Environmental Monitoring and Assessment

, Volume 176, Issue 1–4, pp 531–547 | Cite as

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia

  • Christian HüttichEmail author
  • Martin Herold
  • Ben J. Strohbach
  • Stefan Dech


Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.


Harmonization Standardization Time series Random forest Remote sensing Phenology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

10661_2010_1602_MOESM1_ESM.doc (337 kb)
(DOC 337 KB)


  1. Archibald, S., & Scholes, R. J. (2007). Leaf green-up in a semi-arid African savanna—separating tree and grass responses to environmental cues. Journal of Vegetation Science, 18, 583–594.Google Scholar
  2. Breiman, L. E. (2001). Random forests. Machine Learning, 26, 5–32.CrossRefGoogle Scholar
  3. Childs, S. (1989). Phenoloy of nine common woody species in semi-arid, deciduous Kalahari Sand vegetation. Vegetatio, 79, 151–163.CrossRefGoogle Scholar
  4. Colditz, R., Conrad, C., Wehrmann, T., Schmidt, M., & Dech, S. (2008). TiSeG: Flexible software tool for time-series generation of MODIS data utilizing the quality assessment science data set. IEEE Transactions on Geoscience and Remote Sensing, 46, 3296–3308.CrossRefGoogle Scholar
  5. Cord, A., Conrad, C., Schmidt, M., & Dech, S. (2010). Standardized FAO-LCCS land cover mapping in heterogeneous tree savannas of West Africa. Journal of Arid Environments, 74(9), 1083–1091. doi: 10.1016/j.jaridenv.2010.03.012.CrossRefGoogle Scholar
  6. Defourny, P., Bicheron, P., Brockman, C., Bontemps, S., et al. (2009). The first 300 m global land cover map for 2005 using ENVISAT MERIS time series: A product of the GlobCover system. In Proceedings of the 33th international symposium of remote sensing of environment (pp. 1–4), 4–8 May, Stresa.Google Scholar
  7. DeFries, R., Hansen, M., Townsend, J., Janetos, A., & Loveland, T. (2000). A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology, 6, 247–254.CrossRefGoogle Scholar
  8. Di Gregorio, A. (2005). Land cover classification system. Classification concepts and user manual. Software version 2 2nd ed., FAO, Environment and Natural Resources Series number 8, Rome.Google Scholar
  9. Edwards, D. (1983). A broad-scale structural classification of vegetation for practical purposes. Bothalia, 14, 705–712.Google Scholar
  10. FAO (2009). Advanced Database Gateway (ADG). Glaobal Land Cover Network (p. 1). Retrieved from
  11. Fritz, S., Belward, A., Mayaux, P., & Bartholome, E. (2004). A new land-cover map of Africa for the year 2000. Journal of Biogeography, 31, 861–877.CrossRefGoogle Scholar
  12. Frost, P. (1996). The ecology of miombo woodlands (Africa), CIFOR, Jakarta. Retrieved from;Q11997000079. Accessed 25 October 2009.
  13. Gessner, U., Klein, D., Conrad, C., Schmidt, M., & Dech, S. (2009). Towards an automated estimation of vegetation cover fractions on multiple scales: Examples of Eastern and Southern Africa. In Proceedings of the 33th international symposium of remote sensing of environment (pp. 1–4), 4–8 May, Stresa.Google Scholar
  14. GLCF (2007). Global land cover facility. Landsat Imagery. Accessed 19 July 2007.
  15. Guerschman, J. P., Hill, M. J., Renzullo, L. J., Barrett, D. J., et al. (2009). Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sensing of Environment, 113(5), 928–945.CrossRefGoogle Scholar
  16. Hanan, N., Sankaran, M., Ratnam, J., Dangelmayr, G., et al. (2006). Biocomplexity in African Savannas. Scientific Reports on Research Projects undertaken in the Kruger National Park during 2005 (pp. 47–48).Google Scholar
  17. Hansen, C., Defries, S., Townshend, G., Sohlberg, R., et al. (2002). Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data. Remote Sensing of Environment, 83, 303–319.CrossRefGoogle Scholar
  18. Hansen, M. C., Defries, R. S., Townshend, J. R., Carroll, M., et al. (2003). Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interactions, 7(10), 1–14.CrossRefGoogle Scholar
  19. Herold, M., Mayaux, P., Woodcock, C., Baccini, A., & Schmullius, C. (2008). Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112(5), 2538–2556.CrossRefGoogle Scholar
  20. Hollingsworth, A., Uppala, S., Klinker, E., Burridge, D., et al. (2005). The transformation of earth-system observations into information of socio-economic value in GEOSS. Quarterly Journal of the Royal Meteorological Society, 131(613), 3493–3512.CrossRefGoogle Scholar
  21. Huete, A., Didan, K., Miura, T., Rodriguez, P., et al. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.CrossRefGoogle Scholar
  22. Hüttich, C., Gessner, U., Herold, M., Strohbach, B. J., et al. (2009). On the suitability of MODIS time series metrics to map vegetation types in dry savanna ecosystems: A case study in the Kalahari of NE Namibia. Remote Sensing, 1(4), 620–643.CrossRefGoogle Scholar
  23. Jansen, L. J., & Gregorio, A. D. (2002). Parametric land cover and land-use classi cations as tools for environmental change detection. Environment, Journal of Applied Remote Sensing, 91, 89–100.Google Scholar
