This study aimed to compare unmanned aerial vehicle (UAV) based real-time kinematic (RTK) and post-processing kinematic (PPK) methods via five approaches: an RTK-CORS method (M1), a short-baseline PPK method obtaining corrections from a GNSS base station (M2), and three long-baseline PPK methods that obtained corrections from the three Turkish RTK-CORS network TUSAGA-Aktif reference stations (M3: IZMI, M4: CESM, and M5: KIKA). The comparison was based on the accuracy of the corrected camera positions, the average error of the camera locations computed in the photo-alignment and optimization process, georeferencing errors of the models via nine GCPs based on four scenarios, and Root Mean Square (RMS) errors in the Z-direction for different surface types (i.e. roads, shadows, shrubs, boulders, trees, and ground). For the surface types of “ground”, “roads”, and “shrubs”, RMS error rates were obtained 10 cm lower than that of other surface types in all methods except M4. The greatest differences were obtained over trees and shadowed areas. The conclusion of these comparisons was that the lowest RMS error rate was determined on a solid textured surface. The consideration of mean RMS error regardless of surface type in such model comparisons is misleading.

In the summer of 2021, Türkiye experienced unprecedented forest fire events. Throughout that fire season, a total of 291 fire incidents, covering an area of 202,361 hectares, dominated the public agenda. This study aimed to document and analyze the 30 large fires (affecting over 100 hectares) of 2021 using remote sensing and GIS techniques. A comprehensive fire database was established, encompassing information on burned areas, fire severity, and fuel types, determined from forest-stand types and topographical properties including slope, elevation, and aspect (in eight directions). Sentinel-2 satellite images were utilized to calculate dNBR values for assessing fire severity, analyzed in the Google Earth Engine platform. Three GIS-integrated Python scripts were developed to construct the fire database. In total, 164,658 hectares were affected by these large fires, occurring solely in three regions of Türkiye: the Mediterranean, Aegean, and Eastern Anatolian. The majority of the burned area was situated in the Mediterranean region (59%), with only 3% in Eastern Anatolia. The burned areas ranged from a minimum of 150 hectares to a maximum of 58,798 hectares. Additionally, 679 hectares of residential areas and 22,601 hectares of agricultural land were impacted by the fire events. For each fire, 21 fuel types and their distribution were determined. The most prevalent fire-prone class, “Pure Turkish pine species (Pr-Çz),” accounted for 59.56% of the total affected area (99,516 hectares). Another significant fire-prone pine species, the “Pure Black pine species (Pr-Çk),” covered 7.67% (12,811 hectares) of the affected area. Fuel types were evaluated by considering both forest-stand development stages and canopy closure. Regarding forest-stand development stages, the largest area percentage burned belonged to the “Mature” class (26.48%).

A destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year⁻¹ and between − 24 and 6 mm year⁻¹, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year⁻¹ and between − 18 and 20 mm year⁻¹, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes.

This study tested the potential of a short time series of very high spatial resolution (cm to dm) remote sensing datasets obtained from unmanned aerial system (UAS)-based photogrammetry and terrestrial laser scanning (TLS) to monitor snow cover ablation in the upper Dischma valley (Davos, Switzerland). Five flight missions (for UAS) and five scans (for TLS) were carried out simultaneously: Four during the snow-covered period (9, 10, 11, and 27 May 2016) and one during the snow-free period (24 June 2016 for UAS and 31 May 2016 for TLS). The changes in both the areal extent of the snow cover and the snow depth (HS) were assessed together in the same case study. The areal extent of the snow cover was estimated from both UAS- and TLS-based orthophotos by classifying pixels as snow-covered and snow-free based on a threshold value applied to the blue band information of the orthophotos. Also, the usage possibility of TLS-based orthophotos for mapping snow cover was investigated in this study. The UAS-based orthophotos provided higher overall classification accuracy (97%) than the TLS-based orthophotos (86%) and allowed for mapping snow cover in larger areas than the ones from TLS scans by preventing the occurrence of gaps in the orthophotos. The UAS-based HS were evaluated and compared to TLS-based HS. Initially, the CANUPO (CAractérisation de NUages de POints) binary classification method, a proposed approach for improving the quality of models to obtain more accurate HS values, was applied to the TLS 3D raw point clouds. In this study, the use of additional artificial ground control points (GCPs) was also proposed to improve the quality of UAS-based digital elevation models (DEMs). The UAS-based HS values were mapped with an error of around 0.1 m during the time series. Most pixels representing change in the HS derived from the UAS data were consistent with the TLS data. The time series used in this study allowed for testing of the significance of the data acquisition interval in the monitoring of snow ablation. Accordingly, this study concluded that both the UAS- and TLS-based high-resolution DSMs were biased in detecting change in HS, particularly for short time spans, such as a few days, where only a few centimeters in HS change occur. On the other hand, UAS proved to be a valuable tool for monitoring snow ablation if longer time intervals are chosen.