TLSL<scp>e</scp>AF: automatic leaf angle estimates from single‐scan terrestrial laser scanning

Stovall, Atticus; Masters, B. C.; Fatoyinbo, Lola; Yang, Xi

doi:10.1111/nph.17548

Cited by 28 publications

(27 citation statements)

References 45 publications

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“…Terrestrial LiDAR scanning is increasingly used to measure canopy structure using point cloud data, which can capture detailed 3D structural information of the canopy [54,55,60,61,94]. Most of the terrestrial LiDAR studies have been focused on tree canopies.…”

Section: Canopy Leaf Angle Distribution and Light Capturementioning

confidence: 99%

“…Most of the terrestrial LiDAR studies have been focused on tree canopies. Stovall et al [54] developed an automatic leaf angle estimation algorithm from single-scan terrestrial laser scanning. The algorithm can capture leaf curvature by estimating multiple angles per leaf through capturing incidence angle at the pixel level [55], which provides a more precise representation of leaf angle distribution.…”

Section: Canopy Leaf Angle Distribution and Light Capturementioning

confidence: 99%

“…The algorithm can capture leaf curvature by estimating multiple angles per leaf through capturing incidence angle at the pixel level [55], which provides a more precise representation of leaf angle distribution. Implementing leaf angle distribution with the full angular distribution of sub-leaf curvature will provide more realistic estimates of canopy light interception [54]. Its applicability for rice has yet to be studied.…”

Section: Canopy Leaf Angle Distribution and Light Capturementioning

confidence: 99%

“…Many methods have been developed for estimating leaf angle and/or leaf angle distribution, including inclinometers [34], protractors [27], 3D digitizers [35,36], ground-based digital photography [32,[37][38][39][40][41][42][43][44][45], LiDAR (light detection and ranging laser scanning) [46][47][48][49][50][51][52][53][54][55], aerial photography including drones [56][57][58][59], and smartphone photography [43]. Terrestrial LiDAR scanning is increasingly used to measure the canopy structure using point cloud data, which can capture detailed 3D structural information of the canopy [54,55,60,61]. Most of the terrestrial LiDAR studies have been focused on tree canopies, including work by Itakura and Hosoi [51] on isolated tress and Liu et al [62] on natural beech forest.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling

Yang¹,

Paleari

Wilson³

et al. 2021

Agronomy

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The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.

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Section: Canopy Leaf Angle Distribution and Light Capturementioning

confidence: 99%

Section: Canopy Leaf Angle Distribution and Light Capturementioning

confidence: 99%

Section: Canopy Leaf Angle Distribution and Light Capturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling

Yang¹,

Paleari

Wilson³

et al. 2021

Agronomy

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“…Since the early 2000s, terrestrial laser scanning (TLS) has matured into a robust, and tractable means of measuring forest structural attributes using light detection and ranging (LiDAR) from the ground [1]. TLS has been used to model branch architecture [2], quantify leaf angle distributions [3,4], assess habitat quality [5], estimate fuel loads [6][7][8], map forest microtopograpy [9,10], examine biodiversity gradients [11,12], and estimate forest biomass [13][14][15][16][17][18]. Forest biomass carbon storage and productivity and can be inferred from estimates of above-ground biomass [19,20].…”

Section: Introductionmentioning

confidence: 99%

Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

Stovall¹,

Atkins²

2021

Preprint

Self Cite

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The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

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Remote Detection and Monitoring of Plant Traits: Theory and Practice

Angel

Shiklomanov

2022

Annual Plant Reviews Online

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A means for tracking global plant biodiversity and understanding ecological processes is to identify general functioning principles and quantify underpinning common traits across species. Cumulative knowledge from applied spectroscopy in plant functioning has demonstrated the use of hyperspectral remote sensing in quantifying leaf and canopy biochemical and physiological traits. This article aims to provide an overview of the existing theory, research, and applications of remote sensing of plant functioning traits and wraps up future directions and challenges for ecologists and remote sensing users in assessing global biodiversity and ecological functions. It covers the physics, chemistry, and physiological ecology underlying the relationships between leaf and canopy optical properties with functional traits. Within the scope of the article, descriptions and commonly used physical and empirical trait retrieval approaches are included for both leaf and canopy scales. The last section considers the advancing of retrieving frameworks through emerging hyperspectral remote sensing technology and data‐driven analytics. The article concludes by calling for a greater infusion of biological and ecological principles into remote trait retrieval techniques to ultimately improve understanding of ecosystem function and dynamics across different spatial and temporal scenarios.

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TLSLeAF: automatic leaf angle estimates from single‐scan terrestrial laser scanning

Cited by 28 publications

References 45 publications

Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling

Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling

Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

Remote Detection and Monitoring of Plant Traits: Theory and Practice

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