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

1
Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
2
Department of Built Environment, Aalto University, 00076 Aalto, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(2), 189; https://doi.org/10.3390/rs11020189
Received: 8 November 2018 / Revised: 14 January 2019 / Accepted: 14 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue 3D Point Clouds in Forests)
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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 R 2 = 0.84 to R 2 = 0.86 and from R 2 = 0.80 to R 2 = 0.91 for the most relevant features in the delineation of patches with similar height structure and water cycle conditions, respectively. View Full-Text
Keywords: forest structure; k-means clustering; iterative bi-partitioning; overlap merging; ecosystem processes; forestry forest structure; k-means clustering; iterative bi-partitioning; overlap merging; ecosystem processes; forestry
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bruggisser, M.; Hollaus, M.; Wang, D.; Pfeifer, N. Adaptive Framework for the Delineation of Homogeneous Forest Areas Based on LiDAR Points. Remote Sens. 2019, 11, 189.

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