Abstract
Landslides pose a threat to life and infrastructure and are influenced by anthropogenic modifications associated with land development. These modifications can affect susceptibility to landslides, and thus quantifying their influence on landslide occurrence can help design sustainable development efforts. Although landslide susceptibility has been shown to increase following urban expansion, the long-lasting effect of urbanization on landslide susceptibility remains largely unquantified. Hence, susceptibility maps developed based on inventories from non-urbanized areas may incorrectly evaluate the hazard in urbanized areas. To quantify this effect, we analyzed a landslide inventory from southwestern Pennsylvania, where the pulse of urbanization occurred more than a decade before the inventory was created. Using road density as a proxy for urbanization, the study area was divided into urbanized and non-urbanized areas. Susceptibility patterns were computed using statistical analyses of a post-urbanization landslide inventory together with maps of topographic, land cover, and geologic factors. A pre-urbanization landslide inventory was used as a control. Our findings indicate that urbanization has a decades-long effect on landslide susceptibility, where urbanized areas are generally more susceptible to landslides. In urbanized areas landslides are strongly associated with distance from roads and topographic curvature, whereas in non-urbanized landslides are strongly associated with stratigraphic formation and distance from streams. The consistent differences in susceptibility patterns between urbanized and non-urbanized areas indicate that urbanization has a long-lasting effect on landslide susceptibility and that susceptibility estimates should be made separately for these different environments to account for the persistent influence of urbanization.
Similar content being viewed by others
Data availability
Data that support the findings of this study are available from the corresponding author (Tyler Rohan), upon reasonable request.
References
Ackenheil AC (1955) A soil mechanics and engineering geology analysis of landslides in the area of Pittsburgh, Pennskylvania (Doctoral dissertation, University of Pittsburgh). Available from ProQuest Dissertations & Theses Global database. (UMI No. 301997212)
Alexander D (1986) Landslide damage to buildings. Environ Geol Water Sci 8(3):147–151. https://doi.org/10.1007/BF02509902
Archer KJ (2010) rpartOrdinal: an R package for deriving a classification tree for predicting an ordinal response. J Stat Softw 34:7. https://doi.org/10.18637/jss.v034.i07
Ashland FX (2021) Critical shallow and deep hydrologic conditions associated with widespread landslides during a series of storms between February and April 2018 in Pittsburgh and vicinity, western Pennsylvania, USA. Landslides 18(6):2159–2174. https://doi.org/10.1007/s10346-021-01665-x
Ávila FF, Alvalá RC, Mendes RM, Amore DJ (2021) The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models. Nat Hazards 105(1):1139–1161. https://doi.org/10.1007/s11069-020-04324-x
Bernardie S, Vandromme R, Thiery Y, Houet T, Grémont M, Masson F, Grandjean G, Bouroullec I (2021) Modelling landslide hazards under global changes: the case of a Pyrenean valley. Nat Hazard 21(1):147–169. https://doi.org/10.5194/nhess-21-147-2021
BeVille SH, Mirus BB, Ebel BA, Mader GG, Loague K (2010) Using simulated hydrologic response to revisit the 1973 Lerida Court landslide. Environ Earth Sci 61(6):1249–1257. https://doi.org/10.1007/s12665-010-0448-z
Bogaard TA, Greco R (2016) Landslide hydrology: from hydrology to pore pressure. Wiley Interdiscip Rev Water 3(3):439–459. https://doi.org/10.1002/wat2.1126
Braun A, Urquia ELG, Lopez RM, Yamagishi H (2019) Landslide susceptibility mapping in Tegucigalpa, Honduras, using data mining methods. In IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018-Volume 1 (pp. 207–215). Springer, Cham. https://doi.org/10.1007/978-3-319-93124-1_25
Briggs RP, Pomeroy JS, Davies WE (1975) Landsliding in Allegheny County, Pennsylvania. U.S. Geological Survey Circular 728:18. https://doi.org/10.3133/cir728
Cantarino I, Carrion MA, Goerlich F, Ibañez VM (2019) A ROC analysis-based classification method for landslide susceptibility maps. Landslides 16(2):265–282. https://doi.org/10.1007/s10346-018-1063-4
