This study explores the potential of photogrammetric datasets and remote sensing methods for the assessment of a meteorological catastrophe that occurred in Ordu, Turkey in August 2018. During the event, flash floods and several landslides caused losses of lives, evacuation of people from their homes, collapses of infrastructure and large constructions, destruction of agricultural fields, and many other economic losses. The meteorological conditions before and during the flood were analyzed here and compared with long-term records. The flood extent and the landslide susceptibility were investigated by using multisensor and multitemporal data. Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel-2 optical data, and aerial photogrammetric datasets were employed for the investigations using machine learning techniques. The changes were assessed both at a local and regional level and evaluated together with available damage reports. The analysis of the rainfall data recorded during the two weeks before the floods and landslides in heavily affected regions shows that the rainfall continued for consecutive hours with an amount of up to 68 mm/hour. The regional level classification results obtained from Sentinel-1 and Sentinel-2 data by using the random forest (RF) method exhibit 97% accuracy for the flood class. The landslide susceptibility prediction performance from aerial photogrammetric datasets was 92% represented by the Area Under Curve (AUC) value provided by the RF method. The results presented here show that considering the occurrence frequency and immense damages after such events, the use of novel remote sensing technologies and spatial analysis methods is unavoidable for disaster mitigation efforts and for the monitoring of environmental effects. Although the increasing number of earth observation satellites complemented with airborne imaging sensors is capable of ensuring data collection requirement with diverse spectral, spatial, and temporal resolutions, further studies are required to automate the data processing, efficient information extraction, and data fusion and also to increase the accuracy of the results.

Suitable route determination for linear engineering structures is a fundamental problem in engineering geology. Rapid evaluation of alternative routes is essential, and novel approaches are indispensable. This study aims to integrate various InSAR (Interferometric Synthetic Aperture Radar) techniques for sinkhole susceptibility mapping in the Kirikkale-Delice Region of Turkey, in which sinkhole formations have been observed in evaporitic units and a high-speed train railway route has been planned. Nine months (2019–2020) of ground deformations were determined using data from the European Space Agency’s (ESA) Sentinel-1A/1B satellites. A sinkhole inventory was prepared manually using satellite optical imagery and employed in an ANN (Artificial Neural Network) model with topographic conditioning factors derived from InSAR digital elevation models (DEMs) and morphological lineaments. The results indicate that high deformation areas on the vertical displacement map and sinkhole-prone areas on the sinkhole susceptibility map (SSM) almost coincide. InSAR techniques are useful for long-term deformation monitoring and can be successfully associated in sinkhole susceptibility mapping using an ANN. Continuous monitoring is recommended for existing sinkholes and highly susceptible areas, and SSMs should be updated with new results. Up-to-date SSMs are crucial for the route selection, planning, and construction of important transportation elements, as well as settlement site selection, in such regions.

Flooding events have been frequently observed throughout the world and may have devastating effects on the environment. Mapping of flood extent is important for taking the necessary mitigation measures for future. The freely available Sentinel-1 radar and Sentinel-2 optical images allow analysis of flood extent with adequate spatial resolution. However, temporal resolution may be insufficient, and currently only events with the suitable satellite orbital passes can be analyzed with Sentinel sensors. In addition, clouds are often in scene during and shortly after a flood event, which hinders the use of optical imagery. Here, we investigated the complementary use of Sentinel-1 and Sentinel-2 data with a land cover classification approach based on random forest over a part of northern Türkiye, which frequently confronts floods and landslides. We expanded the feature set with principal components (PC) of gray-level co-occurrence matrix (GLCM) variables obtained from Sentinel-1 polarization and Sentinel-2 spectral bands, and also the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) images produced from the optical data. The training and test data were manually extracted from pre- and post-event optical data. The findings demonstrated that using GLCM PCs significantly increased the overall accuracy (OA = 99% with GLCM and OA = 93% without GLCM) of the classification. Furthermore, the flooded vegetation differs in textural features when compared with the other inundated surfaces, and also permanent water. Therefore, by allowing the separation of flooded vegetation and the other flooded areas, the GLCM data considerably increased the map quality.

Efforts on flood mapping from active and passive satellite Earth Observation sensors increased in the last decade especially due to the availability of free datasets from European Space Agency’s Sentinel-1 and Sentinel-2 platforms. Regular data acquisition scheme also allows observing areas prone to natural hazards with a small temporal interval (within a week). Thus, before and after datasets are often available for detecting surface changes caused by flooding. This study investigates the contribution of textural variables to the predictive performance of a data-driven machine learning algorithm for detecting the effects of a flooding caused by Sardoba Dam break in Uzbekistan. In addition to the spectral channels of Sentinel-2 and polarization bands of Sentinel-1, two spectral indices (normalized difference vegetation index and modified normalized difference water index), and textural features of gray-level co-occurrence matrix (GLCM) were used with the Random Forest. Due to high dimensionality of input variables, principal component (PC) analysis was applied to the GLCM features and only the most significant PCs were used for modeling. The feature stacks used for learning were derived from both pre- and post-event Sentinel-1 and Sentinel-2 images. The models were validated through model test measures and external reference data obtained from PlanetScope imagery. The results show that the GLCM features improve the classification of flooded areas (from 82% to 93%) and flooded vegetation (from 17% to 78%) expressed in user’s accuracy. As an outcome of the study, the use of textural features is recommended for accurate mapping of flooded areas and flooded vegetation.

The Sivrice earthquake (Mw 6.8) occurred on 24 January 2020 along the East Anatolian Fault (EAF) zone of Türkiye, and epicentral information and focal mechanism solutions were published by two national and six international seismic stations. Here, we analyzed epicentral locations and the major fault trace using aerial photogrammetric images taken two days after, and synthetic aperture radar (SAR) interferometry. Although the focal mechanism solutions were similar, the epicenters were largely displaced. Several bright lineaments with a stair-like geometry were observed in aerial images of the Euphrates River channel along the fault trace. These lineaments, also called en echelon fractures in structural geology, are like right-lateral segments of a fault plane aligning the river channel, cut and offset by those similar in trend with the EAF and with alignments of a left lateral sense, as is the EAF motion sense. We interpret that the river local channel follows a right-lateral fault structure. The traces were lost a few days later, which proves the essentiality of remote sensing technologies for obtaining precise information in large regions. The time series analysis for one year period from Sentinel-1 SAR data also illustrated the displacements in the region sourced from the earthquake.