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The paper “Model-based local thresholding for canopy hemispherical photography” describes a method that is a framework in which different algorithms could be developed. The basic software needed for algorithm developing was programmed in the R package CAnopy IMage ANalysis (https://github.com/GastonMauroDiaz/caiman). The algorithm described in the paper is a R script published as a gist (https://goo.gl/zygWDN). This project contains data needed to run the script. Also, includes the templates needed to calibrate the camera.

The in situ measurement of the leaf area index (LAI) from gap fraction is often affected by terrain slope. Path length correction (PLC) is commonly used to mitigate the topographic effect on the LAI measurements. However, the terrain-induced uncertainty and the accuracy improvement of the PLC for LAI measurements have not been systematically analyzed, hindering the establishment of an appropriate protocol for LAI measurements over mountainous regions. In this article, the above knowledge gap was filled using a computer simulation framework, which enables the estimated LAI before and after PLC to be benchmarked against the known and precise model truth. The simulation was achieved by using CANOPIX software and a dedicatedly designed ray-tracing method for continuous and discrete canopies, respectively. Simulations show that the slope distorts the angular pattern of the gap fraction, i.e., increasing the gap fraction in the down-slope direction and reducing it in the up-slope direction. The horizontally equivalent hemispheric gap fraction from the PLC can reconstruct the azimuthally symmetric angular pattern of the real horizontal surface. The azimuthally averaged gap fraction for sloping terrain can both be underestimated or overestimated depending on the LAI and can be successfully corrected through PLC. The topography-induced uncertainty in LAI measurements is found to be ~14.3% and >20% for continuous and discrete canopies, respectively. This uncertainty can be, respectively, reduced to ~1.8% and

The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography. When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy. Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.

Within-field variation of leaf area index (LAI) plays an essential role in field crop monitoring and yield forecasting. Although unmanned aerial vehicle (UAV)-based optical remote sensing method can overcome the spatial and temporal resolution limitations associated with satellite imagery for fine-scale within-field LAI estimation of field crops, image correction and calibration of UAV data are very challenging. In this study, a physical-based method was proposed to automatically calculate crop effective LAI (LAIe) using UAV-based 3-D point cloud data. Regular high spatial resolution RGB images were used to generate point cloud data for the study area. The proposed method, simulated observation of point cloud (SOPC), was designed to obtain the 3-D spatial distribution of vegetation and bare ground points and calculate the gap fraction and LAIe from a UAV-based 3-D point cloud dataset at vertical, 57.5°, and multiview angle of a winter wheat field in London, Ontario, Canada. Results revealed that the derived LAIe using the SOPC multiview angle method correlates well with the LAIe derived from ground digital hemispherical photography, R2 = 0.76. The root mean square error and mean absolute error for the entire experiment period from May 11 to May 27 were 0.19 and 0.14, respectively. The newly proposed method performs well for LAIe estimation during the main leaf development stages (BBCH 20-39) of the growth cycle. This method has the potential to become an alternative approach for crop LAIe estimation without the need for ground-based reference measurements, hence save time and money.

Estimates of clumping index (Ω) are required to improve the indirect estimation of leaf area index (L) from optical field-based instruments such as digital hemispherical photography (DHP). A widely used method allows estimation of Ω from DHP using simple gap fraction averaging formulas (LX). This method is simple and effective but has the disadvantage of being sensitive to the spatial scale (i.e., the azimuth segment size in DHP) used for averaging and canopy density. In this study, we propose a new method to estimate Ω (LXG) based on ordered weighted gap fraction averaging (OWA) formulas, which addresses the disadvantages of LX and also accounts for gap size distribution. The new method was tested in 11 broadleaved forest stands in Italy; Ω estimated from LXG was compared with other commonly used clumping correction methods (LX, CC, and CLX). Results showed that LXG yielded more accurate Ω estimates, which were also more correlated with the values obtained from the gap size distribution methods (CC and CLX) than Ω obtained from LX. Leaf area index estimates, adjusted by LXG, are only 5%–6% lower than direct measurements obtained from litter traps, while other commonly used clumping correction methods yielded more underestimation.

Leaf area index (LAI) is an important vegetation structure parameter in terrestrial ecosystem modeling. Although the spaceborne Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud and land Elevation Satellite (ICESat) has been proved to have potential for deriving forest LAI, previous methods were only applicable to estimate the effective LAI. In this study, a physical method based on the gap fraction model was proposed to retrieve the LAI correcting the between-crown clumping in discontinuous forest using GLAS full-waveform data. Landsat TM imagery was utilized as auxiliary data for providing crown cover information within the footprint. Using the gap probability from GLAS data and the crown coverage fraction from Landsat imagery, the method corrects the between-crown clumping, which has been proved to contribute most to the total clumping effect, and accurately estimates the LAI in discontinuous forest (R2 = 0.83, RMSE = 0.39, n = 47). Additionally, the forest LAI underestimation caused by between-crown clumping was analyzed in practice and theory. Results show that the between-crown clumping has a nonnegligible influence on forest LAI estimation in cases that the forest area within the footprint is close to the nonforest area, and the average LAI of individual tree is high. This study may shed some light on the development of clumping effect and quantitative LAI inversion models.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

