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1.
Comput Intell Neurosci ; 2021: 2677453, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34899888

RESUMO

With the rapid development of the economy and society, geological disasters such as landslides, collapses, and mudslides have shown an intensifying trend, seriously endangering the safety of people's lives and property, and affecting the sustainable development of the economy and society. Aiming at the problems of merging different data layers and determining the weighting of data stacking in the statistical analysis model based on GIS technology in the evaluation of the risk of geological disasters, this study proposes a logistic regression model combined with the RBFNN-GA algorithm, that is, the determination of the occurrence of geological disasters. The fusion coefficient (CF value) with the RBFNN-GA algorithm model, and with the help of SPSS statistical analysis software, solves the problem of factor selection, heterogeneous data merging, and weighting of each data layer in the risk assessment. In the experimental stage, this study adopts the method of geological hazard certainty coefficients to carry out the sensitivity analysis of the geological hazards in the study area. Using homogeneous grid division, the spatial quantitative evaluation of the risk of geological disasters is realized, and at the same time, the results of the spatial quantitative evaluation of the risk of geological disasters are tested according to the latest landslide points in the region. The existing classification mainly depends on the acquisition of land use/cover information or the processing method of the acquired information, but the existing information acquisition will be limited by time, space, and spectral resolution. The results show that the number of landslide points per unit area in the extremely unstable zone and the unstable zone is 0.0395 points/km2 and 0.0251 points/km2, respectively, which is much higher than 0.0038 points/km2 in the stable zone, indicating the evaluation results and actual landslide conditions.


Assuntos
Desastres , Deslizamentos de Terra , Algoritmos , Sistemas de Informação Geográfica , Humanos , Tecnologia
2.
Artigo em Inglês | MEDLINE | ID: mdl-34769662

RESUMO

The current study site of the project Inform@Risk is located at a landslide prone area at the eastern slopes of the city of Medellín, Colombia, which are composed of the deeply weathered Medellín Dunite, an ultramafic Triassic rock. The dunite rock mass can be characterized by small-scale changes, which influence the landslide exposition to a major extent. Due to the main aim of the project, to establish a low-cost landslide early warning system (EWS) in this area, detailed field studies, drillings, laboratory and mineralogical tests were conducted. The results suggest that the dunite rock mass shows a high degree of serpentinization and is heavily weathered up to 50 m depth. The rock is permeated by pseudokarst, which was already found in other regions of this unit. Within the actual project, a hypothesis has for the first time been established, explaining the generation of the pseudokarst features caused by weathering and dissolution processes. These parameters result in a highly inhomogeneous rock mass and nearly no direct correlation of weathering with depth. In addition, the theory of a secondary, weathering serpentinization was established, explaining the solution weathering creating the pseudokarst structures. This contribution aims to emphasize the role of detailed geological data evaluation in the context of hazard analysis as an indispensable data basis for landslide early warning systems.


Assuntos
Deslizamentos de Terra , Cidades , Colômbia , Geologia , Tempo (Meteorologia)
3.
Environ Monit Assess ; 193(12): 832, 2021 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-34799766

RESUMO

Among the natural disasters on the planet, especially in the mountainous and foothill regions, it is widespread erosive-debris flow events, which have the most significant environmental and economic damage to humanity. Georgia is no exception. This paper aims to develop a new methodology to calculate the predictive quantities of debris flow, essential for implementing anti-debris flow measures. Based on the available data and various calculations, a completely new empirical approach has been adopted to calculate predictive quantities of debris flow spent, predicting debris flow spent in the mountains and foothill regions of Georgia. The suggested methodology reflects the physics of debris-flow processes at a very high level and can be applied to calculate debris flow in various world regions.


Assuntos
Monitoramento Ambiental , Deslizamentos de Terra , Georgia
4.
Sensors (Basel) ; 21(21)2021 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-34770339

RESUMO

This paper shows the results of the monitoring of the deformations of a tunnel, carried out using a distributed optical fiber strain sensor based on stimulated Brillouin scattering. The artificial tunnel of the national railway crosses the accumulation zone of an active landslide, the Varco d'Izzo earthflow, in the southern Italian Apennines. Severely damaged by the landslide movements, the tunnel was demolished and rebuilt in 1992 as a reinforced concrete box flanked by two deep sheet pile walls. In order to detect the onset of potentially dangerous strains of the tunnel structure and follow their time trend, the internal deformations of the tunnel are also monitored by a distributed fiber-optic strain sensor since 2016. The results of the monitoring activity show that the deformation profiles are characterized by strain peaks in correspondence of the structural joints. Furthermore, the elongation of the fiber strands crossing the joints is consistent with the data derived by other measurement systems. Experiments revealed an increase in the time rate of the fiber deformation in the first and last part of the monitoring period when the inclinometers of the area also recorded an acceleration in the landslide movements.


