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Forests
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Trees: Recorders of Past Soil Erosion and Landslide Events
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Trees: Recorders of Past Soil Erosion and Landslide Events

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (10 February 2021) | Viewed by 20556

Special Issue Editor

Dr. Karel Šilhán

E-Mail Website1 Website2
Guest Editor
Department of Physical Geography and Geoecology, University of Ostrava, Ostrava, Czech Republic
Interests: dendrogeomorphology; slope processes; tree sensitivity to geomorphic processes; tree ring anatomy; triggers of geomorphic events; dendrochronology

Special Issue Information

Dear Colleagues,

Trees via their tree rings can serve as recorders of geomorphic events and provide very valuable information about natural hazards behavior in the past. General principles of tree growth responses to the external geomorphic disturbances are well known. Nevertheless, recent studies suggest a more complex and difficult event-response relationship than previously expected. A detailed understanding of the above-mentioned tree growth aspects is crucial for attaining a meaningful spatio-temporal reconstruction of past geomorphic processes.

This Special Issue is focused on various aspects of tree-geomorphic process relationships. Specifically, soil erosion and landsliding, as very frequent processes, are objects of interest. Papers dealing with methodical progress and innovations in the reconstruction of past soil erosion and landslide behaviour, detailed studies of the tree growth responses to such processes (even on an anatomical level), or their spatio-temporal reconstruction using tree rings are welcome.

Dr. Karel Šilhán
Guest Editor

Manuscript Submission Information

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Keywords

  • Landsliding
  • Soil erosion
  • Tree rings
  • Growth responses
  • Trees
  • Dating
  • Spatio-temporal reconstruction
  • Dendrochronology
  • Dendrogeomorphology

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Published Papers (4 papers)

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Research

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27 pages, 14156 KiB  
Article
Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms
by Viet-Ha Nhu, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, Marten Geertsema, Victoria R. Kress, Sadra Karimzadeh, Khalil Valizadeh Kamran, Wei Chen and Hoang Nguyen
Forests 2020, 11(8), 830; https://doi.org/10.3390/f11080830 - 30 Jul 2020
Cited by 68 | Viewed by 6624
Abstract
We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total [...] Read more.
We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area. Full article
(This article belongs to the Special Issue Trees: Recorders of Past Soil Erosion and Landslide Events)
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28 pages, 9008 KiB  
Article
Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran
by Viet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Wei Chen, John J Clague, Marten Geertsema, Abolfazl Jaafari, Mohammadtaghi Avand, Shaghayegh Miraki, Davood Talebpour Asl, Binh Thai Pham, Baharin Bin Ahmad and Saro Lee
Forests 2020, 11(4), 421; https://doi.org/10.3390/f11040421 - 9 Apr 2020
Cited by 98 | Viewed by 5771
Abstract
We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on [...] Read more.
We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping. Full article
(This article belongs to the Special Issue Trees: Recorders of Past Soil Erosion and Landslide Events)
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15 pages, 5665 KiB  
Article
Tree Ring-Based Estimation of Landslide Areal Reactivation as a Fundament of Magnitude–Frequency Assessment
by Karel Šilhán
Forests 2020, 11(4), 400; https://doi.org/10.3390/f11040400 - 3 Apr 2020
Cited by 8 | Viewed by 2611
Abstract
Magnitude–frequency (M–F) relationships represent important information on slope deformation and are used in hazard assessment or as supporting data for urban planning. Various approaches have been used to extract such relationships in the past, but most of these methods drove at the problem [...] Read more.
Magnitude–frequency (M–F) relationships represent important information on slope deformation and are used in hazard assessment or as supporting data for urban planning. Various approaches have been used to extract such relationships in the past, but most of these methods drove at the problem of exact events´ frequency determination. Dendrogeomorphic (tree ring-based) approaches are actually thought to be the most precise method of dating past mass movement events that occurred within the last several centuries. Together with information on the spatial positions of the analysed trees, they represent a potentially very valuable tool for reconstructing M–F relationships, although their use for this purpose has been very rare in the past. In this study, M–F relationships are reconstructed using dendrogeomorphic methods for three landslides of different types (a translational slide, a flow-like slide, and a rotational slide) occurring in different geological materials (thick-bedded flysch, limestone marls, and volcanic breccia). In total, 572 disturbed trees were analysed, and chronologies of mass movement events were built. Landslide magnitudes were expressed in three ways: (i) the value of the standard It index; (ii) the area, as determined using homogenous morphological units; and (iii) the area, as determined using tree buffers. The power-law nature of M–F relationships was confirmed for all the landslides that were studied and using all the approaches that were applied. All of the combinations of results yielded high correlation values; nevertheless, differences were noted. The advantages and limitations of each approach used to reconstruct M–F relationships are also discussed. Full article
(This article belongs to the Special Issue Trees: Recorders of Past Soil Erosion and Landslide Events)
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Review

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19 pages, 6253 KiB  
Review
Dendrogeomorphology of Different Landslide Types: A Review
by Karel Šilhán
Forests 2021, 12(3), 261; https://doi.org/10.3390/f12030261 - 25 Feb 2021
Cited by 19 | Viewed by 4972
Abstract
The dating of past landslide events is one of the most crucial aspects of landslide research, leading to a better understanding of past landslide activity. Landslides can be extremely dangerous natural hazards, and thus, solving the relationships between their activity and climate variations [...] Read more.
The dating of past landslide events is one of the most crucial aspects of landslide research, leading to a better understanding of past landslide activity. Landslides can be extremely dangerous natural hazards, and thus, solving the relationships between their activity and climate variations is of high importance. For these purposes, data about past landslide activity are fundamental for such analyses. Various methods of landslide absolute dating exist, but the most precise approach that dates back several centuries is based on tree-ring analysis (dendrogeomorphology). Landslide movements can affect the growth of trees in response to specific growth disturbances. Although dendrogeomorphic methods are successfully used for dating other geomorphic processes, their use in landslide research is actually the most frequent. Dendrogeomorphic research on landslides is strongly influenced by general approaches of landslide signal extraction from tree-ring series of disturbed trees and by the type of landslide (varying by morphology, material and mechanism of movement). This study provides an overview of basic aspects of dendrogeomorphic research on landslides, and more specifically, it reviews basic tree-ring-based approaches of landslide dating. Presented review focuses on various landslide types and their effect on dendrogeomorphic dating. This review is built from the extensive database of all accessible dendrogeomorphic studies of landslides from 1893 to 2020. Moreover, recommendations for specific sampling and approach choice in individual landslide types are presented. Finally, limits of tree-ring-based approaches are presented, including provided proposals for further research. Full article
(This article belongs to the Special Issue Trees: Recorders of Past Soil Erosion and Landslide Events)
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