Canopy hemispherical photography (HP) is widely used to estimate forest structural variables. To achieve good results with HP, a classification algorithm is needed to produce binary images to accurately estimate the gap fraction. Our aim was to develop a local thresholding method for binarizing carefully acquired hemispherical photographs. The method was implemented in the R package “caiman”. Working with photographs of artificial structures and using a linear model, our method turns the cumbersome problem of finding the optimal threshold value into a simpler one, which is estimating the digital number (DN) of the sky. Using hemispherical photographs of a deciduous forest, we compared our method with several standard and state-of-the-art binarization techniques. Our method was as accurate as the best-tested binarization techniques, regardless of the exposure, as long as it was between 0 and 2 stops over the open sky auto-exposure. Moreover, our method did not require knowing the exact relative exposure. Intending to balance accuracy and practicality, we mapped the sky DN using the values extracted from gaps. However, we discussed whether a more accurate but less practical way to map sky DN could provide, along with our method, a new benchmark. Fil: Díaz, Gastón Mauro. Centro de Investigación y Extensión Forestal Andino Patagónico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Lencinas, José Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentina. Centro de Investigación y Extensión Forestal Andino Patagónico; Argentina

The year-to-year variability of the blooming phenology of cherry trees is important as a proxy climate indicator and strongly affects cultural ecosystem services. Observation of blooming phenology at multiple points requires a simple and flexible approach. We examined changes in the canopy gap fraction extracted from binarized upward images taken periodically beneath a Cerasus × yedoensis 'Somei-yoshino' tree. The gap fraction decreased rapidly after the start of bloom, reached a minimum value at full bloom, and began to increase again, but then decreased rapidly during leaf flush. These changes reflect the phenology of blooming and leaf flush after flower drop of 'Somei-yoshino'. These characteristics allow detection of the year-to-year variability of the bloom and leaf-flush phenology of cherry and other deciduous tree species that show the same patterns.

The use of LiDAR in the study of gap regimes has seen significant progress in recent years. Researchers have assumed that canopy gaps detected in this manner are ecologically equivalent to gaps sampled in situ by more traditional methods. However, those latter methods usually include canopy gaps only and ignore non-regenerating openings that are produced by causes limiting tree establishment. We developed a predictive model capable of discriminating between canopy gaps and non-regenerating openings using LiDAR-derived data. Selected predictive variables were related to conditions that limit tree establishment such as zones of moisture accumulation and steep slopes or to the resulting vegetation physiognomy. The model was applied to three old-growth maple forests to predict the fractions of canopy openings belonging to these two types. On average, non-regenerating openings represented 19.5% of the total area detected as canopy openings and occupied 1.37% of the sites. Canopy gaps formed 80.5% of the total area in canopy openings and covered 5.71% of sites that were studied. The non-regenerating opening seemed more frequent on thin surficial deposits. The canopy gap fraction was similar among study sites but had lower values than usually reported for temperate deciduous forest.

Gap fraction (P gap) and vertical gap fraction profile (vertical P gap profile) are important forest structural metrics. Accurate estimation of P gap and vertical P gap profile is therefore critical for many ecological applications, including leaf area index (LAI) mapping, LAI profile estimation and wildlife habitat modelling. Although many studies estimated P gap and vertical P gap profile from airborne LiDAR data, the scan angle was often overlooked and a nadir view assumed. However, the scan angle can be off-nadir and highly variable in the same flight strip or across different flight strips. In this research, the impact of off-nadir scan angle on P gap and vertical P gap profile was evaluated, for several forest types. Airborne LiDAR data from nadir (0°∼7°), small off-nadir (7°∼23°), and large off-nadir (23°∼38°) directions were used to calculate both P gap and vertical P gap profile. Digital hemispherical photographs (DHP) acquired during fieldwork were used as references for validation. Our results show that angular P gap from airborne LiDAR correlates well with angular P gap from DHP (R 2 = 0.74, 0.87, and 0.67 for nadir, small off-nadir and large off-nadir direction). But underestimation of P gap from LiDAR amplifies at large off-nadir scan angle. By comparing P gap and vertical P gap profiles retrieved from different directions, it is shown that scan angle impact on P gap and vertical P gap profile differs amongst different forest types. The difference is likely to be caused by different leaf angle distribution and canopy architecture in these forest types. Statistical results demonstrate that the scan angle impact is more severe for plots with discontinuous or sparse canopies. These include coniferous plots, and deciduous or mixed plots with between-crown gaps. In these discontinuous plots, P gap and vertical P gap profiles are maximum when observed from nadir direction, and then rapidly decrease with increasing scan angle. The results of this research have many important practical implications. First, it is suggested that large off-nadir scan angle of airborne LiDAR should be avoided to ensure a more accurate P gap and LAI estimation. Second, the angular dependence of vertical P gap profiles observed from airborne LiDAR should be accounted for, in order to improve the retrieval of LAI profiles, and other quantitative canopy structural metrics. This is especially necessary when using multi-temporal datasets in discontinuous forest types. Third, the anisotropy of P gap and vertical P gap profile observed by airborne LiDAR, can potentially help to resolve the anisotropic behavior of canopy reflectance, and refine the inversion of biophysical and biochemical properties from passive multispectral or hyperspectral data.

