Abstract
One of the most essential data sets used in landslide susceptibility mapping is the digital elevation model (DEM). As a result, it is critical to figure out how DEM spatial resolution affects the landslide susceptibility model. In this study, the implications of DEM spatial resolution of the advanced spaceborne thermal emission and reflection (ASTER) data source on landslide susceptibility mapping were investigated. The ASTER DEM (30 m) was resampled to 45-, 60-, 75-, and 90-m spatial resolutions. For each DEM, eight landslide-governing factors, and landslide inventory, a set of 10 geodatabases was built using a Geographic Information System. Maps of landslide susceptibility were produced from the statistical relationship of landslide and landslide factors using frequency ratio (FR) and certainty factor (CF) statistical methods, which were classified into very low, low, moderate, high, and very high susceptibility classes. The models’ performance was evaluated using landslide density and area under the curve (AUC) methods for each DEM resolution. A coarser DEM resolution (90 m) offered the best performance and prediction accuracy, according to the predictive curve value of AUC. According to this, coarser DEM resolution produces higher anticipated accuracy in landslide susceptibility mapping than fine resolution, depending on the size of individual landslides in the area. The FR model performed exceptionally well at coarser DEM resolutions (75 and 90). The AUC analysis prediction rate curve value for the FR model varies from 86 to 92%, while the CF model’s prediction rate curve value ranges from 81to 89%, revealing that the models are fairly accurate in predicting future landslide occurrence in the study area. As a result, statistical methodologies (frequency ratio and certainty factor) are acceptable for landslide susceptibility mapping with regard to DEM resolution.
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The corresponding author may provide all of the datasets that were used and analyzed during the current work upon reasonable request.
References
Abraham MT, Satyam N, Jain P, Pradhan B, Alamri A (2021) Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms. Geomatics Nat Hazards Risk 12:3381–3408. https://doi.org/10.1080/19475705.2021.2011791
Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54:1127–1143. https://doi.org/10.1007/s00254-007-0882-8
Anis Z*, Gallala W, Vakhshoori V, Habib S, Gaied ME (2019)GIS-based landslide susceptibility mapping using bivariate statistical methods. Open Geosci 11:708–726.https://doi.org/10.1515/geo-2019-0056
Ayalew L, Yamagishi H (2005) the application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda- Yahiko Mountains, Central Japan. Geomorphology 65:15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Bonham-Carter GF (1994) Geographic information systems for geoscientists. Modeling with GIS. Pergamon, Oxford 398:416
Brock J, Schratz P, Petschko H, Muenchow J, Micu M, Brenning A (2020) The performance of landslide susceptibility models critically depends on the quality of digital elevation models. Geomatics Nat Hazards Risk 11:1075–1092. https://doi.org/10.1080/19475705.2020.1776403
Bui DT, Tuan TA, Hoang ND, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area Vietnam using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14(2):447–458. https://doi.org/10.1007/s10346-016-0711-9
Cama M, Conoscenti C, Lombardo L, Rotigliano E (2016) Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment Sicily, Italy. Environ Earth Sci 75(3):238. https://doi.org/10.1007/s12665-015-5047-6
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazard Earth Syst 13(11):2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
Chandak PG, Sayyed SS, Kulkarni YU, Devtale MK (2016) Landslide hazard zonation mapping using information value method near Parphi village in Garhwal Himalaya. Ljemas 4:228–236
Chen Z, Wang J (2007) Landslide hazard mapping using a logistic regression model in Mackenzie Valley, Canada. Nat Hazard 42(1):75–89. https://doi.org/10.1007/s11069-006-9061-6
Chen Z, Ye F, Fu W, Ke Y, Hong H (2020) The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China. Nat Hazards 101:853–877. https://doi.org/10.1007/s11069-020-03899-9
Chung CJ, Fabbri AG (2012) Systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models. In: Glade T, Anderson MG, Crozier MJ (eds) Landslide hazard and risk. Wiley, New York, pp 139–177. https://doi.org/10.1002/9780470012659.ch4
Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard Mapping. Nat Hazard 30(3):451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
Ciampalini A, Raspini F, Frodella W, Bardi F, Bianchini S, Moretti S (2016) Thethe effectiveness of high-resolution LiDAR data combined with PSInSAR data in landslide study. Landslides 132:399–410
Corominas J, Van Westen C, Frattini P et al (2014) Recommendations or the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228. https://doi.org/10.1016/S0169-555X(01)00087-3
Das G, Lepcha K (2019) Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India. SN Appl Sci 1:1453. https://doi.org/10.1007/s42452-019-1499-8
Dietrich WE, Montgomery DR (1998) SHALSTAB: a digital terrain model for mapping shallow landslide potential. NCASI National Council of the Paper Industry for Air and Stream Improvement Technical Report 1998
Donati L, Turrini MC (2002) Anan objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Eng Geol 63:277–289. https://doi.org/10.1016/S0013-7952(01)00087-4
Dou J, Oguchi T, Hayakawa YS, Uchiyama S, Saito H, Paudel U (2014) GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu area, central Japan. In: Landslide Science for a Safer Geoenvironment. Springer International Publishing, pp 419–424. https://doi.org/10.1007/978-3-319-05050-8_65
Duman TY, Can T, Gokceoglu C, Nefesliogocu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmee area, Istanbul, Turkey. Verlag 51:2412–256
Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard, and risk zoning for land-use planning, joint technical committee (JTC-1) on landslides and engineered slopes. Eng Geol 102(3–4):85–98. https://doi.org/10.1016/j.enggeo.2008.03.022
Gaidzik K, Ramírez-Herrera MT (2021) The importance of input data on landslide susceptibility mapping. Sci Rep 11:19334. https://doi.org/10.1038/s41598-021-98830-y
Gorsevski PV, Gessler P, Foltz RB (2000) Spatial prediction of landslide hazard using discriminant analysis and GIS. In: GIS in the Rockies 2000 Conference and Workshop 10
Haoyuan H, Chen W, Chong Xu, Youssef AM, Pradhan B, Bui DT (2016) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int 32(2):139–154. https://doi.org/10.1080/10106049.2015.1130086
Heckeman (1986) Probabilistic interpretation of MYCIN’s certainty factors. In: Kanal LN, Lemmer JF (eds) Uncertainty in artificial intelligence. Elsevier, New York, pp 298–311
Jia N, Xie M, Mitani Y, Ikemi H, Djamaluddin I (2010) A GIS-based spatial data processing system for slope monitoring. Int Geoinf Res Dev J 1(4)
Kamiński M (2020) The impact of quality of digital elevation models on the result of landslide susceptibility modeling using the method of weights of evidence. Geosciences 10(12):488. https://doi.org/10.3390/geosciences10120488
Kanungo DP, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor south-facing, and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards 59(3):1491–1512. https://doi.org/10.1007/s11069-011-9847-z
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2009) Landslide susceptibility zonation mapping a review. J South Asia Disaster Stud 2:81–105
Karimi NS, Ranjbar H, Akbar S (2010) Susceptibility assessment of the terrain for slope failure using remote sensing and GIS, a case study of Maskoon area, Iran. Int Geoinf Res Dev J 1(3)
Kifle W (2013) Review of the occurrences and influencing factors of landslides in the highlands of Ethiopia with implications for infrastructural development. Momona Ethiopian J Sci 5(1):3. https://doi.org/10.4314/mejs.v5i1.85329
Kouhpeima S, Feizniab H, Ahmadib, Moghadamniab AR (2017) Landslide susceptibility mapping using logistic regression analysis in Latyan catchment. Desert 22(1):85–95
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41. https://doi.org/10.1007/s10346-006-0047-y
Lee S, Choi J, Woo I (2004) The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun. Geosci J 8(1):51–60. https://doi.org/10.1007/BF02910278
Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115(6):661–667. https://doi.org/10.1007/s12040-006-0004-0
Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. J Environ Geol 47:982–990. https://doi.org/10.1007/s00254-005-1228-z
Lee MF, Wang SF, Lin TC (2010) The effect of spatial resolution on landslide mapping—a case study in Chi-Shan river basin, Taiwan. In: 31st Asian conference on remote sensing 2010 (ACRS 2010)
