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Natural Hazards

, Volume 97, Issue 3, pp 1127–1149 | Cite as

Shalstab mathematical model and WorldView-2 satellite images to identification of landslide-susceptible areas

  • Téhrrie KönigEmail author
  • Hermann J. H. Kux
  • Rodolfo M. Mendes
Original Paper
  • 46 Downloads

Abstract

Natural hazards, occurring all over the world, may become a disaster when humans and nature interact. In Brazil, landslides triggered by heavy rainfall are the most common phenomenon that affects the population. Due to the economic and social losses and deaths, the identification and monitoring of risk areas are extremely important. Therefore, this study aims to identify the landslide-susceptible areas in Vila Albertina and Britador neighborhood, located in Campos do Jordão city in São Paulo state, Brazil. Using the Shalstab mathematical model, which analyzes the slope stability, and satellite images from WorldView-2 sensor with data mining techniques, it was identified the most susceptible areas for this phenomenon and the main characteristics of human occupation that might induce landslides. To achieve this goal, three scenarios were simulated for each neighborhood, changing the values of the geotechnical parameters, used as input on Shalstab. The results of susceptibility areas were consistent with the reality observed in these neighborhoods and the landslide scars corroborate with the assumption that anthropic changes induce landslides. The satellite image allowed the identification of different types of human interaction and its changes in steep slope areas.

Keywords

Landslide Susceptibility Shalstab WorldView-2 Data mining 

Notes

Acknowledgements

The authors thank Mr. Devon Libby, from Digital Globe, for kindly providing the WorldView-2 images used in this study.

