Rapid mapping of forested landslide from ultra-high resolution unmanned aerial vehicle data

2018 Geoinformation for Disaster Management Conference, Gi4DM 2018 -- 18 March 2018 through 21 March 2018 -- -- 135177The Black Sea Region is one of the most landslide prone area due to the high slope gradients, heavy rainfall and highly weathered hillslope material conditions in Turkey. The landslide occurrences in this region are mainly controlled by the hydro-climatic conditions and anthropogenic activities. Rapid regional landslide inventory mapping after a major event is main difficulties encountered in this densely vegetated region. However, landslide inventories are first step and necessary for susceptibility assessment since considering the principle that the past is the key to the future thus, future landslides will be more likely to occur under similar conditions, which have led to past and present instability. In this respect, it is important to apply rapid mapping techniques to create regional landslide inventory maps of the area. This study presents the preliminary results of the semi-automated mapping of landslides from unmanned aerial vehicles (UAV) with object-based image analysis (OBIA) approach. Within the scope of the study, ultra-high resolution aerial photographs were taken with fixed wing UAV system on Aug 17, 2017 in the landslide zones which are triggered by the prolonged heavy rainfall event on August 12-13, 2016 at Bartin Kurucaşile province. 10 cm resolution orthomosaic and Digital Surface Model (DSM) data of the area were produced by processing the obtained photographs. A test area was selected from the overall research area and semi-automatic landslide detection was performed by applying object-based image analysis. OBIA has been implemented in three steps: image segmentation, image object metric calculation and classification. The accuracy of the resulting maps is assessed by comparisons with expert based landslide inventory map of the area. As a result of the comparison, 80% of the 240 landslides in the area were detected correctlyFirat University Scientific Research Projects Management Unit: 1608F607This study was supported by Anadolu University Scientific Research Projects Commission under the grant number 1608F607. The authors would like to thank İsmail Bulut from General Directorate of Combating Desertification and Erosion for his valuable suggestions on the site-selection

Sinkhole development in the Sivas gypsum karst, Turkey

The extensive gypsum karst of Sivas, Turkey is one of the most outstanding examples of bare gypsum karst in the world. It displays a number of remarkable geomorphic features, including: (1) two stepped planation surfaces cut-across folded gypsum developed during an initial phase of slow base level deepening punctuated by periods of stability; (2) unusual deeply entrenched gypsum canyons related to a subsequent phase of rapid fluvial incision and water table lowering; (3) a polygonal karst of superlative quality mainly developed in the upper surface; (4) relict valleys disrupted by sinkholes in the lower erosional surface; (5) a large number of bedrock collapse sinkholes mostly associated with the lower surface; and (6) numerous cover subsidence sinkholes developed in the valley floors. This work analyses the spatial distribution, characteristics and evolution of the sinkholes within the broad Plio-Quaternary geomorphological and paleohydrological evolution of the epigene karst system dominated by autogenic recharge. A cartographic sinkhole inventory has been produced in an area covering 2820 km(2) with morphometric data and including 295 bedrock collapse sinkholes and 302 cover subsidence sinkholes. The different sinkhole types show a general spatial zonation controlled by the hydrogeological functioning of the different sectors: (1) solution sinkholes (polygonal karst) in the upper recharge area; (2) bedrock collapse sinkholes in the lower denudation surface and close to the base level, where well developed caves are inferred; and (3) cover subsidence sinkholes, with high densities probably associated with areas of preferred groundwater discharge. The morphology of the bedrock collapse sinkholes, varying from small cylindrical holes to large and deep tronco-conical depressions with gentle slopes reflect to geomorphic evolution of these sinkholes that reach exceptionally large hectometre-scale diameters. Their evolution, involving substantial enlargement and deepening, is attributed to the solutional removal as solute load of large volumes of gypsum by downward vadose flow. This type of morphological evolution with significant post-collapse solutional denudation differs from that observed in carbonate rocks characterised by lower solubility and erodibility. The analysis of historical imagery reveals that bedrock collapse sinkholes currently have a very low probability of occurrence and that buried cover subsidence sinkholes are used for urban development creating risk situations. (C) 2021 The Author(s). Published by Elsevier B.V

Analysis of deformation patterns through advanced DINSAR techniques in Istanbul megacity

As result of the Turkey’s economic growth and heavy migration processes from rural areas, Istanbul has experienced a high urbanization rate, with severe impacts on the environment in terms of natural resources pressure, land-cover changes and uncontrolled sprawl. As a consequence, the city became extremely vulnerable to natural and man-made hazards, inducing ground deformation phenomena that threaten buildings and infrastructures and often cause significant socio-economic losses. Therefore, the detection and monitoring of such deformation patterns is of primary importance for hazard and risk assessment as well as for the design and implementation of effective mitigation strategies. Aim of this work is to analyze the spatial distribution and temporal evolution of deformations affecting the Istanbul metropolitan area, by exploiting advanced Differential SAR Interferometry (DInSAR) techniques. In particular, we apply the Small BAseline Subset (SBAS) approach to a dataset of 43 TerraSAR-X images acquired, between November 2010 and June 2012, along descending orbits with an 11-day revisit time and a 3 m × 3 m spatial resolution. The SBAS processing allowed us to remotely detect and monitor subsidence patterns over all the urban area as well as to provide detailed information at the scale of the single building. Such SBAS measurements, effectively integrated with ground-based monitoring data and thematic maps, allows to explore the relationship between the detected deformation phenomena and urbanization, contributing to improve the urban planning and management

