scholarly journals Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

2019 ◽  
Vol 11 (3) ◽  
pp. 344 ◽  
Author(s):  
Kenta Itakura ◽  
Fumiki Hosoi
Keyword(s):  
Inclination Angle ◽  
Three Dimensional ◽  
Absolute Error ◽  
Light Detection And Ranging ◽  
Angle Distribution ◽  
Angle Estimation ◽  
3D Images ◽  
Leaf Inclination ◽  
The Difference ◽  
Different Parts

The leaf inclination angle is a fundamental variable for determining the plant profile. In this study, the leaf inclination angle was estimated automatically from voxel-based three-dimensional (3D) images obtained from lidar (light detection and ranging). The distribution of the leaf inclination angle within a tree was then calculated. The 3D images were first converted into voxel coordinates. Then, a plane was fitted to some voxels surrounding the point (voxel) of interest. The inclination angle and azimuth angle were obtained from the normal. The measured leaf inclination angle and its actual value were correlated and indicated a high correlation (R2 = 0.95). The absolute error of the leaf inclination angle estimation was 2.5°. Furthermore, the leaf inclination angle can be estimated even when the distance between the lidar and leaves is about 20 m. This suggests that the inclination angle estimation of leaves in a top part is reliable. Then, the leaf inclination angle distribution within a tree was calculated. The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.

scholarly journals Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Guangjian Yan ◽  
Hailan Jiang ◽  
Jinghui Luo ◽  
Xihan Mu ◽  
Fan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Inclination Angle ◽  
Three Dimensional ◽  
Good Choice ◽  
Optical Remote Sensing ◽  
Angle Distribution ◽  
Area Index ◽  
Leaf Inclination

Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

scholarly journals Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3576 ◽  
Author(s):  
Kenta Itakura ◽  
Fumiki Hosoi
Keyword(s):  
Leaf Area ◽  
Inclination Angle ◽  
Spatial Information ◽  
Structural Parameters ◽  
Plant Management ◽  
Seed Region ◽  
3D Images ◽  
Initial Segmentation ◽  
Leaf Inclination ◽  
Leaf Segmentation

Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management.

scholarly journals Estimating Needle and Shoot Inclination Angle Distributions and Projection Functions in Five Larix principis-rupprechtii Plots via Leveled Digital Camera Photography

Forests ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 30
Author(s):  
Jie Zou ◽  
Peihong Zhong ◽  
Wei Hou ◽  
Yong Zuo ◽  
Peng Leng
Keyword(s):  
Inclination Angle ◽  
Digital Camera ◽  
Angle Distribution ◽  
Automatic Method ◽  
Projection Function ◽  
Forest Canopies ◽  
Inclination Angles ◽  
Projection Functions ◽  
Leaf Inclination ◽  
Photography Method

The leaf inclination angle distribution function is a key determinant that influences radiation penetration through forest canopies. In this study, the needle and shoot inclination angle distributions of five contrasting Larix principis-rupprechtii plots were obtained via the frequently used leveled digital camera photography method. We also developed a quasi-automatic method to derive the needle inclination angles based on photographs obtained using the leveled digital camera photography method and further verified using manual measurements. Then, the variations of shoot and needle inclination angle distributions due to height levels, plots, and observation years were investigated. The results showed that the developed quasi-automatic method is effective in deriving needle inclination angles. The shoot and needle inclination angle distributions at the whole-canopy scale tended to be planophile and exhibited minor variations with plots and observation years. The small variations in the needle inclination angle distributions with height level in the five plots might be caused by contrasting light conditions at different height levels. The whole-canopy and height level needle projection functions also tended to be planophile, and minor needle projection function variations with plots and observation years were observed. We attempted to derive the shoot projection functions of the five plots by using a simple and applicable method and further evaluated the performance of the new method.

scholarly journals Dynamic Simulation on Canopy Light Distribution and Dry Matter Accumulation of Natural Colored Cotton in Alar Reclamation Area of Xinjiang

