Advertisement

Land Use/Land Cover Classification of the Natural Environment

  • Rajesh Thapa
  • Stefan Lang
  • Elisabeth Schöepfer
  • Stefan Kienberger
  • Petra Füreder
  • Peter Zeil
Chapter

Abstract

Land use/land cover (LULC) information is one of the most important spatial input for environmental modelling and a crucial indicator to identify and quantify natural and socioeconomic impacts triggered by LULC changes. Such impacts are related to glacier, snow cover, and permafrost melting, the forming of GLOFs, erosion by land sides, discharge and sediment transport dynamics of alpine rivers, and the socioeconomic regional urban and rural development to name some of them.

Keywords

Digital Elevation Model LULC Change Permafrost Melting Object Base Image Analysis LULC Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Benestad RE, Hanssen-Bauer I, Chen D (2008) Empirical-statistical downscaling. World Scientific Publishing Company, p 228. ISBN: 978-981-281-912-3Google Scholar
  2. Benz U, Hoffman P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens 58:239–258CrossRefGoogle Scholar
  3. Bicheron P, Amberg V, Bourg L, Petit D, Huc M, Miras B, Brockmann C, Hagolle O, Delwart S, Ranera F, Leroy M, Arino O (2011) Geolocation assessment of MERIS GlobCover orthorectified products. IEEE Trans Geosci Remote Sens 49(8):2972–2982CrossRefGoogle Scholar
  4. Campbell J (1981) Spatial correlation effects upon accuracy of supervised classification of land cover. Photogrammetric Eng Remote Sens 47(3):355–363Google Scholar
  5. Civco D (1989) Topographic normalization of landsat thematic mapper digital imagery. Photogramm Eng Remote Sens 55(9):1303–1309Google Scholar
  6. Füreder P (2010) Topographic correction of satellite images for improved LULC classification in alpine areas. In: Kaufmann V, Sulzer W (eds) Proceedings of the 10th International Symposium on High Mountain Remote Sensing Cartography, Vol 45. Grazer Schriften der Geographie und Raumforschung, Graz, pp 187–194Google Scholar
  7. IPCC (2000) Special report on emissions scenarios. A special report of working Group III of the intergovernmental panel on climate change, 27 p. Nakicenovic N, Swart R (eds) Cambridge University Press, UK. http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=0
  8. IPCC, Intergovernmental Panel on Climate Change (2003) Good practice guidance for land use. Land-use change and forestryGoogle Scholar
  9. Ji Y, Li G, Guan H (2005) Review of remote sensing classification technology for LULC. Agric Netw Inf 8:36–38 (in Chinese)Google Scholar
  10. Kralisch S, Böhm B, Böhm C, Busch C, Fink M, Fischer C, Schwartze C, Selsam P, Zander F, Flügel W-A (2012) ILMS—a software platform for integrated environmental management. In: Seppelt R, Voinov AA, Lange S, Bankamp D (eds) iEMSs Proceedings, 2012 international congress on environmental modelling and software managing resources of a limited planet, Sixth Biennial Meeting, Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings
  11. Lang S, Blaschke T (2006) Bridging remote sensing and GIS—what are the most supportive pillars? In: Lang S, Blaschke T, Schöpfer E (eds) Proceedings of the 1st international conference on object-based image analysis, July 4–5, 2006Google Scholar
  12. Lang S, Kääb A, Pechstädt J, Flügel W-A, Zeil P, Lanz E, Kahuda D, Frauenfelder R, Casey K, Füreder P, Sossna I, Wagner I, Janauer G, Exler N, Boukalova Z, Thapa R, Lui J, Sharma N (2011) Assessing components of the natural environment of the Upper Danube and Upper Brahmaputra river basins. Adv Sci Res 7:21–36. doi:10.5194/asr-7-21-2011. www.adv-sci-res.net/7/21/2011/ CrossRefGoogle Scholar
  13. Srengers B, Leemans R, Eickhout B, de Vries B, Bouwman L (2004) The land-use projections and resulting emissions in the IPCC SRES scenarios as simulated by the IMAGE 2.2 model. GeoJournal 61:381–391CrossRefGoogle Scholar
  14. Subba B (2001) Himalayan waters. The Panos Institute, South Asia, 286 pGoogle Scholar
  15. Thompson M (1996) A standard land-cover classification scheme for remote-sensing applications in South Africa. S Afr JSci 92(1):34–42Google Scholar
  16. Tötzer T, Köstl M, Steinnocher K (2007) Scenario of land use change in Europe based on socio-economic and demographic driving factors. In: Schrenk M, Popovich V, Benedikt J (eds) Real corp 007: 12th International conference on urban planning, regional development and information society. May 21st–23rd, Vienna. 141-150 CD-ROMGoogle Scholar
  17. Verburg PH, Rounsevell MDA, Veldkamp A (2006) Scenario-based studies of future land use in Europe. Agric Ecosyst Environ 114:1–6CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Rajesh Thapa
    • 1
  • Stefan Lang
    • 2
  • Elisabeth Schöepfer
    • 3
  • Stefan Kienberger
    • 2
  • Petra Füreder
    • 2
  • Peter Zeil
    • 4
  1. 1.Riverine Landscapes Research Laboratory, Geography and PlanningUniversity of New EnglandArmidaleAustralia
  2. 2.Interfaculty Department of Geoinformatics—Z_GISUniversity of SalzburgSalzburgAustria
  3. 3.German Aerospace Center (DLR)German Remote Sensing Data Center (DFD)OberpfaffenhofenGermany
  4. 4.European Commission, DG Enterprise & Industry, Copernicus Services, G2 & Interfaculty Department of Geoinformatics—Z_GISUniversity of SalzburgSalzburgAustria

Personalised recommendations