In a comparison of six methods of estimating foliage clumping, logarithmic methods were found to be the most suitable for validating remote sensing-based clumping maps. The clumping index is a measure describing the spatial aggregation of foliage elements and has an important effect on radiation transfer in canopies. Quantifying the clumping index from field-based measurements has shown to be complicated, as values presented in literature vary substantially between methods. The goal of this work was to improve on previous comparison of beyond-shoot clumping quantification methods over Jarvselja RAMI test sites by including additional methods as well as emphasizing the temporal variability of the clumping index by including two phenologically contrasting moments in the growing season. We show that while apparent clumping, digital cover photography-based methods and clumping from gap size distribution coincide well, they result in the maximum clumping value that should be considered in any case, whereas logarithmically based methods might provide more accurate estimates of clumping. The results are of great importance for the correct assessment of foliage clumping from space, as well as for current attempts to assess clumping using airborne and terrestrial laser scanning.

Currently, Unmanned Aerial Vehicle (UAV)-based remote sensing is a flexible and reliable approach to gather data for agricultural crop intra-field monitoring. This study proposes real-time and low-cost approaches for crop leaf area index (LAI) estimation using UAV-based 3D point cloud data at field-scale. Crop LAI is an indicator of crop growth variation within crop fields which is one of the most essential crop parameters in crop growth models to predict other crop parameters including chlorophyll, biomass and final yield. After converting a circle with a radius of 2 meters 3D point cloud data to spherical projection, the sampling 3D point cloud data will be converted to a hemispherical photograph. The crop canopy LAI is then calculated from this hemispherical photograph using the gap fraction method. From the experiments over a winter wheat field, the estimated LAI from the UAV-based 3D point cloud data is highly correlated with the LAI estimated from an in-situ fisheye camera, the R2 are 0.8995 and 0.8658 for 4 rings and 5 rings view angles calculation, respectively.

Leaf area index (LAI) is a forest canopy variable that is closely related to forest growth and health. The LAI-2000 Plant Canopy Analyzer is widely used for indirect measurement of LAI; however, use of the instrument is limited to diffuse sky conditions, greatly restricting sampling. A new bidirectional transmission model allows measurements to be obtained under clear sky conditions. This is accomplished by calculating a correction factor to reduce the impact of scattered light from canopy elements on gap fraction estimates. In this study we evaluate this technique by contrasting LAI measurements taken under diffuse and clear skies in a Pinus radiata D. Don plantation. We also evaluate the importance of obtaining accurate needle optical properties to parameterise the scattering correction model. Clear sky LAI estimates calculated with (a) measured optical values of P. radiata needles, (b) default values from the instrument software, and (c) maximum published values were compared to diffuse sky LAI estimates. Agreement was strongest where measured optical properties were used ( R 2 = 0.87), with the relationship weakening under the default ( R 2 = 0.78), and maximum value ( R 2 = 0.67) scenarios. Under these three scenarios average clear sky LAI exceeded diffuse sky LAI by 16%, 17%, and 22%, respectively. The disagreement was due in part to erroneous measurements from the outer sensor ring under diffuse skies. With these data removed agreement improved markedly under all scenarios ( R 2 = 0.94, 0.89, and 0.75, respectively), and the mean differences under each scenario declined to 8%, 9%, and 15%, respectively. Measurement of LAI under clear skies appeared to reduce error in the outer ring, greatly reduced logistical constraints, and reduced errors associated with sky variability.

