Creative Commons License

Karakas G., Kocaman S., Gokceoglu C.

24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Nice, France, 6 - 11 June 2022, vol.5-3, pp.525-531 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 5-3
  • Doi Number: 10.5194/isprs-annals-v-3-2022-525-2022
  • City: Nice
  • Country: France
  • Page Numbers: pp.525-531
  • Keywords: Aerial Photogrammetry, Landslide Susceptibility Mapping, Machine Learning, Random Forest, EU-DEM
  • Hacettepe University Affiliated: Yes


Generating precise and up-to-date landslide susceptibility maps (LSMs) in landslide-prone areas is important to identify hazard potential in the future. The data quality and the method selection affect the accuracy of the LSMs. In this context, the accuracy and precision of the digital elevation models (DEMs) used as input are among the most important performance elements. Therefore, the influence of DEM accuracy and spatial resolution in producing LSMs was investigated here. A high accuracy DEM with 5 m grid spacing produced from aerial photographs and the EU-DEM v1.1 freely accessible from Copernicus Land Monitoring Service with 25 m spatial resolution were used for producing two different LSMs using the Random Forest (RF) method in this study. The RF method has proven success for this purpose. A total of eight conditioning factors, which include topographical and geological features, was used as model input. The landslide inventory was derived with the help of aerial stereo images with 20 cm and 30 cm ground sampling distances. The performances of the LSMs were assessed with receiver operating characteristics (ROC) area under curve (AUC) values. In addition, the results were compared with visual inspection. The results show that although the AUC values obtained from the aerial DEM (0.95) and EU-DEM v1.1 (0.93) were comparable; based on the visual assessments, the LSM obtained from the higher resolution DEM was found more successful in detecting the landslides and thus exhibited better prediction performance.