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Developing A Regional Scale Landslide Early Warning System In A Data Sparse Region Using Remote Sensing Geostatistics And Google Earth Engine
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Book Synopsis Developing a Regional Scale Landslide Early Warning System in a Data-sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine by :
Download or read book Developing a Regional Scale Landslide Early Warning System in a Data-sparse Region Using Remote Sensing, Geostatistics, and Google Earth Engine written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : The Landslide Early Warning System (LEWS) is a non-structural approach to mitigate landslide risk by alerting vulnerable communities at an early stage. This study aimed to develop a regional LEWS for rain-induced shallow landslides in Idukki, a mountainous district in India with sparse rainfall data. The landslide model consists of a rainfall component and a slope stability component. Satellite precipitation data can be used in data-sparse regions, but they must be calibrated because they tend to underestimate rainfall. To improve the accuracy of satellite data, this study used a geostatistics-based multi-criteria approach to identify optimal locations to install new rain gauges, thus enhancing the rain gauge network's monitoring capability. A rainfall threshold was developed for Idukki, accounting for intra-seasonal variations in rainfall patterns and extreme rainfall events. The slope stability component of the model is limited by the lack of high-resolution soil properties, which are time-consuming and impractical to acquire using conventional methods. To overcome this limitation, this research proposed developing empirical relationships between sub-surface resistivity and soil properties, providing a regional-scale high-resolution soil property dataset for slope susceptibility assessment. Finally, a cloud-based LEWS was developed using Google Earth Engine, combining the rainfall threshold and high-resolution slope stability models, with the advantage of readily available near real-time data, processing power, user accessibility, and the opportunity for future updates.
Book Synopsis Landslide Analysis and Early Warning Systems by : Benni Thiebes
Download or read book Landslide Analysis and Early Warning Systems written by Benni Thiebes and published by Springer Science & Business Media. This book was released on 2012-01-20 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent landslide events demonstrate the need to improve landslide forecasting and early warning capabilities in order to reduce related risks and protect human lives. In this thesis, local and regional investigations were carried out to analyse landslide characteristics in the Swabian Alb region, and to develop prototypic landslide early warning systems. In the local study area, an extensive hydrological and slope movement monitoring system was installed on a seasonally reactivated landslide body located in Lichtenstein- Unterhausen. Monitoring data was analysed to assess the influence of rainfall and snow-melt on groundwater conditions, and the initiation of slope movements. The coupled hydrology-slope stability model CHASM was applied to detect areas most prone to slope failures, and to simulate slope stability using a variety of input data. Subsequently, CHASM was refined and two web-based applications were developed: a technical early warning system to constantly simulate slope stability integrating rainfall measurements, hydrological monitoring data and weather forecasts; and a decision-support system allowing for quick calculation of stability for freely selectable slope profiles. On the regional scale, available landslide inventory data were analysed for their use in evaluation of rainfall thresholds proposed in other studies. Adequate landslide events were selected and their triggering rainfall and snow-melting conditions were compared to intensity-duration and cumulative thresholds. Based on the results, a regional landslide early warning system was developed and implemented as a webbased application. Both, the local and the regional landslide early warning systems are part of a holistic and integrative early warning chain developed by the ILEWS project, and could easily be transferred to other landslide prone areas.
Book Synopsis Developing Early Warning Systems for Debris Flows and Harmful Algal Blooms by : Sita Karki
Download or read book Developing Early Warning Systems for Debris Flows and Harmful Algal Blooms written by Sita Karki and published by . This book was released on 2019 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study focused on developing early warning systems for two types of geohazards using methods that heavily rely on remote sensing data. The first investigation attempted to develop a prototype version of an early warning system for landslide development, whereas the second focused on harmful algal bloom prediction. Construction of intensity-duration (ID) thresholds, and early warning and nowcasting systems for landslides (EWNSL) are hampered by the paucity of temporal and spatial archival data. This work represents significant steps towards the development of prototype EWNSL to forecast and nowcast landslides over Faifa Mountains in the Red Sea Hills. The developed methodologies rely on temporal, readily available, archival Google Earth and Sentinel-1A imagery, precipitation measurements, and limited field data to construct an ID threshold for Faifa. Adopted procedures entailed the generation of an ID threshold to identify the intensity and duration of precipitation events that cause landslides in the Faifa Mountains, and the generation of pixel-based ID curves to identify locations where movement is likely to occur. Spectral and morphologic variations in temporal Google Earth imagery following precipitation events were used to identify landslide-producing storms and to generate the Faifa ID threshold (I = 4.89*D−0.65). Backscatter coefficient variations in radar imagery were used to generate pixel-based ID curves and to identify locations where mass movements are likely to occur following landslide-producing storms. These methodologies accurately distinguished landslide-producing storms from non-landslide producing ones and identified the locations of these landslides with an accuracy of 60%. Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. I developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. I constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.