Background Hemispherical photography (HP) is a popular method to estimate canopy structure and understorey light environment, which analyses photographs acquired with wide view-angle lens (i.e. fisheye lens). To increase HP accuracy, the approaches of most previous studies were to increase the preciseness of exposure and thresholding of photographs, while ours quantified effects of canopy properties (gap fraction and length of sky-canopy border (SCB)) and errors of exposure and thresholding on the accuracy of HP. Results Through analysing photographs of real and model canopies, it was showed that HP inaccuracy resulted from the mismatch between exposure and thresholding rather than exposure or thresholding errors alone. HP inaccuracy was a function of the SCB length and the extent of exposure and thresholding errors, but independent of gap fraction. Discussion In photographs, SCBs are recorded as grey pixels which greyness is in between that of sky and canopy pixels. When there are exposure and thresholding errors, grey pixels are those prone to be misclassified in image analysis. Longer (vegetation with taller canopies) and wider (lower image sharpness) SCBs in photographs can both result in a higher amount of grey pixels and ultimately higher HP inaccuracy for a given extent of exposure and threshold errors. Conclusions Using lenses with view angle narrower rather than that of fisheye lens can shorten the SCB length in photographs and in turn reduce HP estimation inaccuracy for canopy structure and understorey light environment. Since short SCBs and low levels of exposure and thresholding errors can both result in low HP inaccuracy, to identify the true performance of new exposure and thresholding methods for HP, photographs recording canopies with long SCBs and acquired with fisheye lenses should be used. Because HP inaccuracy in a function of the amount of grey pixels resulting from SCBs, the amount of these pixels in photographs can be used as a universal parameter to quantify canopy properties influential to HP estimation and in turn make cross-study comparisons feasible. Electronic supplementary material The online version of this article (10.1186/s40529-018-0235-9) contains supplementary material, which is available to authorized users.

Leaf area index (LAI) is one of the key biophysical variables required for crop modeling. Direct LAI measurements are time consuming and difficult to obtain for experimental and commercial fruit orchards. Devices used to estimate LAI have shown considerable errors when compared to ground-truth or destructive measurements, requiring tedious site-specific calibrations. The objective of this study was to test the performance of a modified digital cover photography method to estimate LAI in apple trees using conventional digital photography and instantaneous measurements of incident radiation (Io) and transmitted radiation (I) through the canopy. Leaf area of 40 single apple trees were measured destructively to obtain real leaf area index (LAI(D)), which was compared with LAI estimated by the proposed digital photography method (LAI(M)). Results showed that the LAI(M) was able to estimate LAI(D) with an error of 25% using a constant light extinction coefficient (k = 0.68). However, when k was estimated using an exponential function based on the fraction of foliage cover (f(f)) derived from images, the error was reduced to 18%. Furthermore, when measurements of light intercepted by the canopy (Ic) were used as a proxy value for k, the method presented an error of only 9%. These results have shown that by using a proxy k value, estimated by Ic, helped to increase accuracy of LAI estimates using digital cover images for apple trees with different canopy sizes and under field conditions.

Forest snow interception can account for large snow storage differences between open and forested areas. The effect of interception can also lead to significant variations in sublimation, with estimates varying from 5 to 60% of total snowfall. Most current interception models utilize canopy closure and LAI to partition interception from snowfall and calculate interception efficiency as an exponential decrease of interception efficiency with increasing precipitation. However, as demonstrated, these models can show specific deficiencies within heterogeneous canopy. Seven field areas were equipped with 1932 surveyed points within various canopy density regimes in three elevation bands surrounding Davos, Switzerland. Snow interception measurements were taken from 2012 to 2014 (∼9000 samples) and compared with measurements at two open sites. The measured data indicated the presence of snow bridging from a demonstrated increase in interception efficiency as precipitation increased until a maximum was reached. As precipitation increased beyond this maximum, the data then exhibited a decrease in interception efficiency. Standard and novel canopy parameters were developed using aerial LiDAR data. These included estimates of LAI, canopy closure, distance to canopy, gap fraction, and various tree size parameters. These canopy metrics and the underlying efficiency distribution were then integrated to formulate a conceptual model based upon the snow interception measurements. This model gave a ∼27% increase in the r2 (from 0.39 to 0.66) and a ∼40% reduction in RMSE (from 5.19 to 3.39) for both calibration and validation data sets when compared to previous models at the point scale. When upscaled to larger grid sizes, the model demonstrated further increases in performance.

a b s t r a c t Estimates of the canopy gap fraction, on which calculations of leaf area index (LAI) are based, are sensi- tive to photographic exposure in upward-facing images. In this article we describe a simple, automated method of image acquisition and processing that eliminates both subjectivity and the need for the oper- ator to consider photographic exposure in the field. A key strength of our methodology was the use of a test apparatus (perforated aluminum screen) with a precisely known gap fraction; this allowed us to separate the confounding effects of gap size and gap fraction on the optimal photographic exposure for a canopy. We took photographs of the test apparatus at different photographic exposures; we varied the gap fraction by covering a proportion of the holes in the screen, and also varied gap size by varying the distance of the camera from the screen. We acquired both raw images and JPEG images. We found that the optimal exposure of JPEG images varied with both gap size and gap fraction, not just gap fraction as previously assumed. Underexposing by one stop yielded raw data that were never clipped resulting in data loss, but that used most of the 14-bit range of the raw file. We also found that it was easily possible to standardize photographic exposure during image processing by acquiring raw images in the field; thus eliminating the variation in estimated gap fraction and LAI associated with exposure variations. This result was replicated in both fisheye images and cover images that we acquired in real canopies. We recommend the following protocol for acquiring canopy images in future studies

