2,986
Views
13
CrossRef citations to date
0
Altmetric
Article

Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 572-600 | Received 29 Nov 2019, Accepted 18 Feb 2020, Published online: 28 Mar 2020

References

  • Baatz M, Schäpe A. 2000. Multi resolution segmentation: an optimum approach for high quality multi scale image segmentation. In: Beutrage zum AGIT-Symposium. Salzburg, Heidelberg, 2000. p. 12–23
  • Baird C, Healy T, Johnson K, Bogie A, Dankert EW, Scharenbroch C. 2013. A comparison of risk assessment instruments in juvenile justice. Madison, (WI): National Council on Crime and Delinquency
  • Bartelletti C, Giannecchini R, D’Amato Avanzi G, Galanti Y, Mazzali A. 2017. The influence of geological–morphological and land use settings on shallow landslides in the Pogliaschina T. basin (northern Apennines, Italy). J Maps. 13:142–152
  • Blaschke T. 2010. Object based image analysis for remote sensing ISPRS. J Photogramm Remote Sens. 65:2–16
  • Cabrera-Barona P, Ghorbanzadeh O. 2018. Comparing classic and interval analytical hierarchy process methodologies for measuring area-level deprivation to analyze health inequalities. Int J Environ Res Public Health. 15:140
  • Casagli, N, Cigna, F, Bianchini, S, Holbling, D, Fureder, P, Righini, G, Del Conte, S, Friedl, B, Schneiderbauer, S, Iasio, C. et al. 2016. Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER. RSA: Soc Envir. 4:92–108
  • Chen W, Pourghasemi HR, Naghibi SA. 2017. A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. B Eng Geol Environ. 77:647–664
  • Demir G, Aytekin M, Akgün A, İkizler SB, Tatar O. 2012. A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards. 65:1481–1506
  • Dragut L, Csillik O, Eisank C, Tiede D. 2014. Automated parameterisation for multi-scale image segmentation on multiple layers ISPRS J Photogramm Remote Sens. 88:119–127
  • Drǎguţ L, Tiede D, Levick SR. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int J Geogr Inf Sci. 24:859–871
  • Feizizadeh B, Blaschke T. 2014. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. Int J Geogr Inf Sci. 28:610–638
  • Ghorbanzadeh O, Blaschke T, Aryal J, Gholaminia K. 2018a. A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J Spat Sci. 94:1–17
  • Ghorbanzadeh O, Feizizadeh B, Blaschke T. 2017. Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses. Geomat Nat Haz Risk. 9:127–151
  • Ghorbanzadeh O, Feizizadeh B, Blaschke T, Khosravi R. 2018b. Spatially explicit sensitivity and uncertainty analysis for the landslide risk assessment of the gas pipeline networks. Paper presented at: The 21st AGILE conference on Geo-information science., Lund, Sweden,
  • Ghorbanzadeh O, Moslem S, Blaschke T, Duleba S. 2018c. Sustainable urban transport planning considering different stakeholder groups by an interval-AHP decision support model. Sustainability. 11:1–18
  • Ghorbanzadeh O, Rostamzadeh H, Blaschke T, Gholaminia K, Aryal J. 2018d. A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping. Nat Hazards. 94:497–517
  • Glade T, Petschko H, Bell R, Bauer C, Granica K, Heiss G, Leopold P, Pomaroli G, Proske H, Schweigl J. 2012. Landslide susceptibility maps for Lower Austria–Methods and Challenges. Koboltschnig, G., Hübl, J., Braun, J, editors. International Research Society INTERPRAEVENT, vol. 1 Grenoble, France: p. 497–508
  • Gordo C, Zêzere JL, Marques R. 2019. Landslide susceptibility assessment at the basin scale for rainfall- and earthquake-triggered shallow slides. Geosciences 9:1–22
  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T. 2012. Landslide inventory maps: New tools for an old problem. Earth-Sci Rev. 112:42–66
  • Hagenlocher M, Kienberger S, Lang S, Blaschke T. 2014. Implications of spatial scales and reporting units for the spatial modelling of vulnerability to vector-borne diseases GI_Forum 2014:197
  • Haque U, Blum P, da Silva P F, Andersen P, Pilz J, Chalov SR, Malet J-P, Auflič MJ, Andres N, Poyiadji E. et al. 2016. Fatal landslides in Europe. Landslides. 13:1545–1554
  • Höller P. 2009. Avalanche cycles in Austria: an analysis of the major events in the last 50 years. Nat Hazards. 48:399–424
  • Hong H, Pradhan B, Xu C, Tien Bui D. 2015. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena. 133:266–281
  • Kayastha P, Dhital MR, De Smedt F. 2013. Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci. 52:398–408
  • Kienberger S, Lang S, Zeil P. 2009. Spatial vulnerability units - expert-based spatial modelling of socio-economic vulnerability in the Salzach catchment, Austria. Nat Hazards Earth Syst Sci. 9:767–778
  • Lang S, Kienberger S, Tiede D, Hagenlocher M, Pernkopf L. 2014. Geons – domain-specific regionalization of space. Cartography Geo Inf Sci. 41:214–226
  • Lee S, Pradhan B. 2006. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4:33–41