Assuntos
Deslizamentos de Terra , Fibras Ópticas , Tecnologia de Fibra Óptica , Monitorização Fisiológica
5.
Sensors (Basel) ; 21(20)2021 Oct 13.
Artigo em Inglês | MEDLINE | ID: mdl-34696012

RESUMO

Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests.


Assuntos
Deslizamentos de Terra , Humanos , Interferometria , Radar , Projetos de Pesquisa , Tempo (Meteorologia)
6.
Artigo em Inglês | MEDLINE | ID: mdl-34682717

RESUMO

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.


Assuntos
Desastres , Deslizamentos de Terra , Sistemas de Informação Geográfica , Geologia , Aprendizado de Máquina
7.
Artigo em Inglês | MEDLINE | ID: mdl-34574372

RESUMO

Landslides are generated by natural causes and by human action, causing various geomorphological changes as well as physical and socioeconomic loss of the environment and human life. The study, characterization and implementation of techniques are essential to reduce land vulnerability, different socioeconomic sector susceptibility and actions to guarantee better slope stability with a significant positive impact on society. The aim of this work is the bibliometric analysis of the different types of landslides that the United States Geological Survey (USGS) emphasizes, through the SCOPUS database and the VOSviewer software version 1.6.17, for the analysis of their structure, scientific production, and the close relationship with several scientific fields and its trends. The methodology focuses on: (i) search criteria; (ii) data extraction and cleaning; (iii) generation of graphs and bibliometric mapping; and (iv) analysis of results and possible trends. The study and analysis of landslides are in a period of exponential growth, focusing mainly on techniques and solutions for the stabilization, prevention, and categorization of the most susceptible hillslope sectors. Therefore, this research field has the full collaboration of various authors and places a significant focus on the conceptual evolution of the landslide science.


Assuntos
Deslizamentos de Terra , Sistemas de Informação Geográfica , Geologia , Humanos
8.
Sensors (Basel) ; 21(15)2021 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-34372428

RESUMO

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet-, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models' type, layers, and sample set, based on tests with a large number of samples.


Assuntos
Terremotos , Deslizamentos de Terra , Aprendizado de Máquina
9.
Sensors (Basel) ; 21(15)2021 Aug 03.
Artigo em Inglês | MEDLINE | ID: mdl-34372479

RESUMO

Post-seismic vegetation recovery is critical to local ecosystem recovery and slope stability, especially in the Wenchuan earthquake area where tens of thousands of landslides were triggered. This study executed a decadal monitoring of post-seismic landslide activities all over the region by investigating landslide vegetation recovery rate (VRR) with Landsat images and a (nearly) complete landslide inventory. Thirty thousand landslides that were larger than nine pixels were chosen for VRR analysis, to reduce the influence of mixed pixels and support detailed investigation within landslides. The study indicates that about 60% of landslide vegetation gets close to the pre-earthquake level in ten years and is expected to recover to the pre-earthquake level within 20 years. The vegetation recovery is significantly influenced by topographic factors, especially elevation and slope, while it is barely related to the distance to epicenter, fault ruptures, and rivers. This study checked and improved the knowledge of vegetation recovery and landslide stability in the area, based on a detailed investigation.


Assuntos
Terremotos , Deslizamentos de Terra , Ecossistema , Humanos , Rios
10.
Sensors (Basel) ; 21(13)2021 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-34283153

RESUMO

Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations-Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.


