Gap Fraction Measurements Research Articles

Leaf area index (LAI) and gap fraction. A discussion

Methodological aspects of estimating leaf area from gap fraction measurements are discussed. Instead of the common practice of linking in the Beer-Lambert law leaf area index and clumping factor together, the clumping factor and Ross-Nilson geometry function as two structure parameters should be combined into the effective geometry function, which considers both the leaf angle distribution and clumping/regularity of foliage in the expression of the gap fraction of a vegetation layer. Key words: leaf area index; foliage clumping; gap fraction; LAI-2000; G-function

Daily leaf area index from photosynthetically active radiation for long term records of canopy structure and leaf phenology

Leaf area index (LAI) is a critical biophysical indicator that describes foliage abundance in ecosystems. An accurate and continuous estimation of LAI is therefore desirable to quantify ecosystem status and function (e.g. carbon and water exchange between the land surface and the atmosphere). However, deriving accurate LAI measurements at regular temporal intervals remains challenging, requiring either destructive sampling or manual collection of canopy gap fraction measurements at discrete time intervals. In this study, we present four methods to obtain continuous LAI data, simply derived from above and below canopy measurements of photosynthetically active radiation (PAR) at the Borden Forest Research Station from 1999 to 2018. We compared LAI derived using the four PAR-based methods to independent measurements of LAI from optical methods and the MODIS satellite LAI product. LAI derived from all four PAR-based methods captured the seasonal changes in observed and remotely sensed LAI and showed a close linear correspondence with one another (R2 of 0.55 to 0.76 compared to MODIS LAI, and R2 of 0.78 to 0.84 compared to LAI-2000 measurements). A PAR-based method using Miller's Integral theorem showed the strongest linear relationship with LAI-2000 measurements (R2=0.84, p<0.001, SE=0.40). In many years MODIS LAI indicated an earlier start of season and earlier end of season than the daily PAR-based LAI datasets showing systematic biases in the MODIS assessment of growing season.The four PAR-based LAI methods outlined in this study provide an LAI dataset of unprecedented temporal resolution. These methods will allow precise determination of phenological events, improve leaf to canopy scaling in process-based models, and provide valuable insight into dynamic vegetation responses to global climate change.

Influence of image pixel resolution on canopy cover estimation in poplar plantations from field, aerial and satellite optical imagery

Accurate estimates of canopy cover (CC) are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used to estimate CC from the complement of gap fraction measurements obtained with restricted-view sensors. In this short note we evaluated the influence of the image pixel resolution (ground sampling distance; GSD) on CC estimation in poplar plantations obtained from field (cover photography; GSD < 1 cm), unmanned aerial (UAV; GSD <10 cm) and satellite (Sentinel-2; GSD = 10 m) imagery. The trial was conducted in poplar tree plantations in Northern Italy, with varying age and canopy cover. Results indicated that the coarser resolution available from satellite data is suitable to obtain estimates of canopy cover, as compared with field measurements obtained from cover photography; therefore, S2 isrecommended for larger scale monitoring and routine assessment of canopy cover in poplar plantations. The higher resolution of UAV compared with Sentinel-2 allows finer assessment of canopy structure, which could also be used for calibrating metrics obtained from coarser-scale remote sensing products, avoiding the need of ground measurements.

