Unmanned aerial vehicles (UAV) have been used in a variety of fields in the last decade. In forestry, they have been used to estimate tree heights and crowns with different sensors. This approach, with a consumer-grade onboard system camera, is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-light detecting and ranging systems (UAV-LiDAR). In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from a coniferous urban forest. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, digital elevation models, and point clouds in one flight. Canopy height model, obtained by extracting the digital surface model from the digital terrain model, was filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94% correlation and a root-mean-square error of 28 cm. This study highlights the accuracy of the method and compares favourably to more expensive methods.

Landslides cause considerable loss of life and property; therefore, it is important to determine landslide susceptibility. The aim of this study is to produce a geographical information system (GIS)-based landslide susceptibility map (LSM) by the frequency ratio (FR) and weight-of-evidence (WOFE) methods for the selected study area (Şavşat city, Turkey), to calculate the accuracy of the produced LSM, and to perform all these operations fully automatically. User-interface programs were developed in a GIS environment. Slope, aspect, elevation, lithology, distance to fault, distance to river network, normalized difference vegetation index, land use/land cover, and rainfall parameters were included to produce the LSM. The area-under-curve value obtained in receiver operating characteristic (ROC) analysis of the LSM produced by the FR method is 0.78 and that obtained in ROC analysis of the LSM produced by the WOFE method is 0.74. The LSM production process typically takes a long time. This study presents a different perspective on the production of LSM by developing a new method to automatically perform landslide susceptibility analysis. The method presented here, which includes user-interface programs, could be applied to any landslide area.