Assuntos
Deslizamentos de Terra , Sistemas de Informação Geográfica , Aprendizado de Máquina , Redes Neurais de Computação , Tailândia
11.
Artigo em Inglês | MEDLINE | ID: mdl-34068563

RESUMO

Romania is one of the countries severely affected by numerous natural hazards, where landslides constitute a very common geomorphic hazard with strong economic and social impacts. The analyzed area, known as the "Ciuperca Hill", is located in Oradea (NW part of Romania) and it has experienced a number of landsliding events in previous years, which have endangered anthropogenic systems. Our investigation, focused on the main causal factors, determined that landslide events have rather complex components, reflected in the joint climatological characteristics, properties of the geological substrate, and human activity that further contributed to the intensive change of landscape and acceleration of slope instability. Analysis of daily precipitation displays the occurrence and intensive distribution between May and September. Higher values of rainfall erosivity (observed for the 2014-2017 period), are occurring between April and August. Erosivity density follows this pattern and indicates high intensity events from April until October. SPI index reveals the greater presence of various wet classes during the investigated period. Geological substrate has been found to be highly susceptible to erosion and landsliding when climatological conditions are suitable. Accelerated urbanization and reduced vegetation cover intensified slope instability. The authors implemented adequate remote-sensing techniques in order to monitor and assess the temporal changes in landslide events at local level. Potential solutions for preventative actions are given in order to introduce and conduct qualitative mitigation strategies for shaping sustainable urban environments. Results from this study could have implications for mitigation strategies at national, regional, county, and municipality levels, providing knowledge for the enhancement of geohazard prevention and appropriate response plans.


Assuntos
Deslizamentos de Terra , Cidades , Monitoramento Ambiental , Sistemas de Informação Geográfica , Humanos , Conceitos Meteorológicos , Medição de Risco , Romênia
12.
Artigo em Inglês | MEDLINE | ID: mdl-34072874

RESUMO

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1-3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017-2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.


Assuntos
Desastres , Deslizamentos de Terra , China , Sistemas de Informação Geográfica , Humanos , Aprendizado de Máquina
13.
Big Data ; 9(4): 289-302, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34085838

RESUMO

An exponential progression in the miniaturization of communicating devices has proliferated the generation of a large volume of data termed as "big data." The technological advancements in the micro-electro/mechanical system has made it possible to design the low-cost, low-power consuming artificial intelligence (AI)-based wireless sensor nodes to gather the big data belonging to various attributes from their surroundings. These nodes help in the early detection and prediction for the occurrence of landslides, which are among the catastrophic hazards. A profusion of research has focused on exploiting the potential of sensors for continuous monitoring and detecting the landslides at the earliest. However, the limited energy resources of sensor nodes give rise to the huge challenge for the network longevity pertaining to landslide detection. To address this concern, in this article, we propose an optimized routing and big data gathering system for landslide detection using (AI)-based wireless sensor network (WSN) (ORLAW). Since we propose a distributed routing mechanism, AI has a major role to play in the intelligent detection of landslides that too without the intervention of an external entity. We use the Dynamic Salp Swarm Algorithm for the cluster head selection in ORLAW. Two data collecting sinks are deployed on the opposite sides of the network, which is assumed to be a mountainous area. It is discerned from the simulation examination that ORLAW elongates the reliability period by 23.9% compared with the recently proposed cluster-based intelligent routing protocol, and also outperforms many others in the perspective of energy efficient management of big data.


Assuntos
Internet das Coisas , Deslizamentos de Terra , Inteligência Artificial , Big Data , Redes de Comunicação de Computadores , Reprodutibilidade dos Testes , Tecnologia sem Fio
14.
Environ Monit Assess ; 193(7): 386, 2021 Jun 05.
Artigo em Inglês | MEDLINE | ID: mdl-34091764

RESUMO

Detection and mapping of landslides is one of the most important techniques used for reducing the impact of natural disasters especially in the Himalaya, owing to its high amount of tectonic deformation, seismicity, and unfavorable climatic conditions. Moreover, the northeastern part of the Himalaya, severely affected by landslides every monsoon, is poorly studied. The information on the inventories is inhomogeneous and lacking. In this context, satellite-based earth observation data, which has significantly advanced in the last decade and often serves as a potential source for data collection, monitoring, and damage assessment for disasters in a short time span, has been implemented. Keeping in mind the above framework, this study aims to exploit the potentials of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for identifying new landslides in vegetated and hilly areas of the northeastern part of India. In order to assess the potentials of our data and methodology, a landslide event which occurred on 13 August 2016 13:30 h (IST) in North Sikkim, India, triggered due to rainfall has been explored in detail. The landslide also resulted in the formation of a lake, 2.2 km in length and 290 m in width. Difficulty in procurement of cloud-free datasets immediately after the event led us to the use of Sentinel-1 SAR backscatter data, to assess its potential for this purpose. It is observed that the potential of SAR amplitude imagery is limited to different aspects as per the sensor look direction during the mode of acquisition. Furthermore, the present study also incorporates a change detection algorithm to evaluate the performance of the Sudden Landslide Identification Product (SLIP) model to identify new landslides using Sentinel-2 multispectral imagery. Overall, the results exhibit that integrated usage of both optical and SAR amplitude imagery may provide a plethora of information for identification and mapping of new landslides for damage assessment and early warning. All the above results combined together suggest this method for rapid identification of landslides in the Himalayan terrain with special emphasis on the northeastern part of the Himalaya. The automation of this method for future operational usage is also suggested.