Recently, Armston et al. (2013) have demonstrated that a new, physically-based method for direct retrieval of canopy gap probability Pgap from waveform lidar can improve the estimation of Pgap over discrete return lidar data. The success of the approach was demonstrated in a savanna woodland environment in Australia. The huge advantage of this method is that it uses the data themselves to solve for the canopy contrast term i.e. the ratio of the reflectance from crown and ground, ρv/ρg. In this way the method avoids local calibration that is typically required to overcome differences in either ρv or ρg. To be more generally useful the method must be demonstrated on different sites and in the presence of slope and different sensor and survey configurations. If it is robust to these things, slope in particular, then we would suggest it is likely to be widely useful. Here, we test the robustness of the retrieval of Pgap from waveform lidar using the Watershed Allied Telemetry Experimental Research dataset, over the Heihe River Basin region of China. The data contain significant canopy, terrain and survey variations, presenting a rather different set of conditions to those previously used. Results show that ρv/ρg is seen to be stable across all flights and for all levels of spatial aggregation. This strongly supports the robustness of the new Pgap retrieval method, which assumes that this relationship is stable. A comparison between Pgap estimated from hemiphotos and from the waveform lidar showed agreement with Pearson correlation coefficient R=0.91. The waveform lidar-derived estimates of Pgap agreed to within 8% of values derived from hemiphotos, with a bias of 0.17%. The new waveform model was shown to be stable across different off-nadir scan angles and in the presence of slopes up to 26° with R≥0.85 in all cases. We also show that the waveform model can be used to calculate Pgap using just the mean value of canopy returns, assuming that their distribution is unimodal. Lastly, we show that the method can also be applied to discrete return lidar data, albeit with slightly lower accuracy and higher bias, allowing Pgap comparisons with previously-collected lidar datasets. Our results show the new method should be applicable for estimating Pgap robustly across large areas, and from lidar data collected at different times and using different systems; an increasingly important requirement.

Both vegetation multi-angular and LiDAR (light detection and ranging) remote sensing data are indirectly and directly linked with 3D vegetation structure parameters, such as the tree height and vegetation gap fraction, which are critical elements in above-ground biomass and light profiles for photosynthesis estimation. LiDAR, particularly LiDAR using waveform data, provides accurate estimates of these structural parameters but suffers from not enough spatial samplings. Structural parameters retrieved from multiangular imaging data through the inversion of physical models have larger uncertainties. This study searches for an analytical approach to fuse LiDAR and multiangular data. We explore the relationships between vegetation structure parameters derived from airborne vegetation LiDAR data and multiangular data and present a new potential angle vegetation index to retrieve the tree height and gap fraction using multi-angular data in Howland Forest, Maine. The BRDF (bidirectional reflectance distribution factor) index named NDMM (normalized difference between the maximum and minimum reflectance) linearly increases with the tree height and decreases with the gap fraction. In addition, these relationships are also dependent on the wavelength, tree species, and stand density. The NDMM index performs better in conifer (R = 0.451 for tree height and R = 0.472 for the gap fraction using the near infrared band) than in deciduous and mixed forests. It is superior in sparse (R = 0.569 for tree height and R = 0.604 for the gap fraction using the near infrared band) compared to dense forest. Moreover, the NDMM index is more strongly related to tree height and the gap fraction at the near infrared band than at the three visible bands. This study sheds light on the possibility of using multiangular data to map vegetation’s structural parameters in larger regions for carbon cycle studies through the fusion of LiDAR and multiangular remote sensing data.

The increasing availability of high-quality sensors and computational power on low-cost mobile devices like smartphones and tablets is opening new possibilities for adopting this kind of technology for monitoring biophysical processes of interest for agronomic and environmental studies. A method for leaf area index (LAI) estimates based on gap fraction, derived from the segmentation of images acquired at 57° below the canopy, was recently proposed and implemented in the smartphone app PocketLAI ® , and successfully tested against commercial devices for paddy rice. In this study, PocketLAI was tested against the AccuPAR ceptometer on canopy structures (maize, row-seeded giant reed and natural grassland) that strongly deviate from the ideal assumption behind the simplified model for light transmittance into the canopy used in the app (i.e., random distribution of infinitely small leaves). The comparison between PocketLAI and AccuPAR showed overall good performances for the app, with root mean square error of 0.41, 0.49 and 0.96 m 2 m −2 for grassland, maize and giant reed respectively, and R 2 of 0.86, 0.92 and 0.88. A saturation effect was observed for PocketLAI for LAI values higher than 5 m 2 m −2 especially for giant reed, with the LAI values obtained with the app markedly underestimating those provided by AccuPAR. Although further studies are needed to better investigate the need for calibrating the app in case of low-quality devices, these results confirm the possible role of PocketLAI in providing a suitable alternative to the commercial tools available for indirect LAI estimates in contexts characterized by few economic resources or when a high portability is needed.

