Neural networks, multitemporal landsat thematic mapper data and topographic data to classify forest damages in the Czech republic

Research output: Contribution to journalArticle


This study uses multitemporal Landsat Thematic Mapper data and topographic data for the purpose of classifying coniferous forest damage in the Czech Republic using an artificial neural network. Comparing the neural network-based classification with earlier studies and a multinominal logistic regression using identical training and test data indicates that the back propagation algorithm is comparable, but not superior, to conventional methods. The dependence on the randomly set input weights and the more time-consuming back propagation training make neural network less useful for classification of forest damages than conventional classification algorithms. However, the ability to integrate and extract information from multisource data with different or unknown distributions are advantages of neural networks.


External organisations
  • University of Twente
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Environmental Sciences
Original languageEnglish
Pages (from-to)217-229
Number of pages13
JournalCanadian Journal of Remote Sensing
Issue number3
StatePublished - 1997