Cascini L, Bonnard C, Corominas J, Jibson R, Montero-Olarte J (2005) Landslide hazard and risk zoning for urban planning and development. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) Landslide risk management. CRC Press, London, pp 209–246
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazard 13(11):2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
Chen CY, Huang WL (2013) Land use change and landslide characteristics analysis for community-based disaster mitigation. Environ Monit Assess 185(5):4125–4139. https://doi.org/10.1007/s10661-012-2855-y
Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad BB (2019) Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena 172:212–231. https://doi.org/10.1016/j.catena.2018.08.025
Costanzo D, Irigaray C (2020) Comparing forward conditional analysis and forward logistic regression methods in a landslide susceptibility assessment: a case study in Sicily. Hydrology 7(3):37. https://doi.org/10.3390/hydrology7030037
Cruden DM, Varnes DJ (1996) Landslide types and processes. Transportation Research Board, U.S. National Academy of Sciences, Special Report 247:36–75
Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88(11):2783–2792. https://doi.org/10.1890/07-0539.1
Dai F, Lee CF (2002) Landslides on natural terrain. Mt Res Dev 22(1):40–47. https://doi.org/10.1659/0276-4741(2002)022[0040:LONT]2.0.CO;2
Davis JC, Chung CJ, Ohlmacher GC (2006) Two models for evaluating landslide hazards. Comput Geosci 32(8):1120–1127. https://doi.org/10.1016/j.cageo.2006.02.006
Dragićević S, Lai T, Balram S (2015) GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat Int 45(2):114–125. https://doi.org/10.1016/j.habitatint.2014.06.031
Ebel BA, Rengers FK, Tucker GE (2015) Aspect-dependent soil saturation and insight into debris-flow initiation during extreme rainfall in the Colorado Front Range. Geology 43(8):659–662. https://doi.org/10.1130/G36741.1
Fell R, Ho KK, Lacasse S, Leroi E (2005) A framework for landslide risk assessment and management. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) Landslide risk management. CRC Press, London, pp 13–36
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232. https://doi.org/10.1214/aos/1013203451
Frodella W, Ciampalini A, Bardi F, Salvatici T, Di Traglia F, Basile G, Casagli N (2018) A method for assessing and managing landslide residual hazard in urban areas. Landslides 15(2):183–197. https://doi.org/10.1007/s10346-017-0875-y
Giarratani F, Houston DB (1989) Structural change and economic policy in a declining metropolitan region: implications of the Pittsburgh experience. Urban Stud 26(6):549–558. https://doi.org/10.1080/00420988920080661
Gibbons JD, Chakraborti S (2014) Nonparametric statistical inference. In: Lovric M (ed) International encyclopedia of statistical science (pp. 977–979), Springer, Berlin. https://doi.org/10.1007/978-3-642-04898-2_420
Glade T (2003) Vulnerability assessment in landslide risk analysis. Erde 134(2):123–146
Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10(3):395–415. https://doi.org/10.1111/j.1467-9671.2006.01004.x
Gray RE, Hamel JV, Adams WR (2011) Landslides in the vicinity of Pittsburgh, Pennsylvania. In: Ruffolo RM, Ciampaglio CN (eds) GSA field guide 20: from the shield to the sea (pp. 61–85), Geological Society of America, Boulder, CO. https://doi.org/10.1130/2011.0020(04)
Hamel JV, Flint NK (1972) Failure of colluvial slope. J Soil Mech Found Div 98(2):167–180. https://doi.org/10.1061/JSFEAQ.0001736
Hastie T, Tibshirani R, Friedman J (2009) Random forests. In: Hastie T, Tibshirani R, Friedman J (eds) The elements of statistical learning, 2nd edition (pp. 587–604). Springer, New York, NY. https://doi.org/10.1007/978-0-387-84858-7_15
He Y, Lee E, Warner TA (2017) A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sens Environ 199:201–217. https://doi.org/10.1016/j.rse.2017.07.010
Istanbulluoglu E, Bras RL (2005) Vegetation-modulated landscape evolution: effects of vegetation on landscape processes, drainage density, and topography. J Geophys Res Earth Surf 110(F2):F02012. https://doi.org/10.1029/2004JF000249
Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910. https://doi.org/10.1029/2000WR900090
Johnston EC, Davenport FV, Wang L, Caers JK, Muthukrishnan S, Burke M, Diffenbaugh NS (2021) Quantifying the effect of precipitation on landslide hazard in urbanized and non‐urbanized areas. Geophys Res Lett 48(16):e2021GL094038. https://doi.org/10.1029/2021GL094038