2020 ◽  
Vol 10 (3) ◽  
pp. 123-134
Author(s):  
Zhenqi Fan ◽  
◽  
Lixin Zhang
Keyword(s):  
Leaf Area ◽  
Inclination Angle ◽  
Dry Matter ◽  
Light Distribution ◽  
Physiological Age ◽  
Dry Matter Accumulation ◽  
Angle Distribution ◽  
Area Index ◽  
Colored Cotton ◽  
Leaf Inclination

Based on Ross’s theory of optical radiation transmission and full consideration of influences of vertical distribution of canopy leaf area and leaf inclination angle distribution of colored cotton on the light distribution, the Gaussian 5-point distance was used to divide the canopy into 5 layers on basis of the leaf area index. The leaf inclination angle on each layer was divided into 6 equal parts by 15°. The types of radiation in canopy, spatial distribution of light radiation, as well as diurnal variation with solar hour angles were quantified in detail. After comprehensively considering influences of temperature, physiological age and other factors on photosynthesis and respiration, the canopy light distribution, photosynthetic production and dry matter accumulation of colored cotton were simulated with strong mechanistic and physiological & ecological significance. The colored cotton samples sown on April 16, 2019 were used to verify the model. The RMSEs of simulated and measured canopy PAR values at Beijing time 10:00, 12:00, 14:00 and 16:00 on July 30 were 58.2, 64.1, 43.4 and 39.7 µmol•m-2•s-1, respectively. The RMSE of simulated and observed values of the dry matter accumulation above ground was 412.6 kgDM•hm-2, reflecting the good predictability of the model.

Comparison of Head Shape Outcomes in Metopic Synostosis Using Limited Strip Craniectomy and Open Vault Reconstruction Techniques

2020 ◽  
pp. 105566562096929
Author(s):  
Mark Philip Pressler ◽  
Rami R. Hallac ◽  
Emily L. Geisler ◽  
James R. Seaward ◽  
Alex A. Kane
Keyword(s):  
Cross Sections ◽  
Three Dimensional ◽  
Analytical Techniques ◽  
Head Shape ◽  
3D Images ◽  
Metopic Synostosis ◽  
The Difference ◽  
Shape Analyses ◽  
Strip Craniectomy ◽  
Normal Head

Aim: Metopic craniosynostosis (MCS), with its trigonocephalic head shape, is often treated with either limited incision strip craniectomy (LISC) followed by helmet orthotic treatment, or open cranial vault reconstruction techniques (OCVR). There is controversy regarding resultant shape outcomes among craniofacial surgeons. Those adverse to LISC claim normal head shape is never attained, while proponents believe there is gradual correction to an equivalent outcome. This study aims to quantitate, over time, the three-dimensional (3D) head shapes in patients who have undergone LISC or OCVR intervention for MCS. Methods: Sixty-three 3D images of 26 patients with MCS were analyzed retrospectively. Head shape analyses were performed at: (1) preoperative, (2) 1-month postoperative, (3) 10 to 14 months postoperative (1 year), and (4) 2 years postoperative. Composite 3D head shapes of patients were compared at each time point. Two-dimensional (2D) standardized cross sections of the forehead were also compared. Results: Composite head shapes for both groups were nested, to allow visual comparison as the child’s forehead grows and expands. The difference between LISC and OCVR 2D cross sections was calculated; 108.26 mm preoperatively, 127.18 mm after 1-month postoperative, 51.05 mm after 10 to 14 months postoperative, and 27.03 mm after 2 years postoperative. Conclusions: This study found excellent head shape outcomes for both the LISC and OCVR techniques at 2 years of age. It also corroborates the slow and progressive improvement in head shape with the LISC technique. This study highlights the advantages of 3D photography for measurement of contour outcomes, utilizing both 2D vector and 3D whole head analytical techniques.

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