MONITORING THE SLOWLY DEVELOPING LANDSLIDE WITH THE INSAR TECHNIQUE IN SAMSUN PROVINCE, NORTHERN TURKEY

Landslides are prominent natural events with high destructive power. Since they affect large areas, it is important to monitor the areas they cover and analyse their movement. Remote sensing data and image processing techniques have been used to monitor landslides in different areas. Synthetic aperture radar (SAR) data, particularly with the Interferometric SAR (InSAR) method, is used to determine the velocity vector of the surface motion. This study aims to detect the landslide movements in Samsun, located in the north of Turkey, using persistent scattering InSAR method. Archived Copernicus Sentinel-1 satellite images taken between 2017 and 2022 were used in both descending and ascending directions. The results revealed surface movements in the direction of the line of sight, ranging between −6 and 6 mm/year in the study area. Persistent Scatterer (PS) points were identified mainly in human structures such as roads, coasts, ports, and golf courses, especially in settlements. While some regions exhibited similar movements in both descending and ascending results, opposite movements were observed in some regions. The results produced in both descending and ascending directions were used together and decomposed into horizontal and vertical deformation components. It was observed that the western coastal part experienced approximately 4.5 cm/year vertical deformation, while the central part there is more significant horizontal deformation, reaching up to approximately 6 cm/year

Control of style-of-faulting on spatial pattern of earthquake-triggered landslides

Predictive mapping of susceptibility to earthquake-triggered landslides (ETLs) commonly uses distance to fault as spatial predictor, regardless of style-of-faulting. Here, we examined the hypothesis that the spatial pattern of ETLs is influenced by style-of-faulting based on distance distribution analysis and Fry analysis. The Yingxiu–Beichuan fault (YBF) in China and a huge number of landslides that ruptured and occurred, respectively, during the 2008 Wenchuan earthquake permitted this study because the style-of-faulting along the YBF varied from its southern to northern parts (i.e. mainly thrust-slip in the southern part, oblique-slip in the central part and mainly strike-slip in the northern part). On the YBF hanging-wall, ETLs at 4.4–4.7 and 10.3–11.5 km from the YBF are likely associated with strike- and thrust-slips, respectively. On the southern and central parts of the hanging-wall, ETLs at 7.5–8 km from the YBF are likely associated with oblique-slips. These findings indicate that the spatial pattern of ETLs is influenced by style-of-faulting. Based on knowledge about the style-of-faulting and by using evidential belief functions to create a predictor map based on proximity to faults, we obtained higher landslide prediction accuracy than by using unclassified faults. When distance from unclassified parts of the YBF is used as predictor, the prediction accuracy is 80%; when distance from parts of the YBF, classified according to style-of-faulting, is used as predictor, the prediction accuracy is 93%. Therefore, mapping and classification of faults and proper spatial representation of fault control on occurrence of ETLs are important in predictive mapping of susceptibility to ETLs

Recommendations for the quantitative analysis of landslide risk

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts

Control of style-of-faulting on spatial pattern of earthquake-triggered landslides

Predictive mapping of susceptibility to earthquake-triggered landslides (ETLs) commonly uses distance to fault as spatial predictor, regardless of style-of-faulting. Here, we examined the hypothesis that the spatial pattern of ETLs is influenced by style-of-faulting based on distance distribution analysis and Fry analysis. The Yingxiu–Beichuan fault (YBF) in China and a huge number of landslides that ruptured and occurred, respectively, during the 2008 Wenchuan earthquake permitted this study because the style-of-faulting along the YBF varied from its southern to northern parts (i.e. mainly thrust-slip in the southern part, oblique-slip in the central part and mainly strike-slip in the northern part). On the YBF hanging-wall, ETLs at 4.4–4.7 and 10.3–11.5 km from the YBF are likely associated with strike- and thrust-slips, respectively. On the southern and central parts of the hanging-wall, ETLs at 7.5–8 km from the YBF are likely associated with oblique-slips. These findings indicate that the spatial pattern of ETLs is influenced by style-of-faulting. Based on knowledge about the style-of-faulting and by using evidential belief functions to create a predictor map based on proximity to faults, we obtained higher landslide prediction accuracy than by using unclassified faults. When distance from unclassified parts of the YBF is used as predictor, the prediction accuracy is 80%; when distance from parts of the YBF, classified according to style-of-faulting, is used as predictor, the prediction accuracy is 93%. Therefore, mapping and classification of faults and proper spatial representation of fault control on occurrence of ETLs are important in predictive mapping of susceptibility to ETLs