Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Developing an algorithm for automated geometric analysis and classification of landslides... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Earth Sciences Springer Journals

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

Pirasteh, Saied; Li, Jonathan
Environmental Earth Sciences , Volume 77 (11) – Jun 1, 2018
Download PDF
15 pages

Loading next page...
 
/lp/springer_journal/developing-an-algorithm-for-automated-geometric-analysis-and-1IWE686RWf
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Earth Sciences; Geology; Hydrology/Water Resources; Geochemistry; Environmental Science and Engineering; Terrestrial Pollution; Biogeosciences
ISSN
1866-6280
eISSN
1866-6299
DOI
10.1007/s12665-018-7583-3
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Environmental Earth Sciences – Springer Journals

Published: Jun 1, 2018

References

  • Geological applications of Landsat Enhanced Thematic Mapper (ETM) data and Geographic Information System (GIS): mapping and structural interpretation in south-west Iran, Zagros Structural Belt
    Ali, SA; Pirasteh, S
  • Remote Sensing and GIS study of tectonics and net erosion rates in the Zagros Structural Belt, southwestern Iran
    Ali, SA; Rangzan, K; Pirasteh, S
  • Use of digital elevation model for study of drainage morphometry and identification stability and saturation zones in relations to landslide assessments in parts of the Shahbazan area
    Ali, SA; Kazem, R; Pirasteh, S
  • Impact of mapping errors on the reliability of landslide hazard maps
    Ardizzone, F; Cardinali, M; Carrara, A; Guzzetti, F; Reichenbach, P
  • Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar
    Ardizzone, F; Cardinali, M; Galli, M; Guzzetti, F; Reichenbach, P
  • Detecting translational landslide scars using segmentation of Landsat ETMC and DEM data in the northern Cascade Mountains, British Columbia
    Barlow, J; Martin, Y; Franklin, S
  • Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills
    Booth, AM; Roering, JJ; Perron, JT
  • A simple definition of a landslide
    Cruden, DM
  • Landslide recognition. Identification, movement and causes
  • Calculating catchment areas with divergent flow based on a regular grid
    Freeman, GT
  • Landslide manual and automated inventories, and susceptibility mapping using LIDAR in the forested mountains of Guerrero, Mexico
    Gaidzik, K; Ramírez-Herrera, MT; Bunn, M; Leshchinsky, BA; Olsen, M; Regmi, NR
  • Landslide displacement monitoring using 3D range flow on airborne and terrestrial LiDAR data
    Ghuffar, S; Szekely, B; Roncat, A; Pfeifer, N
  • Automated derivation and spatio-temporal analysis of landslide properties in southern Kyrgyzstan
    Golovko, D; Roessner, S; Behling, R; Kleinschmit, B
  • Morphometric analysis of relic landslides using detailed landslide distribution maps: implications for forecasting travel distance of future landslides
    Hattanji, T; Moriwaki, H
  • The Varnes classification of landslide types, an update
    Hungr, O; Leroueil, S; Picarelli, L
  • Use of LIDAR in landslide investigations: a review
    Jaboyedoff, M; Thierry, O; Abella´n, A; Marc-Henri, D; Loye, A; Metzger, R; Pedrazzini, A
  • Advanced engineering mathematics
    Kreyszig, E; Kreyszig, H; Norminton, EJ
  • Improving mass wasting inventories by incorporating debris flow topographic signatures
    Lyons, NJ; Mitasova, H; Wegmann, KW
  • Landslide inventories and their statistical properties
    Malamud, BD; Turcotte, DL; Guzzetti, F; Reichenbach, P
  • Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods
    Martha, TR; Kerle, N; Jetten, V; Westen, CJ; Vinod Kumar, K
  • Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry
    McKean, J; Roering, J
  • Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images
