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• Leaf inclination angle (LIA) was first measured at three vertical layers during the growth period. • 1D canopy reflectance simulation with spherical leaf angle distribution (LAD) only holds before the jointing stage. • Vertical variation of leaf spectra affects canopy reflectance during the maturation stage. • Angularly-averaged clumping index (CI) slightly improves the 1D canopy reflectance simulations. • Leaf inclination angle (LIA) profile can remarkably improve the 1D canopy reflectance simulations. Physical model simulations have been widely utilized to simulate the reflectance of vegetation canopies. Such simulations can be used to estimate key biochemical and physical vegetation parameters, such as leaf chlorophyll content (LCC), leaf area index (LAI), and leaf inclination angle (LIA) from remotely sensed data via model inversion. In simulations, field crops are typically regarded as one-dimensional (1D) vegetation canopies with constant leaf properties in the vertical direction and across the growing season. We investigated the seasonal effects of these two simplifications, 1D canopy structure, and vertically constant leaf properties, on canopy reflectance simulations in a rice field using in situ measurements and the 3D discrete anisotropic radiative transfer model (DART). We also developed a new methodology for reconstructing 3D crop canopy architecture, which was validated using measurements of gap fraction and canopy reflectance. Our results revealed that the 1D canopy assumption only holds during the early stage of the growing season, then leaf clumping affects canopy reflectance from the jointing stage onwards. Consideration of the 3D canopy structure and its seasonal variation significantly reduced the deviation between simulated and measured canopy reflectance in the green and near-infrared wavelengths when compared to the typical 1D canopy assumption and produced the closest multi-angular distribution pattern to the measurements. The vertical heterogeneity of leaf spectra affected canopy reflectance weakly during the maturation stage when senescence started from the bottom of the canopy. Consideration of seasonal and vertical variation in LIAs significantly improved the results of 1D canopy reflectance simulations, including the multi-angular distribution patterns. In contrast, the directionally-averaged clumping index (CI) only slightly improved the 1D canopy reflectance simulation. To summarize, these findings can be used to reduce the simulation bias of canopy reflectance and improve the retrieval accuracy of key vegetation parameters in crop canopies at the seasonal scale. [ABSTRACT FROM AUTHOR]

Forest structure is realized in a variety of ways in forest radiation regime models. Depending on model complexity, canopy structure can be quantified by simple parameters such as leaf area index, describing the density of canopies, and an associated clumping index, quantifying the degree of deviation of the spatial distribution of leaves from a uniform distribution, or by realistic three-dimensional models of tree crowns in ray tracing models, for example. Complex models, while being more realistic, are generally difficult to apply to large scale and high temporal frequency observations, as is common in passive optical remote sensing. To this end, simpler forest radiation regime models are useful, and complex models provide valuable insights for calibrating, improving, or training simpler models. The aim of this dissertation is to use a high degree of spatial complexity to analyze how simpler parameters of forest structure behave in different types of forests. In simpler models, forest structure is described through the leaf area index and clumping index. These two variables are capable of quantifying forest structure, however, the clumping index is typically difficult to measure. In the so-called spectral invariants theory, forest structure is summarized by a single parameter, the photon recollision probability, i.e. the probability that a photon, upon being scattered by a canopy element, will again interact with the canopy. Closely associated to this parameter is the silhouette to total area ratio (STAR), i.e. the ratio between the projected area of a body and its total surface area. STAR has commonly been used in shoot clumping correction and been studied in simulated tree crowns. In this dissertation I developed a method to measure STAR using terrestrial laser scanning (TLS) point clouds, and expanded its application to forest stands. I used this method to assess the capability of TLS to analyze forest structure in general, and clumping in particular. To this end, I validated the STAR measurement method using individual tree crowns and destructive reference measurements, and measured STAR at the stand level in 38 forest stands to explore the range of both STAR and clumping index that can be found in different boreal, hemiboreal, and temperate forests. Since STAR is a driver of the forest radiation regime, I explored the relationship between stand STAR and forest reflectance. Forest reflectance was closely related to STAR in spectral regions that are known to be sensitive to forest structure, i.e., especially the red portion of the visible spectrum. In addition, I inverted the forest reflectance model PARAS and showed that realistic prior distributions are required to obtain unbiased LAI estimates. I have shown that TLS may accurately measure stand level STAR, thus allowing to quantify forest structure and clumping in forest radiation regime models. This new method can help to improve forest structure quantification in forest radiation regime models as well as in the measurement of leaf area index in general, and thus contribute to remote sensing applications of forests. [ABSTRACT FROM AUTHOR]