Kafy AA, Rahman MS, Ferdous L (2017) Exploring the association of land cover change and landslides in the Chittagong hill tracts (CHT): a remote sensing perspective. In Proceedings of the International Conference on Disaster Risk Management, Dhaka, Bangladesh (Vol. 23)
Kim JC, Lee S, Jung HS, Lee S (2018) Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang. Korea Geocarto Int 33(9):1000–1015. https://doi.org/10.1080/10106049.2017.1323964
Kumar SV, Bhagavanulu DVS (2008) Effect of deforestation on landslides in Nilgiris district—a case study. J Indian Soc Remote Sens 36(1):105–108. https://doi.org/10.1007/s12524-008-0011-5
Lee CF, Huang WK, Chang YL, Chi SY, Liao WC (2018) Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan. Geomorphology 300:113–127. https://doi.org/10.1016/j.geomorph.2017.10.019
Lee G, Kim M (2016) Shallow landslide assessment considering the influence of vegetation cover. J Korean GEO-environmen Soc 17(4):17–31. https://doi.org/10.14481/jkges.2016.17.4.17
Lessing P, Kulander BR, Wilson BD, Dean SL, Woodring SM (1976) West Virginia landslides and slide-prone areas. West Virginia Geol Economic Survey Environ Geol Bull EGB-15a, 64 pp. (1:24,000 scale, 28 maps on 27 sheets)
Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22
Marjanović M (2013) Comparing the performance of different landslide susceptibility models in ROC space. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice (pp. 579–584). Springer, Berlin. https://doi.org/10.1007/978-3-642-31325-7_76
Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: an applied review. Int J Remote Sens 39(9):2784–2817. https://doi.org/10.1080/01431161.2018.1433343
McAdoo BG, Quak M, Gnyawali KR, Adhikari BR, Devkota S, Rajbhandari PL, Sudmeier-Rieux K (2018) Roads and landslides in Nepal: how development affects environmental risk. Nat Hazard 18(12):3203–3210. https://doi.org/10.5194/nhess-18-3203-2018
McGuire LA, Rengers FK, Kean JW, Coe JA, Mirus BB, Baum RL, Godt JW (2016) Elucidating the role of vegetation in the initiation of rainfall-induced shallow landslides: insights from an extreme rainfall event in the Colorado Front Range. Geophys Res Lett 43(17):9084–9092. https://doi.org/10.1002/2016GL070741
Merghadi A, Yunus AP, Dou J, Whiteley J, Thai Pham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225
Miles CE, Whitfield GT, and others (2001) Bedrock geologic units of Pennsylvania, scale 1:250,000. based on: Berg TM, Edmunds WE, Geyer AR, Glover AD, Hoskins DM, MacLachlan DB, Root SI, Sevon WD, Socolow AA (1980). Geologic map of Pennsylvania, scale 1:250000. Pennsylvania Geological Survey, Map 1
Mirus BB, Ebel BA, Loague K, Wemple BC (2007) Simulated effect of a forest road on near-surface hydrologic response: redux. Earth Surf Proc Land 32(1):126–142. https://doi.org/10.1002/esp.1387
Mirus BB, Jones ES, Baum RL, Godt JW, Slaughter S, Crawford MM, Lancaster J, Stanley T, Kirschbaum DB, Burns WJ, Schmitt RG, Lindsey KO, McCoy KM (2020) Landslides across the USA: occurrence, susceptibility, and data limitations. Landslides 17:2271–2285. https://doi.org/10.1007/s10346-020-01424-4
National Oceanic and Atmospheric Administration, National Centers for Environmental Information (2021) Precipitation Frequency Data Server. Available at https://hdsc.nws.noaa.gov/hdsc/pfds/. (Accessed April 2021)
Okagbue CO (1986) An investigation of landslide problems in spoil piles in a strip coal mining area, West Virginia (USA). Eng Geol 22(4):317–333. https://doi.org/10.1016/0013-7952(86)90002-5
Ozdemir A (2009) Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Environ Geol 57(7):1675–1686. https://doi.org/10.1007/s00254-008-1449-z
Papathoma-Köhle M, Glade T (2013) The role of vegetation cover change for landslide hazard and risk. In: Renaud G, Sudmeier-Rieux K, Estrella M (eds) The role of ecosystems in disaster risk reduction. UNU-Press, Tokyo, pp 293–320
Pennsylvania Spatial Data Access (PASDA) (2007) PAMAP program—roads. Available at https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=8. (Accessed April 2021)
Pennsylvania Spatial Data Access (PASDA) (2000) Fractional vegetation cover for southwest Pennsylvania, 2000 Available at https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=357. (Accessed April 2021)
