Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives

Yan, Guangjian; Hu, Ronggui; Luo, Jinghui; Weiss, Marie; Jiang, Haijun; Mu, Xihan; Xie, Donghui; Zhang, Wuming

doi:10.1016/j.agrformet.2018.11.033

Cited by 331 publications

(218 citation statements)

References 176 publications

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“…Quantitative analysis of vertical distribution and dynamic changes of LAI for crops is of great importance for early nutrition diagnosis and breeding research. Therefore, it is necessary to accurately estimate the LAI of crops.Currently, LAI measurement uses mainly direct and indirect methods [4,5]. Direct measurement methods include destructive sampling, allometric growth equations, and the litter collection method [6].…”

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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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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 estimation of this range requires a deep understanding of the relationship between leaf dimensions, leaf density, and device specifications. As for increasing footprint size, the estimated P gap from the PNB methods becomes insensitive to LAI or leaf area density variation as well as within-crown clumping, even with the path length variation considered [23,27]. Sensitivity studies demonstrated that IB methods for physically estimating P gap and eLAI were accurate and less influenced by variations in footprint size, leaf area, vegetation cover, and foliar dimensions than the PNB methods.…”

Section: Discussionmentioning

confidence: 96%

“…Information and accuracy losses during waveform discretization can also undermine the use of lidar points for biophysical parameter retrieval [25,26]. Given the influence of ambiguous coefficients and residual radiometric issues, there is considerable controversy over the point-cloud inversion methods used to estimate P gap from a computed laser penetration index (LPI), and in a further step the effective and the true Plant/Leaf Area Index (PAI/LAI) of vegetation [27]. For example, the accuracy of TLS inversions is affected by partial hits that depend on the dimensions of the laser beam and leaves [18,28,29].…”

Section: Introductionmentioning

confidence: 99%

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Yin

Cook

et al. 2019

Remote Sensing

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Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability (P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.

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“…hemiphoto), laser scanning and multispectral imaging (Li et al, ; Parker, Harding, & Berger, ; Pisek, Chen, Lacaze, Sonnentag, & Alikas, ; Ryu et al, ; Vicari et al, ). Underlying these diverse tools is the use of the same physical principle—gap probability modelling—to formulate light–vegetation interactions and interpret data (Pisek, Lang, Nilson, Korhonen, & Karu, ; Yan et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…This weakness is inherent to all classical LAI algorithms (Leblanc, Chen, Fernandes, Deering, & Conley, ) and has been addressed in various ways (Chianucci, Zou, Leng, Zhuang, & Ferrara, ). For example, arbitrary modifications to zero fractions have been suggested by adding one artificial gap pixel to the problematic annuli (Yan et al, ), but this subjective practice is often discouraged, especially due to its severe influences on LAI estimation for dense canopies (Van Gardingen, Jackson, Hernandez‐Daumas, Russell, & Sharp, ; Yan et al, ).…”

Section: Introductionmentioning

confidence: 99%

How to better estimate leaf area index and leaf angle distribution from digital hemispherical photography? Switching to a binary nonlinear regression paradigm

Zhao

Ryu

et al. 2019

Methods Ecol Evol

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Probabilistic modelling of gaps for light–canopy interactions has long served as a theoretical basis to estimate vegetation structural parameters—leaf area index (LAI) and leaf angle distribution (LAD)—from optical measurements such as hemispherical photos. Direct inversion of such probabilistic models provides a reliable statistical algorithm for parameter estimation, but this inferential paradigm has been seldom explored. Even worse, many classical LAI algorithms implicitly assume “wrong” statistical models inconsistent with the underlying probabilistic gap models—a subtle issue not articulated before but known to cause practical issues. Here, we clarified how to improve LAI and LAD estimation by directly inverting binary gap/non‐gap data of hemispherical photos via binary nonlinear regression (BNR). We implemented the new BNR method and some classical algorithms in an R package “hemiphoto2LAI”, comprising a total of 135 models for LAI estimation. Compared to classical algorithms, BNR features many theoretical advantages and allows estimating LAI and LAD simultaneously. BNR can address questions difficult to answer by classical algorithms (e.g. how better is one LAD than another?). We demonstrated the utility of the BNR paradigm based on both synthetic and real data. Overall, BNR is statistically more justifiable but its potential has been under‐appreciated. We encourage the community to embrace this new paradigm for reliable analyses of hemispherical photos or other gap data for canopy research.

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Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives

Cited by 331 publications

References 176 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

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

How to better estimate leaf area index and leaf angle distribution from digital hemispherical photography? Switching to a binary nonlinear regression paradigm

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