    Mondini, AC; Guzzetti, F; Reichenbach, P; Rossi, M; Ardizzone, F
  • Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs
    Niculitˇa, M
  • Automatic extraction of landslide flow direction using geometric processing and DEMs
    Niculiță, M
  • Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling
    Petschko, H; Bell, T; Glade, R
  • The geometric signature: quantifying landslide-terrain types from Digital Elevation Models
    Picke, RJ
  • Landslides investigations from geoinformatics perspective: quality, challenges, and recommendations
    Pirasteh, S; Li, J
  • Probabilistic frequency ratio (PFR) model for quality improvement of landslides susceptibility mapping from LiDAR point clouds
    Pirasteh, S; Li, J
  • Geo-information technology (GiT) and tectonic signatures: the River Karun and Dez, Zagros Orogen in south-west Iran
    Pirasteh, S; Woodbridge, K; Rizvi, SM
  • Coupling of DEM and remote sensing based approaches for semi-automated detection of regional geo-structural features in Zagros Mountain, Iran
    Pirasteh, S; Pradhan, B; Safari, H
  • Implementation of the damage index approach to rapid evaluation building resistance for earthquake
    Pirasteh, S; Li, J; Attarzadeh, I
  • Use of LiDAR-derived DEM and a stream length-gradient index approach to investigation of landslides in Zagros Mountains, Iran
    Pirasteh, S; Li, J; Chapman, M
  • Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping
    Pradhan, B; Pirasteh, S
  • Landslide susceptibility revealed by LIDAR imagery and historical records
    Schulz William, H
  • Holocene fault scarps near Tacoma
    Sherrod, BL; Brocher, TM; Weaver, CS; Bucknam, RC; Blakely, RJ; Kelsey, HM; Nelson, AR; Haugerud, R
  • Shallow landslide susceptibility assessment using a novel hybrid intelligence approach
    Shirzadi, A; Bui, DT; Pham, BT; Solaimani, K; Chapi, K; Kavian, A; Shahabi, H; Revhaug, I
  • Influence of vegetation, slope and LiDAR sampling angle on DEM accuracy
    Su, JG; Bork, EW
  • Aerial photo interpretation of landslides along the Ohio and Mississippi Rivers
    Su, WJ; Stohr, C
  • Terrestrial laser scanner to detect landslide displacement fields: a new approach
    Teza, G; Galgaro, A; Zaltron, N; Genevois, R
  • Effects of raster resolution on landslide susceptibility mapping: a case study of Shenzhen
    Tian, Y; XiaO, C; Liu, Y
  • Monitoring landslide displacements during a controlled rain experiment using a long-range terrestrial laser scanning (TLS)
    Travelletti, J; Oppikofer, T; Delacourt, C; Malet, J; Jaboyedoff, M
  • Special report 176: landslides: analysis and control
    Varnes, DJ
  • Probabilistic predictions of landslide tsunamis off Southern California
    Watts, P
  • Airborne laser scanning: An introduction and overview
    Wehr, A; Lohr, U
  • Landslide susceptibility assessment using the certainty factor and analytic hierarchy process
    Wen, F; Xin, S; Cao, WY; Zheng, B
  • Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview
    Westen, CJ; Van, CE; Kuriakose, SL
  • A distributed slope stability model for steep forested watersheds
    Wu, W; Sidle, RC
  • Study on DEM-derived primary topographic attributes for hydrologic applications: sensitivity to elevation data resolution
    Wu, S; Li, J; Huang, GH
  • The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability (CP) and artificial neural network (ANN)
    Yilmaz, I
  • Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia
    Yousef, AM; Pourghasemi, HR; El-Haddad, BA; Dhahry, BK
  • Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms
    Zare, M; Pourghasemi, HR; Vafakhah, M; Pradhan, B
  • Derivative-Based Trapezoid Rule for the Riemann-Stieltjes Integral
    Zhao, W; Zhang, Z
  • Spatial probabilistic modelling of slope failure using integrated GIS Monte Carlo simulation approach
    Zhou, G; Esaki, T; Mitani, M; Xie, M; Mori, J