Abstract
Human activity and climate variability has always changed the Earth’s surface and both will mainly contribute to future alteration in land cover and land use changes. In this chapter we demonstrate a land cover and land use classification approach for Central Asia addressing regional characteristics of the study area. With the aim of regional classification map for Central Asia a specific classification scheme based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organisation of the United Nations Environment Programme (FAO-UNEP) was developed. The classification was performed by using a supervised classification method applied on metrics, which were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data with 250 m spatial resolution. The metrics were derived from annual time-series of red and near-infrared reflectance as well as from Normalized Difference Vegetation Index (NDVI) and thus reflect the temporal behavior of different land cover types. Reference data required for a supervised classification approach were collected from several high resolution satellite imagery distributed all over the study area. The overall accuracy results for performed classification of the year 2001 and 2009 are 91.2 and 91.3 %. The comparison of both classification maps shows significant alterations for different classes. Water bodies such as Shardara Water Reservoir and Aral Sea have changed in their extent. Whereby, the size of the Shardara Water Reservoir is very dynamic from year to year due to water management and the eastern lobe of southern Aral Sea has decreased because of the lack of inflow from Amu Darja. Furthermore, some large scale changes were detected in sparsely vegetated areas in Turkmenistan, where spring precipitation mainly affects the vegetation density. In the north of Kazakhstan significant forest losses caused by forest fires and logging were detected. The presented classification approach is a suitable tool for monitoring land cover and land use in Central Asia. Such independent information is important for accurate assessment of water and land recourses.
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Acknowledgments
This study was performed within the Regional Research Network CAWa (Water in Central Asia). We thank the German Federal Foreign Office (AA) for funding CAWa and this research. We appreciate that MODIS data, Landsat images and SRTM data were provided free of charge by the NOAA/USGS and GPCC Full Data Reanalysis Precipitation Data by the Global Precipitation Climatology Centre hosted at Deutscher Wetterdienst (DWD). Furthermore, we would like to thank CAIAG for providing us with vector datasets of the study region. We grateful thank the editors and ZALF to give us the opportunity to publish our results in this book.
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Klein, I., Gessner, U., Künzer, C. (2014). Generation of Up to Date Land Cover Maps for Central Asia. In: Mueller, L., Saparov, A., Lischeid, G. (eds) Novel Measurement and Assessment Tools for Monitoring and Management of Land and Water Resources in Agricultural Landscapes of Central Asia. Environmental Science and Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-01017-5_19
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