• Bayesian inversion of PARAS model produced promising LAI retrieval accuracy. • Non-uniform priors improve inversion by counteracting optical saturation effects. • Landsat 8 OLI data showed better LAI retrieval accuracy than Sentinel-2 MSI. • We highlight benefits of uncertainty quantification in reflectance model inversion. The inversion of reflectance models is a generalizable tool to obtain estimates on forest biophysical parameters, such as leaf area index, with theoretically little information need from a study area, instead relying on the knowledge about physical processes in the forest radiation regime. The use of prior information can greatly improve the reflectance model inversion, however, the literature does not yet provide much information on the selection of priors and their influence on the inversion results. In this study, we used a Bayesian approach to invert the PARAS forest reflectance model and retrieve leaf area index from Sentinel-2 MSI and Landsat 8 OLI multispectral satellite images. The PARAS model is based on the theory of spectral invariants, which describes the influence of wavelength-independent parameters on forest radiative transfer. The Bayesian inversion approach is highly flexible, provides uncertainty quantification, and enables the explicit incorporation of prior knowledge into the inversion process. We found that the choice of prior information is crucial in inverting a forest reflectance model to predict leaf area index. Regularizing and informative priors for leaf area index strongly improved the predictions, relative to an uninformative prior, in that they counteracted the saturation effect of the optical signal occuring at high values for leaf area index. The predictions of leaf area index were more accurate for Landsat 8 than for Sentinel-2, due to potential inconsistencies in the visible bands of Sentinel-2 in our data, and the higher spectral resolution. [ABSTRACT FROM AUTHOR]

Highlights • Latest progress and prospects of indirect LAI measurement. • Comprehensive review of theories, algorithms, instruments, and challenges. • Appealing new means of terrestrial, airborne, and spaceborne laser scanners. • Recent progress on laser scanner, cover photography, and within-crown clumping. • Future directions on scale, LAD, isolated/sparse vegetation, slope, and validation. Abstract Leaf area index (LAI) is a key parameter of vegetation structure in the fields of agriculture, forestry, and ecology. Optical indirect methods based on the Beer-Lambert law are widely adopted in numerous fields given their high efficiency and feasibility for LAI estimation. These methods have undergone considerable progress in the past decades, thereby making them operational in ground-based LAI measurement and even in airborne estimation. However, several challenges remain, given the requirement of increasing accuracy and new applications. Clumping effect correction attained significant progress for continuous canopies with non-randomly disturbed leaves while non-continuous canopies are rarely studied. Convenient and operational measurement of leaf angle distribution and woody components is lacked. Accurate and comprehensive validations are still very difficult due to the limitations of direct measurement. The introduction of active laser scanning technology is a driving force for addressing several challenges, but its three-dimensional information has not been fully explored and utilized. In order to update the general knowledge and identify the possible error source, this study comprehensively reviews the temporal development, theoretical framework, and issues of indirect LAI measurement, followed by current methods, instruments, and platforms. Latest methods and instruments are introduced and compared to traditional ones. Current challenges, recent advances, and future perspectives are discussed to provide recommendations for further research. [ABSTRACT FROM AUTHOR]

Highlights • Path length distribution model is implemented on TLS data for LAI estimation. • Alpha hull is used for 3D crown modeling and calculation of path length. • The proposed TLS-extracted LAI compared well with the hemispherical photography. • Two simulated plots were built to provide more objective validation. Abstract Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential. [ABSTRACT FROM AUTHOR]