Pennsylvania Spatial Data Access (PASDA) (2017) Previously active documented landslides in southwestern Pennsylvania. Available at https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=1622. (Accessed April 2021)
Pfeil-McCullough E, Bain DJ, Bergman J, Crumrine D (2015) Emerald ash borer and the urban forest: changes in landslide potential due to canopy loss scenarios in the City of Pittsburgh, PA. Sci Total Environ 536:538–545. https://doi.org/10.1016/j.scitotenv.2015.06.145
Pham BT, Nguyen-Thoi T, Qi C, Van Phong T, Dou J, Ho LS, Van Le H, Prakash I (2020) Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena 195:104805. https://doi.org/10.1016/j.catena.2020.104805
Pisano L, Zumpano V, Malek Ž, Rosskopf CM, Parise M (2017) Variations in the susceptibility to landslides, as a consequence of land cover changes: a look to the past, and another towards the future. Sci Total Environ 601:1147–1159. https://doi.org/10.1016/j.scitotenv.2017.05.231
Pomeroy JS (1977) Preliminary reconnaissance map showing landslides in Butler County, Pennsylvania. U.S. Geological Survey Open-File Report 77–246:3 pp. https://doi.org/10.3133/ofr77246
Pomeroy JS (1982) Landslides in the greater Pittsburgh region, Pennsylvania. U.S. Geological Survey Professional Paper 1229:48 pp., 12 plates. https://doi.org/10.3133/pp1229
Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed. Iran Nat Hazards 63(2):965–996. https://doi.org/10.1007/s11069-012-0217-2
Rappaport J (2003) U.S. urban decline and growth, 1950 to 2000. Econ Rev 88(3):15–44
Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742. https://doi.org/10.1007/s12517-012-0807-z
Reichenbach P, Mondini AC, Rossi M (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manage 54(6):1372–1384. https://doi.org/10.1007/s00267-014-0357-0
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12:77. https://doi.org/10.1186/1471-2105-12-77
Rohan TJ, Wondolowski N, Shelef E (2021) Landslide susceptibility analysis based on citizen reports. Earth Surf Proc Land 46(4):791–803. https://doi.org/10.1002/esp.5064
Running SW, Nemani RR, Hungerford RD (1987) Extrapolation of synoptic meteorological data in mountainous terrain and its use for simulating forest evapotranspiration and photosynthesis. Can J Res 17(6):472–483. https://doi.org/10.1139/x87-081
Sassa K, Wang G, Fukuoka H, Wang F, Ochiai T, Sugiyama M, Sekiguchi T (2004) Landslide risk evaluation and hazard zoning for rapid and long-travel landslides in urban development areas. Landslides 1(3):221–235. https://doi.org/10.1007/s10346-004-0028-y
Schwanghart W, Scherler D (2014) TopoToolbox 2–MATLAB-based software for topographic analysis and modeling in Earth surface sciences. Earth Surf Dyn 2(1):1–7. https://doi.org/10.5194/esurf-2-1-2014
Senanayake S, Pradhan B, Huete A, Brennan J (2020) Assessing soil erosion hazards using land-use change and landslide frequency ratio method: a case study of Sabaragamuwa Province. Sri Lanka Remote Sens 12(9):1483. https://doi.org/10.3390/rs12091483
Shelef E, Hilley GE (2014) Symmetry, randomness, and process in the structure of branched channel networks. Geophys Res Lett 41(10):3485–3493. https://doi.org/10.1002/2014GL059816
Sidle RC, Ziegler AD, Negishi JN, Nik AR, Siew R, Turkelboom F (2006) Erosion processes in steep terrain—truths, myths, and uncertainties related to forest management in Southeast Asia. For Ecol Manage 224(1–2):199–225. https://doi.org/10.1016/j.foreco.2005.12.019
Simon N, Crozier M, de Roiste M, Rafek AG, Roslee R (2015) Time series assessment on landslide occurrences in an area undergoing development. Singap J Trop Geogr 36(1):98–111. https://doi.org/10.1111/sjtg.12096
Smyth CG, Royle SA (2000) Urban landslide hazards: incidence and causative factors in Niterói, Rio de Janeiro State. Brazil Appl Geogr 20(2):95–118. https://doi.org/10.1016/S0143-6228(00)00004-7
Soma AS, Kubota T (2017) The performance of land use change causative factor on landslide susceptibility map in Upper Ujung-Loe Watersheds South Sulawesi, Indonesia. Geoplan: J Geomatics Plan 4(2):157–170. https://doi.org/10.14710/geoplanning.4.2.157-170
Tarolli P, Sofia G (2016) Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology 255:140–161. https://doi.org/10.1016/j.geomorph.2015.12.007
Theodorou P, Baltz LM, Paxton RJ, Soro A (2021) Urbanization is associated with shifts in bumblebee body size, with cascading effects on pollination. Evol Appl 14(1):53–68. https://doi.org/10.1111/eva.13087
