The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km2) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m2. This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).
The need for landslide maps of wider areas has increased with the understanding that proper planning will considerably decrease the construction and maintenance cost of structures. The main objective of the paper is to present types of data and information on landslides that can be derived from landslide inventory and landslide susceptibility maps and their use for spatial and urban planning. Recent examples of landslide zonation maps from Croatia are given to show the possibility of the derivation of data about landslides by using LIDAR (Light Detection and Ranging) DTM (Digital Terrain Model) for the compilation of historical landslide inventory. The application of data about landslide phenomena is compared with the application of information from landslide susceptibility zonation maps. It is concluded that a multi-level and hierarchical approach is necessary to reach the cost-effectiveness of nationwide production of landside maps for land-use planning.
Rock mass characterization is a very important part of engineering geological investigation. For a better understanding of the rock mass behaviour, it is crucially important to obtain as much as possible information about the discontinuity network, especially about orientation and the number of dominant discontinuity sets. The traditional methodology includes field mapping which dominantly produces a limited amount of data and consequently only a rough estimate about discontinuity network. To increase the number of measurements and to eliminate orientation bias, rock mass on the Špičunak rock slope in Gorski kotar, Croatia, was analysed using a combination of 3D Point Cloud and Textured Mesh Model generated from 3D Point Cloud by Poisson surface reconstruction. Both models were obtained from Terrestrial Laser Scanning. Two considerably different parts of a rock slope, with different weathering conditions and different degrees of fracturing were mapped. Discontinuities were mapped in the field and on the models using manual mapping techniques and semi-automated methods. Manual mapping on a 3D Point Cloud and Textured Mesh Model was done by Compass plugin and by Trace a polyline tool in Cloud Compare software version V2.12 and semi-automated mapping methods were done by Discontinuity Set Extractor and qFacet Fast Marching plugin for Cloud Compare software version V2.12. This study was used to show how the application of different methodologies, for the detection of geometric properties of discontinuities, influences the result. Statistical analyses were performed on the collected data to determine differences in the accuracy between the mapping techniques. Manual mapping on the 3D Point Cloud and high-resolution Textured Mesh Model showed good agreement with field measurements, apart from the higher number of discontinuities mapped by remote sensing methods. On the other hand, significant deviations were found between manual and semi-automated mapping techniques. Semiautomated methods did not correctly detect certain discontinuities, especially bedding planes that are perpendicular to a rock face. Also, semi-automated methods overestimate the number of discontinuity sets, especially in a highly weathered and highly fractured rock mass. These differences between methods can influence kinematic analysis results. Based on the results, an appropriate methodology was proposed to utilize the advantages of both manual and semiautomated methods. The proposed approach presents a powerful tool to accurately map and measure discontinuity orientation with results comparable to the field measurements.
<p>As identified by previous work, landslides present a significant hazard in the Umbria Region, Central Italy. We present a Weight of Evidence (WoE) and Random Forest (RF) approach for deriving landslide susceptibility maps (LSMs) for the defined slope units (SU) cartographic unit. Used input data in this study includes a layer containing 7360 SU with 26 landslide conditioning factors (LCFs) and two landslide presence flags. Namely, &#8222;presence1&#8220; (P1) and &#8222;presence2&#8220; (P2) describe 3594 and 2271 SU as unstable, respectively. LCFs were reclassified using Natural Breaks into 10 classes, followed by testing collinearity which resulted in selecting 11 for the further analyses. Unstable SU were randomly split in two equal sets, one for deriving LSMs, and the other for validation. Using only unstable SU for WoE, the landslide dataset applied in RF included additionally an equal amount of stable SU. Stable SU were randomly selected from the area which had excluded only the previously selected unstable SU, simulating a temporal inventory for landslide validation. The latter ensured application of the model to unseen data, as well as unbiased landslide dataset for training the model. Model evaluation and LSM validation included determining Area Under the Curve (AUC) for the LSM area defined with Cumulative percentage of study area in susceptibility classes and the Cumulative percentage of landslide area in susceptibility classes. For model evaluation, 50% of unstable SU were examined, whereas to validate it, the remaining 50% of unstable SU were used. For model classification parameters, all SU were used to define Overall Accuracy (OA) and a Hit Rate and False Alarm Rate curve for which AUC was calculated. RF model performed excellent, having 86.16 and 90.00 AUC values for P1 and P2 scenarios, respectively. Significantly worse, the WoE P1 and P2 scenarios have 62.09 and 69.41 AUC values, respectively. LSM validation on unseen data goes in favor of WoE with 60.46 (P1) and 66.17 (P2) AUC values, compared to 45.06 (P1) and 56.68 (P2) AUC values for RF, indicating a random guess prediction. Considering OA and AUC as classification parameters, OA values for P1 and P2 scenarios in RF are 74.36 and 77.60 whereas AUC values are 81.65 and 84.61. Significantly less, WoE method has 66.03 and 69.14 OA values for P1 and P2 scenario, respectively. Similarly, WoE AUC values for P1 is 74.09 whereas for P2 it is 77.07. Showing better results in all four studied parameters in both methods, we point out the P2 scenario as a better option for defining landslide datasets concerning the amount of unstable and stable SU. Due to having a relatively big portion of unstable SU in the input data we argue that classification parameters should be prioritized when choosing the optimal method and scenario, as they take to consideration both unstable and stable SU for the entire study area. Based on the conducted research, we suggest using RF due to better classification performance as an approach for landslide susceptibility analyses and future zonation in the study area.</p>
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