Assuntos
Deslizamentos de Terra , Monitoramento Ambiental , Índia , Radar , Siquim
16.
PLoS One ; 16(5): e0251212, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33979359

RESUMO

As natural backwater structures, landslide dams both threaten downstream human settlement or infrastructure and contain abundant hydro-energy and tourism resources, so research on their development feasibility is of great significance for permanently remedying them and effectively turning disasters into benefits. Through an analysis of the factors influencing landslide dam development and utilization, an index system (consisting of target, rule, and index layers) for evaluating development feasibility was constructed in this paper. Considering uncertainty and randomness in development feasibility evaluation, a cloud model-improved evaluation method was proposed to determine membership and score clouds based on the uncertainty reasoning of cloud model, and a cloud model-improved analytic hierarchy process (AHP-Cloud Model) was introduced to obtain weights. Final evaluation results were obtained using a hierarchical weighted summary. The improved method was applied to evaluate the Hongshiyan and Tangjiashan landslide dams and the results were compared with the maximum membership principle results. The results showed that the cloud model depicted the fuzziness and uncertainty in the evaluation process. The improved method proposed in this paper overcame the loss of fuzziness in the maximum membership principle evaluation results, and was capable of more directly presenting evaluation results. The development feasibility of the Hongshiyan landslide dam was relatively high, while that of the Tangjiashan landslide dam was relatively low. As suggested by these results, the evaluation model proposed in this paper has great significance for preparing a long-term management scheme for landslide dams.


Assuntos
Deslizamentos de Terra/prevenção & controle , China , Computação em Nuvem , Desastres/prevenção & controle , Estudos de Viabilidade , Humanos , Modelos Teóricos
17.
PLoS One ; 16(4): e0250418, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33882115

RESUMO

The weak interlayer in a rock slope often plays a significant role in seismic rockslides; however, the effect of weak interlayer on the seismic slope response and damage process is still not fully understood. This study presents a series of shaking test tests on two model slopes containing a horizontal weak interlayer with different thicknesses. A recorded Wenchuan earthquake ground motion was scaled to excite the slopes. Measurements from accelerometers embedded at different elevations of slope surface and slope interior were analyzed and compared. The effect of the weak interlayer thickness on the seismic response was highlighted by a comparative analysis of the two slopes in terms of topographic amplification, peak accelerations, and deformation characteristics as the input amplitude increased. It was found that the structure deterioration and nonlinear response of the slopes were manifested as a time lag of the horizontal accelerations in the upper slope relative to the lower slope and a reduction of resonant frequency and Fourier spectral ratio. Test results show that under horizontal acceleration, both slopes exhibited significant topographic amplification in the upper half, and the difference in amplification between slope face and slope interior was more pronounced in Slope B (with a thin weak interlayer) than in Slope A (with a thick weak interlayer). A four-phased dynamic response process of both slopes was observed. Similar deformation characteristics including development of strong response zone and macro-cracks, vertical settlement, horizontal extrusion and collapse in the upper half were observed in both slopes as the input amplitude increased; however, the deformations were more severe in Slope B than in Slope A, suggesting an energy isolation effect of the thick interlayer in Slope A.


Assuntos
Terremotos , Deslizamentos de Terra , Aceleração , Fenômenos Físicos
18.
Sci Total Environ ; 776: 145933, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-33647661

