Derivation of earthquake-induced landslide distribution using aerial photogrammetry: the January 24, 2020, Elazig (Turkey) earthquake

Karakas, Gizem; Nefeslioğlu, Hakan A.; Kocaman, Sultan; Buyukdemircioglu, M.; Yürür, Tekin; Gökçeoğlu, Candan

doi:10.1007/s10346-021-01660-2

Cited by 30 publications

(15 citation statements)

References 78 publications

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“…As a result of this complexity, they form a nappe-stack, several kilometersthick, between the Malatya-Keban Metamorphics of the Tauride-Anatolide terrane in the north and the Bitlis-Pütürge Metamorphics of the Arabian Plate in the South [35]. The region is also prone to seismic hazards, which often trigger the landslides [36]. The study area has twenty-seven lithological units in the area as shown in Table 2 and Figure 2.…”

Section: Study Area Characteristics and The Datasetsmentioning

confidence: 99%

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

2021

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The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km2 with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index.

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Section: Study Area Characteristics and The Datasetsmentioning

confidence: 99%

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

2021

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“…The study area is located inside Elazig and Malatya Provinces, Turkey. The topography in the region is very young and steep due to high seismicity and active tectonism (Karakas et al, 2021). According to the Disaster and Emergency Management Authority (AFAD) of Turkey, the earthquake event with Mw 6.8 occurred on January 24, 2020, at 20:55 local time on Hazar-Sincik segment of East Anatolian Fault Zone (EAFZ) (AFAD, 2020).…”

Section: Study Area Characteristics and The Earthquake Eventmentioning

confidence: 99%

“…2D displacements obtained from the matching of orthophotos of before and after the earthquake were visualized in order to analyze the changes in planimetry. The residuals obtained from the DSM comparison were derived for visualizing the differences in vertical direction (Karakas et al, 2021). The photogrammetric datasets, their processing and the volumetric change detection analyses were described in more detail in Karakas et al (2021).…”

Section: Study Datasetsmentioning

confidence: 99%

“…5000 buildings and various infrastructure were damaged (AFAD, 2020). It has also triggered several landslides (Gokceoglu et al, 2020;Karakas et al, 2021). Besides the emergency response teams, accurate assessment of the changes on the land surface has become important for the geoscientists for efficient analysis of the hazardous event (Gokceoglu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Development of a Geo-Analytics Platform for Post-Disaster Ground Assessment

Atasoy

Kocaman

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Earthquake is a frequently encountered natural hazard and can cause losses of lives and assets. In order to understand the nature of seismic hazards and the risks sourced from them, careful investigations are needed by using diverse geoinformation types. Thanks to the availability of multi-platform and high-resolution geospatial datasets, vast amount of geodata can be collected and analyzed for obtaining timely information on land’s surface before and after an earthquake event; and to assist the disaster management authorities and first responders for supporting the mitigation efforts. On January 24, 2020, an earthquake with Mw 6.8 occurred in Elazig Province, Turkey; caused 41 deaths and damaged the buildings and infrastructure. Although various geodatasets could be collected and processed by the geomatics experts, their utilization by other stakeholders, such as geoscientists, local authorities, and citizens remained limited due to the accessibility issues, and high complexity in their visualization, processing and interpretation. In this study, a web-based platform called EQ4D was designed and implemented to present the multi-sensor and multi-platform 4D data to the stakeholders, and to allow for basic geo-analytical processes such as change detection, 3D surface measurements, etc. Cesium JS and Potree libraries were employed for 3D visualization. PostGIS spatial database management system was used for storing and managing the data, and for performing spatial queries. Currently EQ4D is suitable for the use of geoscientists, and can be further customized for generic use at similar geohazards and developed for the use of inexperienced users, such as citizen scientists.

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“…More recently, this topic has been also examined for the whole Chinese territory by Lin et al (2021) and Wang et al (2022), who stressed the negative influence of an incomplete landslide inventory and the necessity to find ways to reduce the propagation of this spatial bias onto the final susceptibility map. Notably, recent technological developments are contributing to reduce this issues through accurate aerial photograph interpretation (Karakas et al, 2021) and automated landslide mapping routines (Meena et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory

Loche

Alvioli

Marchesini

et al. 2022

Earth-Science Reviews

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Derivation of earthquake-induced landslide distribution using aerial photogrammetry: the January 24, 2020, Elazig (Turkey) earthquake

Cited by 30 publications

References 78 publications

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

Development of a Geo-Analytics Platform for Post-Disaster Ground Assessment

Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory

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