Book Synopsis Landslides: Detection, Prediction and Monitoring by : P. Thambidurai
Download or read book Landslides: Detection, Prediction and Monitoring written by P. Thambidurai and published by Springer Nature. This book was released on 2023-03-02 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book intends to decipher the knowledge in the advancement of understanding, detecting, predicting, and monitoring landslides. The number of massive landslides and the damages they cause has increased across the globe in recent times. It is one of the most devastating natural hazards that cause widespread damage to habitat on a local, regional, and global scale. International experts provide their experience in landslide research and practice to help stakeholders mitigate and predict potential landslides. The book comprises chapters on: Dynamics, mechanisms, and processes of landslides; Geological, geotechnical, hydrological, and geophysical modelling for landslides; Mapping and assessment of hazard, vulnerability, and risk associated with landslides; Monitoring and early warning of landslides; Application of remote sensing and GIS techniques in monitoring and assessment of landslides. The book will be of interest to researchers, practitioners, and decision-makers in adapting suitable modern techniques for landslide study.
Book Synopsis Analysis of Spatial Data from Terrain Models for Landslide Predictive Mapping by : Rubini Santha
Download or read book Analysis of Spatial Data from Terrain Models for Landslide Predictive Mapping written by Rubini Santha and published by . This book was released on 2014 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landslides are a pervasive hazard that can result in substantial damage to properties and loss of life throughout the world. To understand the nature and scope of the hazard, landslide hazard mapping has been an area of intense research by identifying areas most susceptible to landslides in order to mitigate against these potential losses. Advanced GIS and remote sensing techniques are a fundamental component to both generate landslide inventories of previous landslides and identify landslide prone regions. A Digital Elevation Model (DEM) is one of the most critical data sources used in this GIS analysis to describe the topography. A DEM can be obtained from several remote sensing techniques, including satellite data and Light Detection and Ranging (LiDAR). While a DEM is commonly used for landslide hazard analysis, insufficient research has been completed on the influence of DEM source and resolution on the quality of landslide hazard mapping, particularly for high resolution DEMs such as those obtained by LiDAR. In addition to topography, multiple conditioning factors are often employed in landslide susceptibility mapping; however, the descriptive accuracy and contribution of the data representing these factors to the overall analysis is not fully understood or quantified. In many cases, the data available for these factors may be of insufficient quality, particularly at regional scales. These factors are often integrated into a wide assortment of analysis techniques, which can result in inconsistent mapping and hazard analysis. To this end, the principal objectives of this study are to 1) evaluate the influence of DEM source and spatial resolution in landslide predictive mapping, 2) asses the predictive accuracy of landslide susceptibility mapping produced from fewer critical conditioning factors derived solely from LiDAR data, 3) compare six widely used and representative landslide susceptibility mapping techniques to evaluate their consistency, 4) create a seismically-induced landslide hazard map for landside-prone Western Oregon, and 5) develop automated tools to generate landslide susceptibility maps in a regional scale. In this study, semi-qualitative, quantitative and hybrid mapping techniques were used to produce a series of landslide susceptibility maps using 10 m, 30 m and 50 m resolution datasets obtained from ASTER (Advance Space borne Thermal Emission and Reflection Radiometer), NED (National Elevation Dataset) and LiDAR (Light Detection and Ranging). The results were validated against detailed landslide inventory maps highlighting scarps and deposits derived by geologic experts from LiDAR DEMs. The output map produced from the LiDAR 10 m DEM was identified as the optimum spatial resolution and showed higher predictive accuracy for landslide susceptibility mapping. Higher resolution DEMs from LIDAR data was also investigated; however, they were not significantly improved over the 10 m DEM. Next, a series of landslide susceptibility maps were compared from six widely used statistical techniques using slope, slope roughness, elevation, terrain roughness, stream power index and compound topographic index derived from LiDAR DEM. The output maps were validated using both confusion matrix and area of curve methods. Statistically, the six output maps produced, showed accepTable prediction rate for landslide susceptibility. However, visual effects and limitations