Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds

Abstract

Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of under-story layers can be derived. This paper presents a tree segmentation approach for multistory stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an over-story and multiple under-story tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. Unlike previous work that stripped stiff layers within a constrained area, the procedure stratifies the point cloud to flexible tree canopy layers over an unconstrained area with minimal over/under-segmentations of tree crowns across the layers. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies with a variety of stand structures. We applied the proposed approach to the University of Kentucky Robinson Forest -a natural deciduous forest with complex terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting under-story trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented under-story trees (increased from 1% to 16%), while barely affecting the segmentation quality of overstory trees. Results of vertical stratification of canopy showed that the point density of under-story canopy layers were suboptimal for performing reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds (becoming affordable due to advancements of the sensor technology and platforms) would allow more improvements in segmenting under-story trees.

Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

“…where d n denotes the point density of the n th canopy layer, which converges to zero as n increases because point density of individual canopy layers generally decreases with proximity to ground level ( Fig. 1) 41,53,54 . To normalize point densities, we divide both sides of Eq.…”

Assessing the performance of object‐oriented LiDARpredictors for forest bird habitat suitability modeling

“…Nevertheless, recent progress in LiDAR processing may soon fill this gap and allow the extraction of objects below the canopy (Hamraz et al. ). In light of these results, the optimal habitat for capercaillie should include trees of diverse heights from 5 m to 20 m, at both small and large scales.…”

“…Yet, understory is known to constitute an essential component of the capercaillie habitat, which may explain why OO models performed worse than PC area-based models. Nevertheless, recent progress in LiDAR processing may soon fill this gap and allow the extraction of objects below the canopy (Hamraz et al 2017). In light of these results, the optimal habitat for capercaillie should include trees of diverse heights from 5 m to 20 m, at both small and large scales.…”

Remote Sensing of Forests using Discrete Return Airborne LiDAR

“…The canopy stratification procedure [19] uses all LiDAR points binned into the horizontal grid. It then analyzes the height histogram of all LiDAR points within a circular locale around each individual grid cell.…”