Os desastres devido à ocorrência dos escorregamentos têm aumentado significativamente, causando consequências que vão além de prejuízos reparáveis, acabando por afetar muitas vidas. Várias ferramentas como modelos computacionais aliados à Sistemas de Informações Geográficas (SIG), estão subsidiando tomadas de decisões por parte dos gestores públicos. Dentre tais ferramentas, o modelo SHALSTAB está sendo amplamente utilizado para geração de mapas de previsão de escorregamentos. Isso devido às diversas vantagens que vão desde o fácil acesso pela internet até a pequena quantidade de parâmetros necessários, dentre outras. Embora estes aspectos sejam positivos, deve-se atentar para o uso, muitas vezes indiscriminado, pela desconsideração das limitações e alguns aspectos que influenciam diretamente no grau de confiabilidade do modelo. Dentre as limitações inerentes aos aspectos originais do modelo, algumas foram contornadas nos últimos anos. No Brasil importante avanço científico ocorreu em função da modificação do algoritimo do SHALSTAB com a inserção de novas variáveis, além da discretização dos parâmetros físicos do solo. Ainda que isso tenha melhorado a qualidade dos mapas gerados no Brasil, observa-se uma lacuna na bibliografia no que se refere o efeito dos processos inerentes à caracterização das unidades geotécnicas dada pelas propriedades dos solos. À Vista disso pouco se sabe sobre a repercussão nos resultados gerados pelo SHALSTAB, quanto à espacialidade e a quantidade de dados amostrais de solo que caracterizam as unidades geotécnicas. Sendo assim, o presente estudo tem por objetivo verificar o efeito da caracterização das unidades geotécnicas no cálculo de estabilidade de encostas por intermédio do modelo SHALSTAB. Para tal fim, foram propostos diversos cenários, para bacia do arroio Jaguar, em Alto Feliz - RS, com valores dos parâmetros de solo obtidos da bibliografia. Ademais, adoção do inventário de cicatrizes de escorregamentos foi imprescindível no apoio à verificação do desempenho dos cenários. Foram adotadas duas metodologias comumente utilizadas para avaliar o desempenho do modelo. Os resultados mostraram que a variabilidade espacial dos pontos de amostragens de solo aliada à quantidade máxima dos mesmos por unidade geotécnica influenciaram na aproximação dos resultados à realidade local. Isso ficou evidenciado nas simulações que não contemplaram estas duas condições, resultando em cenários com menor eficiência em representar o contexto da bacia.