Highlights • Effects of both foliage clumping and woody components are corrected for LAI estimation using terrestrial LiDAR data. • Leaf angle distribution was retrieved for both deciduous and coniferous forests. • Zenith angle is successfully used as a feature in separating leaf from woody components. Abstract Leaf area index (LAI) has frequently been measured in the field using traditional optical methods such as digital hemispherical photography (DHP). However, in the DHP retrieved LAI, there is always contribution of woody components due to the difficulty in distinguishing woody and foliar materials. In addition, the leaf angle distribution which strongly affects the estimation of LAI is either ignored while using the convergent angle 57.5°, or inversed simultaneously with LAI using multiple directions. Terrestrial laser scanning (TLS) provides a 3-dimensional view of the forest canopy, which we used in this study to improve LAI estimation by directly retrieving leaf angle distribution, and subsequently correcting foliage clumping and woody effects. The leaf angle distribution was retrieved by estimating the angle between the leaf normal vectors and the zenith vectors. The clumping index was obtained by using the gap size distribution method, while the woody contribution was evaluated based on an improved point classification between woody and foliar materials. Finally, the gap fraction derived from TLS was converted to effective LAI, and thence to LAI. The study was conducted for 31 forest plots including deciduous, coniferous and mixed plots in Bavarian Forest National Park. The classification accuracy was improved by approximately 10% using our method. Results showed that the clumping caused an underestimation of LAI ranging from 1.2% to 48.0%, while woody contribution led to an overestimation from 3.0% to 31.9% compared to the improved LAI. The combined error ranged from −46.2% to 32.6% of the leaf area index (LAI) measurements. The error was largely dependent on forest types. The clumping index of coniferous plots on average was lower than that of deciduous plots, whereas deciduous plots had a higher woody-to-total area ratio. The proposed method provides a more accurate estimate of LAI by eliminating clumping and woody effects, as well as the effect of leaf angle distribution. [ABSTRACT FROM AUTHOR]

The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology. [ABSTRACT FROM AUTHOR]

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to >0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible. [ABSTRACT FROM AUTHOR]

Clumping is critical for quantifying the radiation regime of forest canopies, but challenging to measure. We developed a method to measure clumping index (CI) of forest stands using voxel-based estimates of leaf area density from terrestrial lidar data. Our method uses the principle of the silhouette to total area ratio (STAR), a commonly used shoot clumping correction approach. We adapted the concept to forest stands, and derived that STAR at canopy scale (STAR f) is no longer simply a clumping index, but a summary variable for forest structure in general. CI can be calculated from STAR f when leaf area index is known. We measured CI and STAR f of 38 forest stands in Finland, Estonia, and Czechia to study the natural range of these variables, their relationships to other forest variables, and to Landsat 8 OLI surface reflectance. CI did not include clumping below voxel scale (20 cm), and ranged from 0.6 to 0.9, with the lowest values (i.e., the most clumped canopies) in conifer forests and temperate oak forests, and the highest CI values (i.e., the most random canopies) in boreal broadleaved forests. CI was closely correlated with surface reflectance in conifer forests, which may be explained by contradicting influence of clumping that decreases canopy reflectance, but increases visibility of the forest floor. From the viewpoint of forest reflectance modeling, STAR is a useful variable due to its close relationship with the photon recollision probability, i.e., the probability that a photon will interact with a canopy element after being scattered from another canopy element. The photon recollision probability is used to model the influence of forest structure on reflectance. Our method provides a physically-based means of measuring STAR f , and thus the photon recollision probability, hence contributing to the development of new methods for interpreting forest canopy structure from optical remote sensing data. • We quantified clumping in 38 forest stands comprising ∼ 2100 trees in Europe. • Clumping obtained from TLS data using the silhouette to total area ratio of stands. • Conifer and temperate floodplain forests had the highest degrees of clumping. • Clumping correlated with L8 OLI surface reflectance, especially in conifer forests. • Clumping at stand level is best measured by TLS, indirect estimates are insufficient. [ABSTRACT FROM AUTHOR]

