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Landslides

, Volume 16, Issue 1, pp 165–174 | Cite as

Visual interpretation of stereoscopic NDVI satellite images to map rainfall-induced landslides

  • Federica Fiorucci
  • Francesca ArdizzoneEmail author
  • Alessandro Cesare Mondini
  • Alessia Viero
  • Fausto Guzzetti
Technical Note

Abstract

Landslide inventory maps are commonly prepared through the visual interpretation of stereoscopic aerial photographs and field checks. Stereoscopic satellite images can also be interpreted visually to recognize and map landslides. When interpreting stereoscopic imagery, shadows can conceal the photographic elements typical of landslides, hampering the recognition and mapping of the landslides. To mitigate the problem, we propose a method that exploits normalized difference vegetation index (NDVI) images and digital stereoscopy for the 3D visual recognition and mapping of landslides in shadowed areas. We tested the method in the 25 km2 Pogliaschina catchment, northern Italy, where intense rainfall caused abundant landslides on 25 October 2011. Using a PLANAR® StereoMirror™ digital stereoscope, we prepared an event landslide inventory map (E-LIM) through the visual interpretation of a pair of NDVI images obtained from a WorldView-2 stereoscopic multispectral bundle. We compared the event inventory with two independent E-LIMs for the same area and landslide event. The 3D vision of the NDVI stereoscopic image pair maximized the use of the radiometric (color and tone) and the terrain (elevation, slope, relief, and convexity) information captured by the stereoscopic multispectral images, allowing for the recognition of more landslides and more landslide areas than the other E-LIMs in the shadowed areas. Our results confirm that use of NDVI images facilitates the visual recognition and mapping of landslides in terrain affected by shadows. We expect that the proposed method can help trained interpreters to map landslides more accurately in areas affected by shadows.

Keywords

Digital stereoscopic vision Landslide inventory map Normalized difference vegetation index WorldView-2 

Notes

Funding information

This work was supported by the EU LAMPRE project (EC contract no. 312384). Federica Fiorucci was supported by a grant of the Regione dell’Umbria under contract PoR-FESR 861, 2012.

Compliance with ethical standards

Disclosure

Any use of trade, product, or firm names in this work is for descriptive purposes only and does not imply endorsement by the authors or their institutions.