were noted that vary based on each technique. This study also showed that a single LiDAR DEM was capable of producing a satisfactory susceptibility map without additional data sources that may be difficult to obtain for large areas. In western Oregon, landslides are widespread and account for major direct and indirect losses on a frequent basis. A variety of factors lead to these landslides, which makes them difficult to analyze at a regional scale where detailed information is not available. For this study, a seismically-induced landslide hazard map was created using a multivariate, ordinary least squares approach. Various data sources, including combinations of topography (slope, aspect), lithology, vegetation indices (NDVI), mean annual precipitation, seismic sources (e.g., PGA, PGV, distance to nearest fault), and land use were rigorously evaluated to determine the relative contributions on each parameter on landslide potential in western Oregon. Results of the analysis showed that slope, PGA, PGV and precipitation were the strongest indicators of landslide susceptibility and other factors had minimal influence on the resulting map. An automated tool kit was a byproduct of this analysis which can be used to simply the hazard mapping process and selection of parameters to include in the analysis.
Book Synopsis Advancing Physically-based Landslide Prediction from Site-based to Regional Scale by Integrating High Resolution Remote Sensing Data by : Zonghu Liao
Download or read book Advancing Physically-based Landslide Prediction from Site-based to Regional Scale by Integrating High Resolution Remote Sensing Data written by Zonghu Liao and published by . This book was released on 2010 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Enhancement of Rainfall-triggered Shallow Landslide Hazard Assessment at Regional and Site Scales Using Remote Sensing and Slope Stability Analysis Coupled with Infiltration Modeling by : Thilanki Maneesha Dahigamuwa Rajaguru Mudiyanselage
Download or read book Enhancement of Rainfall-triggered Shallow Landslide Hazard Assessment at Regional and Site Scales Using Remote Sensing and Slope Stability Analysis Coupled with Infiltration Modeling written by Thilanki Maneesha Dahigamuwa Rajaguru Mudiyanselage and published by . This book was released on 2018 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landslides cause significant damage to property and human lives throughout the world. Rainfall is the most common triggering factor for the occurrence of landslides. This dissertation presents two novel methodologies for assessment of rainfall-triggered shallow landslide hazard. The first method focuses on using remotely sensed soil moisture and soil surface properties in developing a framework for real-time regional scale landslide hazard assessment while the second method is a deterministic approach to landslide hazard assessment of the specific sites identified during first assessment. In the latter approach, landslide inducing transient seepage in soil during rainfall and its effect on slope stability are modeled using numerical analysis.
Book Synopsis Understanding and Reducing Landslide Disaster Risk by : Nicola Casagli
Download or read book Understanding and Reducing Landslide Disaster Risk written by Nicola Casagli and published by Springer Nature. This book was released on 2020-12-21 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a part of ICL new book series “ICL Contribution to Landslide Disaster Risk Reduction” founded in 2019. Peer-reviewed papers submitted to the Fifth World Landslide Forum were published in six volumes of this book series. This book contains the followings: • One theme lecture and one keynote lecture• Monitoring and remote sensing for landslide risk mitigation, including one keynote lecture• Landslide early warning systems, forecasting models and time prediction of landslides Prof. Nicola Casagli is a Vice President and President-elect of the International Consortium on Landslides (ICL) for 2021–2023. He is Professor of engineering geology at the Department of Earth Sciences, University of Florence, and President of the National Institute of Oceanography and Applied Geophysics – OGS, Trieste, Italy. Dr. Veronica Tofani is an Associate Professor at the Department of Earth Sciences, University of Florence, and Program Coordinator of the UNESCO Chair on Prevention and Sustainable Management of Geo-hydrological hazards, University of Florence. Prof. Kyoji Sassa is the Founding President and the Secretary-General of the International Consortium on Landslides (ICL). He has been the Editor-in-Chief of International Journal Landslides since its foundation in 2004. Prof. Peter Bobrowsky is the President of the International Consortium on Landslides. He is a Senior Scientist of Geological Survey of Canada, Ottawa, Canada. Prof. Kaoru Takara is the Executive Director of the International Consortium on Landslides. He is a Professor and Dean of Graduate School of Advanced Integrated Studies (GSAIS) in Human Survivability (Shishu-Kan), Kyoto University.
