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
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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.
KeywordsHarmonization Standardization Time series Random forest Remote sensing Phenology
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- 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
- 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
- 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
- Edwards, D. (1983). A broad-scale structural classification of vegetation for practical purposes. Bothalia, 14, 705–712.Google Scholar
- FAO (2009). Advanced Database Gateway (ADG). Glaobal Land Cover Network (p. 1). Retrieved from http://www.glcn.org/sof_4_en.jsp.
- Frost, P. (1996). The ecology of miombo woodlands (Africa), CIFOR, Jakarta. Retrieved from http://www.fao.org/agris/search/display.do?f=./1998/v2402/Q11997000079.xml;Q11997000079. Accessed 25 October 2009.
- 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
- GLCF (2007). Global land cover facility. Landsat Imagery. http://glcf.umiacs.umd.edu/data/landsat/. Accessed 19 July 2007.
- 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
- 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
- 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
- Landgrebe, D., & Biehl, L. (2001). An introduction to MultiSpec (pp. 1–171). http://cobweb.ecn.purdue.edu/~biehl/MultiSpec/Intro5_01.pdf. Accessed 17 May 2009.
- 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
- Mendelsohn, J., & Obeid, S. (2002). The communal lands in Eastern Namibia. Windhoek: Raison.Google Scholar
- Strohbach, B. J. (2001). Vegetation survey of Namibia. Journal of the Namibia Scientific Society, 49, 93–124.Google Scholar
- 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
- 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