An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning

Accurate in situ estimates of leaf area index (LAI) are essential for a wide range of ecological studies and applications. Due to the destructiveness and impracticality of direct measurements, indirect optical methods have mostly been used in the field to derive estimates of LAI from gap fraction measurements. Terrestrial laser scanning (TLS) is strongly supporting use of this active technology, which possesses several advantages compared to passive sensors. However, edge effects and partial beam interceptions are significantly challenges for the accurate retrieval of gap fraction from 3D point cloud data available from TLS, particularly in phase-shift instruments, which in turns require point cloud filtering to correct erroneous point measurements.As the limitations above influences the point cloud, we proposed a new method which is based only on the laser return intensity (LRI) information derived from raw TLS data, which are used to generate 2D intensity images. The intensity image contains all the unfiltered LRI information captured by TLS, which is used to separate gap from non-gap pixels, using a procedure comparable to the standard image analysis processing of digital hemispherical images. This allows a theoretically consistent comparison between active and passive optical measurements of gap fraction across all the zenith angle range.The method was tested in real and simulated forests. Gap fraction, canopy openness and effective leaf area index derived from real and simulated intensity TLS images were compared with those obtained using digital hemispherical photography (DHP). Results indicated that the intensity, image-based method outperformed DHP, as the higher pixel resolution of the intensity images and the larger distance covered by TLS allowed detection of many small canopy elements, particularly at higher zenith angles (longer optical distance), which are not detected in DHP. The main findings support the reliability of the intensity, image-based method to standardize protocols for TLS phase-shift scan data processing and use of the produced canopy estimates as a benchmark for passive optical measurements.

Comparison of Seven Inversion Models for Estimating Plant and Woody Area Indices of Leaf-on and Leaf-off Forest Canopy Using Explicit 3D Forest Scenes

Optical methods require model inversion to infer plant area index (PAI) and woody area index (WAI) of leaf-on and leaf-off forest canopy from gap fraction or radiation attenuation measurements. Several inversion models have been developed previously, however, a thorough comparison of those inversion models in obtaining the PAI and WAI of leaf-on and leaf-off forest canopy has not been conducted so far. In the present study, an explicit 3D forest scene series with different PAI, WAI, phenological periods, stand density, tree species composition, plant functional types, canopy element clumping index, and woody component clumping index was generated using 50 detailed 3D tree models. The explicit 3D forest scene series was then used to assess the performance of seven commonly used inversion models to estimate the PAI and WAI of the leaf-on and leaf-off forest canopy. The PAI and WAI estimated from the seven inversion models and simulated digital hemispherical photography images were compared with the true PAI and WAI of leaf-on and leaf-off forest scenes. Factors that contributed to the differences between the estimates of the seven inversion models were analyzed. Results show that both the factors of inversion model, canopy element and woody component projection functions, canopy element and woody component estimation algorithms, and segment size are contributed to the differences between the PAI and WAI estimated from the seven inversion models. There is no universally valid combination of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size that can accurately measure the PAI and WAI of all leaf-on and leaf-off forest canopies. The performance of the combinations of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size to estimate the PAI and WAI of leaf-on and leaf-off forest canopies is the function of the inversion model as well as the canopy element and woody component clumping index estimation algorithm, segment size, PAI, WAI, tree species composition, and plant functional types. The impact of canopy element and woody component projection function measurements on the PAI and WAI estimation of the leaf-on and leaf-off forest canopy can be reduced to a low level (&lt;4%) by adopting appropriate inversion models.

A robust leaf area index algorithm accounting for the expected errors in gap fraction observations

The leaf area index, LAI, representing the physiological and structural functions of vegetation canopies, can be estimated from gap fraction measurements obtained at different zenith angles. Earlier works have provided practical and convenient theoretical solution to retrieve LAI based on the integration of contact numbers (a projected area of leaves on a plane perpendicular to the view or solar zenith angle) over zenith angles as obtained by a linear regression, i.e., LAI=2(A+B), where A and B are the coefficients of the regression of contact numbers against zenith angles. This graphical procedure is equivalent to the more accurate method of LAI retrieval by integrating gap fraction measurements from nadir through horizon angles. However, using an ordinary least-squares regression on inherently unsteady relationship between contact numbers and zenith angles limited the use of a simple graphical procedure for LAI estimation. In this study, we introduce the use of robust procedure to retrieve regression coefficients (i.e., A and B), and assess the performance of the new procedure using numerically derived hypothetical data, computer simulated and real measurements of hemispherical photographs. Our results indicated, the new procedure not only outperformed the ordinary least-squares solution for graphical procedure, but also outperformed all existing LAI methods We conclude from analyses using numerically derived hypothetical data, computer simulated and real measurements of hemispherical photographs that estimating A and B (where LAI=2(A+B)) using a robust procedure is a convenient and sufficiently accurate method for estimating LAI from field measurements of gap fractions at different zenith angles.

