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Remote Sensing of Land-Cover and Land-Use Dynamics

  • Philippe Mayaux
  • Hugh Eva
  • Andreas Brink
  • Frédéric Achard
  • Alan Belward
Chapter

Abstract

Land is changing at a rate never achieved before. This evolution needs to be documented by robust and repeatable figures. Earth Observation tools play a key-role in the production of regular estimates of the landscape changes. In this chapter, we discuss the utility of Remote Sensing data for producing information on land-cover and on land-cover/land-use changes. Basic guidelines in terms of legend, data acquisition, classification techniques and validation are explained. For illustrating global land-cover projects, the recent Global Land Cover 2000 project is described.

Keywords

Land Cover Remote Sensing Land Cover Change Congo Basin Agricultural Expansion 
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.

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References

  1. Achard F., Eva H., Glinni A., Mayaux P., Richards T., & Stibig H.J., (1998). Identification of deforestation hot spot areas in the humid tropics, TREES Publications Series B, N°4, European Commission, Luxembourg, EUR 18079, 102.Google Scholar
  2. Achard, F., Eva, H., Stibig, H. J., Mayaux, P., Gallego, J., Richards, T., & Malingreau, J. P., (2002). Determination of deforestation rates of the world’s humid tropical forests, Science 297, 999–1002.CrossRefGoogle Scholar
  3. Arino O., Leroy, M. F., Ranera, D. Gross, P. Bicheron, F., & Niño, C. et al. (2007). GLOBCOVER: A Global Land Cover Service, ENVISAT Symposium, Montreux Switzerland 23–27 April 2007.Google Scholar
  4. Bartalev, S., Belward, A. S., Ershov, D., & Isaev, A. S. (2003). A New SPOT4-VEGETATION Derived Land Cover Map of Northern Eurasia. Intern. Journal of Remote Sensing, 24, 1977–1982.CrossRefGoogle Scholar
  5. Bartholomé, E., & Belward, A. S. (2005 May). GLC2000: A new approach to global land cover mapping from earth observation data. International Journal of Remote Sensing, 26(9), 1959–1977.CrossRefGoogle Scholar
  6. Belward, A. S., Estes, J. E., & Kline, K. D. (1999). The IGBP-DIS global 1 km land cover data set DISCover: A project overview. Photogrammetric Engineering and Remote Sensing, 65, 1013–1020.Google Scholar
  7. Boulvert, Y. (1986). Carte phytogéographique de la République Centrafricaine á 1:1,000,000, Notice Explicative No104, Editions de l’ORSTOM, Paris.Google Scholar
  8. Brink, A., & Eva, H. D. (2007). Monitoring 25 years of land cover change dynamics in Africa: A sample based remote sensing approach. Journal of Land Use Science – in preparation.Google Scholar
  9. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Digital change detection methods in ecosystem monitoring: A review. Int. J. Remote Sens. 25(9), 1565–1596.CrossRefGoogle Scholar
  10. Czaplewski, R. L., (2002). Can a sample of Landsat sensor scenes reliably estimate the global extent of tropical deforestation? Int. J. Remote Sensing, 24, 1409–1412, 2003.CrossRefGoogle Scholar
  11. D’Souza, G., Malingreau, J. P., & Eva, H. D. (1995). Tropical forest cover of South and Central America as derived from analyses of NOAA-AVHRR data. TREES Publications Series B3, EUR 16274 EN, Luxembourg, European Commission, 52 p.Google Scholar
  12. Di Gregorio, A., & Jansen, L. (2000). Land cover classification system, classification concepts and user manual, Food and Agriculture Organisation of the United Nations:Rome.Google Scholar
  13. Duveiller, G., Defourny, P., Desclée, B., & Mayaux, P., (2007). Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts, Remote Sensing of Environment, in press.Google Scholar
  14. Eva, H. D., Belward, A. S., De Miranda, E. E., Di Bella, C. M., Gond, V., & Huber, O. S. et al. (2004). A land cover map of South America. Global Change Biology, 10, 1–14.Google Scholar
  15. Eva, H. D., Brink, A., & Simonetti, D. (2006). Monitoring Land Cover Dynamics in sub-Saharan Africa. /EUR 22498. Office for Official Publications of the European Communities, Luxembourg.Google Scholar
  16. FAO, (2001). Global Forest Resources Assessment 2000 Main report, FAO Forestry paper 140, 479 pp., Food and Agriculture Organization of the UN, Rome.Google Scholar
  17. Foley, J. A., Defries, R., Asner, G. P., et al. (2005). Global consequences of land use changes. Science, 309, 570–574CrossRefGoogle Scholar
  18. Friedl, M. A., McIver, D. K. Hodges, J. C. F., Zhang, X. Y., Muchoney, D., & Strahler, A. H. et al. (2002). Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83(1–2), 287–302.Google Scholar
