On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR

Béland, Martin; Baldocchi, Dennis; Widlowski, Jean-Luc; Fournier, Richard; Verstraete, M. M.

doi:10.1016/j.agrformet.2013.09.005

Cited by 227 publications

(214 citation statements)

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“…1 can be obtained using (Béland et Although this probability is very low when N is high, such an event may happen quite often in 188 any large voxelized scene, especially when the vegetation is dense. These cases can simply be 189 ignored, as proposed in Béland et al (2014a), considering these cases as "occluded", but it 190 will be shown later that it leads to biases and loss of efficiency. 191 192…”

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confidence: 99%

Estimators and Confidence Intervals for Plant Area Density at Voxel Scale with T-LiDAR

Pimont¹,

Allard²,

Soma³

et al. 2017

Preprint

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9Terrestrial LiDAR becomes more and more popular to estimate leaf and plant area density. 10Voxel-based approaches account for this vegetation heterogeneity and significant work has 11 been done in this recent research field, but no general theoretical analysis is available. 2• Our unbiased estimators are consistent in a wider range of validity than the usual ones, 26 especially for the unbiased MLE, which is consistent when the beam number is as low as 5. 27The unbiased MLE is efficient, meaning it reaches the lowest residual errors that can be 28 expected (for an unbiased estimator). Also the unbiased MLE does not require any bias 29 correction when path lengths are unequal. 30• When elements are small (or voxel is large), 10 3 beams entering the voxel leads to some 31 confidence intervals ≈ ±10%, but when elements are larger (or voxel smaller), it can remain 32 wider than ±50%, even for a large beam number. This is explained by the variability of 33 element positions between vegetation samples. Such a result shows that a significant part of 34 residual error can be explained by random effects. 35• Confidence intervals are much smaller (±5 to 10%) when LAD estimates are averaged over 36 several small voxels, typically within a horizontal layer or in the crown of individual plants. 37In this context, our unbiased estimators show a reduction of 50% of the radius of confidence 38 intervals, in comparison to usual estimators. 39Our study provides some new ready-to-use estimators and confidence intervals for attenuation 40 coefficients, which are consistent and efficient within a fairly large range of parameter values. 41The consistency is achieved for a low beam number, which is promising for application to 42 airborne LiDAR data. They entail to raise the level of understanding and confidence on LAD Highlights: 52• Voxel-based estimations of LAD/PAD may lack of consistency and efficiency 53• We propose new estimators based on theoretical derivation and numerical simulations 54• Estimators for confidence intervals are also provided 55• New estimators should help determine the most appropriate voxel resolution 56 57

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mentioning

confidence: 99%

Estimators and Confidence Intervals for Plant Area Density at Voxel Scale with T-LiDAR

Pimont¹,

Allard²,

Soma³

et al. 2017

Preprint

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“…This variation corresponds to differences in the number of laser measurements and differences in the targets intercepted by the laser which can correspond to a different part of the same branch, trunk or leaf. The random displacement of branches and leaves by the wind causes these differences in the targets hit by every scan during the same night of data collection [12].…”

Section: Discussionmentioning

confidence: 99%

“…Terrestrial Laser Scanner measurements are generally mounted on a survey tripod about 1.5 m above the ground. The majority of TLS instruments sample the forest with a high spatial resolution (typically about half a centimeter between consecutive laser pulses) and a relatively small laser footprint size with respect to the typical dimensions of leaves and other tree organs [12]. When laser pulses emitted in the visible or near-infrared comes into contact with an object, part of that energy is reflected back toward the instrument and triggers the recording of its distance and intensity [12].…”

