Using GIS-based multicriteria decision support system for planning road networks with visual quality constraints: a case study of protected areas in Ankara, Turkey.

Protected areas are important zones due to their natural and cultural assets and their biodiversity preservation functions. Ecotourism activities in these areas have gained great importance for visitors in recent decades. Road networks established in protected areas have ecotourism-related functions, such as providing visitors with continuous access to/within these areas and offering visual richness to visitors while cruising on the roads. Road network planning that prioritizes visual quality is one of the scientific issues discussed today regarding the sustainable management of protected areas. This study focuses on planning new road networks that prioritize visual quality in protected areas and determining the optimum route that maximizes the visual quality experience of visitors. The study area was selected from the protected areas between the Kizilcahamam and Çamlidere Districts of Ankara, Turkey, and their surroundings. In the model application, a road network was planned using the multicriteria decision support system (MDSS) method by considering visual quality parameters. In this stage, the road network that prioritized visual quality during spring and autumn seasons was investigated. Hence, weighted linear combination (WLC) was used as a geographic information system (GIS)-based MDSS method. Then, the GIS-based network analysis method was used to determine the optimum route that provided access to the scenic viewpoints (existing and proposed viewpoints) in the study area and maximized the visual quality during both seasons. In the new road network planned by considering the visual quality parameters, the total road length was calculated as 121.21 km for the spring and 129.47 km for the autumn. The lengths of the optimum routes that allowed visitors to reach the scenic viewpoints and ensured the maximum visual quality were 30.91 km and 30.70 km on the new road network for the spring and autumn seasons, respectively. This study introduced a new methodology that utilized GIS-based decision support systems to plan a road network that prioritized visual quality and determined the optimum route with the maximum visual quality. It is anticipated that this methodology can be used for sustainable management and effective planning of protected areas to reach and protect resources with high visual quality.

Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study.

In the present study, UAV-based monitoring of the Gallenzerkogel landslide (Ybbs, Lower Austria) was carried out by three flight missions. High-resolution digital elevation models (DEMs), orthophotos, and density point clouds were generated from UAV-based aerial photos via structure-from-motion (SfM). According to ground control points (GCPs), an average of 4 cm root mean square error (RMSE) was found for all models. In addition, light detection and ranging (LIDAR) data from 2009, representing the prefailure topography, was utilized as a digital terrain model (DTM) and digital surface model (DSM). First, the DEM of difference (DoD) between the first UAV flight data and the LIDAR-DTM was determined and according to the generated DoD deformation map, an elevation difference of between - 6.6 and 2 m was found. Over the landslide area, a total of 4380.1 m3 of slope material had been eroded, while 297.4 m3 of the material had accumulated within the most active part of the slope. In addition, 688.3 m3 of the total eroded material had belonged to the road destroyed by the landslide. Because of the vegetation surrounding the landslide area, the Multiscale Model-to-Model Cloud Comparison (M3C2) algorithm was then applied to compare the first and second UAV flight data. After eliminating both the distance uncertainty values of higher than 15 cm and the nonsignificant changes, the M3C2 distance obtained was between - 2.5 and 2.5 m. Moreover, the high-resolution orthophoto generated by the third flight allowed visual monitoring of the ongoing control/stabilization work in the area.

Causes of the 1998 Bartin river flood in Western Black Sea region of Turkey.

A vast flood in the Western Black Sea region of Turkey in May 1998 caused great loss of life and significant damage. Communication network, transportation, and construction cost of the disaster was estimated around US $500 million. Since flood area was relatively large, only Bartin river watershed were analysed and investigated within the scope of this study. It is very common having intense summer showers, which results in floods and landslides in the region. Land use changes in Turkey are rapid; therefore, actual land use format and its recent change were determined using remote sensing. Geographic Information System (GIS) was employed to evaluate the data collected in the area. Prolonged rainfall on saturated soil by antecedent rainfall; misuse of land both in upper and lower watersheds are main reasons affecting the formation of such a flood in Bartin river watershed.