Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.

[1]  M. Walter,et al.  Evaluating topographic wetness indices across central New York agricultural landscapes , 2013 .

[2]  C. Westen,et al.  Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake , 2010 .

[3]  Berthold K. P. Horn,et al.  Hill shading and the reflectance map , 1981, Proceedings of the IEEE.

[4]  D. Kawabata,et al.  Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN) , 2009 .

[5]  K. L. Frankel,et al.  Characterizing arid region alluvial fan surface roughness with airborne laser swath mapping digital topographic data , 2007 .

[6]  Jay Gao Identification of topographic settings conducive to landsliding from dem in Nelson county, Virginia, U.S.A. , 1993 .

[7]  W. Mohd,et al.  Evaluation of Vertical Accuracy of Digital Elevation Models Generated from Different Sources : Case Study of Ampang and Hulu Langat , Malaysia , 2014 .

[8]  John P. Wilson,et al.  DEM resolution dependencies of terrain attributes across a landscape , 2007, Int. J. Geogr. Inf. Sci..

[9]  A. FentonGordon,et al.  Landslide hazard assessment using digital elevation models , 2013 .

[10]  Rubini Mahalingam,et al.  Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study) , 2016 .

[11]  Wenzhong Shi,et al.  Robust methods for assessing the accuracy of linear interpolated DEM , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[12]  C. Rizos,et al.  Assessment of digital elevation models using RTK GPS , 2004 .

[13]  Peter F. Fisher,et al.  Causes and consequences of error in digital elevation models , 2006 .

[14]  Federica Bardi,et al.  The effectiveness of high-resolution LiDAR data combined with PSInSAR data in landslide study , 2016, Landslides.

[15]  Mark W. Smith Roughness in the Earth Sciences , 2014 .

[16]  Phillip N Flentje,et al.  Landslide Recognition using LiDAR derived Digital Elevation Models-Lessons learnt from selected Australian examples , 2010 .

[17]  Norman Kerle,et al.  Landslide hazard and risk assessment using semi-automatically created landslide inventories , 2013 .

[18]  Saro Lee,et al.  Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping , 2012 .

[19]  Piotr Jankowski,et al.  Discerning landslide susceptibility using rough sets , 2008, Comput. Environ. Urban Syst..

[20]  Berthold K. P. Horn Understanding Image Intensities , 1977, Artif. Intell..

[21]  F. Loddo,et al.  Digital elevation models for landslide evolution monitoring: application on two areas located in the Reno River Valley (Italy) , 2004 .

[22]  Masahiro Chigira,et al.  Landslides induced by the 2008 Wenchuan earthquake, Sichuan, China , 2010 .

[23]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[24]  Yanli Wu,et al.  Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China) , 2016, Environmental Earth Sciences.

[25]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[26]  Guoyuan Li,et al.  Geometric integration of high-resolution satellite imagery and airborne LiDAR data for improved geopositioning accuracy in metropolitan areas , 2015 .

[27]  B. Pham,et al.  Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. , 2019, The Science of the total environment.

[28]  T. Farr,et al.  The roughness of natural terrain: A planetary and remote sensing perspective , 2001 .

[29]  P. Peduzzi,et al.  Global landslide and avalanche hotspots , 2006 .

[30]  Saro Lee,et al.  Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .

[31]  Zhenfeng Shao,et al.  Challenges and opportunities for the development of MEGACITIES , 2018, Int. J. Digit. Earth.

[32]  Jin Teng,et al.  Impact of DEM accuracy and resolution on topographic indices , 2010, Environ. Model. Softw..

[33]  Andrew D. Weiss Topographic position and landforms analysis , 2001 .

[34]  K. Korzeniowska Mapping gullies using terrain-surface roughness , 2022 .

[35]  S. Uhlenbrook,et al.  Modeling spatial patterns of saturated areas: An evaluation of different terrain indices , 2004 .

[36]  Murat Yakar Digital elevation model generation by robotic total station instrument , 2009 .

[37]  Qing Zhu,et al.  Digital terrain modeling - principles and methodology , 2004 .

[38]  Qiuhua Liang,et al.  Effective Identification of Terrain Positions from Gridded DEM Data Using Multimodal Classification Integration , 2018, ISPRS Int. J. Geo Inf..

[39]  G. Toz,et al.  DEM (DIGITAL ELEVATION MODEL) PRODUCTION AND ACCURACY MODELING OF DEMS FROM 1:35.000 SCALE AERIAL PHOTOGRAPHS , 2008 .

[40]  Lorenzo Marchi,et al.  Characterisation of the surface morphology of an alpine alluvial fan using airborne LiDAR , 2008 .

