Differentiation of Crop Types and Grassland by Multi-scale Analysis of Seasonal Satellite Data

  • Thomas Esch
  • Annekatrin Metz
  • Mattia Marconcini
  • Manfred Keil
Chapter
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 18)

Abstract

The implementation of productive and sustainable cultivation procedures is a major effort regarding the agricultural production in the European Community. However, political, economic and environmental factors impact the cultivation strategies directly and indirectly, and therewith strongly determine the condition and transformation of the cultivated and natural landscape. To assess the actual status, identify basic trends and mitigate major threats with respect to the agricultural production and its impact on the cultural and natural landscape, a frequent and area-wide monitoring of cropland and grassland is required. Satellite-based earth observation (EO) provides ideal capabilities for the area-wide and spatially detailed provision of up-to-date geo-information on the agricultural land use and the properties of the cultivated landscape. A specific benefit of EO is given by analysing multi-seasonal data acquisitions. Intra-annual time series facilitate the analysis of the phenological behaviour of the main crop and grassland types – key information with respect to the characterisation of the land use intensity and its impacts on the environment.

Experimental results for a test area in Germany assess the effectiveness of the proposed approach and demonstrate that a multi-scale and multi-temporal analysis of satellite data can provide spatially detailed and thematically accurate geo-information on crop types and the cropland-grassland distribution, respectively.

Keywords

Normalize Difference Vegetation Index Synthetic Aperture Radar Data Land Parcel Grassland Type Overall Accuracy 
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.

Notes

Acknowledgements

The authors would like to thank the German Federal Agency for Cartography and Geodesy (BKG) for providing GeoBasis-DE data (ATKIS) for this study and the GAF AG and EUROMAP GmbH for the provision of IRS-P6 AWiFS data in the context of the IRS-P6 Scientific Data Pool.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Thomas Esch
    • 1
  • Annekatrin Metz
    • 2
  • Mattia Marconcini
    • 1
  • Manfred Keil
    • 1
  1. 1.Department Land SurfaceGerman Aerospace Center (DLR), Earth Observation Center (EOC), German Remote Sensing Data Center (DFD)OberpfaffenhofenGermany
  2. 2.Institute for Geoinformatics and Remote SensingUniversity of OsnabrückOsnabrückGermany

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