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Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 24999

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Prof. Dr. Tomás Manuel Fernández

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Department of Cartographic Engineering, Geodesy and Photogrammetry, University of Jaén, 23071 Jaén, Spain
Interests: hazards, sustainability and resilience; geomatics, remote sensing and GIS techniques; landslide and erosion monitoring; landslide and erosion hazards’ assessment and mapping; early warning systems
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Prof. Dr. Christian Conoscenti

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Department of Earth and Marine Sciences, University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Interests: gully erosion; stochastic approach to landslide susceptibility modelling; GIS; machine learning to model soil erosion
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Special Issue Information

Dear Colleagues,

In recent decades, landslides and erosion studies have undergone a great amount of development with the application of geospatial techniques. Point data capture is addressed by means of different instruments, such as total station, GNSS and in situ sensors (movement, wetness, etc.), but also with LiDAR techniques, both terrestrial (TLS) and aerial (ALS), that allow the acquisition of massive point clouds. Meanwhile, images are captured from sensors and cameras on board of different platforms: terrestrial, unmanned aerial systems (UAS), aerial or satellites.

These point clouds and images are processed via different approaches based on conventional photogrammetry and computer vision methods, which allow the preparation of high-quality data for further analysis. Moreover, the repetition of data acquisition along time allows the monitoring of landslide and erosion processes regarding both geometric features (position, limits, displacements, etc.) and non-geometric information (soil properties, wetness, elements affected, etc.). Multispectral analyses (vegetation and water indexes, classifications and object-based methods) complete the techniques used for landslide inventories and factors modelling.

Meanwhile, geospatial analysis and modelling in GIS environments are addressed to the scientific knowledge of landslide and erosion processes, and especially to the assessment of hazard and risk. Thus, susceptibility modelling, very useful in these processes, is performed by different techniques, from index-based methods to multivariate statistics methods (linear o logistic regression, discriminant analysis, etc.), machine learning methods (decision trees, random forest, support vector machines, etc.) or deep learning methods (neural networks). Hazard also includes time series analysis and predictive modelling, and risk considers engineering and economic information.

Therefore, we encourage scientists and experts in different disciplines to send their contributions to this Special Issue on topics focused on the application of geospatial techniques for landslides and erosion studies. These include data capture, monitoring, analysis and modeling of both landslides (shallow and deep landslides), and erosion (laminar and gully erosion).

Prof. Dr. Tomás Fernández
Prof. Dr. Christian Conoscenti
Guest Editors

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Keywords

Landslides
Erosion
Gully erosion
Optical remote sensing
InSAR
Photogrammetry
LiDAR
Monitoring
Risk analysis
Machine learning
Modeling

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Published Papers (6 papers)

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Research

19 pages, 4175 KiB

Open AccessArticle

Gully Morphological Characteristics and Topographic Threshold Determined by UAV in a Small Watershed on the Loess Plateau

by Ziguan Wang, Guanghui Zhang, Chengshu Wang and Shukun Xing

Remote Sens. 2022, 14(15), 3529; https://doi.org/10.3390/rs14153529 - 23 Jul 2022

Cited by 16 | Viewed by 3197

Abstract

Gully erosion is an important sediment source in small watershed, and causes severe land degradation, particularly in semi-arid regions. Accurately measuring gully morphological characteristics, and determining its topographic threshold, are vital for gully erosion simulation and control. In this study, 910 gullies were visually interpreted by unmanned aerial vehicle (UAV) technology combined with field measurement. Ten gully morphological characteristics were extracted from the digital orthophoto map (DOM) and digital elevation model (DEM) generated by UAV images, including gully length (L), circumference (C), plane area (PA), surface area (SA), volume (V), depth (D), top width (TW), mean width (MW), cross-sectional area (CSA), and ratio of top width to depth (TW/D). The morphological characteristics of 30 reachable gullies were measured by a real time kinematic (RTK) to validate the parameters extracted from the UAV images. The topographic thresholds were determined based on the local slope gradient (S) and upland drainage area (A), using a dataset of 365 gully heads and their corresponding land-use types. The results show that the mean absolute percentage errors (MAPE) of the 2D and 3D gully characteristics are less than 10% and 20%, respectively, demonstrating a high accuracy of gully characteristic extraction from UAV images. Gully V is significantly related to the other nine parameters. Significant power functions were fitted between V, and L, C, PA, and SA. The gully volume could be well-estimated by SA (V = 0.212 SA^0.982), with a R² of 0.99. For all land-use types, the topographic threshold could be described as S = 0.61 A^0.48, implying that water erosion is the dominant process controlling gully erosion in this region. The topographic threshold is land-use-dependent, and shrubland is hardest for gully incision, followed by grassland and cropland. The results are helpful to rapidly estimate gully erosion, and identify the areas for gully erosion mitigation in small watershed. Full article