Estimating the proportion of woody-to-total plant material ‘α’ is an essential step to convert Plant Area Index ‘PAI’ estimates into Leaf Area Index ‘LAI’. α has also been shown to have a significant impact on the passive optical remote sensing signal for retrieval of biophysical parameters in forests, woodlands, and savannas. However, benchmarked indirect α retrieval methods are lacking and thus it is common for this pivotal correction to be ignored. In this paper we validate an α retrieval method using a 3D radiative transfer simulation framework, enabling the retrieval method to be benchmarked against a known and precise model truth. The 3D framework consists of a representative and highly detailed 3D explicit Eucalypt forest reconstructed from field measurements. The 3D structure is coupled with a 3D scattering model to enable simulation of remote sensing instruments. The retrieval method utilises classified hemispherical photography ‘HP’, but is applicable to all ground-based optical instruments that can separate leaf and woody elements. The method is applicable to evergreen forests and thus independent of the estimation of PAI or LAI. The unknown degree of mutual shading or occlusion of leaf and woody elements was traditionally a key impediment to the operational use of this method and was therefore closely examined. The indirect α method utilising classified HP imagery agreed on average to within 0.01 α of the reference ( αref = 0.37). In addition, the method demonstrated robustness to a range of LAI, stem density, and stem distribution values, matching to within ±0.05 α of the reference. Angular dependence on indirect α retrieval was also found; where the entire HP image (180° FOV) was needed to produce the most accurate estimate. Conversely, the classified narrow view zenith angle range around 55−60° zenith also provided an α estimate matching the reference. At this narrow zenith angle the method is insensitive to leaf angle distribution. As such, careful consideration of zenith angle range utilised from the instrument is recommended. The results demonstrate the method’s applicability for accurate indirect estimation of α in single-storey forest types. The simple and efficient method can be used to convert estimates of PAI into LAI from a variety of optical ground-based instruments. Quantitative α estimates can and should be used to aid interpretation of the remote sensing signal from satellite imagery, which has been shown to be sensitive to the proportion and spatial distribution of woody canopy materials. [ABSTRACT FROM AUTHOR]

This study presents an improved theoretical formulation of the gap probability ( Pgap ) model, typically applied to indirectly estimate LAI in woody ecosystems. Specifically, we present the woody element projection function ( G W ), which characterises the angular contribution of non-leaf facets in woody ecosystems, and explain how it may be used to improve the accuracy of indirect LAI retrieval via the application of the Pgap model. G W enables separate treatment of the leaf and wood projection functions in the theoretical model, important in the typical case when Pgap includes the influence of both leaf and wood canopy elements. This study then validates the improved theoretical model using experimental data. Here, Pgap was calculated from a 3D scattering model, parameterised with highly-detailed 3D explicit tree models reconstructed from empirical data of a sampled forest stand. The experimental data was then used to quantify additional effects of view zenith angle ( VZA ), leaf angle distribution (LAD), and the influence of woody components on the indirect estimation of LAI and within-crown clumping via application of the Pgap model. Additionally, we quantify within-crown clumping of reconstructed tree models for leaf and woody elements both together and separately for the first time. LAI errors up to 25% at zenith were found when ignoring G W and were shown to be a function of VZA . Conversely, at the approximate 57.3° (1 radian) VZA , results show that there was no effect of G W due to the wood projection function converging with leaf projection functions. Within-crown clumping factors for the modelled dataset were as low as 0.35. Consequently, making a common assumption of a random distribution of canopy elements at the crown scale would lead to an LAI error of up to 65% for the 3D forest stand. We also conclude that when estimating LAI via the Pgap model, separate treatment of canopy material projection ‘ G ’ functions are required at VZA’s other than 1 radian. The findings of this study and the extended physical formulation presented here impact upon indirect Pgap LAI retrieval methods from sensors of all platforms in clumped canopy environments or canopies with woody (non-leaf) elements contributing to the extinction of light. [ABSTRACT FROM AUTHOR]

