Investigation of Landslides and Debris Flows in Tachia Watershed Between Maan Dam and Techi Dam

The Chi-Chi earthquake and subsequent typhoon events induced severe landslides and debris flows in the watershed of Tachia river. It inflicted severe damage to the power generation facilities and highway links. For the rehabilitation planning, quantitative assessment of landslides, debris flows and river deposits were conducted by using aerial photos and satellite images obtained at six stages of earthquake and typhoon events. The future trends of landslide and debris flow were also investigated by using empirical models. The long-term deposition or scouring was also conducted by numerical simulation. The results show that over 50,000,000 to 70,000,000m3 of sliding volume were induced in the Chi-Chi earthquake and subsequent typhoon events during 1999 to 2005. By conservative estimation, 60% of the debris still remain in the watershed, which will cause silting of the main river channel in the future. The deposition in the main river channel will increase with decreasing rate in the future, and river channel scouring is not expected to occur in the future 20 to 30 years

A simple and efficient GIS tool for volume calculations of submarine landslides

A numeric tool is presented for calculating volumes of topographic voids such as slump scars of landslides, canyons or craters (negative/concave morphology), or alternatively, bumps and hills (positive/convex morphology) by means of digital elevation models embedded within a geographical information system (GIS). In this study, it has been used to calculate landslide volumes. The basic idea is that a (singular) event (landslide, meteorite impact, volcanic eruption) has disturbed an intact surface such that it is still possible to distinguish between the former (undisturbed) landscape and the disturbance (crater, slide scar, debris avalanche). In such cases, it is possible to reconstruct the paleo-surface and to calculate the volume difference between both surfaces, thereby approximating the volume gain or loss caused by the event. I tested the approach using synthetically generated land surfaces that were created on the basis of Shuttle Radar Topography Mission data. Also, I show the application to two real cases, (1) the calculation of the volume of the Masaya Slide, a submarine landslide on the Pacific continental slope of Nicaragua, and (2) the calculation of the void of a segment of the Fish River Canyon, Namibia. The tool is provided as a script file for the free GIS GRASS. It performs with little effort, and offers a range of interpolation parameters. Testing with different sets of interpolation parameters results in a small range of uncertainty. This tool should prove useful in surface studies not exclusively on earth

Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis

The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS), sequential Gaussian simulation (SGS) and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, Moran’I and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI) images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE). Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management

Forecasting the magnitude of potential landslides based on InSAR techniques

A new method, combining empirical modeling with time series Interferometric Synthetic Aperture Radar (InSAR) data, is proposed to provide an assessment of potential landslide volume and area. The method was developed to evaluate potential landslides in the Heitai river terrace of the Yellow River in central Gansu Province, China. The elevated terrace has a substantial loess cover and along the terrace edges many landslides have been triggered by gradually rising groundwater levels following continuous irrigation since 1968. These landslides can have significant impact on communities, affecting lives and livelihoods. Developing effective landslide risk management requires better understanding of potential landslide magnitude. Fifty mapped landslides were used to construct an empirical power-law relationship linking landslide area (AL) to volume (VL) (VL = 0.333 × AL1.399). InSAR-derived ground displacement ranges from −64 mm/y to 24 mm/y along line of sight (LOS). Further interpretation of patterns based on remote sensing (InSAR & optical image) and field survey enabled the identification of an additional 54 potential landslides (1.9 × 102 m2 ≤ AL ≤ 8.1 × 104 m2). In turn this enabled construction of a map that shows the magnitude of potential landslide activity. This research provides significant further scientific insights to inform landslide hazard and risk management, in a context of ongoing landscape evolution. It also provides further evidence that this methodology can be used to quantify the magnitude of potential landslides and thus contribute essential information towards landslide risk management

Digital Elevation Modeling of Inaccessible Slope by Using Close-range Photogrammetric Data

Digital Elevation Model (DEM) currently is extensively used extensively in various applications such as for natural hazard assessment and monitoring of high risk areas. DEM data source of inaccessible areas can be collected by using several methods, but mostly are costly and requires sophisticated instruments. Due to these conditions, close-range photogrammetry offers a low cost alternative solution. Materials presented in this thesis are based on the experiments to explain the application of close-range photogrammetry with the aid of commercial digital pocket camera as DEM data collection tools, applied on inaccessible slope areas. The analysis covers calibration of the camera and surveying instruments, DEM data collections, data processing and visualization, together with DEM quality measures. The data collections are accomplished on several study areas with different topographical characteristics by using close-range photogrammetry technique. The sampling points were selected on stereo model, by using three types of sampling methods. The DEM quality measures are assessed by following elevation interpolation error and volumetric difference error analyses. The representation of the DEM is generated using TIN-based (Triangular Irregular Network) approach. The result shows that the method is able to be applied for three dimensional (3D) modeling of potentially unstable slope areas, with accuracy of less than 15 cm in RMS for elevation error and is less than 1% in volume error. The result has indicated that topographical condition has not affected the accuracy of generated DEM. Improvement of point density radically enhances the DEM’s quality, up to a certain level of point density beyond which the increment of the accuracy is not significant. The difference setting of focal length has also influences the quality of captured images, and drastically affects the accuracy of the DEM. If the accuracy of the DEM is a matter of concern, the preferred sampling method is selective sampling, while if accuracy and DEM’s time generation are the concern the most effective sampling method is regular sampling method. Since there was no permanent points on the observed slope surface, velocity and direction of landslide could not be accurately determined. However the distribution of massmovement and elevation changed on the slope surfaces can be modeled through spatialcalculation of overlaying DEMs together with profiling of cross-section and longitudinalsection of the generated DEMs

Digital Elevation Modeling oflnaccessible Slope by Using Close-range Photogrammetric Data

Digital Elevation Model (DEM) currently is extensively used extensively in vanous applications such as for natural hazard assessment and monitoring of high risk areas. DEM data source of inaccessible areas can be collected by using several methods, but mostly are costly and requires sophisticated instruments. Due to these conditions, close-range photograrnmetry offers a low cost alternative solution. Materials presented in this thesis are based on the experiments to explain the application of close-range photogrammetry with the aid of commercial digital pocket camera as DEM data collection tools, applied on inaccessible slope areas. The analysis covers calibration of the camera and surveying instruments, DEM data collections, data processing and visualization, together with DEM quality measures. The data collections are accomplished on several study areas with different topographical characteristics by using close-range photograrnmetry technique. The sampling points were selected on stereo model, by using three types of sampling methods. The DEM quality measures are assessed by following elevation interpolation error and volumetric difference error analyses. The representation of the DEM is generated using TIN-based (Triangular Irregular Network) approach. The result shows that the method is able to be applied for three dimensional (3D) modeling of potentially unstable slope areas, with accuracy of less than 15 em in RMS for elevation error and is less than 1% in volume error. The result has indicated that topographical condition has not affected the accuracy of generated DEM. Improvement of point density radically enhances the DEM's quality, up to a certain level of point density beyond which the increment of the accuracy is not significant. The difference setting of focal length has also influences the quality of captured images, and drastically affects the accuracy of the DEM. If the accuracy of the DEM is a matter of concern, the preferred sampling method is selective sampling, while if accuracy and DEM's time generation are the concern the most effective sampling method is regular sampling method. Since there was no permanent points on the observed slope surface, velocity and direction of landslide could not be accurately determined. However the distribution of massmovement and elevation changed on the slope surfaces can be modeled through spatialcalculation of overlaying DEMs together with profiling of cross-section and longitudinalsection of the generated DEMs

Digital Elevation Modeling of Inaccessible Slope by Using Close-range Photogrammetric Data

Digital Elevation Model (DEM) currently is extensively used extensively in various applications such as for natural hazard assessment and monitoring of high risk areas. DEM data source of inaccessible areas can be collected by using several methods, but mostly are costly and requires sophisticated instruments. Due to these conditions, close-range photogrammetry offers a low cost alternative solution. Materials presented in this thesis are based on the experiments to explain the application of close-range photogrammetry with the aid of commercial digital pocket camera as DEM data collection tools, applied on inaccessible slope areas. The analysis covers calibration of the camera and surveying instruments, DEM data collections, data processing and visualization, together with DEM quality measures. The data collections are accomplished on several study areas with different topographical characteristics by using close-range photogrammetry technique. The sampling points were selected on stereo model, by using three types of sampling methods. The DEM quality measures are assessed by following elevation interpolation error and volumetric difference error analyses. The representation of the DEM is generated using TIN-based (Triangular Irregular Network) approach. The result shows that the method is able to be applied for three dimensional (3D) modeling of potentially unstable slope areas, with accuracy of less than 15 cm in RMS for elevation error and is less than 1% in volume error. The result has indicated that topographical condition has not affected the accuracy of generated DEM. Improvement of point density radically enhances the DEM’s quality, up to a certain level of point density beyond which the increment of the accuracy is not significant. The difference setting of focal length has also influences the quality of captured images, and drastically affects the accuracy of the DEM. If the accuracy of the DEM is a matter of concern, the preferred sampling method is selective sampling, while if accuracy and DEM’s time generation are the concern the most effective sampling method is regular sampling method. Since there was no permanent points on the observed slope surface, velocity and direction of landslide could not be accurately determined. However the distribution of massmovement and elevation changed on the slope surfaces can be modeled through spatialcalculation of overlaying DEMs together with profiling of cross-section and longitudinalsection of the generated DEMs

Landslide Geoanalytics Using LiDAR-derived Digital Elevation Models

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