(This article belongs to the Special Issue Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling)

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18 pages, 7648 KiB

Open AccessArticle

A Simple Deposition Model for Debris Flow Simulation Considering the Erosion–Entrainment–Deposition Process

by Seungjun Lee, Hyunuk An, Minseok Kim, Hyuntaek Lim and Yongseong Kim

Remote Sens. 2022, 14(8), 1904; https://doi.org/10.3390/rs14081904 - 14 Apr 2022

Cited by 12 | Viewed by 3447

Abstract

This study aimed to determine the depositional effect and improve the identification of debris flow risk zones. To accomplish this goal, we developed a two-dimensional debris flow model (Deb2D) based on a hyperbolic conservation form of the mass and the momentum balance equation with consideration of the erosion–entrainment effect as well as the depositional effect. In this model, we implemented a widely-used rheological equation—the Voellmy equation—and a quadtree adaptive grid-based shallow-water equation. This model was applied to two study sites to assess the depositional effect. The impact area, volume of soil loss, maximum velocity, inundated depth, and erosion depth resulting from the debris-flow modeling were compared with the field data. The simulation results with/without deposition were evaluated using the receiver operating characteristic method. The implementation results of the erosion–entrainment model with deposition showed superior accuracy when estimating the damage range and flow time. Full article

(This article belongs to the Special Issue Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling)

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23 pages, 5304 KiB

Open AccessArticle

Quantitative Evaluation of Gully Erosion Using Multitemporal UAV Data in the Southern Black Soil Region of Northeast China: A Case Study

by Ranghu Wang, Huan Sun, Jiuchun Yang, Shuwen Zhang, Hanpei Fu, Nan Wang and Qianyu Liu

Remote Sens. 2022, 14(6), 1479; https://doi.org/10.3390/rs14061479 - 18 Mar 2022

Cited by 28 | Viewed by 3792

Abstract

The black soil region of northeast China is experiencing severe gully erosion. The lack of periodic, high-resolution, short–medium-term, annual, and seasonal observations considerably limit the comprehensive understanding of the processes and mechanisms of gully erosion caused by multiple forces at the watershed scale. Therefore, in this study, we periodically monitored the geomorphic, morphological, and volume changes of a stabilized gully both annually and seasonally in a small agricultural watershed (6 ha) in the southern black soil region in northeast China based on the centimeter-level resolution of unmanned aerial vehicle (UAV)-derived orthoimages and digital terrain models (DTMs) from 2015 to 2020. Compared with submeter-resolution satellite images, the multitemporal UAV data exhibited strong adaptability and various advantages for the assessment of short–medium-term (≤5 years) gully erosion rates in this region. The results demonstrated that the gully has an actively retreating headcut that was always the main source of sediment yield. The linear, areal, and volumetric gully headcut retreat (GHR) rates were 0.74 m year⁻¹, 7.29 m² year⁻¹, and 9.66 m³ year⁻¹, respectively. GHR in the rainy season accounted for 94.62% of the annual linear erosion and 87.64% of the areal erosion. In particular, sidewall collapse and gully head expansion dominated in the early rainy season, which accounted for 66.67% of the annual linear erosion and 49% of the areal erosion. Our results provide high-resolution orthoimages and a DTM time series produced by a UAV to evaluate short–medium-term (5 years) GHR rate and quantify the contribution of freeze–thaw processes, snowmelt, and rainfall to gully erosion in the region. The findings contribute to understanding the gully erosion processes induced by multiple forces in the southern black soil region of northeast China. Full article

(This article belongs to the Special Issue Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling)

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25 pages, 12497 KiB

Open AccessArticle

Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective

by Tao Jin, Xiewen Hu, Bo Liu, Chuanjie Xi, Kun He, Xichao Cao, Gang Luo, Mei Han, Guotao Ma, Ying Yang and Yan Wang