RESUMO

Dendrogeomorphic dating of past landslide events is a valuable tool for the assessment of landslide activity, providing unique data for the analysis of triggers or the modelling of landslide behaviour in the future. Unfortunately, tree-ring-based methods as well as dating approaches suffer from some limitations. One of the less frequently addressed limits of dendrogeomorphic analysis concerns the changing capacity of trees to record landslide events in their tree-ring series with increasing age. This study uses, to date, the most extensive database of tree-ring series (1736) of 868 disturbed individuals of Picea abies (L.) H. Karst. subjected to 20 landslides in the Outer Wester Carpathians for the assessment of their age-dependent sensitivity. The distribution of the total number of 1485 growth disturbances (reaction wood - RW and abrupt growth suppression - GS) throughout all decades of tree life shows evidence of distinct changes in the capacity for trees to record landslide signals with increasing age. The occurrence of RW dominated in the juvenile decades of tree life and then increased again in the 9th decade. The frequency of GS gradually increased and culminated during the 7th and 8th decades. The two intensities of growth disturbance (strong and moderate) expressed temporally balanced ratios, suggesting an effect of disturbance intensity rather than changing tree age. The important factors controlling age-dependent tree sensitivity to landslide movements based on the results seem to be changing stem elasticity, decreasing annual increment rates, root system development and increasing tree body weight. Moreover, this study demonstrates that landslide type (e.g., rigid block vs. plastic flow-like landslides) and bedrock geology distinctly modify age-dependent sensitivity that should be taken into account during the sampling of trees for landslide dating.


Assuntos
Abies , Deslizamentos de Terra , Picea , Humanos , Árvores , Madeira
19.
J Environ Manage ; 286: 112194, 2021 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-33652255

RESUMO

Silvopastoralism in New Zealand's highly erodible hill country is an important form of erosion and sediment control. Yet, there has been little quantitative work to establish the effectiveness of space-planted trees in reducing shallow landslide erosion. We propose a method to provide high-resolution spatially explicit individual tree influence models at landscape scale for the dominant species in pastoral hill country. The combined hydrological and mechanical influence of trees on slopes is inferred through the spatial relationship between trees and landslide erosion. First, we delineate individual tree crowns and classify these into four dominant species classes found in New Zealand's pastoral hill country. This is the first species classification of individual trees at landscape scale in New Zealand using freely accessible data, achieving an overall accuracy of 92.6%. Second, we develop tree influence models for each species class by means of inductive inference. The inferred empirical tree influence models largely agree with the shape and distribution of existing physical root reinforcement models. Of exotic species that were planted for erosion and sediment control, poplars (Populus spp.) and willows (Salix spp.) make up 51% (109,000 trees) in pastoral hill country at a mean density of 3.2 trees/ha. In line with previous studies, poplars and willows have the greatest contribution to slope stability with an average maximum effective distance of 20 m. Yet, native kanuka (Kunzea spp.) is the most abundant woody vegetation species in pastoral hill country within the study area, with an average of 24.1 stems per ha (sph), providing an important soil conservation function. A large proportion (56% or 212.5 km2) of pastoral hill-country in the study area remains untreated. The tree influence models presented in this study can be integrated into landslide susceptibility modelling in silvopastoral landscapes to both quantify the reduction in landslide susceptibility achieved and support targeted erosion and sediment mitigation plans.


Assuntos
Deslizamentos de Terra , Árvores , Hidrologia , Nova Zelândia , Solo
20.
Environ Sci Pollut Res Int ; 28(27): 36753-36764, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33710488

RESUMO

Understanding the effect of acid rain to landslides is crucial for a better landslide risk assessment. This work aims to reveal the unsuspected but key role of acid rain in Panzhihua airport landslide, China. Firstly, we propose a hypothesis that acid rain may aggravate the slaking behavior of mudstone at weak interlayer and make it more fragmented, eventually further reducing its shear strength and predisposing the Panzhihua airport landslide. Subsequently, mudstone samples are subjected to slaking durability test, respectively, using water with a pH of 7 and two dilute hydrochloric acid solution with pH of 5 and 3. Slaking durability index (Idn) is adopted aiming to quantitatively evaluate the impact of acid rain on the slaking. Moreover, the mechanisms of acid rain affecting the slaking behavior of mudstone are revealed by (1) analyzing cation compositions changes in different pH slaking fluid and (2) observing micro-structure change of mudstone-chip before and after acid rain treatment. Finally, three works are conducted as evidences to prove that acid rain indeed plays a key role in the occurrence of Panzhihua airport landslide, including (1) analysis of the link between the slaking behavior of mudstone and its shear strength, (2) comparison of cations between spring water at the edge of the toe of landslide and acid rain, and (3) comparison of mineral contents of mudstone samples collected from different locations. These findings have implications for comprehensively analyzing the formation mechanism of landslide in acid rain area (such as Europe, North America, and China).


Assuntos
Chuva Ácida , Deslizamentos de Terra , Aeroportos , China , Europa (Continente) , América do Norte
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