  • Li R, Wang N. 2019. Landslide susceptibility mapping for the muchuan county (China): a comparison between bivariate statistical models (WoE, EBF, and IoE) and their ensembles with logistic regression. Symmetry. 11:762
  • Lima P, Steger S, Glade T, Tilch N, Schwarz L, Kociu A (2017) Landslide susceptibility mapping at national scale: a first attempt for Austria. In: Mikos M, Tiwari B, Yin Y, Sassa K, editors. Advancing Culture of Living with Landslides. , Advances in Landslide Science. Volume 2. Springer, Cham, 2017, 943–951.
  • Linden A. 2006. Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. J Eval Clin Pract. 12:132–139
  • Mahalingam R, Olsen MJ, O’Banion MS. 2016. Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study) Geomatics, Nat Haz Risk. 7:1884–1907
  • Malczewski J. 1999. GIS and multicriteria decision analysis. :John Wiley & Sons, New York
  • Malczewski J. 2006. GIS‐based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci. 20:703–726
  • Meena S, Ghorbanzadeh O, Blaschke T. 2019a. A Comparative study of statistics-based landslide susceptibility models: a case study of the region affected by the Gorkha earthquake in Nepal. ISPRS Int. J. Geo-Inf. 8: 1–23.
  • Meena S, Mishra B, Tavakkoli Piralilou S. 2019b. A hybrid spatial multi-criteria evaluation method for mapping landslide susceptible areas in Kullu Valley, Himalayas. Geosciences. 9: 1–18
  • Persichillo MG, Bordoni M, Meisina C. 2017. The role of land use changes in the distribution of shallow landslides. Sci Total Environ. 574:924–937
  • Petschko H, Brenning A, Bell R, Goetz J, Glade T. 2014. Assessing the quality of landslide susceptibility maps–case study Lower Austria Nat Hazards Earth Syst Sci. 14:95–118
  • Pham BT, Indra P, Khosravi K, Chapi K, Trinh PT. Ngo TQ, Hosseini SV, Bui DT. 2018. A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto Int. 34:1385–1407
  • Pirnazar M, Karimi A, Feizizadeh B, Ostad-Ali-Askari K, Eslamian S, Hasheminasab H, Ghorbanzadeh O, Hamedani M. 2017. Assessing flood hazard using gis based multi-criteria decision making approach; study area: east-azerbaijan province (Kaleybar Chay basin). J flood engr. 8: 203–223
  • Pourghasemi HR, Rahmati O. 2018. Prediction of the landslide susceptibility: Which algorithm, which precision?. Catena. 162:177–192
  • Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A. 2017. Correction to: landslide susceptibility mapping of the sera river Basin using logistic regression model. Nat Hazards. 91:1423–1423
  • Roodposhti MS, Aryal J, Pradhan B. 2019. A Novel Rule-based Approach In Mapping Landslide Susceptibility.Sensors (Basel). 19(10), 2274.
  • Saaty TL. 1980. The analytic process: planning, priority setting, resources allocation New York: McGraw
  • Saaty TL, Vargas LG. 1984. Inconsistency and rank preservation. J Math Psychol. 28:205–214
  • Saaty TL, Vargas LG. 1991. Prediction, projection, and forecasting: applications of the analytic hierarchy process in economics, finance, politics, games, and sports. :Kluwer Academic Pub.
  • Sahnoun H, Serbaji MM, Karray B, Medhioub K. 2011. GIS and multi-criteria analysis to select potential sites of agro-industrial complex. Envi Earth Sci. 66:2477–2489
  • Scaioni M, Longoni L, Melillo V, Papini M. 2014. Remote sensing for landslide investigations: an overview of recent achievements and perspectives. Remote Sens. 6:9600–9652
  • Schicker R, Moon V. 2012. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology. 161-162:40–57
  • Sestraș P, Bilașco Ș, Roșca S, Naș S, Bondrea MV, Gâlgău R, Vereș I, Sălăgean T, Spalević V, Cîmpeanu SM. 2019. Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area. Sustainability. 11:
  • Taha R, Dietrich J, Dehnhardt A, Hirschfeld J. 2019. Scaling effects in spatial multi-criteria decision aggregation in integrated river Basin management. Water 11, 355.
  • Tay LT, Lateh H, Hossain K, KamilAA. 2014. Landslide Hazard Mapping of Penang Island Using Poisson Distribution with Dominant Factors. J Civ Eng Res. 4(3A): 72–77
  • Tiede D, Lang S, Albrecht F, Holbling D. 2010. Object-based class modeling for cadastre-constrained delineation of geo-objects. Photogramm Eng Rem S. 76:193–202
  • Tien Bui D, Khosravi K, Shahabi H, Daggupati P, Adamowski JF, Melesse AM, Pham BT, Pourghasemi HR, Mahmoudi M, Bahrami S. et al. 2019. Flood spatial modeling in Northern Iran using remote sensing and GIS: a comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sens. 11
  • Tsangaratos P, Ilia I. 2016. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena 145:164–179
  • Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera province, Indonesia. Catena. 118:124–135
  • Wang Q, Li W. 2017. A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping. Phys Geogr. 38:318–337
  • Wu Y, Li W, Wang Q, Liu Q, Yang D, Xing M, Pei Y, Yan S. 2016. Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arab. J. Geosci. 9
  • Yalcin A, Bulut F. 2006. Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards. 41:201–226