Digital photography for tracking the phenology of an evergreen conifer stand

Gap fraction measurements with digital a hemispherical camera were carried out in 2007–2016 in a Scots pine (Pinus sylvestris L.) stand growing on transitional Sphagnum bog in Järvselja, Estonia. The measurements were done at the time when (1) the pines had maximum foliage, or (2) after autumn needle fall or before bud burst. Data for gap fraction calculation were extracted from camera raw files. For data recording, the sensor output signal maximum was kept according to the image brightness histogram within the interquartile range of the sensor dynamic range. A linear conversion method (LinearRatio) was used for image processing. The relative needle loss in autumn (20–30%) increased gap fraction by 0.02–0.05 at view zenith angles from 10° to 60° and decreased the mean plant area index from 1.86 to 1.68 after autumn litter fall. The measurement results were in good agreement with a theoretical gap fraction model simulation and with gap fraction estimates made from cover photos. The measurement and data processing protocol is appropriate for long-term monitoring in permanent sample plots.

Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model

The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3<GAI<6 and data simulated with the 3D structure model with 1<GAI<7.Results confirm that using only the GF provides poor estimates of GAI (0.89<RMSE<1.28; 0.22<rRMSE<0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48<RMSE<0.55; 0.12<rRMSE<0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used.

Measuring leaf area index in rubber plantations − a challenge

In order to estimate water use, water requirements and carbon sequestration of tropical plantation systems such as rubber it is adamant to have accurate information on leaf area development of the plantation as the main determinant of evapotranspiration. Literature commonly suggests a number of different methods on how to obtain leaf area index (LAI) information from tree plantation systems. Methods include destructive measurements of leaf area at peak LAI, indirect methods such as gap fraction methods (i.e. Hemiview and LAI 2000) and radiation interception methods (i.e. SunScan) or litter fall traps. Published values for peak LAI in rubber plantation differ widely and show no clear trend to be explained by management practices or the influence of local climate patterns. This study compares four methods for determining LAI of rubber plantations of different ages in Xishuangbanna, Yunnan, PR China. We have tested indirect measurement techniques such as light absorption and gap fraction measurements and hemispherical image analysis against litter fall data in order to obtain insights into the reliability of these measuring techniques for the use in tropical tree plantation systems. In addition, we have included data from destructive harvesting as a comparison. The results presented here clearly showed that there was no consistent agreement between the different measurements. Site, time of the day and incoming radiation all had a significant effect on the results depending on the devices used. This leaves us with the conclusion that the integration of published data on LAI in rubber into broad ranging assessments is very difficult to accomplish as the accuracy of the measurements seems to be very sensitive to a number of factors. This diminishes the usefulness of literature data in estimating evapotranspiration from rubber plantations and the induced environmental effects, both on local as well as regional levels.

Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography

Abstract Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products.

Leaf area index mapping with optical methods and allometric models in SMEAR flux tower footprint at Järvselja, Estonia

