A Semi-Automated Approach for the Generation of a New Land Use and Land Cover Product for Germany Based on Landsat Time-Series and Lucas in-Situ Data. Mack, B., Leinenkugel, P., Kuenzer, C., & Dech, S. Remote Sensing Letters, 8(3):244–253, December, 2016.
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Information on land cover and land use at high spatial resolutions is essential for advancing earth system science as well as for environmental monitoring to support decision-making and reporting processes. In view of this, we present the first version of the DFD Land Use and Land Cover Product for Germany, DFD-LULC_DE, for the year 2014, generated from 702 Landsat-7 and Landat-8 scenes at 30 m resolution. The results were derived based on a fully automated preprocessing chain that integrates data acquisition, radiometric, atmospheric and topographic correction, as well as spectral-temporal feature extraction for all Landsat surface reflectance bands, brightness temperature and various spectral indices. The classification followed a two-step approach: first, an initial classification is performed using a Random Forest classifier trained on ground truth data obtained from the LUCAS survey of EUROSTAT, followed by a semi-automated sampling of additional training data to further improve the initial classification results. Automatic selection of appropriate training samples is based on the vote entropy derived from the initial classification, thereby keeping manual user interaction low. The approach demonstrated is promising, also with respect to a European wide application, and contributes towards the advancement and enhancement of the DLR-DFD's processing chains, which are directed towards the generation of land cover products at regular intervals being of central importance to related land monitoring and reporting services.
@article{mackSemiautomatedApproachGeneration2016,
  title = {A Semi-Automated Approach for the Generation of a New Land Use and Land Cover Product for {{Germany}} Based on {{Landsat}} Time-Series and {{Lucas}} in-Situ Data},
  author = {Mack, Benjamin and Leinenkugel, Patrick and Kuenzer, Claudia and Dech, Stefan},
  year = {2016},
  month = dec,
  volume = {8},
  pages = {244--253},
  issn = {2150-704X},
  doi = {10.1080/2150704x.2016.1249299},
  abstract = {Information on land cover and land use at high spatial resolutions is essential for advancing earth system science as well as for environmental monitoring to support decision-making and reporting processes. In view of this, we present the first version of the DFD Land Use and Land Cover Product for Germany, DFD-LULC\_DE, for the year 2014, generated from 702 Landsat-7 and Landat-8 scenes at 30 m resolution. The results were derived based on a fully automated preprocessing chain that integrates data acquisition, radiometric, atmospheric and topographic correction, as well as spectral-temporal feature extraction for all Landsat surface reflectance bands, brightness temperature and various spectral indices. The classification followed a two-step approach: first, an initial classification is performed using a Random Forest classifier trained on ground truth data obtained from the LUCAS survey of EUROSTAT, followed by a semi-automated sampling of additional training data to further improve the initial classification results. Automatic selection of appropriate training samples is based on the vote entropy derived from the initial classification, thereby keeping manual user interaction low. The approach demonstrated is promising, also with respect to a European wide application, and contributes towards the advancement and enhancement of the DLR-DFD's processing chains, which are directed towards the generation of land cover products at regular intervals being of central importance to related land monitoring and reporting services.},
  journal = {Remote Sensing Letters},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14617987,~to-add-doi-URL,classification,clc,germany,land-cover,land-use,landsat,lucas,modelling,random-forest,remote-sensing},
  lccn = {INRMM-MiD:c-14617987},
  number = {3}
}
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