Remote Sens. 2022, 14(6), 1306; https://doi.org/10.3390/rs14061306 - 8 Mar 2022

Cited by 23 | Viewed by 3110

Abstract

The post-fire debris flow (PFDF) is a commonly destructive hazard that may persist for several years following the wildfires. Susceptibility mapping is an effective method for mitigating hazard risk. Yet, the majority of susceptibility prediction models only focus on spatial probability in the specific period while ignoring the change associated with time. This study improves the predictive model by introducing the temporal factor. The area burned by the 30 March 2020 fire in Xichang City, China is selected as an illustrative example, and the susceptibility of the PFDF was predicted for different periods of seven months after the wildfires. 2214 hydrological response events, including 181 debris flow events and 2033 flood events from the 82 watersheds are adopted to construct the sample dataset. Seven conditioning factors consist of temporal factors and spatial factors are extracted by the remote sensing interpretation, field investigations, and in situ tests, after correlation and importance analysis. The logistic regression (LR) is adopted to establish prediction models through 10 cross-validations. The results show that the susceptibility to PFDF has significantly reduced over time. After two months of wildfire, the proportions of very low, low, moderate, high, and very high susceptibility are 1.2%, 3.7%, 24.4%, 23.2%, and 47.6%, respectively. After seven months of wildfire, the proportions of high and very high susceptibility decreased to 0, while the proportions of very low to medium susceptibility increased to 35.4%, 35.6%, and 28.1%, respectively. The reason is that the drone seeding of grass seeds and artificial planting of trees accelerated the natural recovery of vegetation and soil after the fire. This study can give insight into the evolution mechanism of PFDF over time and reflect the important influence of human activity after the wildfire. Full article

(This article belongs to the Special Issue Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling)

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31 pages, 11596 KiB

Open AccessArticle

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

by Guangzhi Rong, Kaiwei Li, Yulin Su, Zhijun Tong, Xingpeng Liu, Jiquan Zhang, Yichen Zhang and Tiantao Li

Remote Sens. 2021, 13(22), 4694; https://doi.org/10.3390/rs13224694 - 20 Nov 2021

Cited by 43 | Viewed by 5408

Abstract

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions. Full article

(This article belongs to the Special Issue Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling)

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32 pages, 18317 KiB

Open AccessArticle

Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China

by Heping Shu, Zizheng Guo, Shi Qi, Danqing Song, Hamid Reza Pourghasemi and Jiacheng Ma

Remote Sens. 2021, 13(18), 3623; https://doi.org/10.3390/rs13183623 - 10 Sep 2021

Cited by 52 | Viewed by 4410

Abstract

Although numerous models have been employed to address the issue of landslide susceptibility at regional scale, few have incorporated landslide typology into a model application. Thus, the aim of the present study is to perform landslide susceptibility zonation taking landslide classification into account using a data-driven model. The specific objective is to answer the question: how to select reasonable influencing factors for different types of landslides so that the accuracy of susceptibility assessment can be improved? The Qilihe District in Lanzhou City of northwestern China was undertaken as the test area, and a total of 12 influencing factors were set as the predictive variables. An inventory map containing 227 landslides was created first, which was divided into shallow landslides and debris flows based on the geological features, distribution, and formation mechanisms. A weighted frequency ratio model was proposed to calculate the landslide susceptibility. The weights of influencing factors were calculated by the integrated model of logistic regression and fuzzy analytical hierarchy process, whereas the rating among the classes within each factor was obtained by a frequency ratio algorithm. The landslide susceptibility index of each cell was subsequently calculated in GIS environment to create landslide susceptibility maps of different types of landslide. The analysis and assessment process were separately performed for each type of landslide, and the final landslide susceptibility map for the entire region was produced by combining them. The results showed that 73.3% of landslide pixels were classified into “very high” or “high” susceptibility zones, while “very low” or “low” susceptibility zones covered only 3.6% of landslide pixels. The accuracy of the model represented by receiver operating characteristic curve was satisfactory, with a success rate of 70.4%. When the landslide typology was not considered, the accuracy of resulted maps decreased by 1.5~5.4%. Full article

(This article belongs to the Special Issue Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling)

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