Leaf area index (LAI) and canopy structure are important parameters affecting grape quality and yield of grapevines. Two different experimental protocols as well as the average LAI value of the different protocols for indirect estimation of LAI by gap fraction analysis in VSP-trained grapevines (Vitis vinifera L. cv. Riesling) were tested in this study using plant area index (PAI). Measurements were performed using a plant canopy analyzer. Directly measured LAI and estimated PAI were compared. Protocol SFC (sensor facing the canopy) gave accurate estimates of LAI by measuring PAI along a diagonal transect including eight vines on each side. The correlation between directly measured LAI and estimated PAI was very high (R2 = 0.93) and the root mean square error was lowest of the methods tested here (RMSE = 0.21). Eight measurements below the canopy were enough to accurately estimate LAI. By applying the empirical calibration equation, the measurements provide accurate LAI estimates. Nevertheless, local calibration is required. The method presented provides a useful tool for rapid and precise LAI estimation in VSP training systems and for supporting canopy or management decisions based on LAI.

In this study, we evaluated methods for reliably estimating leaf area index (LAI) and gap fraction in two different types of broad-leaved forests by the use of airborne light detection and ranging (LiDAR) data. We evaluated 13 estimation variables related to laser height, laser penetration rate, and laser point attributes that were derived from LiDAR analyses. The relationships between LiDAR-derived estimates and field-based measurements taken from the forests were evaluated with simple linear regressions. The data from the two forests were analyzed separately and as an integrated dataset. Among the laser height variables, the coefficient of variation (CV) of all laser point heights had the highest level of accuracy for estimating both LAI and gap fraction. However, we recommend that more evaluations be conducted prior to the use of CV in forests with complex structures. The simplest laser penetration variable, which represents the ratio of the number of ground points to the total number of all points (PALL), also had a high level of accuracy for estimating LAI and gap fraction at the study sites regardless of whether the data were analyzed separately or as an integrated data set. Furthermore, PALL values showed near 1:1 relationships with the field-based gap fraction values. Hence, the use of PALL may be the most practical for estimating LAI and gap fraction in broad-leaved forests, even when the canopies are heavily closed.

At least 10 different methods to determine exposure for hemispherical photographs were used by scientists in the last two decades, severely hampering comparability among studies. Here, an overview of the applied methods is reported. For the standardization of photographic exposure, a time-consuming reference measurement in the open land towards the unobstructed sky was required so far. The two Histogram Methods proposed here make use of the technical advances of digital cameras which enable users to assess a photograph’s histogram directly at the location of measurement. This avoids errors occurring due to variations in sky lighting happening in the time span between taking the reference measurement and reaching the sample location within the forest. The Histogram Methods speed up and simplify taking hemispherical photographs, and introduce an objectively applicable, standardized approach. We highlight the importance of correct exposure by quantifying the overestimation of gap fraction resulting from auto-exposed photographs under a wide range of canopy openness situations. In our study, gap fraction derived from auto-exposed photographs reached values up to 900% higher than those derived from non-overexposed photographs. By investigating the size of the largest gap per photograph and the number of small gaps (gaps contributing less than 0.1% to gap fraction), we concluded that the overestimation of gap fraction resulted mainly from the overexposure of vegetation surrounding large gaps.

The dimensionless leaf area index (LAI) is a fundamental crop characteristic. Since the direct measurement of LAI or leaf area is labour intensive and destructive, fast and reliable indirect methods have been devised to estimate LAI of different crops. The objective of this work was to test indirect methods for the non-destructive estimation of LAI in kohlrabi ( Brassica oleracea var. gongylodes L.) and lettuce ( Lactuca sativa var. capitata L.). Focusing on the gap fraction methodology, digital photographs and simultaneous radiation interception measurements were taken using a Li-Cor plant canopy analyser (LAI-2200) on 12 sampling dates from planting to harvest, with concurrent destructive estimations of the leaf area. Several geometric protocols of the LAI-2200 and inversion algorithms of the accompanying software were evaluated. Very good indirect-direct LAI relationships were obtained for kohlrabi ( R 2 > 0.97, n = 12) and lettuce ( R 2 > 0.99, n = 9) for the most suitable protocols and algorithms.