Abstract Leaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net primary production and gas and energy exchange between the canopy and the atmosphere. The aim of the present study was to test different methods for LAI and effective plant area index (PAIe) estimation in mixed hemiboreal forests in Järvselja SMEAR Estonia (Station for Measuring Ecosystem-Atmosphere Relations) flux tower footprint. We used digital hemispherical images from sample plots, forest management inventory data, allometric foliage mass models, airborne discrete-return recording laser scanner (ALS) data and multispectral satellite images. The free ware program HemiSpherical Project Manager (HSP) was used to calculate canopy gap fraction from digital hemispherical photographs taken in 25 sample plots. PAIe was calculated from the gap fraction for up-scaling based on ALS point cloud metrics. The all ALS pulse returns-based canopy transmission was found to be the most suitable lidar metric to estimate PAIe in Järvselja forests. The 95-percentile (H95 ) of lidar point cloud height distribution correlates very well with allometric regression models based LAI and in birch stands the relationship was fitted with 0.7 m2 m−2 residual error. However, the relationship was specific to each allometric foliage mass model and systematic discrepancies were detected at large LAI values between the models. Relationships between the spectral reflectance and allometric LAI were not good enough to be used for LAI mapping. Therefore, airborne laser scanning data-based PAIe map was created for areas near SMEAR tower. We recommend to establish a network of permanent sample plots for forest growth and gap fraction measurements into the flux footprint of SMEAR Estonia flux tower in Järvselja to provide consistent up to date data for interpretation of the flux measurements.

Novel forest structure metrics from airborne LiDAR data for improved snow interception estimation

A set of 57 novel canopy metrics of potential value for snow modeling were created from airborne LiDAR data. The metrics were meant to estimate size and relative location of gap openings around a point within forested areas, allowing for measures of the spatial arrangement of surrounding canopy elements. These new metrics were correlated with snow interception measured in the field (8488 manually measured snow interception points). The results were further compared to the correlation values between effective interception and traditionally used forest parameters (CC and LAI). The correspondence between all metrics was also analyzed in order to understand the potential cross correlation between each variable. LAI (average R: 0.57) demonstrated low correlations when directly compared to snow interception and further showed a large cross correlation with canopy closure (average R: 0.72). A new metric, ‘mean distance to canopy’ had the highest correlation (average R: 0.78) over all storm events to the effective interception. But in contrast to LAI, this metric did not show any cross correlation with canopy closure (CC). Likewise, ‘total gap area,’ an indirect measurement of apparent gap fraction (another new metric), also showed a high correlation to effective interception (average R: 0.72) without demonstrating a significant cross correlation to CC or to mean distance to canopy. These findings suggest that modeling forest snow processes with both CC and LAI may not be the best option due to both the low correlation of LAI as well as high cross correlation between these parameters. However, the pairing of mean distance to canopy and/or total gap area with canopy closure could give more robust estimations of snow interception within heterogeneous terrain.

Gap Fraction Estimates over Selectively Logged Forests in Western Amazon

Gap fraction is a biophysical variable related to energy balance, forest fauna, micro-climate and regeneration, and is an important indicator of forest management quality. The objective of this study was to compare gap fraction estimates from undisturbed forests and different environments or strata of selectively logged areas. Moreover, gap fraction measurements were collected with two distinct instruments (optical canopy analyzer LAI-2000 and hemispherical photographs). Field data were collected from two sustainable forest management sites at Jamari National Forest, Rondonia State, Brazilian Amazon. Our results indicated significant differences between data acquired using these two instruments. For instance, the LAI-2000 data showed greater variation for each environment compared to hemispherical photographics data, and the data were also more sensitive to the increase in gap fraction. Small variations were found in the gap fraction means for the two study areas, and only data for the undisturbed area were significantly different. A gradient of increasing gap fraction that ranged from primary forests to log decks was observed. Furthermore, a multiple linear regression analysis determined the contribution of the selectively logged environments to decreased forest cover, confirming the observed gradient.

On the correct estimation of gap fraction: How to remove scattered radiation in gap fraction measurements?

