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

Nayyer Saleem1, Md. Enamul Huq1, Nana Yaw Danquah Twumasi1, Akib Javed1, Asif Sajjad1
1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Tóm tắt

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.

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Tài liệu tham khảo

Terrain Analysis (2019, August 25). Dictionary of Military and Associated Terms. Available online: https://www.thefreedictionary.com/terrain+analysis.

Wilson, J.P., and Fotheringham, A.S. (2008). Chapter 23: Terrain Analysis. The Handbook of Geographic Information Science, John Wiley & Sons. [1st ed.].

Mutluoglu, 2010, Investigation of the effect of land slope on the accuracy of digital elevation model (DEM) generated from various sources, Sci. Res. Essays, 5, 1384

Toz, 2008, DEM (Digital Elevation Model) Production and Accuracy Modeling of DEMs from 1: 35000 scale aerial photographs, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVI, 775

Yakar, 2009, Digital Elevation Model Generation by Robotic Total Station Instrument, Soc. Exp. Mech., 33, 52

Li, 2017, DEM generation from contours and a low-resolution DEM, ISPRS J. Photogramm. Remote Sens., 134, 135, 10.1016/j.isprsjprs.2017.09.014

(2019, August 14). USGS, Available online: https://www.usgs.gov/.

Taud, 1999, DEM generation by contour line dilation p, Comput. Geosci., 25, 775, 10.1016/S0098-3004(99)00019-9

Li, Z., and Gold, Q.Z.C. (2005). Digital Terrain Modeling: Principles and Methodology, CRC Press.

Peralvo, 2004, Influence of DEM interpolation methods in drainage analysis, GIS Water Resour., 4, 26

Vaze, J., and Teng, J. (2019, January 01). High‐resolution LiDAR DEM: How good is it? In Proc. MODSIM 2007: Intl. Congress on Modelling and Simulation, 692-698. L. Oxley and D. Kulasiri, eds. Modelling and Simulation Society of Australia and New Zealand, Available online: www.mssanz.org.au/MODSIM07/ papers/12_s27/HighResolution_s27_Vaze_.pdf.

Jongmans, D., Pirard, E., and Trefois, P. (1999). From scanned topographic maps to digital elevation models. International Symposium on Imaging Applications in Geology.

Carter, 1988, Digital Representations of Topographic Surfaces, Photogramm. Eng. Remote Sens., 54, 1577

Soycan, 2009, Digital Elevation Model Production from Scanned Topographic conotur maps via Thin Plate Spline Interpolation, Arab. J. Sci. Eng., 34, 121

Oky, 2002, DEM generation method from contour lines based on the steepest slope segment chain and a monotone interpolation function, ISPRS J. Photogramm. Remote Sens., 57, 86, 10.1016/S0924-2716(02)00117-X

(2019, October 05). NASA JPL ASTER, Available online: https://asterweb.jpl.nasa.gov/gdem.asp.

Farr, 2007, The Shuttle Radar Topography Mission, Rev. Geophys., 45, 1, 10.1029/2005RG000183

USGS (2019, October 05). EROS Archive, Available online: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-30-arc-second-elevation-gtopo30?qt-science_center_objects=0#qt-science_center_objects.

Visser, 1999, Gravity field determination with GOCE and GRACE, Adv. Sp. Res., 23, 771, 10.1016/S0273-1177(99)00154-4

(2019, October 05). NGA. Available online: https://www.nga.mil/ProductsServices/GeodesyandGeophysics/Pages/EarthGravityModel.aspx.

Balmino, 2011, Spherical harmonic modelling to ultra-high degree of Bouguer and isostatic anomalies, J. Geod., 86, 499, 10.1007/s00190-011-0533-4

Wang, 2015, Modelling of Singapore s topographic transformation based on DEMs, Geomorphology, 231, 367, 10.1016/j.geomorph.2014.12.027

Fisher, 2006, Causes and consequences of error in digital elevation models, Prog. Phys. Geogr., 30, 467, 10.1191/0309133306pp492ra

Mercer, 2004, DEMs created from airborne IFSAR–An update, Int. Arch. Photogramm. Remote Sens., 35, 242

Hahn, 1999, Integration of DTMs using wavelets, Int. Arch. Photogramm. Remote Sens., 32, 3

Richardson, D., and van Oosterom, P. (2002). Quantifying Uncertainty of Digital Elevation Models Derived from Topographic Maps. Symposium on Advances in Spatial Data Handling, Springer.