Book Synopsis Partnerships for Reducing Landslide Risk by : National Research Council
Download or read book Partnerships for Reducing Landslide Risk written by National Research Council and published by National Academies Press. This book was released on 2004-03-15 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landslides occur in all geographic regions of the nation in response to a wide range of conditions and triggering processes that include storms, earthquakes, and human activities. Landslides in the United States result in an estimated average of 25 to 50 deaths annually and cost $1 to 3 billion per year. In addition to direct losses, landslides also cause significant environmental damage and societal disruption. Partnerships for Reducing Landslide Risk reviews the U.S. Geological Survey's (USGS)National Landslide Hazards Mitigation Strategy, which was created in response to a congressional directive for a national approach to reducing losses from landslides. Components of the strategy include basic research activities, improved public policy measures, and enhanced mitigation of landslides. This report commends the USGS for creating a national approach based on partnerships with federal, state, local, and non-governmental entities, and finds that the plan components are the essential elements of a national strategy. Partnerships for Reducing Landslide Risk recommends that the plan should promote the use of risk analysis techniques, and should play a vital role in evaluating methods, setting standards, and advancing procedures and guidelines for landslide hazard maps and assessments. This report suggests that substantially increased funding will be required to implement a national landslide mitigation program, and that as part of a 10-year program the funding mix should transition from research and guideline development to partnership-based implementation of loss reduction measures.
Book Synopsis Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning by : Samuele Segoni
Download or read book Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning written by Samuele Segoni and published by MDPI. This book was released on 2021-06-22 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landslides are destructive processes causing casualties and damage worldwide. The majority of the landslides are triggered by intense and/or prolonged rainfall. Therefore, the prediction of the occurrence of rainfall-induced landslides is an important scientific and social issue. To mitigate the risk posed by rainfall-induced landslides, landslide early warning systems (LEWS) can be built and applied at different scales as effective non-structural mitigation measures. Usually, the core of a LEWS is constituted of a mathematical model that predicts landslide occurrence in the monitored areas. In recent decades, rainfall thresholds have become a widespread and well established technique for the prediction of rainfall-induced landslides, and for the setting up of prototype or operational LEWS. A rainfall threshold expresses, with a mathematic law, the rainfall amount that, when reached or exceeded, is likely to trigger one or more landslides. Rainfall thresholds can be defined with relatively few parameters and are very straightforward to operate, because their application within LEWS is usually based only on the comparison of monitored and/or forecasted rainfall. This Special Issue collects contributions on the recent research advances or well-documented applications of rainfall thresholds, as well as other innovative methods for landslide prediction and early warning. Contributions regarding the description of a LEWS or single components of LEWS (e.g., monitoring approaches, forecasting models, communication strategies, and emergency management) are also welcome. We encourage, in particular, the submission of contributions concerning the definition and validation of rainfall thresholds, and their operative implementation in LEWS. Other approaches for the forecasting of landslides are also of interest, such as physically based modelling, hazard mapping, and the monitoring of hydrologic and geotechnical indicators, especially when described in the framework of an operational or prototype early warning system.
