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

Hamraz, Hamid; Contreras, Marco A.; Zhang, Jun

doi:10.1038/s41598-017-07200-0

Cited by 67 publications

(45 citation statements)

References 62 publications

(104 reference statements)

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“…R 11 27.0 0.85 0.085 R 12 24.5 0.90 0.050 R 13 28.6 0.85 0.040 R 14 43.7 0.69 0.060 R 15 39.6 0.70 0.055 R 16 33.3 0.82 0.040 R 17 30.3 0.85 0.040 R 18 42.7 0.67 0.065 R 21 11.2 0.90 0.040 R 22 1.9 0.94 0.050 R 23 3.2 0.92 0.055 R 24 5.8 0.74 0.060 R 25 3.6 0.84 0.050 R 26 1.9 0.93 0.045 R 27 3.4 0.83 0.045 densities, and the optimal voxel sizes were concentrated on 0.040 m to 0.055 m, considering the average ground points distance of approximately 0.0239 m. Therefore, the optimal voxel size is approximately 1.7 to 2.3 times of the average ground point distance. Among them, the highest R 2 was 0.94 and the lowest was 0.70, and the highest NRMSE was 1.9% and the lowest was 39.6%.…”

Section: Routes Nrmse (%) R 2 Optimal Voxel Size (M)mentioning

confidence: 99%

Effect of Leaf Occlusion on Leaf Area Index Inversion of Maize Using UAV–LiDAR Data

Lei

Qiu

et al. 2019

Remote Sensing

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The leaf area index (LAI) is a key parameter for describing crop canopy structure, and is of great importance for early nutrition diagnosis and breeding research. Light detection and ranging (LiDAR) is an active remote sensing technology that can detect the vertical distribution of a crop canopy. To quantitatively analyze the influence of the occlusion effect, three flights of multi-route high-density LiDAR dataset were acquired at two time points, using an Unmanned Aerial Vehicle (UAV)-mounted RIEGL VUX-1 laser scanner at an altitude of 15 m, to evaluate the validity of LAI estimation, in different layers, under different planting densities. The result revealed that normalized root-mean-square error (NRMSE) for the upper, middle, and lower layers were 10.8%, 12.4%, 42.8%, for 27,495 plants/ha, respectively. The relationship between the route direction and ridge direction was compared, and found that the direction of flight perpendicular to the maize planting ridge was better than that parallel to the maize planting ridge. The voxel-based method was used to invert the LAI, and we concluded that the optimal voxel size were concentrated on 0.040 m to 0.055 m, which was approximately 1.7 to 2.3 times of the average ground point distance. The detection of the occlusion effect in different layers under different planting densities, the relationship between the route and ridge directions, and the optimal voxel size could provide a guideline for UAV–LiDAR application in the crop canopy structure analysis.

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Section: Routes Nrmse (%) R 2 Optimal Voxel Size (M)mentioning

confidence: 99%

Effect of Leaf Occlusion on Leaf Area Index Inversion of Maize Using UAV–LiDAR Data

Lei

Qiu

et al. 2019

Remote Sensing

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“…Airborne laser scanning, on the other hand, allows data collection over larger areas hence making it particularly useful for integration with remotely collected animal movement data (see Strandburg-Peshkin, Farine, Crofoot, & Couzin, 2017 for an example) or for viewshed applications in landscape ecology (see below). When collected at sufficiently high density (c. 170 pt/m²), ALS point clouds can also be used to model 3D structure of individual vegetation strata (Hamraz, Contreras, & Zhang, 2017) which allows modelling viewsheds below forest canopies. Airborne laser scanning data (both processed and as point clouds) is becoming freely available for a rapidly increasing number of countries.…”

Section: A Call For "Viewshed Ecology"mentioning

confidence: 99%

“…When collected at sufficiently high density ( c . 170 pt/m²), ALS point clouds can also be used to model 3D structure of individual vegetation strata (Hamraz, Contreras, & Zhang, ) which allows modelling viewsheds below forest canopies. Airborne laser scanning data (both processed and as point clouds) is becoming freely available for a rapidly increasing number of countries.…”

Section: Introductionmentioning

confidence: 99%

A call for viewshed ecology: Advancing our understanding of the ecology of information through viewshed analysis

2017

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There has been rapid increase of interest in the role that information acquisition plays in ecological process and in shaping life histories and their evolution. Compared to auditory and olfactory cues, the range at which visual cues are likely to be informative to animals is particularly sensitive to the spatial structure of the environment. However, quantification of and accounting for availability of visual information in fundamental and applied ecological research remains extremely limited. We argue that a comprehensive understanding of animal behaviour in a spatial context would greatly benefit from objective quantification of the area an animal can potentially obtain visual information from and therefore draw broad attention to viewshed analysis. This analysis identifies all cells of a gridded surface that are connected by lines‐of‐sight to a viewpoint, hence, providing information on how much of the environment surrounding a location can be seen. Although heavily used in non‐ecological disciplines including civil planning and archaeology, viewshed analysis has seldom been applied in an ecological context. Here, we highlight the opportunity to make use of viewshed approaches in conjunction with three‐dimensional remote sensing data and data from animal tracking to make major progress in understanding how visual information influences animal spatial behaviour, ecology and evolution.

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“…If, however, our initial point cloud was a few times denser, the two lower canopy layers might have neared the optimal density, likely boosting segmentation accuracy of under-story trees. In a concurrent study, we modeled how point density of lower canopy layers decreases and estimated that a point cloud density of about 170 pt/m 2 is required to segment under-story trees within as deep as the third canopy layer with accuracies similar to over-story trees (Hamraz et al 2017a). Such dense LiDAR campaigns are slowly becoming affordable given the advancements of the sensor technology and platforms (Swatantran et al 2016).…”

Section: Tree Segmentation Accuracymentioning

confidence: 99%

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

Hamraz

Contreras

Zhang

2017

ISPRS Journal of Photogrammetry and Remote Sensing

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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.

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

Cited by 67 publications

References 62 publications

Effect of Leaf Occlusion on Leaf Area Index Inversion of Maize Using UAV–LiDAR Data

Effect of Leaf Occlusion on Leaf Area Index Inversion of Maize Using UAV–LiDAR Data

A call for viewshed ecology: Advancing our understanding of the ecology of information through viewshed analysis

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

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