Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers

S. Delalieux, B. Somers, B. Haest, C.A. Mücher

Research output: Contribution to journalArticleAcademicpeer-review

69 Citations (Scopus)


Monitoring the conservation status of natural habitats is an essential aspect of effective conservation management. Not only data on habitat occurrence are needed, but also detailed information on the structural and functional characteristics of the habitat patches is crucial for an adequate conservation status assessment. Classification of hyperspectral remote sensing images performs well in discriminating dominant land cover and vegetation classes, but the accuracy drops significantly for the classification of more subtle differences in conservation status that are related to structural characteristics. This study proposes a method to facilitate ecological conservation status assessment based on decision tree modeling of subpixel fraction estimates steered by ecological expert knowledge. In particular, it contributes to the spatially explicit assessment of an important structural aspect of dry heathland vegetation, namely the heather age structure, using Airborne Hyperspectral line-Scanner radiometer (AHS-160) data of the Kalmthoutse Heide in northern Belgium. We implemented a subpixel unmixing approach to identify the percentage of heather, sand and shadow in each heather pixel, and subsequently applied a decision tree classification to allocate each pixel to a certain age class. As such, our method provides a tool that contributes to the information required for an appropriate management and successful conservation of natural heathlands.
Original languageEnglish
Pages (from-to)222-231
JournalRemote Sensing of Environment
Publication statusPublished - 2012


  • land-cover classification
  • vegetation
  • habitats
  • imagery
  • accuracy
  • indicators
  • management
  • selection
  • ecology
  • wetland


Dive into the research topics of 'Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers'. Together they form a unique fingerprint.

Cite this