Chang, 2004, Assessment of digital elevation models using RTK GPS, J. Geospatial Eng., 6, 1

Webster, 2006, The application of lidar-derived digital elevation model analysis to geological mapping: An example from the Fundy Basin, Nova Scotia, Canada, Can. J. Remote Sens., 32, 173, 10.5589/m06-017

Zhang, 2008, Generation of Digital Surface Model From High Resolution, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVI, 785

Capaldo, P., Crespi, M., Fratarcangeli, F., Nascetti, A., Francesca, P., Agugiaro, G., Poli, D., and Remondino, F. (2012, January 22–27). DSM Generation from Optical and SAR high resolution satellite Imagery: Methodology, Problems and Potentialities. Proceedings of the International Geoscience Remote Sensing Symposium (IGARSS), Munich, Germany.

Mohd, 2014, Evaluation of Vertical Accuracy of Digital Elevation Models Generated from Different Sources: Case Study of Ampang and Hulu Langat, Malaysia, FIG Congress, XXV, 1

Wu, 2015, Geometric integration of high-resolution satellite imagery and airborne LiDAR data for improved geopositioning accuracy in metropolitan areas, ISPRS J. Photogramm. Remote Sens., 109, 139, 10.1016/j.isprsjprs.2015.09.006

Yu, 2016, Application of virtual earth in 3D terrain modeling to visual analysis of large-scale geological disasters in mountainous areas, Environ. Earth Sci., 75, 562, 10.1007/s12665-015-5161-5

2018, Towards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment, J. Hydrol., 561, 651, 10.1016/j.jhydrol.2018.04.043

Akturk, 2018, Accuracy Assesment of a Low-Cost UAV Derived Digital Elevation Model (DEM) in a Highly Broken and Vegetated Terrain, Measurement, 136, 382, 10.1016/j.measurement.2018.12.101

(2019, August 20). UNITED NATIONS-SPIDER. Available online: https://www.un-spider.org.

(2019, August 18). UNITED NATIONS-OOSA. Available online: https://www.unoosa.org.

Altan, O., Backhause, R., Boccardo, P., van Manen, N., Trinder, J., and Zlatanova, S. (2013). The Value of Geoinformation for Disaster Risk Management (VALID) Benefit Analysis Stakeholder Assessment, Joint Board of Geospatial Information Societies (JBGIS). [1st ed.].

van Oosterom, F.E.M., and Zlatanova, S.P. (2005). Use of Photogrammetry, Remote Sensing and Spatial Information Technologies in Disaster Management, especially Earthquakes. Geo-Information for Disaster Management, Springer.

Li, 2013, Geomatics for smart cities-concept, key techniques, and applications, Geo-Spatial Inf. Sci., 16, 13, 10.1080/10095020.2013.772803

Li, 2009, The new era for geo-information, Sci. China Ser. F Inf. Sci., 52, 1233, 10.1007/s11432-009-0122-9

Kocaman, 2019, A CitSci app for landslide data collection, Landslides, 16, 611, 10.1007/s10346-018-1101-2

Shao, 2019, Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation, Remote Sens. Environ., 232, 111338, 10.1016/j.rse.2019.111338

Li, 2014, From digital Earth to smart Earth, Chin. Sci. Bull., 59, 722, 10.1007/s11434-013-0100-x

Erasmi, 2014, Evaluating the quality and accuracy of TanDEM-X digital elevation models at archaeological sites in the Cilician Plain, Turkey, Remote Sens., 6, 9475, 10.3390/rs6109475

Alganci, U., Besol, B., and Sertel, E. (2018). Accuracy Assessment of Different Digital Surface Models. ISPRS Int. J. Geo-Inf., 7.

Li, 2018, Challenges and opportunities for the development of MEGACITIES, Int. J. Digit. Earth, 12, 1382, 10.1080/17538947.2018.1512662

(2019, August 24). IFRC. Available online: https://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/what-is-a-disaster/.

Cruden, 1991, A Simple Definition of a Landslide, Int. Assoc. Eng. Geol., 43, 27, 10.1007/BF02590167

Nadim, 2006, Global landslide and avalanche hotspots, Landslides, 3, 159, 10.1007/s10346-006-0036-1

Gorum, 2011, Geomorphology Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake, Geomorphology, 133, 152, 10.1016/j.geomorph.2010.12.030

Crosta, 2005, Small fast-moving flow-like landslides in volcanic deposits: The 2001 Las Colinas Landslide (El Salvador), Eng. Geol., 79, 185, 10.1016/j.enggeo.2005.01.014

Chigira, 2010, Landslides induced by the 2008 Wenchuan earthquake, Sichuan, China, Geomorphology, 118, 225, 10.1016/j.geomorph.2010.01.003

(2019, August 31). Scientific Visualization Studio (NASA), Available online: https://svs.gsfc.nasa.gov/4710.

