Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points

Moritz Bruggisser*, Markus Hollaus, Di Wang, Norbert Pfeifer

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
141 Downloads (Pure)


We propose a flexible framework for automated forest patch delineations that exploits a set of canopy structure features computed from airborne laser scanning (ALS) point clouds. The approach is based on an iterative subdivision of the point cloud using k-means clustering followed by an iterative merging step to tackle oversegmentation. The framework can be adapted for different applications by selecting relevant input features that best measure the intended homogeneity. In our study, the performance of the segmentation framework was tested for the delineation of forest patches with a homogeneous canopy height structure on the one hand and with similar water cycle conditions on the other. For the latter delineation, canopy components that impact interception and evapotranspiration were used, and the delineation was mainly driven by leaf area, tree functional type, and foliage density. The framework was further tested on two scenes covering a variety of forest conditions and topographies. We demonstrate that the delineated patches capture well the spatial distributions of relevant canopy features that are used for defining the homogeneity. The consistencies range from R2=0.84 to R2=0.86 and from R2=0.80 to R2=0.91 for the most relevant features in the delineation of patches with similar height structure and water cycle conditions, respectively.
Original languageEnglish
Article number189
Number of pages30
JournalRemote Sensing
Issue number2
Publication statusPublished - 18 Jan 2019
MoE publication typeA1 Journal article-refereed


  • forest structure
  • k-means clustering
  • iterative bi-partitioning
  • overlap merging
  • ecosystem processes
  • forestry


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