Author Correction: Leaf angle distribution in Johnsongrass, leaf thickness in sorghum and Johnsongrass, and association with response to Colletotrichum sublineola

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

ORIENTATION OF SORBUS AUCUPARIA LEAVES BY THE RESULTS OF FIELD SURVEYS IN THE TERRITORY OF THE LENINGRAD REGION

Methods for retrieving leaf inclination angles in a forest canopy are considered. To acquire data on the orientation of Sorbus aucuparia leaves, a technique based on leveled camera digital photography well suited for conducting surveys in a boreal forest was used. In the course of field and office work, leaf angle distribution data for the specified species in the Priozersky district of the Leningrad region was obtained and analyzed. Values of the Ross-Nielson integral function were estimated.

Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

Leaf angle distribution in Johnsongrass, leaf thickness in sorghum and Johnsongrass, and association with response to Colletotrichum sublineola

Basal leaf angle distribution was surveyed in twenty-one Johnsongrass cultivars near the end of the vegetative stage. The angles increased from the top to the bottom leaves, and compared to cultivated grain sorghums, the average angle was larger in Johnsongrass. When basal leaf angle distribution data were correlated with pathogenicity test data from excised-leaf assays for three isolates of Colletotrichum sublineola, the results showed a weak positive correlation between basal leaf angle and pathogenicity level in Johnsongrass. In order to investigate a protective role of leaf thickness to C. sublineola, leaf thickness was measured in three sorghum cultivars and one Johnsongrass cultivar at the 8-leaf-stage. Leaf thickness near the apex, near the base, and half-way between the two points were measured in the top four leaves of each plant. Thickness of leaf blade and midrib were recorded separately. Using an excised-leaf-assay, the three points were inoculated with C. sublineola, and pathogenicity level was recorded 4-days-post-inoculation. Results showed strong negative correlations between leaf midrib thickness and pathogenicity level in sorghum and Johnsongrass but not in leaf blades.

Simulations of Leaf BSDF Effects on Lidar Waveforms

Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data.

Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR

It is critical to take the variability of leaf angle distribution into account in a remote sensing analysis of a canopy system. Due to the physical limitations of field measurements, it is difficult to obtain leaf angles quickly and accurately, especially with a complicated canopy structure. An application of terrestrial LiDAR (Light Detection and Ranging) is a common solution for the purposes of leaf angle estimation, and it allows for the measurement and reconstruction of 3D canopy models with an arbitrary volume of leaves. However, in most cases, the leaf angle is estimated incorrectly due to inaccurate leaf segmentation. Therefore, the objective of this study was an emphasis on the development of efficient segmentation algorithms for accurate leaf angle estimation. Our study demonstrates a leaf segmentation approach based on a k-means algorithm coupled with an octree structure and the subsequent application of plane-fitting to estimate the leaf angle. Furthermore, the accuracy of the segmentation and leaf angle estimation was verified. The results showed average segmentation accuracies of 95% and 90% and absolute angular errors of 3° and 6° in the leaves sampled from mochi and Japanese camellia trees, respectively. It is our conclusion that our method of leaf angle estimation has high potential and is expected to make a significant contribution to future plant and forest research.