References

  1. Ahrendt A (2005) Movimentos de Massa Gravitacionais—Proposta de um Sistema de Previsão: Aplicação na Área Urbana de Campos do Jordão—SP. Thesis (Doutorado), Escola de Engenharia de São Carlos, Universidade de São Paulo, São CarlosGoogle Scholar
  2. Araújo EHG, Kux HJH, Florenzano TG (2008) Ortorretificação de imagens de satélite QuickBird para aplicações urbanas. Revista Brasileira de Cartografia 60(2):205–213Google Scholar
  3. Baum RL, Savage WZ, Godt JW (2008) TRIGRS—A Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis. US Geological Survey Open-file report 02-0424. https://pubs.usgs.gov/of/2008/1159/. Accessed 14 May 2018
  4. Bortolozo CA, Motta MFB, Andrade MRM, Lavalle LV, Mendes RM, Simões SJC, Mendes TSG, Pampuch LA (2019) Combined analysis of electrical and electromagnetic methods with geotechnical soundings and soil characterization as applied to a landslide study in Campos do Jordão City, Brazil. J Appl Geophys 161:1–14CrossRefGoogle Scholar
  5. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process. Landf 16:427–445CrossRefGoogle Scholar
  6. Centro Nacional de Monitoramento e Alerta de Desastres Naturais—CEMADEN (2017) Histórico de criação do Cemaden. https://www.cemaden.gov.br/historico-da-criacao-do-cemaden/. Accessed 24 Jan 2017
  7. Cheng K, Wei C, Chang S (2004) Locating landslides using multitemporal satellite images. Adv Space Res 33:296–301CrossRefGoogle Scholar
  8. Cruden DM, Varnes DJ (1996) Landslides types and processes. In: Turner AK, Shuster RL (eds) Landslides: investigation and mitigation. Transportation Research Board Business Office, Washington, pp 36–75Google Scholar
  9. Dietrich WE, Montgomery DR (1998) SHALSTAB: a digital terrain model for mapping shallow landslide potential. National Council of the Paper Industry for Air and Stream Improvement (NCASI), Technical Report, 29 pGoogle Scholar
  10. Dietrich WE, Bellugi D, Real de Asua R (2001) Validation of the shallow landslide model, SHALSTAB, for forest management. Water Sci Appl 2:195–227CrossRefGoogle Scholar
  11. Digital Globe (2017). Satellite information. https://www.digitalglobe.com/resources/satellite-information. Accessed 24 Jan 2017
  12. Earth Explorer—USGS (2017). https://earthexplorer.usgs.gov/. Accessed 8 May 2017
  13. EngeSat (2017) Image satellite. http://www.engesat.com.br/imagem-de-satelite/ikonos/. Accessed 4 Jul 2017
  14. Fernandes NF, Guimarães RF, Gomes RAT, Vieira BC, Montgomey DR, Greenberg HM (2004) Topographic controls of landslides in Rio de Janeiro: field evidences and modeling. CATENA 55:163–181CrossRefGoogle Scholar
  15. Gallo-Junior H, Olivato D, Carvalho JL (2010) Sobreposição de Territórios e Gestão de Unidades de Conservação de Proteção Integral: Estudo Aplicado ao Município de Campos do Jordão. In: Encontro Nacional de Geógrafos, Anais XVI, Porto Alegre—RS, julho 25 a 31Google Scholar
  16. GeoStudio (2005) GeoStudio tutorials includes student edition lessons, 1st edn. Geo-Slope International Ltd., Calgary, p 485Google Scholar
  17. Gomes RAT, Guimarães RF, Carvalho Júnior OA, Fernandes NF (2005) Análise de um modelo de previsão de deslizamentos (SHALSTAB) em diferentes escalas cartográficas. Rev Solos e Rocha 28:85–97Google Scholar
  18. Gomes RAT, Guimarães RF, Carvalho Júnior OA, Fernandes NF, Vargas Júnio E (2008) Identification of the affected areas by mass movement through a physically based model of landslide hazard combined with an empirical model of debris flow. Nat Hazards 45:197–209CrossRefGoogle Scholar
  19. Gomes RAT, Guimarães RF, Carvalho Júnior OA, Fernandes NF, Amaral Júnior EV (2013) Combining Spatial Models for shallow landslides and debris-flows prediction. Remote Sens 5:2219–2237CrossRefGoogle Scholar
  20. Guimarães RF, Montgomery DR, Greenberg HM, Fernandes NF, Gomes RAT, Carvalho Júnior OA (2003) Parameterization of soil properties for a model of topographic controls on shallow landsliding. Application to Rio de Janeiro. Eng Geol 69:99–108CrossRefGoogle Scholar
  21. Guimarães RF, Gomes RAT, Carvalho Júnior OA, Martins ES, Fernandes NF (2009) Análise temporal das áreas suscetíveis a escorregamentos rasos no Parque Nacional da Serra dos Órgãos (RJ) a partir de dados pluviométricos. Rev Brasil de Geociênc 39:192–200Google Scholar
  22. Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66CrossRefGoogle Scholar
  23. Hiruma ST, Riccomini C, Modenesi-Gauttieri MC (2001) Neotectônica no planalto de Campos do Jordão, SP. Revista Brasileira de Geociências 31:375–384CrossRefGoogle Scholar
  24. Instituto Brasileiro de Geografia e Estatística—IBGE (2016) Cidades e estados do Brasil Disponível em. http://cidades.ibge.gov.br/xtras/perfil.php?lang=&codmun=350970. Accessed 13 Oct 2016
  25. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefGoogle Scholar
  26. Larsen MC, Torres-Sanchez AJ (1998) The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico. Geomorphology 24:309–331CrossRefGoogle Scholar
  27. Lin PS, Lin JY, Hung HC, Yang MD (2002) Assessing debris flow hazard in a watershed in Taiwan. Eng Geol 66:295–313CrossRefGoogle Scholar
  28. Mendes RM, Valerio-Filho MV (2015) Real-time monitoring of climatic and geotechnical variables during landslides on the slopes of Serra do Mar and Serra da Mantiqueira (São Paulo state—Brazil). Engineering 7:140–159CrossRefGoogle Scholar
  29. Mendes RM, Andrade MRM, Tomasella J, Moraes MAE, Scofield GB (2018a) Understanding shallow landslides in Campos do Jordão municipality—Brazil: disentangling the anthropic effects from natural causes in the disaster of 2000. Nat Hazards Earth Syst Sci 18:15–30CrossRefGoogle Scholar
  30. Mendes RM, Andrade MRM, Graminha CA, Prieto CC, Ávla FF, Camarinha PIM (2018b) Stability analysis on urban slopes: case study of na anthropogenic-induced landslide in São José dos Campos, Brazil. Geotech Geol Eng Int J 36:599–610CrossRefGoogle Scholar