Abstract: Rapid, reliable and meaningful estimates of forest canopy are essential to the characterization of forest ecosystems. In this paper the accuracy of digital hemispherical (DHP) and cover (DCP) photography for the estimation of canopy properties in deciduous forests was evaluated. Leaf area index (LAI) estimated from both these photographic methods and from light transmittance data derived from DHP were compared with direct measurements obtained by litter traps (LAILT) and an AccuPAR ceptometer. Also, comparison with different gap fraction methods used to calculate LAI in DHP and LAI-2000 PCA were performed. We applied these methods in four forest stands of Quercus cerris, two stands of Castanea sativa and four stands of Fagus sylvatica, the most common deciduous species in Italy, where LAILT ranged from 3.9 to 7.3. Both photographic methods provided good indirect estimates of LAILT. The DCP method provided estimates of crown porosity, crown cover, foliage cover and the clumping index at the zenith, but required assumptions about the light extinction coefficient at the zenith (k), to accurately estimate LAI. Cover photography provided good indirect estimates of LAI assuming a spherical leaf angle distribution, even though k appeared to decrease as LAI increased, thus affecting the accuracy of LAI estimates in DCP. In contrast, the accuracy of LAI estimates in DHP appeared insensitive to LAILT values, but the method was sensitive to photographic exposure and more time-consuming than DCP. The studied stands were characterized by higher within-crown clumping than between-crowns clumping; only the segmented analysis of gap fraction for each ring of the fisheye images was found to provide reliable and useful clumping index in DHP. The 1-azimuth segment method employed in PCA poorly detected clumping in dense canopies. The correlation between transmittance estimates by DHP with values measured at noon with the AccuPAR ceptometer was linear and significant, although the variability observed in reference measures suggested that results obtained with the ceptometer should be treated with caution. We conclude both photographic methods are suitable for dense deciduous forests. Cover photography holds great promise as a means to quickly obtain inexpensive estimates of LAI over large areas. However, in situations where no direct reference measurements of k are available, we recommend using both DHP and DCP, in order to cross-calibrate the two methods; DCP could then be used for more routinely indirect measurement and monitoring of LAI. [Copyright &y& Elsevier]

Abstract: Effective leaf area index is routinely quantified with optical instruments that measure gap fraction through the probability of beam penetration of sunlight through the vegetation. However, there have been few efforts to obtain theoretically consistent effective leaf area indices from those measurements. To apply the Beer–Lambert law, multiple gap fraction measurements may be averaged in two ways: (1) by taking the mean of the logarithms of the individual gap fraction values or (2) by taking the logarithm of the mean gap fraction. Based on a theoretical model and gap fraction measurements from 41 sites, we report that effective leaf area index must be quantified using the second approach. The first approach implemented in the LAI-2000 instrument considers clumping effects at scales larger than shoots. Thus, the combination of the first approach with an independent clumping index overestimates leaf area index up to 30% at the investigated sites. Clumping effects accounted for by the LAI-2000 instrument, called the “apparent” clumping index, were dependent on canopy cover, crown shape, and canopy height. A forest gap fraction model showed that short canopy height, vertically prolonged crown shape and higher canopy cover are associated with the lowest apparent clumping indices. We show that the apparent clumping index is a useful quantity to constrain the true clumping index and to investigate spatial and temporal variation of clumping effects. Such information would be useful to evaluate a coarse global clumping index map and improve land surface models. [Copyright &y& Elsevier]

Abstract: One of the crucial steps in image processing is the thresholding, meaning the image segmentation needed to separate the foreground (canopy) from the background (sky). Due to the plethora of factors affecting image grey levels (lens vignetting, gamma correction, heterogeneity of sky irradiance, etc.), with current thresholding procedures the estimation of canopy gap fraction is rather uncertain. Both manual and automatic segmentation methods are highly dependent on exposure, operator experience or assumptions of thresholding algorithms, and therefore produce rather subjective and non-repeatable results. However, since CCD and CMOS sensors used in digital cameras respond linearly to light, it is nowadays possible to obtain images in which digital numbers are proportional to canopy brightness. For this purpose the standard in-camera logarithmic conversion (gamma correction) has to be replaced with a linear conversion of the sensor analog signal. In this work, the potential of the linear conversion of digital images in the estimation of canopy gap fraction is assessed and discussed, comparing the estimates obtained with digital cameras and with the LI-COR LAI-2000. In addition, the performance of the linear conversion is compared with standard automatic or manual thresholding methods, both in terms of canopy gap fraction and of plant architectural parameters. The ability to acquire hemispherical photographs with digital numbers proportional to radiance opens new possibilities in the analysis of canopy structure and microclimate, with important feedbacks in ecology and remote sensing. In particular, using two synchronized cameras above and below the canopy, it is possible to calculate high-resolution gap fraction data, which provide an accurate and objective estimation of the intercepted radiation and of typical architectural parameters, such as LAI. In addition, the effectiveness of this methodology for the estimation of linear and logarithmic averages of canopy gap fraction, makes this technique an important step forward in the estimation of leaf area index of non-random canopies. [Copyright &y& Elsevier]

