ASSESSMENT OF GIS-BASED LANDSLIDE SUSCEPTIBILITY MAPPING IN FOREST ROAD PLANNING: THE TANIR STREAM WATERSHED CASE STUDY

Authors : Kıvanç Yüksel

Pages : 253-264

Doi:10.32328/turkjforsci.1771112

View : 142 | Download : 421

Publication Date : 2025-10-27

Article Type : Research Paper

Abstract :Primer transport in mountainous forest areas is carried out using forest road networks. In Turkey, forest areas are located in high mountainous areas and steep slope land. Road networks planning in mountainous areas should be done by considering negative and positive cardinal points. Landslides and mass movements are triggered after road construction and can lead to massive earth movements. The landslide susceptibility mapping (LSM) is often used to predict landslide-prone areas. GIS-based machine learning methods are frequently used in the creation of landslide susceptibility mapping. In this study, LSM was created using GIS-based logistic regression method. It was selected the Tanır Stream Watershed as the study area, in the Suçatı region of Kahramanmaraş province, where intensive forest management activities are carried out and forest and rural roads are located. The LSM was used to determine the route of a new forest road to be constructed with certain starting and end points. Landslide data were obtained from General Directorate of Mineral Research and Exploration. The parameters of slope, aspect, curvature, land use, lithology, NDVI, distance to road, distance to stream were used to create the LSM. According to LSM, the study area was divided into five classes in terms of landslide potential: very high, high, medium, low and very low. The results show that approximately 60% of the study area consists of areas with high and very high landslide potential. In sensitive forest watersheds where nature-based solutions and minimized ground movement are critical, GIS-based landslide susceptibility maps offer highly accurate alternatives for locating road routes.
Keywords : Orman yolu, heyelan duyarlılık haritalama, CBS, lojistik regresyon

ORIGINAL ARTICLE URL