  24. Jung, M., Henkel, K., Herold, M., & Churkina, G. (2006). Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101(4), 534–553.CrossRefGoogle Scholar
  25. Justice, C. O., Vermote, E., Townshend, J. R., Defries, R., et al. (1998). The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228–1249.CrossRefGoogle Scholar
  26. Landgrebe, D., & Biehl, L. (2001). An introduction to MultiSpec (pp. 1–171). Accessed 17 May 2009.
  27. Latifovic, R. (2004). Land cover mapping of North and Central America—Global Land Cover 2000. Remote Sensing of Environment, 89(1), 116–127.CrossRefGoogle Scholar
  28. Loveland, T. (2008). North America land cover summit. In J. Campbell, K. Jones, J. Smith, & M. Koeppe (Eds.), Association of American Geographers. Washington.Google Scholar
  29. Loveland, T., Reed, B., Brown, J., Ohlsen, D., et al. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6 & 7), 1303–1330.CrossRefGoogle Scholar
  30. Mendelsohn, J., & Obeid, S. (2002). The communal lands in Eastern Namibia. Windhoek: Raison.Google Scholar
  31. Muchoney, D. (2008). Earth observations for terrestrial biodiversity and ecosystems. Remote Sensing of Environment, 112(5), 1909–1911.CrossRefGoogle Scholar
  32. Nagendra, H. (2001). Using remote sensing to assess biodiversity. International Journal of Remote Sensing, 22(12), 2377–2400.CrossRefGoogle Scholar
  33. Neumann, K., Herold, M., Hartley, A., & Schmullius, C. (2007). Comparative assessment of CORINE2000 and GLC2000: Spatial analysis of land cover data for Europe. International Journal of Applied Earth Observation and Geoinformation, 9(4), 425–437.CrossRefGoogle Scholar
  34. Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., et al. (2001). Terrestrial ecoregions of the world: A new map of life on earth. BioScience, 51(11), 933–938.CrossRefGoogle Scholar
  35. Pereira, H. M., & Cooper, H. D. (2006). Towards the global monitoring of biodiversity change. Trends in Ecology & Evolution, 21(3), 123–139.CrossRefGoogle Scholar
  36. Privette, J., Tian, Y., Roberts, G., Scholes, R., et al. (2004). Vegetation structure characteristics and relationships of Kalahari woodlands and savannas. Global Change Biology, 10, 281–291.CrossRefGoogle Scholar
  37. Running, S., Loveland, T., Pierce, L., Nemany, R., & Hunt, E. (1995). A remote sensing based vegetation classification logic for global land cover analysis. Remote Sensing of Environment, 51, 39–48.CrossRefGoogle Scholar
  38. Scanlon, T. M., Albertson, J. D., Caylor, K. K., & Williams, C. A. (2002). Determining land surface fractional cover from NDVI and rainfall time series for a savanna ecosystem. Remote Sensing of Environment, 82, 376–388.CrossRefGoogle Scholar
  39. Scholes, R., & Biggs, R. (2005). A biodiversity intactness index. Nature, 434, 45–49.CrossRefGoogle Scholar
  40. Scholes, R., Frost, P., & Tian, Y. (2004). Canopy structure in savannas along a moisture gradient on Kalahari sands. Global Change Biology, 10, 292–302.CrossRefGoogle Scholar
  41. Scholes, R. J., Mace, G. M., Turner, W., Geller, G. N., et al. (2008). Toward a global biodiversity observing system. Science, 321(5892), 1044–1045.CrossRefGoogle Scholar
  42. Sedano, F., Gong, P., & Ferrao, M. (2005). Land cover assessment with MODIS imagery in southern African Miombo ecosystems. Remote Sensing of Environment, 98(4), 429–441.CrossRefGoogle Scholar
  43. Strohbach, B. J. (2001). Vegetation survey of Namibia. Journal of the Namibia Scientific Society, 49, 93–124.Google Scholar
  44. Strohbach, B., Strohbach, M., Katuahuripa, J., & Mouton, H. (2004). A reconnaissance survey of the landscapes, soils and vegetation of the eastern communal areas (Otjiozondjupa and Omaheke Regions), Namibia (p. 119).Google Scholar
  45. Thompson, M. (1996). A standard land-cover classification scheme for remote-sensing applications in South Africa. South African Journal of Science, 92, 34–42.Google Scholar
  46. Trodd, N. M., & Dougill, A. J. (1998). Monitoring vegetation dynamics in semi-arid African rangelands. Applied Geography, 18(4), 315–330.CrossRefGoogle Scholar
  47. Turner, W. (2003). Remote sensing for biodiversity science and conservation. Trends in Ecology & Evolution, 18(6), 306–314.CrossRefGoogle Scholar
  48. Wagenseil, H., & Samimi, C. (2007). Woody vegetation cover in Namibian savannahs: A modelling approach based on remote sensing. Erdkunde, 61(4), 325–334.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Christian Hüttich
    • 1
    Email author
  • Martin Herold
    • 2
  • Ben J. Strohbach
    • 3
  • Stefan Dech
    • 1
    • 4
  1. 1.Department of Remote Sensing, Institute of GeographyJulius-Maximilians-University WürzburgWürzburgGermany
  2. 2.Center for GeoinformationWageningen UniversityWageningenThe Netherlands
  3. 3.National Botanical Research Institute of NamibiaWindhoekNamibia
  4. 4.German Remote Sensing Data Center, German Aerospace CenterOberpfaffenhofenGermany

Personalised recommendations