Moore, 1991, Digital Terrain Modeling: A review of Hydrological, Geomorphological and Biological applications, Hydrol. Process., 5, 3, 10.1002/hyp.3360050103

Wolock, 1994, Effect of Digital Elevation Model Map Scale and Data Resolution on a Topography-Based Watershed Model, Water Resour. Res., 30, 3041, 10.1029/94WR01971

Lee, E.M., and Jones, D.K.C. (2004). Landslide Risk Assessment, 1 Heron Quay. [1st ed.].

Gao, 1993, Identification of topographic settings conducive to landsliding from DEM in Nelson county, Virginia, U.S.A, Earth Surf. Process. Landf., 18, 579, 10.1002/esp.3290180702

Cardinali, 2002, System Sciences A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy, Nat. Hazards Earth Syst. Sci., 2, 57, 10.5194/nhess-2-57-2002

Costanzo, 2012, Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: Application to the river Beiro basin (Spain), Nat. Hazards Earth Syst. Sci., 12, 327, 10.5194/nhess-12-327-2012

Fenton, 2013, Landslide hazard assessment using digital elevation models, Can. Geotech. J., 50, 620, 10.1139/cgj-2011-0342

Biran, A. (2019). Chapter 5: Curvature. Geometry for Naval Architects, Elsevier Ltd.. [1st ed.].

Stump, 1999, Secondary Mathematics Teachers’ Knowledge of Slope, Math. Educ. Res. J., 11, 124, 10.1007/BF03217065

Horn, 1977, Understanding Image Intensities, Artif. Intell., 8, 201, 10.1016/0004-3702(77)90020-0

Horn, 1981, Hill Shading and the Reflectance Map, Proc. IEEE, 69, 14, 10.1109/PROC.1981.11918

Burrough, P.A. (1986). Principles of Geographical Information Systems for Land Resources Asessment, Clarendon Press. [1st ed.].

Skidmore, 1989, A comparison of techniques for calculating gradient and aspect from a gridded digital elevation model, Int. J. Geogr. Inf. Syst., 3, 323, 10.1080/02693798908941519

Raaflaub, 2006, The effect of error in gridded digital elevation models on the estimation of topographic parameters, Environ. Model. Softw., 21, 710, 10.1016/j.envsoft.2005.02.003

Mclean, A. (2011). Landslide Risk Assessment Using Digital Elevation Models, Dalhousie University.

Zevenbergen, 1987, Quantitative Analysis of Land Surface Topography, Earth Surf. Dyn., 12, 47

Lee, 2004, Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Eng. Geol., 71, 289, 10.1016/S0013-7952(03)00142-X

Rasyid, 2016, Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia, Geoenvironmental Disasters, 3, 1, 10.1186/s40677-016-0053-x

(2019, August 28). ArcMap: Curvature Function. Available online: http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/curvature-function.htm.

Guisan, 1999, GLM versus CCA spatial modeling of plant species distribution, Plant Ecol., 143, 107, 10.1023/A:1009841519580

Weiss, A.D. (2001, January 22–24). Topographic Position and Landforms Analysis. Proceedings of the Poster Presentation at ESRI User Conference, Seattle, WA, USA.

Jebur, 2014, Remote Sensing of Environment Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale, Remote Sens. Environ., 152, 150, 10.1016/j.rse.2014.05.013

Oh, 2018, Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models, Geomatics, Nat. Hazards Risk, 9, 1053, 10.1080/19475705.2018.1481147

Jenness, J., Brost, B., and Beier, P. (2013). Land Facet Corridor Designer. USDA Forest Service Rocky Mountain Research Station.

Enterprises, J. (2019, September 08). Available online: http://www.jennessent.com/arcgis/arcgis_extensions.htm.

Jiang, L., Ling, D., Zhao, M., Wang, C., Liang, Q., and Liu, K. (2018). Effective Identification of Terrain Positions from Gridded DEM Data Using Multimodal Classification Integration. ISPRS Int. J. Geo-Inf., 7.

Grabs, 2009, Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model, J. Hydrol., 373, 15, 10.1016/j.jhydrol.2009.03.031

Grimm, K., Nasab, M.T., and Chu, X. (2018). TWI Computations and Topographic Analysis of Depression-Dominated Surfaces. Water, 10.

