district is covered with forest, with annual average soil loss ranging from 20 to 80 t ha(-1) yr(-1) and prioritized watersheds for conservation efforts identified.
Article
Environmental Sciences
Giovanni Matranga, Francesco Palazzi, Antonio Leanza, Annalisa Milella, Giulio Reina, Eugenio Cavallo, Marcella Biddoccu
Summary: Soil Surface Roughness (SSR) is a physical feature of soil microtopography that plays a key role in hydrological and soil erosion processes. Traditional field measurements are inaccurate, so a novel technique has been adopted to measure SSR and estimate soil erosion rates using models.
ENVIRONMENTAL RESEARCH
(2023)
Article
Environmental Sciences
Wenfeng Gong, Tiedong Liu, Xuanyu Duan, Yuxin Sun, Yangyang Zhang, Xinyu Tong, Zixuan Qiu
Summary: The study examined soil erosion in a mountainous region of North China using satellite images and modeling techniques. The results show that forested land has increased while moderate soil erosion has decreased. Changes in land use have influenced the intensity of soil erosion, but the implementation of ecological measures has helped reduce it.
Article
Geosciences, Multidisciplinary
Liang Dong, Chenyu Ge, Hongming Zhang, Zihan Liu, Qinke Yang, Bei Jin, Coen J. Ritsema, Violette Geissen
Summary: The study introduces an improved method for calculating slope length in the DWESL model, which utilizes the ITF method combining topographical features slope line, contour curvature and cutoff factors. The extracted slope length using this method had the smallest error in verification of mathematical surfaces and its spatial distribution was more consistent with the terrain surface structure compared to the conventional flow direction algorithms in the original DWESL model.
Article
Geosciences, Multidisciplinary
Sumedh R. Kashiwar, Manik Chandra Kundu, Usha R. Dongarwar
Summary: The agricultural land of the whole world is deteriorating due to the loss of top fertile soil, reducing agricultural productivity and groundwater availability. The GIS-based RUSLE model is an efficient tool to evaluate soil erosion losses and provide decision support for policymakers.
Article
Environmental Sciences
Xiaolin Mu, Junliang Qiu, Bowen Cao, Shirong Cai, Kunlong Niu, Xiankun Yang
Summary: This study utilized satellite image data to analyze the changes in soil erosion in the Pearl River Basin. The results showed a decreasing trend in soil erosion over the past 30 years, primarily occurring in the tributary basin of Xijiang River, and strongly influenced by vegetation coverage and land use.
Article
Environmental Sciences
P. Sandeep, K. C. Arun Kumar, S. Haritha
Summary: The study estimated the average soil loss in the Amravati watershed of Tamil Nadu state in South India using the RUSLE model along with GIS and Remote Sensing techniques. The results showed that most of the watershed is at low risk of soil erosion, but there are also some areas with moderate to very high erosion risk.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Geography, Physical
Pingheng Li, Aqil Tariq, Qingting Li, Bushra Ghaffar, Muhammad Farhan, Ahsan Jamil, Walid Soufan, Ayman El Sabagh, Mohamed Freeshah
Summary: In this study, the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) were used to predict the annual rate of soil loss in District Chakwal, Pakistan. The parameters of the RUSLE model were estimated using remote sensing data, and GIS was used to determine erosion probability zones. The results show that the estimated total annual potential soil loss is comparable to the measured sediment loss, and the predicted soil erosion rate due to an increase in agricultural area is also significant. Integrating GIS and remote sensing with the RUSLE model helped achieve the objectives of the study.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Geosciences, Multidisciplinary
Sergey Chalov, Viktor Ivanov
Summary: This paper presents a comparative study of sediment budgets for the largest Siberian rivers in Northern Eurasia. The data suggests that sediment sinks and storage in catchment and river networks dominate the sediment budget, leading to a significant decline in sediment transport compared to catchment erosion.