This study evaluated the performance of digital photography for measuring the green leaf area index (LAI) and green plant area index (GAI) of field crops by quantifying gap fraction either at nadir or at a 57.5° view angle. Clumping index was estimated using the logarithmic averaging method of Lang and Xiang (1986) to derive the total GAI. Measurement uncertainty associated with foliage inclination and view angles was assessed analytically, as was image classification. The GAI derived for corn, soybean and wheat crops was strongly linearly correlated with the destructive GAI for both the nadir and the 57.5° photographic methods (R2 > 0.83, root mean square error < 0.63). Because the destructive LAI and GAI exhibited strong linear correlations for soybean and corn, LAI could be predicted from photographic methods. The LAI and GAI of the wheat canopy were poorly correlated because of a significant proportion of non-leaf tissues contributing to photosynthesis after the start of stem elongation. Clumping had a greater effect on the nadir photographic method than on the 57.5° method. The effective GAI from nadir viewing was better correlated to the destructive GAI than the photographic GAI, whereas the effective GAI from the 57.5° photographic method provided a greater underestimation of the destructive GAI than the photographic GAI. The extinction coefficient at nadir viewing was found to be 22% larger than the assumed value (i.e. k = 0.61 vs. the theoretical value of 0.5). When gap reduction reached the asymptotic saturation level with increasing LAI, higher classification accuracy was required to achieve acceptable LAI measurement accuracy. The 57.5° photographic method was more affected by classification accuracy when LAI was higher than 1.5. With crop canopy development, the decrease in gap size and the decline in signal strength from shadows mean that high-quality photos are required to effectively differentiate gaps from plant tissues.

Angular distribution of leaves is an important parameter determining the transmission and reflection of radiation by vegetation canopies. When inverting canopy transmittance measurements for estimating the leaf area index or canopy clumping, incorrect assumptions on leaf angles may lead to considerable errors. To address this issue, we measured an extensive data set of leaf angle distributions for 58 deciduous broadleaf tree species commonly found in temperate and boreal ecoclimatic regions. Additionally, a small subset of species was sampled several times during the growing season. We found that reliable estimates of leaf angle distributions at the level of a whole tree can be obtained by measuring the leaf inclination angles of 75 leaves distributed across the vertical tree profile using a simple technique based on digital photography. According to our measurements, the common assumption of a spherical leaf angle distribution often used when no measurement data is available is not a valid assumption for most of the studied tree species. Our multitemporal measurements indicated no seasonality of leaf inclination angles of the selected tree species, except directly after budburst. Finally, using a gap fraction model, we illustrate the influence of different leaf angle distributions on the clumping factor and leaf area index. We advocate the use of species-specific leaf angle distributions from actual leaf inclination measurements. However, a planophile or a plagiophile leaf angle distribution appears to be a more appropriate assumption than a spherical one for modeling radiation transmission through temperate and boreal broadleaf stands when no leaf inclination angle measurements are available.

The leaf area index (LAI) of forest canopies can be rapidly estimated by the canopy gap fraction measured using in situ optical instruments. Gap fraction analysis, however, will routinely underestimate LAI when forest canopies exhibit a strong pattern of aggregation at one or more spatial scales. Recent advances in optical-based measurement techniques and gap fraction analyses show that inclusion of a view angle-dependent clumping index Ω(θ) may substantially improve optical-based estimates of LAI. Here we introduce four different estimates of Ω(θ) and demonstrate how these indices can be extracted directly from hemispherical canopy photographs. Our findings suggest that estimates of Ω(θ) can improve LAI estimation in the presence of spatial non-randomness, although their overall effectiveness was strongly dependent on the clumping index chosen, quantity of leaf area, severity and complexity of foliage clumping, and the view angles used for LAI integration.

In this work, for the first time, we give a fully comprehensive overview of the performance of all commonly used methods for estimating clumping from in situ measurements. Estimates based on the gap size distribution and the combination of gap size and logarithm methods performed the best while compared with an independent gap fraction model. Importantly, we illustrate the seasonal variation of clumping cannot be ignored. The results are of great importance for the correct assessment of various clumping methods from space, as well as for current attempts to assess clumping using airborne and terrestrial laser scanning [8, 9].

The efficiency of precision agriculture fundamentally depends on the exploration of bio-physical and bio-chemical plant parameters and the assessment of current crop conditions. The leaf area index (LAI) represents one of the most important crop parameters and is defined as the ratio of foliage area to ground area. It is widely-used in agriculture and agronomy as it indicates yield-limiting processes. In this paper, we present Smart fLAIr (fast LAI retrieval), a novel smartphone application for a low-cost in-situ LAI estimation. This estimation is based on the gap fraction analysis, a widespread indirect and non-destructive methodology. For that purpose, Smart fLAIr leverages the smartphone's internal Ambient Light Sensor (ALS). However, in order to improve the gap fraction accuracy, we enhance the ALS by a diffuser cap combined with an optical band-pass filter. Our prototype is implemented on the Android platform with a focus on usability aspects and its practicability. Conducting a comparative analyses with a commercial instrument, we successfully evaluated this prototype for maize canopies. The convincing performance of our approach in terms of accuracy and stability highlights the potential of Smart fLAIr as a valuable alternative for in-situ LAI assessment.

article i nfo The Echidna Validation Instrument (EVI), a ground-based, near-infrared (1064 nm) 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 re- search, 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 iden- tified 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 den- sities 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 (R 2 =0.866) with those of hemi

