Developing an algorithm for automated geometric analysis and classification of landslides incorporating LiDAR-derived DEM

• Published in: Environmental earth sciences Vol. 77; no. 11; pp. 1 - 15
• Main Authors: Pirasteh, Saied, Li, Jonathan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Automation; Biogeosciences; Classification; computer software; data collection; decision making; Digital Elevation Models; Direction; Earth; Earth and Environmental Science; Earth Sciences; Earth surface; Environmental Science and Engineering; Geochemistry; Geographic information systems; Geographical coordinates; Geographical information systems; Geology; Geometric accuracy; geometry; Hydrology/Water Resources; inventories; Iran; issues and policy; Landslides; Landslides & mudslides; Length; Lidar; Mathematical models; Methods; Mountains; Numerical integration; Original Article; Policies; Satellite navigation systems; Terrestrial Pollution; Width; Iran; Landslide classification; Area; Inventory map; Length; Computing techniques; LiDAR; Trapezoidal rule; Alborz Mountains; Width; MATLAB
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-018-7583-3

Amending landslides inventories is immensely important to policy and decision makers alike. Sliding creates geometric shapes on the Earth’s surface. This study presents the utilization of LiDAR high-resolution digital elevation model (DEM) in the Alborz Mountains, Iran to refurbish the existing landslide inventory dataset by implementing the proposed algorithm. The method consists of the automated derivation of landslide geometry (length, width, and area) followed by classification of landslide types considering length, width and flow direction. This study has used the trapezoidal rule for numerical integration to develop the proposed algorithm. The landslides were then classified into four types (very long, long, very wide, and wide) based on slope, length, and width. This geometric classification of landslides is based on the geographical coordinates, slope angle ( θ ), length ( L ), and width ( W ), and further failure flow direction. A total of 95 landslides were updated from the existing inventory database. The proposed method was verified and evaluated by field observations; and 14 samples were tested to determine the relative error. The results demonstrated that the mean percentage relative error is 0.496% in length and width and 0.008% in area, related to the GIS analysis. The accuracy performance of determining the landslide’s type is 92%. The purposefulness of this algorithm is to increase the accuracy performance of landslides geometry analysis and automated measurements associated with the usual GIS platforms such as ArcGIS.