Article
Environmental Sciences
Olivier S. Humphrey, Odipo Osano, Christopher M. Aura, Andrew L. Marriott, Sophia M. Dowell, William H. Blake, Michael J. Watts
Summary: Soil erosion has significant implications for nutrient cycling, land productivity, livelihoods, and ecosystem services. This study presents a spatio-temporal assessment of soil erosion risk in the Winam Gulf, Kenya using the Revised Universal Soil Loss Equation. The results show that soil erosion rates are influenced by rainfall intensity and land cover management, with different levels of susceptibility in different months.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Agronomy
Yolanda Sanchez Sanchez, Antonio Martinez Grana, Fernando Santos-Frances
Summary: This paper investigates soil erosion before and after a wildfire using remote sensing techniques. The study shows a significant increase in erosion post-fire, with the fire also negatively impacting post-fire vegetation recovery.
Article
Geography
Chhabi Lal Chidi, Wei Zhao, Pankaj Thapa, Basanta Paudel, Suresh Chaudhary, Narendra Raj Khanal
Summary: Farmers in the mountainous region of Nepal use outward sloping terraces to control soil erosion, and a study shows that this traditional topographic management is highly effective in controlling soil erosion, even with varying amounts of rainfall.
Article
Ecology
Xingjian Guo, Quanqin Shao, Ying Luo
Summary: This study used unmanned aerial vehicle remote sensing and the revised universal soil loss equation to evaluate the effects of different management measures on the soil conservation rate in the Loess Plateau. The results showed that human activities improved the soil conservation rate, with artificial management measures having a higher rate compared to non-artificial measures. Terraced structures had a significant impact on steep slopes, but also increased the risk of structural failure. The study provides guidance for future soil and water conservation measures and suggests adjustments to existing low-efficiency measures.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2022)
Article
Geosciences, Multidisciplinary
Zhilu Chang, Huanxiang Gao, Faming Huang, Jiawu Chen, Jinsong Huang, Zizheng Guo
Article
Chemistry, Analytical
Li Zhu, Lianghao Huang, Linyu Fan, Jinsong Huang, Faming Huang, Jiawu Chen, Zihe Zhang, Yuhao Wang
Article
Engineering, Geological
Zhilu Chang, Faming Huang, Jinsong Huang, Shui-Hua Jiang, Chuangbing Zhou, Li Zhu
Summary: Loess fill slopes are susceptible to heavy rainfall due to their water sensitivity and collapsibility. The failure mode of loess fill slopes under continuous rainfall is characterized by gully erosion, slope toe failure, central slope failure, and top slope failure. The study reveals that fissures play a crucial role in slope failure by generating preferential flow.
ENGINEERING GEOLOGY
(2021)
Article
Computer Science, Information Systems
Yijing Li, Ping Liu, Huokun Li, Faming Huang
Summary: This study presents a displacement change detection method for arch dams based on a two-step point cloud registration and contour model comparison method. By using stable rock as the correspondence element, a two-step registration method from rough to fine is proposed using the iterative closest point algorithm to describe the coordinate unification of the two states' data without control network and target. Then, the contour model fitting the point clouds is used to compare the change in distance between models, improving the accuracy of comparing the two surfaces without being affected by the roughness of the point cloud.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Geosciences, Multidisciplinary
Faming Huang, Zhou Ye, Shui-Hua Jiang, Jinsong Huang, Zhilu Chang, Jiawu Chen
Summary: This study investigates the influences of different attribute interval numbers and data-based models on the accuracy of landslide susceptibility prediction. Results demonstrate that the prediction accuracy gradually increases with the increase in attribute interval numbers for a specific model, and the random forest model has the highest prediction accuracy as the attribute interval numbers increase.
Article
Engineering, Geological
Faming Huang, Siyu Tao, Zhilu Chang, Jinsong Huang, Xuanmei Fan, Shui-Hua Jiang, Wenbin Li
Summary: This study proposed the innovative multi-scale segmentation (MSS) method for extracting slope units, using terrain aspect and shaded relief images from digital elevation models as data sources, and automatically optimizing the initial slope units through vector analysis in GIS, achieving a discussion on extraction performance.