[41]  P. Reichenbach,et al.  GIS techniques and statistical models in evaluating landslide hazard , 1991 .

[42]  Manfred F. Buchroithner,et al.  Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia) , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Fausto Guzzetti,et al.  A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy , 2002 .

[44]  A. Mclean LANDSLIDE RISK ASSESSMENT USING DIGITAL ELEVATION MODELS , 2011 .

[45]  Deren Li,et al.  The new era for geo-information , 2009, Science in China Series F: Information Sciences.

[46]  I. Moore,et al.  Sediment Transport Capacity of Sheet and Rill Flow: Application of Unit Stream Power Theory , 1986 .

[47]  C. Fraser,et al.  Generation of Digital Surface Model from High Resolution Satellite Imagery , 2008 .

[48]  David Maidment,et al.  Influence of DEM interpolation methods in Drainage Analysis , 2003 .

[49]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[50]  Pierre Soille,et al.  From scanned topographic maps to digital elevation models by , 2001 .

[51]  Elif Sertel,et al.  Accuracy Assessment of Different Digital Surface Models , 2018, ISPRS Int. J. Geo Inf..

[52]  I. Moore,et al.  Length-slope factors for the Revised Universal Soil Loss Equation: simplified method of estimation , 1992 .

[53]  D. Cruden A simple definition of a landslide , 1991 .

[54]  DEMs Created from Airborne IFSAR – An Update , 2004 .

[55]  Jie Dou,et al.  SPATIAL RESOLUTION EFFECTS OF DIGITAL TERRAIN MODELS ON LANDSLIDE SUSCEPTIBILITY ANALYSIS , 2016 .

[56]  Orhan Altan,et al.  Use of Photogrammetry, Remote Sensing and Spatial Information Technologies in Disaster Management, especially Earthquakes , 2005 .

[57]  Cheng Miao,et al.  Large-scale assessment of landslide hazard, vulnerability and risk in China , 2018 .

[58]  N. Bhandary,et al.  Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia , 2016, Geoenvironmental Disasters.

[59]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[60]  M. Długosz Digital Terrain Model (Dtm) as a Tool for Landslide Investigation in the Polish Carpathians , 2012 .

[61]  Qihao Weng Quantifying Uncertainty of Digital Elevation Models Derived from Topographic Maps , 2002 .

[62]  Biswajeet Pradhan,et al.  Effects of the Spatial Resolution of Digital Elevation Models and Their Products on Landslide Susceptibility Mapping , 2017 .

[63]  P. Reichenbach,et al.  A review of statistically-based landslide susceptibility models , 2018 .

[64]  U. Hassler,et al.  Modelling of Singapore's topographic transformation based on DEMs , 2015 .

[65]  J. Vaze,et al.  High Resolution LiDAR DEM – How good is it ? , 2007 .

[66]  Isik Yilmaz,et al.  Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) , 2009, Comput. Geosci..

[67]  Andrew K. Skidmore,et al.  A comparison of techniques for calculating gradient and aspect from a gridded digital elevation model , 1989, Int. J. Geogr. Inf. Sci..

[68]  C. Gokceoğlu,et al.  Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey) , 2004 .

[69]  A-Xing Zhu,et al.  Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping , 2018, CATENA.

[70]  S. Weiss,et al.  GLM versus CCA spatial modeling of plant species distribution , 1999, Plant Ecology.

[71]  Mukhiddin Juliev,et al.  Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. , 2019, The Science of the total environment.

[72]  J. Seibert,et al.  On the calculation of the topographic wetness index: evaluation of different methods based on field observations , 2005 .

[73]  B. Pradhan,et al.  Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran , 2012 .

[74]  Hind Taud,et al.  DEM generation by contour line dilation , 1999 .

[75]  Peng Guo,et al.  Application of virtual earth in 3D terrain modeling to visual analysis of large-scale geological disasters in mountainous areas , 2016, Environmental Earth Sciences.

[76]  Jie Liu,et al.  Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China , 2018, Entropy.

[77]  J. Leitão,et al.  Towards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment , 2018, Journal of Hydrology.

[78]  Tim Webster,et al.  The application of lidar-derived digital elevation model analysis to geological mapping: an example from the Fundy Basin, Nova Scotia, Canada , 2006 .

[79]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[80]  M. McSaveney,et al.  Landslides and liquefaction generated by the Cook Strait and Lake Grassmere earthquakes , 2013 .

[81]  Kevin Bishop,et al.  Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model , 2009 .

[82]  Deren Li,et al.  Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation , 2019, Remote Sensing of Environment.

[83]  M. R. Mansouri Daneshvar,et al.  Mapping of landslide hazard zonation using GIS at Golestan watershed, northeast of Iran , 2013, Arabian Journal of Geosciences.

