Tree species mapping by combining hyperspectral with LiDAR data

Pieter Kempeneers, Frieke Vancoillie, Wenzhi Liao, Flore Devriendt, Kris Vandekerkhove, Monique Bernier (Editor), Josée Lévesque (Editor), Jean-Marc Garneau (Editor), Ellsworth LeDrew (Editor)

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This study deals with data fusion of hyperspectral and LiDAR sensors for forest applications. In particular, the added value of different data sources on tree species mapping has been analyzed. A total of seven species have been mapped for a forested area in Belgium: Beech, Ash, Larch, Poplar, Copper beech, Chestnut and Oak. Hyperspectral data is obtained from the APEX sensor in 286 spectral bands. LiDAR data has been acquired with a TopoSys sensor Harrier 56 at full waveform. Confirming previous research [1], it has been found that airborne LiDAR data, when combined with hyperspectral data, can improve classification results. The novelty of this study is in the quantification of the contribution of the individual data sources and their derived parameters. LiDAR information was combined with the hyperspectral image in a data fusion approach. Different data fusion techniques were tested, including feature and decision fusion. Decision fucsion produced optimal results, reaching an overall accuracy of 96% (Kappa [3] of 0:95).
Original languageEnglish
Number of pages4
Publication statusPublished - 18 Jul 2014
Event 2014 IEEE Geoscience and Remote Sensing Symposium IGARSS - Quebec City, Canada
Duration: 13 Jul 201418 Jul 2014


Conference 2014 IEEE Geoscience and Remote Sensing Symposium IGARSS
Abbreviated titleIGARSS 2014
CityQuebec City


  • LiDAR
  • hyperspectral images
  • tree species mapping
  • image classification


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