Book Synopsis The Development of a Site Specific Early Warning System for Rainfall Induced Landslides by : Samuel James Harris
Download or read book The Development of a Site Specific Early Warning System for Rainfall Induced Landslides written by Samuel James Harris and published by . This book was released on 2013 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes the development of a site specific early warning system for rainfall induced landslides. In New Zealand alone, rainfall induced landslides cause millions of dollars' worth of damage annually. Worldwide the toll is much greater, with thousands of fatalities and billions of dollars' worth of damage annually. The early warning system developed in this research is a means to mitigate the risk of such landslides. This thesis describes the underlying theory, the methodology used and results of the development of this early warning system. The early warning system is site specific, and based on the assumed failure mechanism of a slope subject to rainfall events of variable magnitude and intensity including prolonged events. The selected site, which the prototype of this early warning system was developed for, is located in Silverdale, New Zealand. A slope was formed at the site by a previous road cut operation to form State Highway One, which lies at the toe of the slope. A landslide occurred at the site in the winter of 2008 following a period of prolonged rainfall. The soil at the site consists of residual soil weathered from the Northland Allochthon formation. Previous research and experience within this soil group suggests it is particularly susceptible to rainfall induced landslides. A variety of laboratory tests were undertaken in this research to better understand the shear strength characteristics of the soil. The results obtained from consolidated drained and constant shear stress drained triaxial tests indicate that this soil may exhibit different shear strength parameters depending on the stress path associated with failure. Many site specific early warning systems have been developed in the literature, however they are usually based on explicitly stating a level which a measured parameter (pore-pressure for instance) must reach before a warning is given. The aim of this research was to develop an early warning system which alerts the user (a) to decrease of the factor of safety to a level defining overall failure or landslide occurrence and (b) the time-frame in which this failure may happen. For the purpose of this research, a factor of safety of one is defined as overall failure. This early warning system utilises field monitoring to determine the factor of safety of the slope against slope failure. Two sites are referred to throughout this thesis. One is the landsite site; the location of the 2008 landslide event. The other is the monitored site. Volumetric water content sensors were installed at various depths and locations along the same cross section of the site, approximately 40m away from the 2008 landslide site. A tipping bucket rain-gauge was used to monitor rainfall events. The rainfall record captured at the site was input into a finite element model (SEEP/W) to replicate the fluctuating water content observed at the monitored site. Once a good agreement was obtained, the matric suction/pore-water pressure profile was coupled with a limit equilibrium analyses (SLOPE/W). Thus, the factor of safety at each time step of the finite element model was obtained. An artificial neural network was trained to predict this factor of safety, using the corresponding readings of the volumetric sensors at the site as inputs. Next, the rate of change of this factor of safety (change of factor of safety with respect to time during a rainfall event) was used to estimate the time until failure. Finally, another artificial neural network was trained to predict the factor of safety at the site in the future, using rainfall forecasts for the site. The user of the early warning system can then use these predictions of the time until failure as a basis for taking any necessary action. For the given monitored site, it is recommended that if failure is predicted to occur within 5 hours, then the warning should consist of lowering speed limits. If failure is predicted to occur within 1 hour, than the warning should consist of diverting traffic to avoid the landslide site. The finite element model was reasonably successful at replicating the observed water content fluctuations in the field. A factor of safety of one was obtained for the 2008 landslide site, using the rainfall record leading up to the landslide failure (using the stratigraphy at the location of the 2008 landslide), verifying the modelling process. The artificial neural network could predict the factor of safety of the monitored site to a reasonable level, using the field monitoring data and rainfall forecasts as inputs, which can form the basis of an early warning system as a means to mitigate the risk of rainfall induced landslides.
Book Synopsis An Integrated Approach (remote Sensing, GIS, Engineering, Data Mining) for Modeling, Assessing and Mitigating Slope Stability Hazards in Mountainous Environments by : Racha El Kadiri
Download or read book An Integrated Approach (remote Sensing, GIS, Engineering, Data Mining) for Modeling, Assessing and Mitigating Slope Stability Hazards in Mountainous Environments written by Racha El Kadiri and published by . This book was released on 2014 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mountainous areas are quite often subjected to hazards related to their steep relief and intense precipitation events. These hazards pose serious threats to settlements and structures that support transportation, agriculture, tourism, and other economic activities. This study applies and develops a wide range of methodologies and tools that take advantage of readily available remote sensing datasets, geographic information system technologies, and artificial intelligence techniques. The developed approaches allow the characterization of both spatial and temporal conditions that controlled mass movement occurrences and use these characteristics to model and mitigate future occurrences. The Jazan area in the southern Red Sea Hills of Saudi Arabia has been selected as a test site. This project incorporates four research topics: In the first section, in an effort to compensate for the paucity or lack of ground systems and historical databases, I implement methodologies that rely heavily on remote sensing datasets to assess and understand the factors controlling debris flows and to predict their distribution on a regional scale. In the second section, I develop a new artificial neural network based approach for susceptibility analyses and evaluate its performance by comparing the model outputs to those extracted using a conventional ANN modeling approach. In the third section, I develop cost effective, remote sensing based solutions for the mitigation of the modeled hazards. They include the determination of optimal locations for civil engineering structures and the development of a warning system for rainfall induced events. The advocated practices will allow mitigation of the hazardous events with sufficient lead time, thus reducing their impacts on local communities. In the fourth section, I use radar interferometry to detect and monitor mass movements that are experiencing slow rates of deformation, but that have the potential for much higher rates of movement during brief rainfall or earthquake events. The four components of the study offer a broad, multidisciplinary range of advanced techniques that provide a better understanding and assessment of typical mountainous slope stability hazards. This effort is a step toward building safe and sustainable communities in data scare mountainous regions.