[84]  Xing-kui Wang,et al.  An approach to estimating sediment transport capacity of overland flow , 2011 .

[85]  Ouml,et al.  Investigation of the effect of land slope on the accuracy of Digital Elevation Model (DEM) generated from various sources , 2010 .

[86]  Jie Li,et al.  DEM generation from contours and a low-resolution DEM , 2017 .

[87]  Ralph Rosenbauer,et al.  Evaluating the Quality and Accuracy of TanDEM-X Digital Elevation Models at Archaeological Sites in the Cilician Plain, Turkey , 2014, Remote. Sens..

[88]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

[89]  Wei Chen,et al.  Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility , 2019, CATENA.

[90]  M. Jaboyedoff,et al.  Use of LIDAR in landslide investigations: a review , 2012, Natural Hazards.

[91]  B. Pradhan,et al.  Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya , 2012, Natural Hazards.

[92]  Prima Oky Dicky Ardiansyah,et al.  DEM generation method from contour lines based on the steepest slope segment chain and a monotone interpolation function , 2002 .

[93]  Giovanni B. Crosta,et al.  Small fast-moving flow-like landslides in volcanic deposits: The 2001 Las Colinas Landslide (El Salvador) , 2005 .

[94]  Candan Gokceoglu,et al.  A CitSci app for landslide data collection , 2018, Landslides.

[95]  Tao Pei,et al.  An approach to computing topographic wetness index based on maximum downslope gradient , 2011, Precision Agriculture.

[96]  Yuan Yao,et al.  Geomatics for Smart Cities - Concept, Key Techniques,and Applications , 2013, Geo spatial Inf. Sci..

[97]  I. Moore,et al.  A contour‐based topographic model for hydrological and ecological applications , 1988, Earth surface processes and landforms.

[98]  Xiaolong Deng,et al.  Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity , 2017, ISPRS Int. J. Geo Inf..

[99]  John P. Wilson,et al.  Chapter 23. Terrain Analysis , 2008 .

[100]  Yukni Arifianti,et al.  Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia , 2018 .

[101]  G. Tayfur Applicability of Sediment Transport Capacity Models for Nonsteady State Erosion from Steep Slopes , 2002 .

[102]  Andreas Persson,et al.  Comparison of DEM Data Capture and Topographic Wetness Indices , 2003, Precision Agriculture.

[103]  Michael E. Hodgson,et al.  Effects of lidar post‐spacing and DEM resolution to mean slope estimation , 2009, Int. J. Geogr. Inf. Sci..

[104]  M. Soycan,et al.  DIGITAL ELEVATION MODEL PRODUCTION FROM SCANNED TOPOGRAPHIC CONTOUR MAPS VIA THIN PLATE SPLINE INTERPOLATION , 2009 .

[105]  S. Stump Secondary mathematics teachers’ knowledge of slope , 1999 .

[106]  Michael J. Collins,et al.  The effect of error in gridded digital elevation models on the estimation of topographic parameters , 2006, Environ. Model. Softw..

[107]  P. Visser Gravity field determination with GOCE and GRACE , 1999 .

[108]  Yuan Yao,et al.  From digital Earth to smart Earth , 2014 .

[109]  C. Thorne,et al.  Quantitative analysis of land surface topography , 1987 .

[110]  Prima Riza Kadavi,et al.  Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models , 2018 .

[111]  Fausto Guzzetti,et al.  Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard , 1999 .

[112]  Mustafa Neamah Jebur,et al.  Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale , 2014 .

[113]  J. Nichol,et al.  Satellite remote sensing for detailed landslide inventories using change detection and image fusion , 2005 .

[114]  Georges Balmino,et al.  Spherical harmonic modelling to ultra-high degree of Bouguer and isostatic anomalies , 2012, Journal of Geodesy.

[115]  Andrzej Borkowski,et al.  Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland , 2017, Natural Hazards.

[116]  H. A. Nefeslioglu,et al.  Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey) , 2016, Bulletin of Engineering Geology and the Environment.

[117]  C. Irigaray,et al.  Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain) , 2012 .

[118]  Mattia Crespi,et al.  DSM generation from optical and SAR high resolution satellite imagery: Methodology, problems and potentialities , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[119]  X. Chu,et al.  TWI Computations and Topographic Analysis of Depression-Dominated Surfaces , 2018 .

[120]  D. Wolock,et al.  Effects of digital elevation model map scale and data resolution on a topography‐based watershed model , 1994 .

[121]  Arif Oguz Altunel,et al.  Accuracy assessment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain , 2019, Measurement.