LANDSLIDE SITE ASSESSMENT AND CHARACTERIZATION USING REMOTE SENSING TECHNIQUES

Landslides are common and dangerous natural hazards that occur worldwide, often causing severe direct impacts on human lives, public and private properties. It is imperative to identify the landslide susceptible areas to avoid or mitigate the possible damage. Landslide prediction can be presented in a slope failure in spatial and/ or temporal terms. If it is presented in spatial term, it is considered a landslide susceptibility map (LSM) defined as the probability of spatial occurrence of slope failures. If it is presented in a combination of spatial and temporal distribution of the landslide susceptibility, it is commonly referred to as landslide hazard map (LHM). This document presents generation and comparison of LHM, and LSM using a remote sensing data. In addition, this paper shows the workflow of using multi-temporal UAV images to detect land movement and estimate soil moisture

Method for landslides detection with semi-automatic procedures: The case in the zone center-east of Cauca department, Colombia

Landslides are a common natural hazard that causes human casualties, but also infrastructure damage and land-use degradation. Therefore, a quantitative assessment of their presence is required by means of detecting and recognizing the potentially unstable areas. This research aims to develop a method supported on semiautomatic methods to detect potential mass movements at a regional scale. Five techniques were studied: Morphometry, SAR interferometry (InSAR), Persistent Scatterer InSAR (PS-InSAR), SAR polarimetry (PolSAR) and NDVI composites of Landsat 5, Landsat 7, and Landsat 8. The case study was chosen within the mid-eastern area of the Cauca state, which is characterised by its mountainous terrain and the presence of slope instabilities, officially registered in the CGS-SIMMA landslide inventory. This inventory revealed that the type `slide' occurred with 77.4% from the entire registries, `fall' with 16.5%, followed by `creeps' with 3%, flows with 2.6%, and `lateral spread' with 0.43%. As a result, we obtained the morphometric variables: slope, CONVI, TWI, landform, which were highly associated with landslides. The effect of a DEM in the processing flow of the InSAR method was similar for the InSAR coherence variable using the DEMs ASTER, PALSAR RTC, Topo-map, and SRTM. Then, a multiInSAR analysis gave displacement velocities in the LOS direction between -10 and 10 mm/year. With the dual-PolSAR analysis (Sentinel-1), VH and VV C-band polarised radar energy emitted median values of backscatters, for landslides, about of -14.5 dB for VH polarisation and -8.5 dB for VV polarisation. Also, L-band fully polarimetric NASA-UAVSAR data allowed to nd the mechanism of dispersion of CGS landslide inventory: 39% for surface scattering, 46.4% for volume dispersion, and 14.6% for double-bounce scattering. The optical remote sensing provided NDVI composites derived from Landsat series between 2012 and 2016, showing that NDVI values between 0.40 and 0.70 had a high correlation to landslides. In summary, we found the highest categories related to landslides by Weight of Evidence method (WofE) for each spaceborne technique applied. Finally, these results were merged to generate the landslide detection model by using the supervised machine learning method of Random Forest. By taking training and test samples, the precision of the detection model was of about 70% for the rotational and translational types.Los deslizamientos son una amenaza natural que causa pérdidas humanas, daños a la infraestructura y degradación del suelo. Una evaluación cuantitativa de su presencia se requiere mediante la detección y el reconocimiento de potenciales áreas inestables. Esta investigación tuvo como alcance desarrollar un método soportado en métodos semi-automáticos para detectar potenciales movimientos en masa a escala regional. Cinco técnicas fueron estudiadas: Morfometría, Interferometría radar, Interferometría con Persistent Scatterers, Polarimetría radar y composiciones del NDVI con los satélites Landsat 5, Landsat 7 y Landsat 8. El caso de estudio se seleccionó dentro de la región intermedia al este del departamento del Cauca, la cual se caracteriza por terreno montañoso y la presencia de inestabilidades de la pendiente oficialmente registrados en el servicio SIMMA del Servicio Geológico Colombiano. Este inventario reveló que el tipo de movimiento deslizamiento ocurrió con una frecuencia relativa de 77.4%, caidos con el 16.5% de los casos y reptaciones con 3%, flujos con 2.6% y propagación lateral con 0.43%. Como resultado, se obtuvo las variables morfométricas: pendiente, convergencia, índice topográfico de humedad y forma del terreno altamente asociados con los deslizamientos. El efecto de un DEM en el procesamiento del método InSAR fue similar para la variable coherencia usando los DEMs: ASTER, PAlSAR RTC, Topo-map y SRTM. Un análisis Multi-InSAR estimó velocidades de desplazamiento en dirección de vista del radar entre -10 y 10 mm/año. El análisis de polarimetría dual del Sentinel-1 arrojó valores de retrodispersión promedio de -14.5 dB en la banda VH y -8.5dB en la banda VV. Las cuatro polarimetrías del sensor aéreo UAVSAR permitió caracterizar el mecanismo de dispersión del Inventario de Deslizamiento así: 39% en el mecanismo de superficie, 46.4% en el mecanismo de volumen y 14.6% en el mecanismo de doble rebote. La información generada en el rango óptico permitió obtener composiciones de NDVI derivados de la plataforma Landsat entre los años 2012 y 2016, mostrando que el rango entre 0.4 y 0.7 tuvieron una alta asociación con los deslizamientos. En esta investigación se determinaron las categorías de las variables de Teledetección más altamente relacionadas con los movimientos en masa mediante el método de Pesos de Evidencias (WofE). Finalmente, estos resultados se fusionaron para generar el modelo de detección de deslizamientos usando el método supervisado de aprendizaje de máquina Random Forest. Tomando muestras aleatorias para entrenar y validar el modelo en una proporción 70:30, el modelo de detección, especialmente los movimientos de tipo rotacional y traslacional fueron clasificados con una tasa general de éxito del 70%.Ministerio de CienciasConvocatoria 647 de 2014Research line: Geotechnics and Geoenvironmental HazardDoctorad