Abstract: A new semi-physical forest reflectance model, PARAS, is presented in the paper. PARAS is a simple parameterization model for taking into account the effect of within-shoot scattering on coniferous canopy reflectance. Multiple scattering at the small scale represented by a shoot is a conifer-specific characteristic which causes the spectral signature of coniferous forests to differ from that of broadleaved forests. This has for long led to problems in remote sensing of canopy structural variables in coniferous dominated regions. The PARAS model uses a relationship between photon recollision probability and leaf area index (LAI) for simulating forest reflectance. The recollision probability is a measurable, wavelength independent variable which is defined as the probability with which a photon scattered in the canopy interacts with a phytoelement again. In this study, we present application results using PARAS in simulating reflectance of coniferous forests for approximately 800 Scots pine and Norway spruce dominated stands. The results of this study clearly indicate that a major improvement in simulating canopy reflectance in near-infrared (NIR) is achieved by simply accounting for the within-shoot scattering. In other words, the low NIR reflectance observed in coniferous areas is mainly due to within-shoot scattering. In the red wavelength the effect of within-shoot scattering was not pronounced due to the high level of needle absorption in the red range. To conclude the paper, further application possibilities of the presented parameterization model are discussed. [Copyright &y& Elsevier]

The theoretical background of modeling the gap fraction and the leaf inclination distribution is presented and the different techniques used to derive leaf area index (LAI) and leaf inclination angle from gap fraction measurements are reviewed. Their associated assumptions and limitations are discussed, i.e., the clumping effect and the distinction between green and non-green elements within the canopy. Based on LAI measurements in various canopies (various crops and forests), sampling strategy is also discussed. [Copyright &y& Elsevier]

Leaf area index (LAI) is the total one‐sided area of leaf tissue per unit ground surface area. It is a key parameter in ecophysiology, especially for scaling up the gas exchange from leaf to canopy level. It characterizes the canopy–atmosphere interface, where most of the energy fluxes exchange. It is also one of the most difficult to quantify properly, owing to large spatial and temporal variability. Many methods have been developed to quantify LAI from the ground and some of them are also suitable for describing other structural parameters of the canopy. This paper reviews the direct and indirect methods, the required instruments, their advantages, disadvantages and accuracy of the results. Analysis of the literature shows that most cross‐validations between direct and indirect methods have pointed to a significant underestimation of LAI with the latter techniques, especially in forest stands. The two main causes for the discrepancy, clumping and contribution of stem and branches, are discussed and some recent theoretical or technical solutions are presented as potential improvements to reduce bias or discrepancies. The accuracy, sampling strategy and spatial validity of the LAI measurements have to be assessed for quality assurance of both the measurement and the modelling purposes of all the LAI‐dependent ecophysiological and biophysical processes of canopies. [ABSTRACT FROM PUBLISHER]