Correct estimates of gap fraction are essential for quantifying canopy architectural variables, such as leaf area and clumping indices, which modify land–atmosphere interactions. However, gap fraction measurements from optical sensors are contaminated by radiation that is scattered by plant elements and ground surfaces. In this study, we propose a simple one-dimensional, invertible, bidirectional transmission model to remove scattering effects from gap fraction measurements. To evaluate how well the proposed model computes scattered radiance under a variety of ecosystem conditions, we compared simulated scattered radiance by the proposed model to a more sophisticated three-dimensional model in four ecosystem types (oak-grass savanna, birch, pine, and spruce stands). The simple model showed good agreement with the three-dimensional model in the scattering factor (scattered radiation from leaves normalized by sky radiation), except for highly reflective stems such as birch. The simple model showed that the scattering factor is highest when the leaf area index (LAI) is low (1–2m2m−2) in a non-clumped canopy, potential errors in estimating the LAI increase with an increase in LAI, and bright land surfaces (e.g., snow and bright soil) and bright stems (e.g., birch) can contribute significantly to scattering effects. By applying the simple model with LAI-2200 data collected in an oak-grass savanna woodland, we found that the scattering factor causes significant underestimation of the LAI (up to 26% for sunny conditions, 7.7% for diffuse sky conditions) and significant overestimation of the apparent clumping index (up to 14% for sunny conditions, 4.3% for diffuse sky conditions). The LAI is underestimated because of the effect of scattered radiation on gap fraction estimates, which cause overestimation of the clumping index. Even under highly diffuse sky conditions, errors in LAI estimates due to scattering effects are not always negligible (up to 7.7% underestimation). The proposed inversion scheme provides an opportunity to quantify gap fractions, LAI, and apparent clumping index even under sunny conditions.

Measuring gap fraction, element clumping index and LAI in Sierra Forest stands using a full-waveform ground-based lidar

The Echidna Validation Instrument (EVI), a ground-based, near-infrared (1064nm) scanning lidar, provides gap fraction measurements, element clumping index measurements, effective leaf area index (LAIe) and leaf area index (LAI) measurements that are statistically similar to those from hemispherical photos. In this research, a new method integrating the range dimension is presented for retrieving element clumping index using a unique series of images of gap probability (Pgap) with range from EVI. From these images, we identified connected gap components and found the approximate physical, rather than angular, size of connected gap component. We conducted trials at 30 plots within six conifer stands of varying height and stocking densities in the Sierra National Forest, CA, in August 2008. The element clumping index measurements retrieved from EVI Pgap image series for the hinge angle region are highly consistent (R2=0.866) with those of hemispherical photos. Furthermore, the information contained in connected gap component size profiles does account for the difference between our method and gap-size distribution theory based method, suggesting a new perspective to measure element clumping index with EVI Pgap image series and also a potential advantage of three dimensional Lidar data for element clumping index retrieval. Therefore further exploration is required for better characterization of clumped condition from EVI Pgap image series.

Large-scale leaf area index inversion algorithms from high-resolution airborne imagery

Large-scale leaf area index (LAI) inversion algorithms were developed to determine the LAI of a forest located in Gatineau Park, Canada, using high-resolution colour and colour infrared (CIR) digital airborne imagery. The algorithms are parameter-independent and developed based on the principles of optical field instruments for gap fraction measurements. Cloud-free colour and CIR images were acquired on 21 August 2007 with 35 and 60 cm nominal ground pixel size, respectively. Normalized Difference Vegetation Index (NDVI), maximum likelihood and object-oriented classifications, and principal component analysis (PCA) methods were applied to calculate the mono-directional gap fraction. Subsequently, LAI was derived from inversion and compared with ground measurements made in 54 plots of 20 by 20 m using hemispherical photography between 10 and 20 August 2007. There was high inter-correlation (the Pearson correlation coefficient, R > 0.5, p < 0.01) among LAI values inverted using the classifications and PCA methods, but neither were highly correlated with LAI inverted from the NDVI method. LAI inverted from the NDVI-based gap fraction significantly correlated with ground-measured LAI (R = 0.63, root mean square error (RMSE) = 0.52), while LAI inverted from the classification and PCA-derived gap fraction showed poor correlation with ground-measured LAI. Consequently, the NDVI method was used to invert LAI for the whole study area and produce a 20‐m resolution LAI map.

Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops

Leaf area index (LAI) was estimated from vertical gap fraction measurements obtained using top-of-canopy digital colour photography over corn, soybean and wheat canopies. A histogram-based threshold technique was used to separate green vegetation tissues from background soil and residue materials in order to derive the canopy vertical gap fraction from the digital photos. The results show that the gap fraction obtained using digital photography was linearly related with the diffuse non-interceptance obtained with a LAI-2000 plant canopy analyzer ( R 2 = 0.78). LAI derived from the photographic method was comparable to the LAI measured using LAI-2000 ( R 2 = 0.83) in the absence of senescence. We recommend using digital photography in addition to conventional equipment for acquiring LAI and gap fraction of agricultural crops, because the approach is less limited by radiation conditions, and the protocol can easily be implemented for extensive sampling at low cost.

GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops

This study describes a technique to estimate green area index (GAI) of row crops from gap fraction measurements at 57.5° perpendicular to the row using downward looking digital photos. This particular directional configuration makes the gap fraction independent from leaf angle distribution and minimizes leaf clumping when plants overlap within the row and when rows overlap from this particular direction which is the case for several crops including wheat, maize, sorghum, sunflower and soybean. This was demonstrated from generic row crop canopy architecture models. Additional simulations over realistic 3D scenes of wheat crop allowed to calibrate the following equation relating the gap fraction ( Po(57.5°)) to GAI for this particular directional configuration: Po(57.5°) = e −0.824· GAI . This relationship appears very robust across development stages, cultivars and variations due to environmental conditions. When comparing with the situation where leaves are randomly distributed, performances degrade significantly, demonstrating that some residual clumping ( Ω = 0.89) has to be accounted for. Field experiments were conducted over wheat crops using colour digital photos taken at 57.5° zenith angle from above in a compass direction perpendicular to the rows. The corresponding gap fraction was computed after image segmentation based on the three colours. The equation derived from wheat architecture model simulations was then used to estimate GAI. Comparison with destructive GAI field measurements shows very good performances with a relative RMSE of 12%. GAI values estimated with this technique were also showing a good consistency with LAI2000 PAI (plant area index) estimates. However, systematic biases between the two estimates were observed, due to canopy elements at the bottom of the canopy not sampled by the instrument because of the height of the LAI2000 sensor, as well as accounting for the residual clumping in the proposed method. These results suggest that this GAI estimation method is very efficient over wheat crops from emergence up to flowering independently from possible architecture variation due to genotype or environmental condition differences. Possible extension to other crops is discussed.

Sampling gap fraction and size for estimating leaf area and clumping indices from hemispherical photographs

Hemispherical photography is becoming a popular technique for gap fraction measurements to characterize biophysical parameters and solar radiation in plant canopies. One of the crucial steps in the measurement of canopy gap fraction using hemispherical photography is determining the resolution of the sampling grid. In this work, the effects of varying resolutions of sampling grids by modifying the angle widths of zenithal annuli and azimuthal sectors were evaluated for leaf area and clumping indices computations. Sensitivity analysis was performed to test these effects using artificial photographs simulating ideal canopies with varying leaf area index and aggregation levels of foliage elements. Contrasting forest types, including natural tropical cloud forest and exotic plantations, were tested as real canopies. Results indicate that leaf area and clumping indices estimates are significantly affected by the variation of sampling grids. A new approach to solve the problem of null-gap segments, obscured completely by foliage, is proposed. However, the determination of optimal combinations of zenithal annuli and azimuthal sector angular widths that suit all canopy types remains a difficult practical problem that is often overlooked. Finally, theoretically sound gap fraction and size sampling regions were demonstrated for reliable estimates of canopy biophysical parameters.

On the correct estimation of effective leaf area index: Does it reveal information on clumping effects?

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.