Use of Geospatial Methods to Characterize Dispersion of the Emerald Ash Borer in Southern Ontario, Canada

Since the introduction of the Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis) to Southern Ontario in 2002, the condition of all species of Ash trees (Fraxinus) in the province is currently at risk. In this research, the effects of positive spatial autocorrelation on the EAB data as a result of sampling bias was addressed by applying a filtering distance threshold. To analyze the impact of environmental and anthropogenic predictors on the distribution of the EAB, logistic regression, Random Forest (RF) and a hybrid of Random Forest and GLM known as the Random Generalized Linear Model (RGLM) were applied to EAB data from 2006-2012 across Ontario. Ultimately, three risk maps were created from the 2006-2012 EAB data to validate the prediction dataset from 2013. In terms of model transferability, RGLM had the best extrapolation accuracy (84%), followed by stepwise backward logistic regression (70%), and Random Forest (52%)

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Modelagem de suscetibilidade e de limiares de precipitação para deslizamentos de terra utilizando métodos de aprendizagem de máquina

O Brasil é o país na América Latina com maior número de deslizamentos fatais provocados por precipitação. Neste trabalho, modela-se a suscetibilidade e os limiares de precipitação antecedente espacializados para ocorrência de deslizamentos de terra, a partir do desenvolvimento e aplicação de modelos baseados em Redes Neurais Artificiais (RNA). A modelagem é feita no âmbito da unidade geomorfológica da Serra Geral, com base em seis eventos passados cujas cicatrizes foram mapeadas com base em imagens de sensoriamento remoto. Os atributos do terreno utilizados como variáveis de entrada dos modelos foram obtidos a partir de um Modelo Digital de Elevação (MDE). O uso dos atributos reprojetados sobre os oito primeiros Componentes Principais acelerou o treinamento das RNAs, mas diminuiu a performance dos modelos. A pesquisa de métodos para a escolha dos locais para a extração das amostras de não-ocorrência proporcionou orientação importante para a composição da amostragem de treinamento dos modelos. O mapeamento da suscetibilidade também foi executado utilizando outros dois métodos de Aprendizagem de Máquina, Sistemas de Inferência Difusos (Fuzzy) (FIS) e Florestas Aleatórias, com bons resultados. Por fim, foram modelados conjuntamente a suscetibilidade a deslizamentos e os limiares de precipitação para a região de estudo, utilizando RNAs de múltiplas saídas treinadas com validação cruzada espacial, com resultados satisfatórios (AUC = 0,90, MEA = 32,77 mm, representando 25,99%). A transferibilidade do modelo de suscetibilidade foi analisada em uma bacia na mesma formação cujos dados não foram utilizados na modelagem, apresentando um AUC de 0,96.Brazil is the Latin American country that concentrates the highest number of deadly rainfall-induced landslides. In this Thesis, landslide susceptibility and spatialized precipitation thresholds for landslide occurrence are modeled based on the development and application of Artificial Neural Networks (ANN). The area of study is the Serra Geral geomorphological unit. The modeling is based on six past events from which scars were mapped based on remote sensing imagery. The terrain attributes used as input variables for the models were obtained from a Digital Elevation Model (DEM). The use of the first eight attributes that were reprojected on Principal Components accelerated the training of the ANNs but decreased the performance of the models. Methods for non-landslide sample selection were investigated, and the results obtained were an important base for composing the training samples in the remaining of this Thesis. Landslide susceptibility was modeled through two other Machine Learning methods, namely Fuzzy Inference Systems (FIS) and Random Forests, attaining good performance. Finally, we modeled the landslide susceptibility and precipitation thresholds concurrently for the entire study region using multiple-output ANNs trained with spatial cross-validation, with satisfactory results (AUC = 0.90, MEA = 32.77 mm, representing 25.99%). The transferability of the susceptibility model was analyzed in a basin within the same geological formation, from which data was not previously acquired for the models, attaining an AUC of 0.96