Section: Introductionmentioning

confidence: 99%

“…The technology has proven highly useful for estimating total aboveground biomass and providing detailed vertical profiles of forest environments [3,12,13]. Terrestrial Laser Scanner measurements are generally mounted on a survey tripod about 1.5 m above the ground.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of TLS instruments sample the forest with a high spatial resolution (typically about half a centimeter between consecutive laser pulses) and a relatively small laser footprint size with respect to the typical dimensions of leaves and other tree organs [12]. When laser pulses emitted in the visible or near-infrared comes into contact with an object, part of that energy is reflected back toward the instrument and triggers the recording of its distance and intensity [12]. TLS systems typically employ vertical and horizontal scanning about a fixed point of observation, providing a hemispherical representation of biomass distribution in the forest leaves, branches and trunks, which allows exploring foliage angle distributions and clumping [3,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Using VEGNET In-Situ Monitoring LiDAR (IML) to Capture Dynamics of Plant Area Index, Structure and Phenology in Aspen Parkland Forests in Alberta, Canada

Portillo-Quintero

Sánchez‐Azofeifa

Culvenor³

2014

Forests

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Abstract:The use of ceptometers and digital hemispherical photographs to estimate Plant Area Index (PAI) often include biases and errors from instrument positioning, orientation and data analysis. As an alternative to these methods, we used an In-Situ Monitoring LiDAR system that provides indirect measures of PAI and Plant Area Volume Density (PAVD) at a fixed angle, based on optimized principles and algorithms for PAI retrieval. The instrument was installed for 22 nights continuously from September 26 to October 17, 2013 during leaf-fall in an Aspen Parkland Forest. A total of 85 scans were performed (~4 scans per night). PAI measured decreased from 1.27 to 0.67 during leaf-fall, which is consistent with values reported in the literature. PAVD profiles allowed differentiating the contribution of PAI per forest strata. Phenological changes were captured in four ways: number of hits, maximum cumulative and absolute PAI values, time series of PAVD profiles and PAI values per forest strata. We also found that VEGNET IML Canopy PAI and MODIS LAI values showed a similar decreasing trend and differed by 2%-15%. Our results indicate that the VEGNET IML has great potential for rapid forest structural characterization and for ground validation of PAI/LAI at a temporal frequency compatible with earth observation satellites. OPEN ACCESSForests 2014, 5 1054

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Forest Canopy Structural Complexity and Light Absorption Relationships at the Subcontinental Scale

et al. 2018

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Understanding how the physical structure of forest canopies influences light absorption is a long‐standing area of inquiry fundamental to the physical sciences, including the modeling and interpretation of biogeochemical cycles. Conventional measures of forest canopy structure used to infer canopy light absorption are often limited to leaf or vegetation area indexes. However, LiDAR‐derived measures of canopy structural complexity (CSC) that describe the arrangement of vegetation may improve prediction of canopy light absorption by providing novel information on canopy‐light interactions not regulated by leaf area alone. We measured multiple indexes of CSC, vegetation area index (VAI), and the fraction of photosynthetically active radiation absorbed (fPAR) across the eastern United States using portable canopy LiDAR to evaluate how different canopy structural attributes relate to fPAR. Our survey included sites from the National Ecological Observation Network and university field stations. Measures of CSC were more strongly coupled with fPAR under high light (>1,000 μmol m−2 s−1 PAR). Under low light conditions, when diffuse light predominates, light scattering weakens the dependency of fPAR on CSC. A multivariate model including CSC parameters and VAI explains ~89% of the intersite variance in fPAR, an improvement of over a VAI only linear model (r2 = 0.73). The inclusion of CSC metrics in canopy light absorption models could increase confidence in predictions of biogeochemical cycles and energy balance.

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On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR

Cited by 227 publications

References 50 publications

Estimators and Confidence Intervals for Plant Area Density at Voxel Scale with T-LiDAR

Estimators and Confidence Intervals for Plant Area Density at Voxel Scale with T-LiDAR

Using VEGNET In-Situ Monitoring LiDAR (IML) to Capture Dynamics of Plant Area Index, Structure and Phenology in Aspen Parkland Forests in Alberta, Canada

Forest Canopy Structural Complexity and Light Absorption Relationships at the Subcontinental Scale

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