Article
Geosciences, Multidisciplinary
Wenbin Li, Yu Shi, Faming Huang, Haoyuan Hong, Guquan Song
Summary: This study examines the uncertainty characteristics of different machine learning models in predicting collapse susceptibility. Six types of machine learning models were used to predict collapse susceptibility in An'yuan County, China, with the random forest model showing the highest prediction accuracy and lowest uncertainty. It is essential to utilize a variety of machine learning models for collapse susceptibility prediction and cross-validation for accuracy comparison.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Environmental Sciences
Xiaojing Wang, Faming Huang, Xuanmei Fan, Himan Shahabi, Ataollah Shirzadi, Huiyuan Bian, Xiongde Ma, Xinxiang Lei, Wei Chen
Summary: This study applies three advanced landslide susceptibility models to evaluate landslide susceptibility in Muchuan County, China. By analyzing the local geo-environmental characteristics and a landslide inventory map, the best model is determined and valuable information for slope stability is provided for local governments and organizations.
ENVIRONMENTAL EARTH SCIENCES
(2022)
Article
Geochemistry & Geophysics
Li Zhu, Gongjian Wang, Faming Huang, Yan Li, Wei Chen, Haoyuan Hong
Summary: This letter proposes a sparse feature extraction network (SFE+) for landslide susceptibility prediction (LSP), which improves the accuracy of traditional machine learning models in capturing nonlinear correlations among environmental factors. The SFE-based ML models show promising prospects for LSP.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geosciences, Multidisciplinary
Zizheng Guo, Yu Shi, Faming Huang, Xuanmei Fan, Jinsong Huang
Summary: This study introduced a machine learning approach based on the C5.0 decision tree model and the K-means cluster algorithm to produce a regional landslide susceptibility map, which outperformed traditional models in terms of model performance according to the validation results.
GEOSCIENCE FRONTIERS
(2021)
Article
Geosciences, Multidisciplinary
Zengzhen Qian, Mingqiang Sheng, Faming Huang, Xianlong Lu
Summary: By adding cement to aeolian sand, significant increases in failure resistance and uplift stiffness of plate anchors can be achieved. However, distinct load-displacement responses were observed in tests on model plate anchors embedded in cement-stabilised aeolian sand and uncemented aeolian sand.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Geosciences, Multidisciplinary
Faming Huang, Jun Yan, Xuanmei Fan, Chi Yao, Jinsong Huang, Wei Chen, Haoyuan Hong
Summary: In landslide susceptibility mapping (LSM), the expression of landslide boundaries and spatial shapes as points or circles instead of accurate polygons can lead to differences in the predicted landslide susceptibility indexes (LSIs) and introduce uncertainties into the LSM. This study compared the uncertainties of LSM modeling using different representations of landslide boundaries and spatial shapes, and found that using polygonal surfaces to represent the landslide boundaries can significantly improve the accuracy of LSM compared to using points and circles. The results also showed that polygon-based models have higher LSM accuracy compared to point- and circle-based models, and the overall accuracy of the random forest (RF) model is superior to that of the support vector machine (SVM) model.
GEOSCIENCE FRONTIERS
(2022)
Article
Geography, Physical
Faming Huang, Jiawu Chen, Weiping Liu, Jinsong Huang, Haoyuan Hong, Wei Chen
Summary: This study focuses on the rainfall-induced landslide hazard, using machine learning models to predict landslide susceptibility and proposing different critical rainfall threshold methods. The coupling of susceptibility maps and critical rainfall threshold values effectively predicts the rainfall-induced landslide hazards.
Article
Geosciences, Multidisciplinary
Lei-Lei Liu, Can Yang, Fa-Ming Huang, Xiao-Mi Wang
Summary: This study uses a new machine learning model- Attentional Factorization Machines (AFM)- to explicitly consider the influence of feature interactions in Landslide Susceptibility Mapping (LSM). The results show that the performance of AFM is slightly better than that of Random Forest (RF) model in terms of AUC metric. Compared with general LSM models, AFM not only ensures model interpretability but also improves model performance by introducing an attention mechanism to learn the weight of different feature combinations.
GEOMATICS NATURAL HAZARDS & RISK
(2021)