Various biotic and abiotic agents are changing forests. Prolonged human activities substantially could cause not only different changes in forests but also could accelerate natural hazards in the Anthropocene. Despite several remote sensing-based research in forest changes, there is a need for a holistic study that could visualize different dimensions of anthropogenic-induced forest changes such as forest loss, forest fragmentation, and forest degradation. Besides, the effects of these changes require to be investigated in the natural hazards’ studies in forest regions. This research was accomplished for holistic assessing of long-term forest loss, forest fragmentation, and forest degradation induced by human activities such as sprawling residential areas and expanding road networks in northeast Iran. Moreover, it has investigated the significance of forest dynamics in analyzing of landslide susceptibility in the forest regions. The time series of Landsat data with the contribution of aerial photos were employed to investigate long-term forest changes in three spans from 1966 to 2016. The expansion of forest roads was extracted from a combination of satellite images and topographic maps. Both pixel– and object-based approaches were used for analyzing forest changes. The spatial autocorrelation indicators and spatial regression models were applied for visualizing patterns of forest changes and possible relationships between forest changes and the expansion of residential areas and road networks. Furthermore, the detection of old and new landslide events was accomplished through Sentinel-1 and -2 images and DEM derivatives using object-oriented random forest method. The significance of conditioning and triggering factors that control the susceptibility of protected and non-protected forests to landslides was explored using the object-based random forest approach as well. Key findings revealed that the expansion of residential areas and rural roads have increasingly heightened the rates of forest loss before 2000. However, the spatial patterns of forest dynamics were changed from forest loss to forest fragmentation and forest degradation– along with the expansion of forest and mine roads– since the 1980s. Although the topographic and hydrologic features were the top influential predictors that control the susceptibility of protected forests to landslides, the natural and anthropogenic triggers have obtained significant values in non-protected forests to the landslides as well; forest fragmentation and logging were the top features of anthropogenic triggers. This research verifies that influential variables are different either for detecting landslides or for assessing landslide susceptibility in different forest regions. The spatial-based regression models showed higher efficiency than the traditional regression model for modelling relationships between forest changes and anthropogenic- induced drivers; however, there was no priority between spatial models. Random forest algorithm demonstrated satisfactory accuracy for mapping of both old and new landslides and landslide susceptibility with higher accuracy in the protected forests. This research has investigated human-induced forest changes; however, other abiotic and biotic agents may cause these changes such as climate hazards, forest fires, insect outbreaks, pathogens, and other natural hazards that need to be explored in the future studies. / Verschiedene biotische und abiotische Faktoren verursachen Veränderungen im Wald. Dauerhafte menschliche Eingriffe im Anthropozän könnten nicht nur zu unterschiedlichen Typen von Veränderungen im Wald selbst führen, sondern auch bestehende Naturgefahren verstärken. Trotz verschiedener fernerkundungsgestützter Forschungsarbeiten zu Waldveränderungen besteht Bedarf an einer holistischen Studie, welche verschiedene Dimensionen anthropogen verursachter Waldveränderungen wie Waldverlust, Waldfragmentierung und Waldschädigung aufzeigen kann. Außerdem ist es notwendig, die Auswirkungen derartiger Veränderungen in Naturgefahrenstudien für Waldgebiete zu untersuchen. Ziel dieser Forschung war es, eine holistische Bewertung von langfristigem Waldverlust, Waldfragmentierung und Waldschädigung durchzuführen, die durch menschliche Aktivitäten wie Ausbreitung von Siedlungsgebieten und Ausbau von Straßennetzen im Nordosten des Iran verursacht werden. Darüber hinaus hat diese Forschungsarbeit die Bedeutung der Walddynamik in der Analyse von Rutschungsneigung innerhalb von Waldgebieten untersucht. Um langfristige Waldveränderungen in drei Intervallen zwischen 1966 und 2016 zu untersuchen, wurden Zeitreihen von Landsat-Daten und zusätzlich von Luftbildern verwendet. Die Erweiterung der Waldwege wurde aus einer Kombination von Satellitenbildern und topographischen Karten extrahiert. Für die Analyse von Waldveränderungen wurden sowohl pixel- als auch objektbasierte Ansätze verwendet. Räumliche Autokorrelationsindikatoren und räumliche Regressionsmodelle wurden eingesetzt, um Muster von Waldveränderungen und Zusammenhänge zwischen Waldveränderungen und der Erweiterung von Wohngebieten und Straßennetzen zu visualisieren. Darüber hinaus wurde die Erkennung alter und neuer Erdrutsche aus Sentinel-1 und -2 Bildern und DEM-Derivaten unter Verwendung der objektorientierten „Random Forest “-Methode durchgeführt. Ebenfalls mit dem objektbasierten „Random Forest “-Ansatz wurde die Bedeutung von Konditionierungs- und Auslösefaktoren untersucht, welche die Suszeptibilität einer Fläche für Erdrutsche in geschützten und nicht geschützten Wäldern kontrollieren. Die zentralen Erkenntnisse sind, dass die Ausbreitung von Siedlungsflächen und der Ausbau von Landstraßen die Waldverluste vor dem Jahr 2000 zunehmend erhöht haben. Mit dem Ausbau von Wald- und Bergbaustraßen änderten sich jedoch seit den 1980er Jahren die räumlichen Muster der Walddynamik von Waldverlust hin zu Waldfragmentierung und Walddegradierung. Obwohl die orographischen und hydrologischen Merkmale die wichtigsten Einflussfaktoren hinsichtlich Suszeptibilität für das Auftreten von Erdrutschen in Schutzwäldern waren, haben weitere natürliche und anthropogene Auslöser Signifikanz erreicht innerhalb der nicht geschützten Waldflächen: Holzeinschlag und Waldfragmentierung waren dabei die dominierenden anthropogenen Auslöser. Die Studie bestätigt zudem, dass die Parameter für die Erkennung von Erdrutschen und für die Beurteilung der Rutschungs-Suszeptibilität an die verschiedenen Waldgebiete anzupassen sind. In der Modellierung der Beziehung zwischen Waldveränderungen und anthropogenen Einflüssen zeigten räumlich basierte Regressionsmodelle eine höhere Effizienz als das traditionelle Regressionsmodell; allerdings gab es keine klare Priorität innerhalb der räumlichen Modelle. Der „Random Forest “-Ansatz zeigte eine zufriedenstellende Genauigkeit sowohl bei der Kartierung historischer Erdrutsche als auch in der Bestimmung der Rutschungs-Suszeptibilität. Dabei lag die erreichte Genauigkeit in den geschützten Waldgebieten höher. Diese Forschungsarbeit hat vom Menschen verursachte Waldveränderungen untersucht. Da jedoch auch andere abiotische und biotische Faktoren die geschilderten Veränderungen verursachen können, sind weitere Studien notwendig. Diese könnten z. B. Klimaparameter, Waldbrände, Insektenschädigung und andere Naturgefahren einschließen.