Li J, Zhou CH (2003) Appropriate grid size for terrain-based landslide risk assessment in Lantau Island, Hong Kong. J Remote Sens (Beijing) 7(2):86–92
Liu M, Chen X, Yang S (2014) Collapse landslide and mudslide hazard zonation; In: Landslide science for a safer geoenvironmental. Springer International Publishing, pp 457–462. https://doi.org/10.1007/978-3-319-050508
Mahalingam R, Olsen MJ (2016) Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping. Geomatics Nat Hazards Risk 7(6):1835–1855. https://doi.org/10.1080/19475705.2015.1115431
Mahalingam R, Olsen MJ, O’Banion MS (2016) Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors, Oregon case study. Geomatics Nat Hazards Risk 7(6):1884–1907. https://doi.org/10.1080/19475705.2016.1172520
Mandal S, Mondal S (2019) Probabilistic approaches and landslide susceptibility. Geoinformatics and modeling of landslide susceptibility and risk. Environ Sci Eng. Springer book series (ESE). 145–163. https://doi.org/10.1007/978-3-030-10495-56
Meten M, Bhandary NP, Yatabe R (2015) GIS-based frequency ratio and logistic regression modeling for landslide susceptibility mapping of Debre Sina area in central Ethiopia. J Mountain Sci 12(6):1355–1372. https://doi.org/10.1007/s11629-015-3464-3
Mezughi TH, Akhir JM, Rafek AG, Abdullah I (2011) Landslide susceptibility assessment using frequency ratio model applied to an area along the E-W Highway (Gerik-Jeli). Am J Environ Sci 7:43–50
Mohammad M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of evidence models. J Asian Earth Sci 61:22136. https://doi.org/10.1016/j.jseaes.2012.10.005
Oh HJ, Lee S, Wisut C, Kim CH, Kwon JH (2009) Predictive landslide susceptibility mapping using spatial information in the Pechabun Area of Thailand. Environ Geol 57:641–651. https://doi.org/10.1007/s00254-008-1342-9
Palamakumbure D, Flentje P, Stirling D (2015) Consideration of optimal pixel resolution in deriving landslide susceptibility zoning within the Sydney Basin, New South Wales, Australia. Comput Geosci 82:13–22. https://doi.org/10.1016/j.cageo.2015.05.002
Paudel U, Oguchi T, Hayakawa Y-R (2016) Landslide susceptibility analysis using a DEM and random forest. Int J Geosci 07:726–743. https://doi.org/10.4236/ijg.2016.75056
Penna D, Borga M, Aronica GT, Brigandì G, Tarolli P (2014) The influence of grid resolution on the prediction of natural and road-related shallow landslides. Hydrol Earth Syst Sci 18(6):2127–2139. https://doi.org/10.5194/hess-18-2127-2014
Pourghasemi HR, Pradhan B, Gokceoglu C, MohammadiMoradi MHR (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365. https://doi.org/10.1007/s12517-012-0532-7
Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012) Application of weights-of evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. https://doi.org/10.1007/s12517-012-0532-7
Pradhan B, Chaudhari A, Adinarayana J, Buchroithner MF (2012) Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia. Environ Monit Assess 184(2):715–727. https://doi.org/10.1007/s10661-011-1996-8
Pradhan B, Lee S, Buchroithner MF (2010) Remote sensing and GIS-based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model. Photogramm Fernerkun 1:17–32. https://doi.org/10.1127/14328364/2010/0037
Pradhan B, Mansor S, Pirasteh S, Buchroithner M (2011) Landslide hazard and risk analyses at a landslide-prone catchment area using the statistical-based geospatial model. Int J Remote Sens 32(14):4075–4087. https://doi.org/10.1080/01431161.2010.484433
Pradhan B, Sameen MI (2017) Effects of the spatial resolution of digital elevation models and their products on landslide susceptibility mapping. In: Pradhan B (ed) Laser Scanning Applications in Landslide Assessment. Springer, Cham, pp 133–150. https://doi.org/10.1007/978-3-319-55342-9_7
Qiqing W, Guo Y, Li W, He J, Zhiyong Wu (2019) Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor. Geomatics Nat Hazards Risk 10(1):820–835. https://doi.org/10.1080/19475705.2018.1549111
Regmi AD, Yoshida K, Pourghasemi HR, Dhital MR, Pradhan B (2014) Landslide susceptibility mapping along Bhalubang-Shiwapur area of mid-western Nepal using frequency ratio and conditional probability models. J Mt Sci 11(5):1266–1285. https://doi.org/10.1007/s11629-013-2847-6
Roy J, Saha S (2019) Landslide susceptibility mapping using knowledge-driven statistical models in Darjeeling District, West Bengal India. Geoenviron Disasters 6:11. https://doi.org/10.1186/s40677-019-0126-8