References

  1. Alkevli T, Ercanoglu M (2011) Assessment of ASTER satellite images in landslide inventory mapping: Yenice-Gökçebey (Western Black Sea Region, Turkey). Bull Eng Geol Environ 70(4):607–617.  https://doi.org/10.1007/s10064-011-0353-z CrossRefGoogle Scholar
  2. Antonini G, Ardizzone F, Cardinali M, Galli M, Guzzetti F, Reichenbach P (2002) Surface deposits and landslide inventory map of the area affected by the 1997 Umbria-Marche earthquakes. Boll Soc Geol Ital 121(2):843–853Google Scholar
  3. Ardizzone F, Fiorucci F, Santangelo M, Cardinali M, Mondini AC, Rossi M, Reichenbach P, Guzzetti F (2013) Very-high resolution stereoscopic satellite images for landslide mapping. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin Heidelberg, pp 95–101.  https://doi.org/10.1007/978-3-642-31325-7_12 CrossRefGoogle Scholar
  4. Bajracharya B, Bajracharya SR (2008) Landslide mapping of the Everest region using high resolution satellite images and 3D visualization. In: Proceedings of the mountain GIS e-conference, Kathmandu, Nepal, p 14–25Google Scholar
  5. 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(2):142–152.  https://doi.org/10.1080/17445647.2017.1279082 CrossRefGoogle Scholar
  6. Barten PGJ (1999) Contrast sensitivity of the human eye and its effects on image quality. Spie optical engineering press, Bellingham, WAGoogle Scholar
  7. Bedi SS, Khandelwal R (2013) Various image enhancement techniques- a critical review. IJARCCE 2(3):1605–1609 ISSN (online): 278-1021Google Scholar
  8. Brivio PA, Lechi G, Zilioli E (2006) Principi e metodi di telerilevamento. CittaStudi (ed), NovaraGoogle Scholar
  9. Carrara A, Cardinali M, Guzzetti F (1992) Uncertainty in assessing landslides hazard and risk. ITC J 2:172–183Google Scholar
  10. Casagli N, Fanti R, Nocetini M, Righini G (2005) Assessing the capabilities of VHR satellite data for debris flow mapping in the Machu Picchu area. In: Sassa K, Fukuoka H, Wang F, Wang G (eds) Landslides: risk analysis and sustainable disaster management. Springer, Berlin, pp 61–70.  https://doi.org/10.1007/3-540-28680-2_6 CrossRefGoogle Scholar
  11. Desiato F, Fioravanti G, Fraschetti P, Perconti W, Toreti A (2011) Gli indicatori del CLIMA in Italia nel 2010. ISPRA, Stato dell’Ambiente 24/2011. http://www.isprambiente.gov.it/it/pubblicazioni/stato-dellambiente/gli-indicatori-del-clima-in-italia-nel-2010-anno. ISBN 978-88-448-0499-2 (in Italian). Accessed 27 Aug 2018
  12. Fiorucci F, Cardinali M, Carlà R, Rossi M, Mondini AC, Santurri L, Ardizzone F, Guzzetti F (2011) Seasonal landslides mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology 129:59–70.  https://doi.org/10.1016/j.geomorph.2011.01.013 CrossRefGoogle Scholar
  13. Galli M, Ardizzone F, Cardinali M, Guzzetti F, Reichenbach P (2008) Comparing landslide inventory maps. Geomorphology 94:268–289.  https://doi.org/10.1016/j.geomorph.2006.09.023 CrossRefGoogle Scholar
  14. 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(1):42–66.  https://doi.org/10.1016/j.earscirev.2012.02.001 CrossRefGoogle Scholar
  15. Hackman RJ (1967) Time, shadows, terrain, and photo-interpretation. US Geol Surv Prof Pap 575:155–159Google Scholar
  16. Haeberlin Y, Turberg P, Retière A, Senegas O, Parriaux A (2004) Validation of SPOT 5 satellite imagery for geological hazard identification and risk assessment for landslides, mud and debris flows in Matagalpa, Nicaragua. In: ISPRS, XXth ISPRS Congress Technical Commission I, Istanbul, Turkey, IAPRS, volume 35, part B1, p 273–278. ISSN 1682-1750Google Scholar
  17. Hsieh YT, Wu ST, Liao CS, Yui YG, Chen JC, and Chung YL (2011) Automatic extraction of shadow and non-shadow landslide area from ADS-40 image by stratified classification. In: Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, Vancouver, BC, 24-29 July 2011Google Scholar
  18. Lawrence RL, Ripple WJ (1998) Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape: Mount St. Helens, Washington. Remote Sens Environ 64:91–102.  https://doi.org/10.1016/S0034-4257(97)00171-5 CrossRefGoogle Scholar
  19. Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491.  https://doi.org/10.1080/01431160412331331012 CrossRefGoogle Scholar
  20. Lillesand TM, Kiefer RW, Chipman JW (1994) Remote sensing and image interpretation. Wiley, New York ISBN: 978-1-118-34328-9Google Scholar
  21. Liu CC (2015) Preparing a landslide and shadow inventory map from high-spatial-resolution imagery facilitated by an expert system. J Appl Remote Sens 9(1):096080.  https://doi.org/10.1117/1.JRS.9.096080 CrossRefGoogle Scholar
  22. Maini R, Aggarwal H (2010) A comprehensive review of image enhancement techniques. J Comput 2(3):8–13Google Scholar