Book Synopsis The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale by : Nixon Alexander Correa-Munoz
Download or read book The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale written by Nixon Alexander Correa-Munoz and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This landslide detection research applied remote sensing techniques. Morphometry to derive both DEM terrain parameters and land use variables. SAR interferometry (InSAR) for showing that InSAR coherence and InSAR displacement obtained with SRTM DEM 30¬†m resolution were strongly related to landslides. InSAR coherence values from 0.43 to 0.66 had a high association with landslides. PS-InSAR allowed to estimate terrain velocities in the satellite line-of-sight (LOS) in the range¬†,àí¬†10 to 10¬†mm/year concerning extremely slow landslide displacement rates. SAR polarimetry (PolSAR) was used over L-band UAVSAR quad-pol data, obtaining the scattering mechanism of volume and surface retrodispersion more associated with landslides. The optical remote sensing with a multitemporal approach for change detection by multi-year Landsat (5, 7 and 8)-NDVI, showed that NDVI related to landslides had values between 0.42 and 0.72. All the information was combined into a multidimensional grid product and crossed with training data containing a Colombian Geologic Service (CGS) landslide inventory. A detection model was implemented using the Random Forest supervised method relating the training sample of landslides with multidimensional explanatory variables. A test sample with a proportion of 70:30 allowed to find the accuracy of detection of about 70.8% for slides type.
Book Synopsis Landslide Recognition and Monitoring with Remotely Sensed Data from Passive Optical Sensors by :
Download or read book Landslide Recognition and Monitoring with Remotely Sensed Data from Passive Optical Sensors written by and published by . This book was released on 2013 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Rainfall-triggered Landslides: Conditions, Prediction, and Warning by : Lisa Victoria Luna
Download or read book Rainfall-triggered Landslides: Conditions, Prediction, and Warning written by Lisa Victoria Luna and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Landslide Geoanalytics Using LiDAR-derived Digital Elevation Models by : Saeid Pirasteh
Download or read book Landslide Geoanalytics Using LiDAR-derived Digital Elevation Models written by Saeid Pirasteh and published by . This book was released on 2018 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landslides are natural hazards that contribute to tremendous economic loss and result in fatalities if there is no well-prepared mitigation and planning. Assessing landslide hazard and optimizing quality to improve susceptibility maps with various contributing factors remain a challenge when working with various geospatial datasets. Also, the system of updating landslide inventories which identify geometry, deformation, and type of landslide with semi-automated computing processes in the Geographic Information System (GIS) can be flawed. This study explores landslide geoanalytics approaches combined with empirical approach and powerful analytics in the Zagros and Alborz Mountains of Iran. Light Detection And Ranging (LiDAR)-derived Digital Elevation Models (DEMs), Unmanned Aerial Vehicle (UAV) images, and Google Earth images are combined with the existing inventory dataset. GIS thematic data in conjunction with field observations are utilized along with geoanalytics approaches to accomplish the results. The purpose of this study is to explore the challenges and techniques of landslide investigations. The study is carried out by studying stream length-gradient (SL) index analysis in order to identify tectonic signatures. A correlation between the stream length-gradient index and the graded Dez River profile with slopes and landslides is investigated. By building on the previous study a quantitative approach for evaluating both spatial and temporal factors contributing to landslides for susceptibility mapping utilizing LiDAR-derived DEMs and the Probability Frequency Ratio (PFR) model is expanded. Furthermore, the purpose of this study is to create an algorithm and a software package in MATLAB for semi-automated geometric analysis to measure and determine the length, width, area, and volume of material displacement and flow direction, as well as the type of landslide. A classification method and taxonomy of landslides are explored in this study. LiDAR-derived DEMs and UAV images help to characterize landslide hazards, revise and update the inventory dataset, and