Saha AK, Gupta RP, Sarkar I, Arora KM, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation with a case study in the Himalayas. Landslides 2(1):61–69. https://doi.org/10.1007/s10346-004-0039-8
Sarkar S, Kanungo D, Ptra A, Kumar P (2006) Disaster mitigation of debris flow, slope failure, and landslides. GIS-based landslide susceptibility case study in Indian Himalaya. Universal Acadamy Press, Tokyo. pp 617–624
Sarkar S, Rjan Martha T, Roy A (2013) Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. Geol Soc India 82:351–362. https://doi.org/10.1007/s12594-013-0162-z
Schlögel R, Marchesini I, Alvioli M, Reichenbach P, Rossi M, Malet JP (2018) Optimizing landslide susceptibility zonation: effects of dem spatial resolution and slope unit delineation on logistic regression models. Geomorphology 301:10–20. https://doi.org/10.1016/j.geomorph.2017.10.018
Shortliffe EH, Buchanan BG (1975) A model of inexact reasoning in medicine. Math Biosci 23(3):351–379. https://doi.org/10.1016/0025-5564(75)90047-4
Silalahi FES*, Pamela, Arifianti Y, Hidayat F (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosci Lett 6:10.https://doi.org/10.1186/s40562-019-0140-4
Sujatha ER, Rajamanickam GV, Kumaravel P (2012) Landslide susceptibility analysis using probabilistic certainty factor approach: a case study on Tevankarai stream watershed, India. J Earth Syst Sci 121(5):1337–1350
Tarolli P, Tarboton DG (2006) A new method for determination of most likely landslide initiation points and the evaluation of digital terrain model scale in terrain stability mapping. Hydrol Earth Syst Sci 10(5):663–677. https://doi.org/10.5194/hess-10-663-2006
Tian Y, Xiao C, Liu Y, Wu L (2008) Effects of raster resolution on landslide susceptibility mapping: a case study of Shenzhen. Sci China Ser E 51(2):188–198
Van Westen CJ, Castellanos E, Kuriakose SL (2008) spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
Varnes DJ (1984) Landslide hazard zonation, a review of principles and practice, International Association of Engineering Geology Commission on Landslides and Other Mass Movements on Slopes., UNESCO, Paris 63
Wubalem A (2020) Modeling of Landslide susceptibility in a part of Abay Basin, northwestern Ethiopia. Open Geosci 12(1):1440–1467. https://doi.org/10.1515/geo-2020-0206
Wubalem A (2021) Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia. Geoenviron Disasters 8(1):1–21. https://doi.org/10.1186/s40677-020-00170-y
Wubalem A*, Meten M (2020) Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci 2:807. https://doi.org/10.1007/s42452-020-2563-0
Yalcin A, Reis S, Aydinoglu A, Yomralioglu T (2011) AGIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics, and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85:274–287. https://doi.org/10.1016/j.catena.2011.01.014
Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: A comparison of logistic regression and neural networks method in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266. https://doi.org/10.1016/j.enggeo.2005.02.002
Yilmaz I, Keskin I (2009) GIS-based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bull Eng Geol Environ 68:459–471. https://doi.org/10.1007/s10064-009-0188-z
Zine El Abidine R, Abdel Mansour N (2019) Landslide susceptibility mapping using information value and frequency ratio for the Arzew sector (Northwestern of Algeria). Bull Miner Res Explor 160:197–211. https://doi.org/10.19111/bulletinofmre.502343
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The author expresses my gratitude to the almighty God for allowing me to complete this study project .Also, the author expresses gratitude to his wonderful family for their invaluable assistance and guidance throughout this research project. Finally, the author expresses gratitude to the University of Gondar, the National Meteorological Agency employees, the Ethiopian Geological Survey, the Disaster Head Office, the Natural Resource Management Head Office, and the rural community for their invaluable information.
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Wubalem, A. The impact of DEM resolution on landslide susceptibility modeling. Arab J Geosci 15, 967 (2022). https://doi.org/10.1007/s12517-022-10241-z
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DOI: https://doi.org/10.1007/s12517-022-10241-z