Engineering geological mapping using airborne LiDAR datasets – an example from the Vinodol Valley, Croatia

Jagodnik, Petra; Gazibara, Sanja Bernat; Arbanas, Željko; Arbanas, Snježana Mihalić

doi:10.1080/17445647.2020.1831980

Cited by 14 publications

(9 citation statements)

References 31 publications

(44 reference statements)

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“…This chapter presents the experience gained in Croatia, collected through detecting and mapping landslides from high-resolution LiDAR images produced based on ALS in different landslide-prone geomorphological settings in Croatia. Recognition of landslides was performed through the visual analysis of a series of LiDAR derivatives by experienced and trained landslide scientists, using a systematic methodology and well-defined interpretation criteria (Bernat Gazibara et al 2019a;Jagodnik et al 2020a;Krkač et al 2022).…”

Section: Historical Inventories Derived From Lidar Imagesmentioning

confidence: 99%

“…98%) of all activity styles (complex, composite, successive, single, multiple), typically activated along the contact between the flysch bedrock and superficial deposits. Regardless on the landslide type, about 80% are developed within the gully landforms (Jagodnik et al 2020a). Because of that, 236 gullies are also mapped, with a total area of 1.89 km 2 .…”

Section: Historical Inventories Derived From Lidar Imagesmentioning

confidence: 99%

“…Based on the maximum value at approximately A L = 1500 m 2 , 58% of the mapped landslides are small (A L < 1500 m 2 ), and 52% are medium-large (A L > 1500 m 2 ) in size. The dominant types of landslides are debris slide and debris slide-debris flow (Jagodnik et al 2020a).…”

Section: Landslide Statistics and Completeness Of The Inventorymentioning

confidence: 99%

See 2 more Smart Citations

Landslide Detection and Spatial Prediction: Application of Data and Information from Landslide Maps

Arbanas

Gazibara

Krkač

et al. 2023

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

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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.

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Section: Historical Inventories Derived From Lidar Imagesmentioning

confidence: 99%

Section: Historical Inventories Derived From Lidar Imagesmentioning

confidence: 99%

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Landslide Detection and Spatial Prediction: Application of Data and Information from Landslide Maps

Arbanas

Gazibara

Krkač

et al. 2023

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

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“…In recent years, lidars have been widely used in autonomous driving [ 1 , 2 , 3 ], aerospace [ 4 ], three-dimensional modeling [ 5 , 6 , 7 , 8 ], and other fields. In these applications, lidars provide the range map of the concerned object or environment, which can be further processed for geological mapping [ 9 , 10 , 11 ], simultaneous localization and mapping (SLAM) [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ], as well as 3D modeling and reconstruction, even for the modeling of human organs, detection and positioning of necrotic tissues, and other aspects in medicine [ 19 ]. Generally, the 3D structure of the concerned object is scanned by lidar and restored after point cloud scan registration, segmentation, and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Cheng

Zhao

Zhang

et al. 2021

Sensors

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Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

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“…In recent decades, digital elevation models (DEMs) derived from airborne laser scanning (ALS) have been widely used for studies of Earth surface processes [14,15], and references therein]. DEMs proved to be an invaluable tool in geomorphological and natural hazards mapping, identifying geomorphological processes, detecting small-scale features, monitoring surface changes, reconstructing landscape evolution, re-evaluating the existing maps, and even determining surface age [16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Towards Better Visualisation of Alpine Quaternary Landform Features on High-Resolution Digital Elevation Models

Novak

Oštir

2021

Remote Sensing

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Alpine topography is formed by a complex series of geomorphological processes that result in a vast number of different landforms. The youngest and most diverse landforms are various Quaternary sedimentary bodies, each characterised by its unique landform features. The formation of Quaternary sedimentary bodies and their features derive from the dominant building sedimentary processes. In recent years, studies of Quaternary sedimentary bodies and processes have been greatly aided by the use of digital elevation models (DEMs) derived by airborne laser scanning (ALS). High-resolution DEMs allow detailed mapping of sedimentary bodies, detection of surface changes, and recognition of the building sedimentary processes. DEMs are often displayed as hillshaded reliefs, the most common visualisation technique, which suffers from the limitation of a single illumination source. As a result, features can be barely visible or even invisible to the viewer if they are parallel to the light source or hidden in the shadow. These limitations become challenging when representing landforms and subtle landscape features in a diverse alpine topography. In this study, we focus on eleven visualisations of Quaternary sedimentary bodies and their sedimentary and morphological features on a 0.5 m resolution DEM. We qualitatively compare analytical hillshading with a set of visualisation techniques contained in the Raster Visualisation Toolbox software, primarily hillshading from multiple directions RGB, 8-bit sky view factor and 8-bit slope. The aim is to determine which visualisation technique is best suited for visual recognition of sedimentary bodies and sedimentation processes in complex alpine landscapes. Detailed visual examination of previously documented Pleistocene moraine and lacustrine deposits, Holocene alluvial fans, scree deposits, debris flow and fluvial deposits on the created visualisations revealed several small-scale morphological and sedimentary features that were previously difficult or impossible to detect on analytical hillshading and aerial photographs. Hillshading from multiple directions resulted in a visualisation that could be universally applied across the mountainous and hilly terrains. In contrast, 8-bit sky view factor and 8-bit slope visualisations created better visibility and facilitated interpretation of subtle and small-scale (less than ten metres) sedimentary and morphological features.

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Engineering geological mapping using airborne LiDAR datasets – an example from the Vinodol Valley, Croatia

Cited by 14 publications

References 31 publications

Landslide Detection and Spatial Prediction: Application of Data and Information from Landslide Maps

Landslide Detection and Spatial Prediction: Application of Data and Information from Landslide Maps

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Towards Better Visualisation of Alpine Quaternary Landform Features on High-Resolution Digital Elevation Models

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