  19. Fuller, R. M., Smith, G. M., Sanderson, J. M., Hill, R. A., & Thomson A. G. (2002). The UK Land Cover Map 2000: Construction of a parcel-based vector map from satellite images. Cartographic Journal, 39, 15–25.Google Scholar
  20. GCOS, (2003). The second report on the adequacy of the global observing systems for climate in support of the UNFCCC, April 2003, GCOS-82, WMO Technical Document 1143 (WMO; Geneva), 74 pp.Google Scholar
  21. GCOS, (2006). Systematic observation requirements for satellite-based products for climate; Supplemental details to the satellite-based component of the “Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCOS-92),” WMO/TD No.1338, (WMO Geneva), 103 pp.Google Scholar
  22. Hansen, M., DeFries, R., Townshend, J.R.G., Dimiceli, C., Carroll, M., & Sohlberg, R. (2003 September). Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS Vegetation continuous fields algorithm, Earth Interactions, 7, 1–15.CrossRefGoogle Scholar
  23. Jeanjean H., & Achard, F., (1997). A new approach for tropical forest area monitoring using multiple resolution data. Int. J. Remote Sensing, 18, 2455–2461.CrossRefGoogle Scholar
  24. Lambin E. F., & Strahler A., (1994). Multitemporal change-vector analysis: A tool to detect and categorise land-cover change processes using high temporal resolution satellite data. Remote Sensing of Environment, 48, 231–244.CrossRefGoogle Scholar
  25. Loveland, T. R., & Belward, A. S. (1998). The International Geosphere Biosphere Programme Data and Information System Global Land Cover Data Set (DISCover): Acta Astronautica, 41(4–10), 681–689.Google Scholar
  26. Loveland, T. R., Zhu, Z., Ohlen, D. O., Brown, J. F., Reed, B. C., & Yang, L., (1999). An analysis of the IGBP Global Land-Cover Characterization Process. Photogrammetric Engineering and Remote Sensing, 65, 1021–1032.Google Scholar
  27. Mayaux P., Bartholomé, E., Fritz, S. & Belward, A. (2004). A new land-cover map of Africa for the year 2000, Journal of Biogeography, 31, 1–17.CrossRefGoogle Scholar
  28. Mayaux, P., Holmgren, P., Achard, F., Eva, H., Stibig, H-J., & Branthomme, A. (2005). Tropical forest cover change in the 1990s and options for future monitoring. Philosphical Transactions of the Royal Society B., 360, 373–384.CrossRefGoogle Scholar
  29. Mayaux, P., Strahler, A., Eva, H., Herold, M., Shefali, A., & Naumov, S. et al. (2006). Validation of the Global Land Cover 2000 Map IEEE-Transactions on Geoscience and Remote Sensing 44 (7), 1728–1739, doi 10.1109/TGRS.2006.864370.Google Scholar
  30. Roy, P. S., & Joshi, P. K., (2002). Forest cover assessment in north-east India – The potential of temporal wide swath satellite sensor data (IRS-1C WiFS). Int. Journ. of Remote Sensing 23(22), 4881–4896.CrossRefGoogle Scholar
  31. Running, S. W., Loveland, T. R., & Pierce, L. L., (1994). A vegetation classification logic based on remote sensing for use in global scale biogeochemical models, Ambio, 23, 77–81.Google Scholar
  32. Sellers, P. J., Dickinson, R. E., Randall, D. A., Betts, A. K., Hall, F. G., & Berry, J. A. et al. (1997). Modelling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 275, 502–509.CrossRefGoogle Scholar
  33. Stephenne, N., & Lambin, E. (2001). Backward land-cover change projections for the Sudano-Sahelian countries of Africa with a dynamic simulation model of land-use change (SALU). In: Matsuno, T., & Kida, H. (Eds.), Present and future of modeling global environmental change: Toward integrated modeling (pp. 255–270). Terrapub, Tokyo.Google Scholar
  34. Stibig, H-J., Belward, A. S., Roy, P. S., Rosalina-Wasrin, U., Agrawal, S., & Joshi, P. K. et al. (2007). A land cover map for South and Southeast Asia derived from SPOT-4 VEGETATION data, Journal of Biogeography 34, 625–637.CrossRefGoogle Scholar
  35. Strahler, A. S., Boschetti, L., Foody, G. M., Friedl, M. A., Hansen, M. C., & Herold, M., et al. (2006). Global land cover validation: Recommendations for evaluation and accuracy assessment of global land cover maps, Luxembourg: Office for Official Publication of the European Communities, EUR 22156 EN, 2006, 48 p.Google Scholar
  36. Tappan, G., Sall, M., Wood, E., & Cushing, M., (2004 March 23). Ecoregions and land-cover trends in Senegal. Journal of Arid Environments 59(3), 427–462.CrossRefGoogle Scholar
  37. White, F (1983). The vegetation of Africa: A Descriptive Memoir to accompany the UNESCO/AEFTAT/UNSO Vegetation Map of Africa. Paris, UNESCO.Google Scholar

Copyright information

© Springer Science + Business Media B.V. 2008

Authors and Affiliations

  • Philippe Mayaux
  • Hugh Eva
  • Andreas Brink
  • Frédéric Achard
  • Alan Belward

There are no affiliations available

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