Beven, 1979, A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant, Hydrol. Sci. Bull., 24, 43, 10.1080/02626667909491834

Schmidt, 2003, Comparison of DEM Data Capture and Topographic Wetness Indices, Precis. Agric., 4, 179, 10.1023/A:1024509322709

Gu, 2004, Modeling Spatial Patterns of Saturated Areas: An Evaluation of Different Terrain Indices, Water Resour. Res., 40, 114

Zinko, 2006, On the calculation of the topographic wetness index: Evaluation of different methods based on field observations, Hydrol. Earth Syst. Sci. Discuss. Eur. Geosci. Union, 10, 101

Zhu, 2011, An approach to computing topographic wetness index based on maximum downslope gradient, Precis. Agric., 12, 32, 10.1007/s11119-009-9152-y

Buchanan, 2014, Evaluating topographic wetness indices across central New York agricultural landscapes, Hydrol. Earth Syst. Sci., 18, 3279, 10.5194/hess-18-3279-2014

Smith, 2014, Roughness in the Earth Sciences, Earth Sci. Rev., 136, 202, 10.1016/j.earscirev.2014.05.016

Korzeniowska, K., and Korup, O. (2016, January 14–17). Mapping Gullies Using Terrain-Surface Roughness. Proceedings of the 19th AGILE conference on Geographic Information Science, Helsinki, Finland.

Riley, 1999, Terrain Ruggedness Index that Quantifies Topographic Heterogeneity, Intermt. J. Sci., 5, 23

Shepard, 2001, A planetary and remote sensing perspective, J. Geophys. Res., 106, 777

Frankel, 2007, Characterizing arid region alluvial fan surface roughness with airborne laser swath mapping digital topographic data, J. Geophys. Res., 112, 1

Cavalli, 2008, Characterisation of the surface morphology of an alpine alluvial fan using airborne LiDAR, Nat. Hazards Earth Syst. Sci., 8, 323, 10.5194/nhess-8-323-2008

Wenjie, 2011, An approach to estimating sediment transport capacity of overland flow, Sci. China Technol. Sci., 54, 2649, 10.1007/s11431-011-4506-x

Moore, 1986, Sediment Transport Capacity of Sheet and Rill Flow: Application of Unit Stream Power Theory, Water Resour. Res., 22, 1350, 10.1029/WR022i008p01350

Moore, 1992, Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation, J. Soil Water Conserv., 47, 423

Tayfur, 2002, Applicability of sediment transport capacity models for nonsteady state erosion from steep slopes, J. Hydrol. Eng., 7, 252, 10.1061/(ASCE)1084-0699(2002)7:3(252)

Chandra, 2013, 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, Nat. Hazards, 65, 135, 10.1007/s11069-012-0347-6

Wang, 2016, Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China), Environ. Earth Sci., 75, 598

Moore, 1988, A Contour-based Topographic Model for Hydrological and Ecological Applications, Earth Surf. Process. Landf., 13, 305, 10.1002/esp.3290130404

Pradhan, 2010, Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia), IEEE Trans. Geosci. Remote Sens., 48, 4164, 10.1109/TGRS.2010.2050328

Pawluszek, 2016, Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Roznow Lake, Poland, Nat. Hazards, 86, 919, 10.1007/s11069-016-2725-y

Yilmaz, 2009, Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey), Comput. Geosci., 35, 1125, 10.1016/j.cageo.2008.08.007

Juliev, 2019, Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan, Sci. Total Environ., 653, 801, 10.1016/j.scitotenv.2018.10.431

Lee, 2005, Probabilistic landslide susceptibility and factor effect analysis, Environ. Geol., 47, 982, 10.1007/s00254-005-1228-z

Oh, 2012, Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping, Int. J. Remote Sens., 33, 3211, 10.1080/01431161.2010.545084

Dlugosz, 2012, Digital Terrain Model (DTM) As a Tool for Landslide Investigation in the Polish Carpathians, Versita, XLVI, 5

Pradhan, 2017, Effects of the Spatial Resolution of Digital Elevation Models and their Products on Landslide Susceptibility Mapping, Laser Scanning Appl. Landslide Assess., 2, 133

Deng, 2007, DEM resolution dependencies of terrain attributes across a landscape, Int. J. Geogr. Inf. Sci., 21, 187, 10.1080/13658810600894364

Vaze, 2010, Impact of DEM accuracy and resolution on topographic indices, Environ. Model. Softw., 25, 1086, 10.1016/j.envsoft.2010.03.014

Chow, 2009, Effects of lidar post-spacing and DEM resolution to mean slope estimation, Int. J. Geogr. Inf. Sci., 23, 1277, 10.1080/13658810802344127