Les glissements de terrain sont à l'origine de la destruction de nombreuses infrastructures routières, de bâtiments et causent la perte de vies humaines. L'observation des glissements de terrain est limitée par l'absence d'archives historiques précises. Sur les glissements de terrain superficiels forestiers, la dendrogéomorphologie permet de reconstruire, avec une résolution temporelle saisonnière et une emprise spatiale décamétrique, l'activité du processus, à partir de l'analyse des perturbations anatomiques contenues dans les cernes de croissance. 13 glissements de terrain superficiels forestiers, localisés dans la vallée de l'Ubaye ont été étudiés. L'analyse dendrogéomorphologique permet de densifier les chroniques historiques, d'obtenir des périodes de retour et d'améliorer nos connaissances sur le comportement spatial du processus. L'approche statistique basée sur un modèle de Poisson, permet de quantifier et de cartographier des probabilités de réactivation. La thèse proposera d'évaluer la robustesse de la reconstruction dendrogéomorphologique ainsi que le rôle des précipitations dans le déclenchement des évènements passé. / Landslides constitute a common mass movement process and a widespread hazard in mountain where they cause damage and destruction to settlements, transportation corridors, or even lead to the loss of life. A major obstacle for the knowledge of past landslide activity is the lack of historical data with satisfying spatial resolution over medium to long timescales and on a continuous basis. In wooded shallow landslides, the use of tree rings may greatly help the documentation of past events and may allow reconstruction of precise chronologies of landslide reactivations over considerable periods of the past. 13 wooded shallow landslides located in the Ubaye valley were analysed using dendrogeomorphic approach. This method enhances the historical chronicles. It permits to quantify return periods of landslide reactivations and to improve our knowledge concerning the spatial behaviour of the process. Coupled with a statistical approach based on a Poisson probabilistic model, it permits to quantify and to map the probability of landslide reactivations. Finally, the coincidence between landslide reactivations and extreme precipitation was examined in order to improve existing threshold values for the triggering of major landslides in the French Alps.

京都大学 / 0048 / 新制・論文博士 / 博士(理学) / 乙第13300号 / 論理博第1563号 / 新制||理||1653(附属図書館) / (主査)准教授 王 功輝, 教授 釜井 俊孝, 教授 福田 洋一 / 学位規則第4条第2項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第22264号 / 理博第4578号 / 新制||理||1657(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)准教授 松四 雄騎, 教授 千木良 雅弘, 教授 岩田 知孝 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

The construction of motorways in Slovakia faces a series of geotechnical problems due to the mountainous relief. One of these problems is also discussed in this diploma thesis, where the motorway route is led through a landslide area. The aim of the thesis is to compare two different proposals of the D1 motorway in part of Hubova - Ivachnova section. The theoretical part deals with the issue of slope movements in connection with transport line constructions. In the practical part, the proposals for specific solutions of the high embankment route variant and variant with the bridge are discussed. Both variants are designed in the Plaxis numeric program. Subsequent comparison and evaluation of variants is performed in relation to the degree of stability, deformation, structural stress and construction costs.

The thesis called Securing of Deep Cut in Slide Area focuses on the basic analysis of the problems connected to landslides and securing of the slopes. It also presents a solution of a certain problem, where the slope stability, potencional landslide launchers and subsequent remediaton are discussed.

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Critical and residual states are key soil conditions relevant to slope stability. Evaluating the available shear strength in relation to these conditions is crucial for reliable stability analyses. The best way to determine the strength parameters is through properly selected laboratory tests of soil mechanics. However, in specific cases, these parameters can be estimated based on other material properties of soils. The work clarifies the applicability of various correlation equations from the literature and proposes new relationships, created from laboratory- determined results of strength and index parameters of soils from the Bohemian Central Mountains. It shows how different equations or different material properties of soils can predict the required value of strength. The work also describes how the residual strength of soils and its estimation can be affected. This is mainly caused by the clay fraction proportion and by the shear rate used in its laboratory determination. The results are applied to a specific geotechnical problem - to estimate the critical state and the residual friction angle of soils from the selected study area of the D8 motorway, which is well known thanks to the stability problems. Last but not least, various errors and misunderstandings are discussed, which are often...

Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.