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant and canopy scales. Radiative transfer models are ideally suited to investigate the complex sources of variability in the SIF signal to guide the interpretation of SIF retrievals from airborne and space-borne platforms. Here, we coupled the Fluspect-Cx model of leaf optical properties and chlorophyll- a fluorescence with the Discrete Anisotropic Radiative Transfer (DART) model to upscale SIF from individual leaves to three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical to SIF simulated in two existing models, SCOPE and mSCOPE (RMSE <0.221 W.m−2.μm−1.sr−1). DART simulations in geometrically explicit 3D canopies offered four important insights regarding the influence of vegetation structure on the multi-angular top-of-canopy SIF signal. First, changes in the 3D canopy architecture of maize crops, represented by leaf density (leaf area index), and plant clumping (canopy closure) had a larger impact on SIF than the modelled photosynthetic efficiency distinction between sun-adapted and shade-adapted foliage. Second, clumping of leaves at the crop and stand levels was identified as one of the key driving factors of multi-angular anisotropy of red and far-red SIF (686 and 740 nm) for both maize and eucalyptus canopies. Third, non-photosynthetic woody material had a significant impact on top-of-canopy SIF in modelled 3D forest stands. Wood shadowing decreased the photosynthetically active radiation absorbed by green leaves, and consequently the SIF emissions, by 10% in sparse and 17% in dense eucalyptus stands. The wood obstruction (blocking) effect, quantified as a relative difference of SIF escape probabilities from canopies with and without wood in the nadir viewing direction, decreased far-red SIF by 4–6% but it had a smaller and sometimes positive influence (by less than 2%) on red SIF. Fourth, DART 3D radiative budget profiles revealed that the majority of the SIF signal from a dense eucalyptus stand originated from the top 25% of the simulated canopy. Interestingly, the introduction of bark-covered woody elements did not alter the simulated balance and omnidirectional escape factor of red SIF in this upper canopy part but did raise significantly both of them in case of far-red SIF. These results demonstrate the importance of 3D radiative transfer and radiative budget simulations for investigating SIF interactions in structurally complex plant canopies and for a better understanding of spatiotemporal and multi-angular remote sensing SIF observations. • 3D solar induced fluorescence (SIF) was simulated by coupled Fluspect-DART models. • SIF of turbid vegetation simulated in DART was identical as in SCOPE/mSCOPE models. • Impact of canopy structure on SIF might be larger than sun/shade leaf adaptations. • Wood shadows in forest have a greater effect than its optical interactions with SIF. • Low forest SIF escape factor is driven by a complex foliage structure less by wood. [ABSTRACT FROM AUTHOR]

• LAI is a core aspect of canopy structure, emphasizing the importance of surfaces. • Difficulty of LAI measurement is related to the primacy of its function. • Total LAI is not clearly a driver of total production, as often supposed. • High LAI may be a consequence of structural complexity. • LAI has components with both ecosystem and biological implications. Leaf Area Index, LAI, the total surface area of leaves per unit of ground area (m2 m−2), is a collective measure of the foliar portion of vegetation canopy structure. The measure underscores the primacy of surface area in many fundamental processes of vegetation-environment interactions. Since its definition by Watson in 1947, LAI has been used to aggregate leaf level characteristics throughout the canopy to the crop or stand level, predict light regimes, assess the total quantity of above ground biomass and estimate the primary production in vegetation among other uses. This property is widely cited as a central parameter in ecosystem or earth system models of production, for schemes to parameterize vegetative surface interactions in climate models and for large-scale estimates of other surface properties. The value of LAI is sensitive to a variety of factors. At a global scale it is related to climate and plant functional type. At a local scale it is affected by weather and site factors such as fertility, stand age, management treatment, disturbance history. It is a difficult quantity to measure, principally because only part of the total is readily apparent. The indirect measurement approaches are affected by several distinct factors: viewpoint effects, occlusion of surfaces and the presence of material that is not green foliage. Much effort has been spent to estimate those hidden parts. Here I review the history of this measure and related ecosystem structure and process concepts, discuss how this structural characteristic has been connected to light distribution, material and energy exchange and used to model a variety of vegetation functions. Although the concept has been applied at the leaf, branch and whole-tree basis, I focus here on canopy and ecosystem scales, mostly in forests. The principal theses are that while LAI is a fundamental characteristic of vegetation and the result of considerable energetic investment by the plants: (1) the absolute value of the index is not always a clear driver of biomass or production, (2) the details of leaf area organization in space and within microclimate gradients is far more important than is the total amount of leaf area and (3) the total LAI can include leaf areas spanning a range of species, strategies and behaviors whose aggregate behavior is a challenge to model. I argue that (1) the value of LAI may be considered more of a consequence than a cause of canopy structural attributes and (2) the total LAI may be considered to have a range of functions, from ecosystem production to sustaining a diversity of biological strategies. Scaling of processes dependent on leaf area is a significant challenge and simple aggregation schemes can be misleading. I suggest that much of the total LAI (beyond about LAI ~ 3) may not be directly relevant for many ecosystem processes but have other important consequences. I illustrate this idea with several examples and suggest some questions related to LAI that might be profitably studied. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]