Multi-data Approach (mda) for Enhanced Land Use / Land Cover Mapping

For spatial decision support and regional (agro-)ecosystem modeling, land use data are of central importance and have to be available in a spatial data infrastructure for regional modeling. Usually, land use data are available, but they lack the desired information detail for many purposes. For example, in official land use maps, agricultural land use is generally differentiated between arable land, grassland, orchards and some special land use classes like paddy fields. For detailed agro-ecosystem modeling, this information resolution is rather poor. Here, disaggregated land use data which provide information about the major crops and crop rotations as well as management data like date of sowing, fertilization, irrigation, harvest etc. are needed. The analysis of multispectral, hyperspectral and/or radar data from satellite or airborne sensors is a standard method to retrieve such kind of information with remote sensing methodologies. By using a Multi-Data Approach (MDA), the retrieved information from multitemporal and multisensoral remote sensing analysis can be integrated into official land use data to enhance both the information level (e. g. crop rotations) of existing land use data and the quality of the land use classification. The workflow of the MDA to generate enhanced land use and land cover data consists basically of two components: (i) the methods and data of the remote sensing analysis and (ii) the methods and data of the GIS analysis.