Ciampalini, 2016, The effectiveness of high-resolution LiDAR data combined with PSInSAR data in landslide study, Landslides, 13, 399, 10.1007/s10346-015-0663-5

Pesci, 2004, Digital elevation models for landslide evolution monitoring: Application on two areas located in the Reno River Valley (Italy), Ann. Geophys., 47, 1339

Mahalingam, 2016, Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study), Geomat. Nat. Hazards Risk, 7, 1884, 10.1080/19475705.2016.1172520

Halounova, 2016, Spatial resolution effects of digital terrain models on landslide susceptibility analysis, The International Archives of the Photogrammetry, Remote Sensing Spatial Information Sciences, Volume XLI-B8, 33

Wang, 2015, Robust methods for assessing the accuracy of linear interpolated DEM, Int. J. Appl. Earth Obs. Geoinf., 34, 198

Carrara, 1999, Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard, Nat. Hazards, 20, 117, 10.1023/A:1008097111310

Ercanoglu, 2004, Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey), Eng. Geol., 75, 229, 10.1016/j.enggeo.2004.06.001

Gorsevski, 2008, Discerning landslide susceptibility using rough sets, Comput. Environ. Urban Syst., 32, 53, 10.1016/j.compenvurbsys.2007.04.001

Yesilnacar, 2005, Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey), Eng. Geol., 79, 251, 10.1016/j.enggeo.2005.02.002

Kawabata, 2009, Geomorphology Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Arti fi cial Neural Network (ANN), Geomorphology, 113, 97, 10.1016/j.geomorph.2009.06.006

Xiaolong, 2017, Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity, ISPRS Int. J. Geo-Inf., 6, 103, 10.3390/ijgi6040103

Liu, 2018, Large-scale assessment of landslide hazard, vulnerability and risk in China, Geomat. Nat. Hazards Risk, 9, 1037, 10.1080/19475705.2018.1502690

Dagdelenler, 2015, Modification of seed cell sampling strategy for landslide susceptibility mapping: An application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey), Bull. Eng. Geol. Environ., 75, 575, 10.1007/s10064-015-0759-0

Liu, J., and Duan, Z. (2018). Quantitative assessment of landslide susceptibility comparing statistical index, index of entropy, and weights of evidence in the Shangnan Area, China. Entropy, 20.

Sadisun, I.A., and Arifianti, Y. (2017, January 18–19). Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia. Proceedings of the IOP Conference Series: Earth Environmental Science, Bandung, Indonesia.

Reichenbach, 2018, Earth-Science Reviews statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60, 10.1016/j.earscirev.2018.03.001

Chen, 2019, Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility, Catena, 172, 212, 10.1016/j.catena.2018.08.025

Carrara, 1991, GIS techniques and statistical models in evaluating landslide hazard, Earth Surf. Process. Landf., 16, 427, 10.1002/esp.3290160505

Nichol, 2005, Satellite remote sensing for detailed landslide inventories using change detection and image fusion, Int. J. Remote Sens., 26, 1913, 10.1080/01431160512331314047

Miner, A.S., Flentje, P., Mazengarb, C., and Windle, D.J. (2010, January 5–10). Landslide Recognition using LiDAR derived Digital Elevation Models-Lessons learnt from selected Australian examples. Proceedings of the Geologically Active Proceedings 11th IAEG Congregalia, Auckland, New Zealand.

Pourghasemi, 2012, Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran, Catena, 97, 71, 10.1016/j.catena.2012.05.005

Jaboyedoff, 2012, Use of LIDAR in landslide investigations: A review, Nat. Hazards, 61, 5, 10.1007/s11069-010-9634-2

Bagherzadeh, 2013, Mapping of landslide hazard zonation using GIS at Golestan watershed, northeast of Iran, Arab J. Geosci., 6, 3377, 10.1007/s12517-012-0583-9

Martha, 2013, Landslide hazard and risk assessment using semi-automatically created landslide inventories, Geomorphology, 184, 139, 10.1016/j.geomorph.2012.12.001

Zhu, 2018, Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping, Catena, 171, 222, 10.1016/j.catena.2018.07.012

Dou, 2019, Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan, Sci. Total Environ., 662, 332, 10.1016/j.scitotenv.2019.01.221

Massey, C., van Dissen, R., McSaveney, M., Townsend, D., Hancox, G., Little, T.A., Ries, W., Perrin, N., Archibald, G., and Dellow, G. (2013). Landslides and Liquefaction Generated by the Cook Strait and Lake Grassmere Earthquakes, A Reconnaissance Report.