Rock slope instability in alpine geomorphic systems, Switzerland

Faced with the hazard potential and geomorphic importance of rock slopes adjusting to glacier retreat and current climate warming, the motivation of this dissertation is to increase our systemic and process understanding of rock slope instability in alpine geomorphic systems. It is hypothesised that a deeper understanding of rock slope instability can be achieved by thinking and working across scales and accounting for the emergence of non-linear, complex rock slope systems. For this reason, a novel hierarchical methodological approach has been developed. The methodology integrates multivariate modelling and geomorphic field mapping at the valley-scale, rockwall-scale geotechnical, geomorphological and sedimentological field surveys in the Turtmann Valley and Swiss National Park as well as numerical frost cracking modelling and laboratory weathering simulations at the intact rock scale. By means of this multi-method and, most importantly, multiscale systems approach, some progress was made towards current research debates about (i) the key controls of rock slope instability in areas affected by glacier retreat, (ii) associated paraglacial and short-term rockfall activity and (iii) their geomorphic consequences for alpine sediment cascade systems.Felsinstabilitäten in alpinen geomorphologischen Systemen, Schweiz Die Instabilität von Felswänden ist ein komplexes Phänomen das in Zeit und Magnitude variiert. Vor allem in Hochgebirgsregionen sind Felsinstabilitäten von großer Relevanz für die langzeitliche Relieferosion und Landschaftsentwicklung, sowie für die Sedimentproduktion und Effizienz von alpinen Sedimentflüssen. Die damit verbundene Disposition von Sturzereignissen stellt zudem ein ernstzunehmendes Naturgefahrenpotenzial für Mensch und Infrastruktur dar. Untersuchungen zeigen weltweit, und speziell für die Schweizer Alpen, eine Zunahme von Felsinstabilitäten unterschiedlicher Magnituden in den letzten Jahrzehnten. Das komplexe Zusammenspiel von topoklimatischen, kryosphärischen und felsmechanischen Kontrollfaktoren, insbesondere in von Gletscherrückzug betroffenen alpinen Tälern, ist jedoch noch unzureichend verstanden. Folglich stehen nur begrenzt Informationen über die kurz- und langzeitlichen Konsequenzen von Felsinstabilitäten bezüglich Magnituden, Intensitäten und Frequenzen von Sturzprozessen in alpinen Kaskadensystem zur Verfügung. Angesichts dieser Wissenslücken hat diese Doktorarbeit zum Ziel unser System- und Prozessverständnis von alpinen Felsinstabilitäten auf unterschiedlichen Zeit- und Raumskalen zu vertiefen. Ein neuer multiskaliger methodologischer Ansatz wird entwickelt, welcher erlaubt die Skalenabhängigkeit und Emergenz von Felssystemen zu adressieren. Die Arbeit umfasst fünf empirische Studien auf unterschiedlichen räumlichen und zeitlichen Skalen mit Untersuchungsgebieten im Turtmanntal (Schweizer Waliser Alpen) und Schweizer National Park. Auf der größten und längsten Skale untersucht diese Arbeit Hauptkontrollfaktoren für die raumzeitliche Aktivität von Felsinstabilitäten in alpinen Tälern seit dem letzten Glazialen Maximum. Zum ersten Mal in der Sturzprozessforschung wird ein Random Forest Klassifikationsalgorithmus angewandt und durch die Kombination mit einem Hauptkomponenten-basierten, logistischen Regressionsmodell weiter entwickelt. Die Modellkombination zeigt auf, dass Permafrostdegradation im Laufe des Gletscherrückzugs einer der wichtigsten Kontrollfaktoren für die Instabilität entgletscherter Felswände darstellt. Mit Hilfe eines ergodischen Ansatzes werden drei Szenarien paraglazialer Felsanpassung entwickelt, welches nichtlineare tektonische und strukturelle Konditionierungen von Permafrostwänden berücksichtigt. Die Arbeit liefert zudem quantitative und qualitative Beweise für die geomorphologische Signifikanz von Felsinstabilitäten für Sedimentkaskaden in alpinen Einzugsgebieten. Die Kombination aus einem GIS-basierten Konnektivitätsmodell und einer detaillierten geomorphologischen Feldkartierung ermöglicht es Sedimentflüsse von instabilen Felswänden zum fluvialen System zu identifiziert und im Hinblick auf ihre Effizienz zu bewerten. Die feld- und modellierungsbasierten Beobachtungen zeigen eine Dominanz von Sturzprozesse kleiner bis