  23. Marcelino EV, Formaggio AR, Maeda EE (2009) Landslide inventory using image fusion techniques in Brazil. Int J Appl Earth Obs Geoinf 11:181–191.  https://doi.org/10.1016/j.jag.2009.01.003 CrossRefGoogle Scholar
  24. Marchi L, Cavalli M, Comiti F, Vela AL, Viero A (2013) Studio dei processi idrologici, idraulici e geomorfologici e della pericolosità ad essi associata nel bacino del Torrente Pogliaschina (Val di Vara, Provincia della Spezia). Technical report (February 2013), 54 pp, (in Italian)Google Scholar
  25. Mondini AC, Guzzetti F, Reichenbach P, Rossi M, Cardinali M, Ardizzone F (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115:1743–1757.  https://doi.org/10.1016/j.rse.2011.03.006 CrossRefGoogle Scholar
  26. Mondini AC, Viero A, Cavalli M, Marchi L, Herrera G, Guzzetti F (2014) Comparison of event landslide inventories: the Pogliaschina catchment test case, Italy. Nat Hazards Earth Syst Sci 14:1749–1759.  https://doi.org/10.5194/nhess-14-1749-2014 CrossRefGoogle Scholar
  27. Moreno RG, Requejo AS, Alonso AT, Barrington S, Díaz MC (2008) Shadow analysis: a method for measuring soil surface roughness. Geoderma 146(1–2):201–208.  https://doi.org/10.1016/j.geoderma.2008.05.026 CrossRefGoogle Scholar
  28. Murillo-García FG, Alcántara-Ayala I, Ardizzone F, Cardinali M, Fiourucci F, Guzzetti F (2015) Satellite stereoscopic pair images of very high resolution: a step forward for the development of landslide inventories. Landslides 12(2):277–291.  https://doi.org/10.1007/s10346-014-0473-1 CrossRefGoogle Scholar
  29. Nichol JE, Shaker A, Wong MS (2006) Application of high-resolution stereo satellite images to detailed landslide hazard assessment. Geomorphology 76:68–75.  https://doi.org/10.1016/j.geomorph.2005.10.001 CrossRefGoogle Scholar
  30. Paine DP, Kiser D (2012) Principles and techniques of aerial image interpretation. In: Paine DP, Kiser JD (eds) Aerial photography and image interpretation, 3rd edn. Copyright © 2012 John Wiley & Sons, Inc., Hoboken, pp 280–305.  https://doi.org/10.1002/9781118110997.ch15 CrossRefGoogle Scholar
  31. Philipson W (1997) The manual of photographic interpretation. American Society for Photogrammetry and Remote Sensing, 2nd edn. American Society of Photogrammetry and Remote Sensing, Bethesda, pp 1–700 ISBN 1570830398 9781570830396Google Scholar
  32. Plank S, Twele A, Martinis S (2016) Landslide mapping in vegetated areas using change detection based on optical and polarimetric SAR data. Remote Sens 8(4):307.  https://doi.org/10.3390/rs8040307 CrossRefGoogle Scholar
  33. Rib HT, Liang T (1978) Recognition, and identification. In: Schuster RL, Krizek RJ (eds) Landslide analysis and control, transportation Research Board special report, 176. National Academy of Sciences, Washington, pp 34–80Google Scholar
  34. Santangelo M, Marchesini I, Bucci F, Cardinali M, Fiorucci F, Guzzetti F (2015) An approach to reduce mapping errors in the production of landslide inventory maps. Nat Hazards Earth Syst Sci 15(9):2111–2126.  https://doi.org/10.5194/nhess-15-2111-2015 CrossRefGoogle Scholar
  35. Schlögel R, Braun A, Torgoev A, Fernandez-Steeger TM, Havenith HB (2013) Assessment of landslides activity in Maily-Say Valley, Kyrgyz Tien Shan. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin Heidelberg, p 111–117.  https://doi.org/10.1007/978-3-642-31325-7_14
  36. Sun W, Tian Y, Mu X, Zhai J, Gao P, Zhao G (2017) Loess landslide inventory map based on GF-1 satellite imagery. Remote Sens 9(4):314.  https://doi.org/10.3390/rs9040314 CrossRefGoogle Scholar
  37. Temesgena B, Mohammeda MU, Korme T (2001) Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet area, Ethiopia. Phys Chem Earth 26(9):665–675.  https://doi.org/10.1016/S1464-1917(01)00065-4 Google Scholar
  38. van Zuidam RA (1985) Aerial photo-interpretation in terrain analysis and geomorphologic mapping International Institute for Aerospace Survey and Earth Sciences (ITC). Smits Publishers, The Hague, p 442Google Scholar
  39. Weirich F, Blesius L (2007) Comparison of satellite and air photo based landslide susceptibility maps. Geomorphology 87:352–364.  https://doi.org/10.1016/j.geomorph.2006.10.003 http://www.planar.com/media/211324/mn-planar-sd2020.pdf. Accessed 27 Aug 2018CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Consiglio Nazionale delle RicercheIstituto di Ricerca per la Protezione IdrogeologicaPerugiaItaly
  2. 2.Private Consultant, Padova, Italy. Formerly Consiglio Nazionale delle RicercheIstituto di Ricerca per la Protezione IdrogeologicaPadovaItaly

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