Tubbs DW (1974) Landslides in Seattle. Department Natural Resources
U. S. Geological Survey EROS Data Center (1999) National elevation dataset 10 meter 7.5x7.5 minute quadrangle for Pennsylvnia. National Cartography & Geospatial Center
Van Beek LPH, Van Asch TW (2004) Regional assessment of the effects of land-use change on landslide hazard by means of physically based modelling. Nat Hazards 31(1):289–304. https://doi.org/10.1023/B:NHAZ.0000020267.39691.39
Van Den Eeckhaut M, Poesen J, Govers G, Verstraeten G, Demoulin A (2007) Characteristics of the size distribution of recent and historical landslides in a populated hilly region. Earth Planet Sci Lett 256(3–4):588–603. https://doi.org/10.1016/j.epsl.2007.01.040
Vieira BC, Fernandes NF (2004) Landslides in Rio de Janeiro: the role played by variations in soil hydraulic conductivity. Hydrol Process 18(4):791–805. https://doi.org/10.1002/hyp.1363
Waltz JP (1971) An analysis of selected landslides in Alameda and Contra Costa Counties, California. Bull Assoc Eng Geol 8(2):153–163
Wang F, Yin Y, Huo Z, Zhang Y, Wang G, Ding R (2013) Slope deformation caused by water-level variation in the Three Gorges Reservoir, China. In: Sassa K, Rouhban B, Briceño S, McSaveney M, He B (eds) Landslides: global risk preparedness (pp. 227–237). Springer, Berlin. https://doi.org/10.1007/978-3-642-22087-6_15
Wang H, Zhang L, Yin K, Luo H, Li J (2021) Landslide identification using machine learning. Geosci Front 12(1):351–364. https://doi.org/10.1016/j.gsf.2020.02.012
Wasowski J (1998) Understanding rainfall-landslide relationships in man-modified environments: a case-history from Caramanico Terme. Italy Environ Geol 35(2):197–209. https://doi.org/10.1007/s00254005030
Wasowski J, Lamanna C, Casarano D (2010) Influence of land-use change and precipitation patterns on landslide activity in the Daunia Apennines, Italy. Q J Eng Geol Hydrogeol 43(4):387–401. https://doi.org/10.1144/1470-9236/08-101
Wasowski J, Pisano L (2020) Long-term InSAR, borehole inclinometer, and rainfall records provide insight into the mechanism and activity patterns of an extremely slow urbanized landslide. Landslides 17(2):445–457. https://doi.org/10.1007/s10346-019-01276-7
Winant G (2021) The next shift. In The next shift. Harvard University Press
Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146:70–80. https://doi.org/10.1016/j.geomorph.2011.12.040
Yang L, Jin S, Danielson P, Homer C, Gass L, Bender SM, Case A, Costello C, Dewitz J, Fry J, Funk M, Granneman B, Liknes GC, Rigge M, Xian G (2018) A new generation of the united states national land cover database: requirements, research priorities, design, and implementation strategies. ISPRS J Photogramm Remote Sens 146:108–123. https://doi.org/10.1016/j.isprsjprs.2018.09.006
Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836. https://doi.org/10.1007/s12665-009-0394-9
Zêzere JL, Ferreira AB, Rodrigues ML (1999) Landslides in the North of Lisbon Region (Portugal): conditioning and triggering factors. Phys Chem Earth Part A 24(10):925–934. https://doi.org/10.1016/S1464-1895(99)00137-4
Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area. China Environ Earth Sci 76(11):405. https://doi.org/10.1007/s12665-017-6731-5
Zhou C, Cao Y, Yin K, Wang Y, Shi X, Catani F, Ahmed B (2020) Landslide characterization applying Sentinel-1 images and InSAR technique: the Muyubao landslide in the three gorges reservoir area. China Remote Sens 12(20):3385. https://doi.org/10.3390/rs12203385
Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG Proceedings: Health Care and Life Sciences, Baltimore, Maryland 9 pp
Zope PE, Eldho TI, Jothiprakash V (2016) Impacts of land use–land cover change and urbanization on flooding: a case study of Oshiwara River Basin in Mumbai, India. Catena 145:142–154. https://doi.org/10.1016/j.catena.2016.06.009
Acknowledgements
Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Funding
The authors would like to thank both the Heinz and Andrew Mellon Foundations for providing the funding to accomplish this project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Rohan, T., Shelef, E., Mirus, B. et al. Prolonged influence of urbanization on landslide susceptibility. Landslides 20, 1433–1447 (2023). https://doi.org/10.1007/s10346-023-02050-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10346-023-02050-6