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

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2019-01-18
Major/Subject
Mcode
Degree programme
Language
en
Pages
30
Series
Remote Sensing, Volume 11, issue 2
Abstract
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.
Description
Keywords
forest structure, k-means clustering, iterative bi-partitioning, overlap merging, ecosystem processes, forestry, COVER, CANOPY-STRUCTURE, MODEL, IMAGE SEGMENTATION, AIRBORNE, STANDS, WATER
Citation
Bruggisser , M , Hollaus , M , Wang , D & Pfeifer , N 2019 , ' Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points ' , Remote Sensing , vol. 11 , no. 2 , 189 . https://doi.org/10.3390/rs11020189