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.

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.

No abstract

No abstract

The objective of this study was using a wide range of dietary concentrate levels to investigate the major effects of limit-feeding on heifers. Twenty-four Holstein heifers were blocked into six groups and fed with one of four diets containing different levels of concentrate (20%, 40%, 60% and 80% on a dry matter (DM) basis) but with same intakes of metabolizable energy for 28 days. Increasing levels of dietary concentrate caused decreased (P ≤ 0.02) intakes of dry matter (DMI) and neutral detergent fiber and total rumination time, but increased (P < 0.01) nonfiberous carbohydrates intake, ruminal concentrations of NH -N, propionate and butyrate, and digestibility of DM and crude protein. Dietary concentrate levels had no significant effect on most plasma concentrations and body measurements. The corrected average daily gain (CADG) and feed efficency (ADG/DMI, CFE) were linearly increased (P < 0.01) with increasing dietary concentrate levels when gut fill impact was removed. In conclusion, heifers limit-fed high concentrate diets increased most ruminal fermentation parameters, CADG and CFE with similar body growth and blood metabolites as heifers fed low concentrate diets, and had the potential to be used as an effective feeding strategy in dairy heifers.