Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 83-90, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-83-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
31 May 2017
TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS
K. Pawłuszek1, A. Borkowski1, and P. Tarolli2 1Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Poland
2Department of Land, Environment, Agriculture and Forestry, University of Padova, Italy
Keywords: Landslide, landslide mapping, DEM resolution, Neural Net classification, Maximum Likelihood classification Abstract. Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1 m, 2 m, 5 m and 10 m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1 m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5 m DEM-resolution for FFNN and 1 m DEM resolution for results. The best performance was found to be using 5 m DEM-resolution for FFNN and 1 m DEM resolution for ML classification.
Conference paper (PDF, 2024 KB)


Citation: Pawłuszek, K., Borkowski, A., and Tarolli, P.: TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 83-90, https://doi.org/10.5194/isprs-archives-XLII-1-W1-83-2017, 2017.

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