mittlerer Magnitude. Allerdings wird deutlich, dass aktuell ein Drittel des gespeicherten Sturzmaterials aufgrund der glazialen Talmorphometrie vom Hauptkaskadensystem entkoppelt ist. Auf der Skale individueller Felswände analysiert diese Arbeit die Ursache-Wirkung Beziehung zwischen Felsverwitterung, Felsinstabilität sowie Materialspeicherung auf Schutthalden in drei vergletscherten Hängetälern. Ein neuer holistischer Ansatz wird vorgestellt, welcher abduktive Schutthaldenuntersuchungen mit deduktiven geotechnischen Kartierungen an Felswänden, einem zweijährigen Felstemperaturmonitoring und numerischer Frostverwitterungsmodellierung integriert. Dieser integrative Ansatz zeigt auf, dass die Komplexität aus Kluftabstand, der vorgegebenen Kinematik aus Haupttrennflächen sowie der tiefenvariierenden Intensität saisonaler Eissegregation wesentlich das jährlich-dekadische Frequenz-Magnituden Spektrum von Sturzprozessen steuert sowie, in Kombination mit Permafrostdegradation, die langzeitliche Variabilität von Sedimentproduktion und Formeigenschaften von Schutthalden kontrolliert. Auf der Skale des intakten Fels widmet sich diese Arbeit der Frage nach der individuellen und synergetischen Verwitterungseffizienz hochfrequenter thermaler Zyklen und täglicher Eiskristallisation in Glimmerschiefer geringer Porosität. Ein neuartiges zweiphasiges Laborexperiment liefert Evidenzen für mikroskalige, strukturabhängige Felsermüdung in Folge wiederholter Frostzyklen, insbesondere in Felsproben, welche zuvor einer Phase thermalen Stresses ausgesetzt waren. Die Langzeitmessungen zeigen sowohl positive als auch negative Feedbackeffekte im Laufe verändernder mechanischer Felseigenschaften. Diese Beobachtungen haben Implikationen für aktuelle Forschungsdebatte über die Rolle subkrtitischer Verwitterungsmechanismen für oberflächennahe Felsinstabilitäten. Diese Arbeit hebt hervor, dass geomorphologische Forschung dringend mehr Aufmerksamkeit auf die Quellgebiete in alpinen Systemen, also Felswände und ihre inhärenten Systemeigenschaften, richten muss. Zudem zeigen die Befunde dieser Arbeit auf, dass die Instabilität von Felswänden eine Skalenfrage ist. Jede räumliche und zeitliche Skale ist mit unterschiedlichen Kausalzusammenhängen und Erklärungen verbunden hinsichtlich Hauptkontrollfaktoren, raumzeitlicher Sturzprozessaktivität und geomorphologischen Effekten für Sedimentkaskaden. Um diese Skalenabhängigkeit und Nichtlinearität von Felssystemen zu adressieren, liefert diese Arbeit verschiedene praktische und philosophische Lösungsansätze für zukünftige Forschung

Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. OBIA and machine learning algorithms are first tested using a combination of multiple datasets for identifying individual tree species. These datasets include Quickbird, LiDAR, and GIS derived terrain data. Improvements in tree species classification were obtained and the best data combination was terrain context (based on slope, elevation, and wetness), tree height, canopy shape, and branch density (based on LiDAR return intensity). The availability of a range of classifiers and different data pre-processing techniques adds to the complexity of image analysis. The combinations of these techniques result in a large number of potential outcomes and these need to be evaluated. Therefore, the second part of this research investigated and compared tree species classification performance for different methods (Naïve Bayes - NB , Logistic Regression - LR, Random Forest - RF, and Support Vector Machine - SVM), combined with various dimensionality reduction (DR) methods (Correlation-based feature selection filter, Information Gain, Wrapper methods, and Principal Component Analysis). When DR was used prior to classification, only the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy, and this was achieved without DR. The final part of this thesis demonstrates a new method using OBIA for mapping the biomass change of mangrove forests in Vietnam between 2000 and 2011 from SPOT images. First, three different mangrove associations were identified using two levels of image segmentation followed by a SVM classifier and a range of spectral, texture and GIS information for classification. The RF regression model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy