Identification of debris flow initiation zones using topographic model and airborne laser scanning data

Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). The three most influencing topographic parameters identified are topographic roughness index (TRI), slope angle, and specific catchment area (SCA) with the percentage values of participation in the model of 100, 93, and 86%, respectively. The chosen function appeared to describe the analysed correlation sufficiently well. Consequently, three stages of optimization were made to determine the optimized source areas is possible with 10 m pixel size, 200 maximum basis functions and 3 maximum interactions, resulting into 82% ROC train and 80% test, GCV 0.189 and 85% correlation coefficient. The result will be of great contribution to the advancement of a broad understanding of the dynamics of debris flows hazard and mitigations at regional level which; that is resourceful for comprehensive slope management for safe urban planning decision-making process and debris flow disaster management

Identification of debris flow initiation zones using topographic model and airborne laser scanning data

Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). The three most influencing topographic parameters identified are topographic roughness index (TRI), slope angle, and specific catchment area (SCA) with the percentage values of participation in the model of 100, 93, and 86%, respectively. The chosen function appeared to describe the analysed correlation sufficiently well. Consequently, three stages of optimization were made to determine the optimized source areas is possible with 10 m pixel size, 200 maximum basis functions and 3 maximum interactions, resulting into 82% ROC train and 80% test, GCV 0.189 and 85% correlation coefficient. The result will be of great contribution to the advancement of a broad understanding of the dynamics of debris flows hazard and mitigations at regional level which; that is resourceful for comprehensive slope management for safe urban planning decision-making process and debris flow disaster management

An assessment of landslide susceptibility in the Faifa area, Saudi Arabia, using remote sensing and GIS techniques

An integrated approach was adopted over Faifa Mountain and its surroundings, in Saudi Arabia, to identify landslide types, distribution, and controlling factors, and to generate landslide susceptibility maps. Given the inaccessibility of the area, we relied on remote sensing observations and GIS-based applications to enable spatial analysis of data and extrapolation of limited field observations. Susceptibility maps depicting debris flows within ephemeral valleys (Type I) and landslides caused by failure along fracture planes (Type II) were generated. Type I susceptibility maps were generated applying linear relationships between normalized difference vegetation index (NDVI) and threshold slope values (30°), both of which were extracted over known debris flow locations. For Type II susceptibility maps, landslides were predicted if fracture planes had strike values similar to (within 20°) those of the slope face strike and dip angles exceeding the friction, but not the slope angles. Comparisons between predicted and observed debris flows yielded success rates of 82% (ephemeral valleys); unverified predictions are interpreted as future locations of debris flows. Our approach could serve as a replicable model for many areas worldwide, in areas where field measurements are difficult to obtain and/or are cost prohibitive

Geophysical, remote sensing, GIS, and isotopic applications for a better understanding of the structural controls on groundwater flow in the Mojave Desert, California

Study region: Mojave Desert, USA. Study focus: An integrated (near-surface geophysics, remote sensing, isotopic analyses) study was conducted in the Mojave River Basin and Morongo Groundwater Basin to investigate potential effects that the Helendale Fault [HF] and basement uplifts might have on groundwater flow in the Mojave Desert. New hydrological insights for the region: The HF traces were mapped using LiDAR and Geoeye-1 imagery (surface) and magnetic profiles (subsurface). Shallow basement parallel to and west of the HF was detected using the Vertical Electrical Soundings (VESs). Conductive water-saturated breccia was detected along the HF using the Very Low Frequency (VLF) electromagnetic measurements. Isotopic analyses (δD and δ18O) for groundwater samples from productive shallow wells, and springs sampled west of the HF and the basement uplift are less depleted (Group I: Fifteenmile Valley Groundwater sub-basin [FVGS]; average δD: −86.8‰; δ18O: −11.8‰) than samples east of the basement uplift (Group II: Lucerne Valley Groundwater sub-basin [LVGS]; average δD: −95.0‰; δ18O: −12.1‰), whereas samples proximal to, the fault have compositions similar to Group I but show evidence for mixing with Group II compositions (Group III; average δD: −88.8‰; δ18O: −11.5‰). Findings are consistent with the HF channeling groundwater from the San Bernardino Mountains with basement uplifts acting as barriers to lateral groundwater flow and could be applicable to similar settings across the Mojave Desert and elsewhere worldwide. Keywords: Mojave desert, Groundwater flow, Structural controls, Geophysics (VLF, Magnetic, VES), Isotopic analyses (O, H), Remote sensing (LiDAR, GeoEye-1