validate the susceptibility model, geometric analysis, and landslide deformation. This study makes the following accomplishments and contributions: 1) Operational use of LiDAR-derived DEMs for landslide hazard assessment is estimated, which is a realistic ambition if we can continue to build on recent achievements; 2) While a steeper gradient could potentially be a signature for landslide identification, this study identifies the geospatial locations of high-gradient indices with potential to landslides; 3) An updated inventory dataset is achieved, this study indicates an improved landslide susceptibility map by implementing the PFR model compared to the existing data and previous studies in the same region. This study shows that the most effective factor is the lithology with 13.7% positive influence; and 4) This study builds a software package in MATLAB that can a) determine the type of landslide, b) calculate the area of a landslide polygon, c) determine and measure the length and width of a landslide, d) calculate the volume of material displacement and determine mass movement (i.e. deformation), and e) identify the flow direction of a landslide material movement. In addition to the contributions listed above, a class taxonomy of landslides is introduced in this study. The relative operating characteristic (ROC) curve method in conjunction with field observations and the inventory dataset are used to validate the accuracy of the PFR model. The validation of the result for susceptibility mapping accuracy is 92.59%. Further, the relative error method is applied to validate the performance of relative percentage of error of the selected landslides computing in the proposed software package. The relative percentage of error of the area, length, width, and volume is 0.16%, 1.67%, 0.30%, and 5.50% respectively, compared to ArcGIS. Marzan Abad and Chalus from Mazandaran Province of Iran and Madaling from Guizhou Province of China are used for validating the proposed algorithm.
Book Synopsis Advancing Culture of Living with Landslides by : Matjaz Mikos
Download or read book Advancing Culture of Living with Landslides written by Matjaz Mikos and published by Springer. This book was released on 2017-06-10 with total page 1148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains peer-reviewed papers from the Fourth World Landslide Forum organized by the International Consortium on Landslides (ICL), the Global Promotion Committee of the International Programme on Landslides (IPL), University of Ljubljana (UL) and Geological Survey of Slovenia in Ljubljana, Slovenia from May 29 to June 2,. The complete collection of papers from the Forum is published in five full-color volumes. This second volume contains the following: • Two keynote lectures • Landslide Field Recognition and Identification: Remote Sensing Techniques, Field Techniques • Landslide Investigation: Field Investigations, Laboratory Testing • Landslide Modeling: Landslide Mechanics, Simulation Models • Landslide Hazard Risk Assessment and Prediction: Landslide Inventories and Susceptibility, Hazard Mapping Methods, Damage Potential Prof. Matjaž Mikoš is the Forum Chair of the Fourth World Landslide Forum. He is the Vice President of International Consortium on Landslides and President of the Slovenian National Platform for Disaster Risk Reduction. Prof. Binod Tiwari is the Coordinator of the Volume 2 of the Fourth World Landslide Forum. He is a Board member of the International Consortium on Landslides and an Executive Editor of the International Journal “Landslides”. He is the Chair-Elect of the Engineering Division of the US Council of Undergraduate Research, Award Committee Chair of the American Society of Civil Engineering, Geo-Institute’s Committee on Embankments, Slopes, and Dams Committee. Prof. Yueping Yin is the President of the International Consortium on Landslides and the Chairman of the Committee of Geo-Hazards Prevention of China, and the Chief Geologist of Geo-Hazard Emergency Technology, Ministry of Land and Resources, P.R. China. Prof. Kyoji Sassa is the Founding President of the International Consortium on Landslides (ICL). He is Executive Director of ICL and the Editor-in-Chief of International Journal“Landslides” since its foundation in 2004. IPL (International Programme on Landslides) is a programme of the ICL. The programme is managed by the IPL Global Promotion Committee including ICL and ICL supporting organizations, UNESCO, WMO, FAO, UNISDR, UNU, ICSU, WFEO, IUGS and IUGG. The IPL contributes to the United Nations International Strategy for Disaster Reduction and the ISDR-ICL Sendai Partnerships 2015–2025.