  31. Meneghetti GT, Kux HJH (2014) Mapeamento da cobertura da terra do município de Raposa (MA) utilizando imagens WorldView-2, o aplicativo interimage e mineração de dados. Revista Brasileira de Cartografia 66:365–377Google Scholar
  32. Metternicht G, Hurni L, Gogu R (2005) Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sens Environ 98:284–303CrossRefGoogle Scholar
  33. Michel GP, Kobiyama M, Goerl RF (2012) Análise Comparativa entre os Modelos SHALSTAB e SINMAP na Identificação de Áreas Susceptíveis a Escorregamentos Translacionais. In X Encontro Nacional de Engenharia de Sedimentos, Foz do Iguaçu—PR, Dez 3–7Google Scholar
  34. Michel GP, Kobiyama M, Goerl RF (2014) Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil. J Soils Sediment 2014(14):1266–1277CrossRefGoogle Scholar
  35. Modenesi-Gauttieri MC, Hiruma ST (2004) A Expansão Urbana no Planalto de Campos do Jordão: Diagnóstico Geomorfológico para Fins de Planejamento. Revista do Instituto Geológico SP 25:1–28CrossRefGoogle Scholar
  36. Montgomery DR (1994) Road surface drainage, channel initiation, and slope stability. Water Resour Res 30:1925–1932CrossRefGoogle Scholar
  37. Montgomery DR, Dietrich WE (1994) A physically-based model for the topographic control on shallow landsliding. Water Resour Res 30:1153–1171CrossRefGoogle Scholar
  38. Montgomey DR, Sullivan K, Greenber MH (1998) Regional test of a model for shallow landsliding. Hydrol Process 12:943–955CrossRefGoogle Scholar
  39. Montgomey DR, Schmidt KM, Greenberg HM, Dietrich WE (2000) Forest clearing and regional landsliding. Geology 28:311–314CrossRefGoogle Scholar
  40. Montgomey DR, Greenber MH, Laprede B, Nasham B (2001) Sliding in Seattle: test of a model of shallow landsliding potential in an urban environment. In: Wigmosta MS, Burges SJ (eds) Land use and watersheds: human influence on hydrology and geomorphology in urban and forest areas-water science and application monograph, vol 2. American Geophysical Union, Washington, pp 59–73Google Scholar
  41. Neto LA, Bráulio N, Salles T, Moura G, Almeida C, Koike K (2006) (coordenadores). Plano Municipal de Redução de Risco. Ministério das CidadesGoogle Scholar
  42. O´Loughlin GH (1986) Prediction of surface saturation zones in natural catchments by topographic analysis. Water Resour Res 22:794–804CrossRefGoogle Scholar
  43. Pack RT, Tarboton DG, Goodwin CN (1998) The Sinmap Approach to terrain stability mapping. In: Proceedings of the 8th congress of the International Association of Engineering Geology, Vancouver, BC, Canada, 21–25 September, pp 21–25Google Scholar
  44. Pesk VA, Disperati AA, Santos JR (2011) Comparação de técnicas de fusão aplicadas à imagens Quickbird-2. Floresta e Ambiente 18:127–134. http://www.floram.org/files/v18n2/v18n2a2.pdf. Accessed 17 Apr 2017
  45. Pinho CMD, Fonseca LMG, Korting TS, Almeida CMA, Kux HJH (2012) Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis. Int J Remote Sens 33:5973–5995CrossRefGoogle Scholar
  46. Polizel SP, Marques ML, Costa NR, Rossi E, Ferreira MV (2011) Aplicação e avaliação de técnicas de fusão em imagens IKONOS e GeoEye. In: Simpósio Brasileiro de Sensoriamento Remoto, 15. 2001. Anais São José dos Campos: INPEGoogle Scholar
  47. Prieto CC, Mendes RM, Simões SJC, Nobre CA (2017) Comparação entre a aplicação do modelo Shalstab com mapas de suscetibilidade e risco de deslizamento na bacia do córrego Piracuama em Campos do Jordão-SP. Revista Brasileira de Cartografia 69:71–87Google Scholar
  48. Pu R, Landry S (2012) A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sens Environ 125:516–533CrossRefGoogle Scholar
  49. Ramos VM, Guimarães RF, Carvalho Júnior OA, Redivo AL, Gomes RAT, Cardoso FBF, Fernandes NF (2007) Algorithm for incorporating soil physical properties of each different soil class in a landslide prediction model (SHALSTAB). Soils Rocks 30:139–148Google Scholar
  50. Reginatto GMP, Maccarini M, Kobiyama M, Higashi RAR, Grando A, Corseuil CW, Caramez ML (2012) SHALSTAB application to identify the susceptible areas of shallow landslides in Cunha River Watershed, Rio dos Cedros city, SC, Brazil. In: Proceedings of the 4th GEOBIA, Rio de Janeiro—Brazil, May 7–9, p 108Google Scholar
  51. Rodrigues TCS (2014) Classificação da cobertura e do uso da terra com imagens WorldView-2 de setores nortes da ilha do Maranhão por meio do aplicativo interIMAGE e de mineração de dados. 2014. Dissertação (Mestrado em Sensoriamento Remoto). Instituto Nacional de Pesquisas Espaciais, São José dos Campos, SPGoogle Scholar
  52. Savage WZ, Godt JW, Baum RL (2004) Modeling time-dependent aerial slope stability. In: Proceedings of 9th international symposium of landslides, landslides-evaluation and stabilization, Rio de Janeiro, RJ, Brazil, 28 June–2 July, vol 1, pp 23–36Google Scholar
  53. Tofani V, Segoni S, Agostini A, Catani F, Casagli N (2013) Use of remote sensing for landslide studies in Europe. Nat Hazards Earth Syst Sci 13:299–309CrossRefGoogle Scholar
  54. Vieira BC, Ramos H (2015) Aplicação do Modelo SHASLTAB para Mapeamento da Susceptibilidade a Escorregamentos Rasos em Caraguatatuba. Revista Departamento de Geografia—USP 29:161–174Google Scholar
  55. Wu W, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31:2097–2110CrossRefGoogle Scholar
  56. Zêrere JL, Trigo RM, Trigo IF (2005) Shallow and deep landslides induced by rainfall in the Lisbon region (Portugal): assessment of relationships with the North Atlantic Oscillation. Nat Hazards Earth Syst Sci 5:332–344Google Scholar
  57. Zhou C, Lee C, Li J, Xu Z (2002) On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology 43:197–207CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.National Institute for Space Research (INPE)São José dos CamposBrazil
  2. 2.National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN/MCTIC)São José dos CamposBrazil
  3. 3.Vale do Paraíba University (UNIVAP/IP&D)São José dos CamposBrazil

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