In even-aged, 120-year-old Norway spruce stands with underplanted beech (in 1995) four permanent research plots were established, each 100 × 100 m in size. Twelve subplots were selected on each plot along a light gradient from complete canopy closure to open light conditions. On each plot, photon flux density (PFD) was measured continuously, and during same time interval (August 2008, 2009), color digital hemispherical photographs were taken. Cumulative PFD values for measuring points were compared with potential PFD radiation obtained from hemispherical photograph analysis for different angle of hemisphere and different parameters from hemispherical image analysis (gap fraction, total openness and direct, indirect and total amount of radiation). Cumulative and average daily values for the plots were compared; 120° hemispherical photograph angle, gap fraction and total openness were the variables that explained the largest proportion of variance in light transmittance. Determination coefficients between direct and total light component were highest for the total potential radiation and lowest for the diffuse light component. Comparison between potential and instantaneous light measurements for radial, height increment and SLA of young beech showed that instantaneous radiation measurements explained height increment best; 120° of hemisphere proved to be the best explaining angle.

In this paper, a method is proposed for spontaneous fire whirl analysis and prediction due to non-regularly or randomly distributed flame sources, by defining an equivalent gap fraction and providing an adapted criterion. The topological structure of the flame source configuration, the eccentric direction of each equivalent gap and the integrated effect of all the gaps are considered. By the application of the equivalent gap fraction criterion, predictions can be made in a real fire disaster for the likelihood, the rotating direction and the rough intensity of the swirl and then suggestions can be provided for configuration design to prevent fire whirls or to reduce the damage.

Pokorný R., Stojnic S. 2012: Leaf area index of Norway spruce stand in relation to its age and defoliation. – Beskydy, 5 (2): 173–180 This contribution presents LAI data of 17 Norway spruce stands of different age (from 15 to 102 years) and health status (defoliation ranging from 0 to 40 %) located in the Těsinske Beskydy Mts. (NE of the Czech Republic). Defoliation degree was estimated by ocular observation and LAI was estimated indirectly on the base of gap fraction method using LAI-2000 PCA (Li-Cor, USA). It was found that LAI decreased with stand age (approximately from 12.6 to 8.5) whereas defoliation increased. Therefore, strong relationship between defoliation and LAI as well as gap fractions was found. LAI appears valuable parameter for health status of stand evaluation due to linear relationship (r2 = 0.87) between LAI and defoliation. As LAI can be estimated more objectively by a canopy analyzer then the degree of canopy defoliation by ocular observation, there is a perspective for a wide use LAI in forest practice. The LAI values can be used for the evaluation of health status of Norway spruce stands.

The “damage accumulation” phenomenon has not been quantitatively demonstrated in clinical cement mantles surrounding femoral hip stems. We stained transverse sections of 11 postmortem retrieved femoral hip components fixed with cement using fluorescent dye-penetrant and quantified cement damage, voids, and cement-bone interface gaps in epifluorescence and white light micrographs. Crack density (Cr.Dn), crack length-density (Cr.Ln.Dn), porosity, and cement-bone interface gap fraction (c/b-gap%) were calculated, normalized by mantle area. Multiple regression tests showed that cement damage (Cr.Ln.Dn. & Cr.Dn.) was significantly positively correlated (r2=0.98, p

Contact: talbot@supagro.inra.fr; International audience; Predicting the temporal and spatial variability of radiation intensity under wide-spaced tree stands is required for many applied issues in savannah-like ecosystems, orchards, agroforestry and urban forestry systems. Numerous authors have advocated the use of simple light interception models that approximate the crown shape with ellipsoids. They have suggested taking into account leaf clumping to improve the efficiency of these simple models, but this was never assessed. We tested this hypothesis together with the impact of including predictions of light interception by woody parts (trunks, branches). We calibrated and evaluated the model using cross-validation across eight walnut trees with field measurements of radiation intensity and spatial heterogeneity using hemispherical photographs. Leafless trees were efficiently modelled using Wood Area Density (WAD, m(2)m(-3)) for branches and an opaque cone for the trunk. We introduced a clumping parameter (mu) but this proved inefficient, clumping being highly variable amongst trees. This results from the limitations of representing the crown as an ellipsoid, a procedure too coarse to be improved by using a clumping parameter. The model proved efficient to predict the light pattern around an average tree, but was not fit for simulating the variability of individual trees. We finally discuss practical recommendations for modelling light competition in integrated agroforestry models simply.

An estimate of the required amount of LAI-2000 or digital hemispheric image measurements to ensure the desired accuracy of the leaf area index value derived from optical analyser measurements by inversion of gap fraction data is given. The estimate is based on a theoretical model of the second order statistics of gap occurrence in forest canopies. The main attention is paid to gap fractions averaged over the angular rings similar to the LAI-2000 instrument. Nilson's (1999) forest gap fraction model is first extended to simulate the bidirectional gap fraction and next the approximate equations for the standard deviation of gap fraction, correlation matrix of gap readings in different LAI-2000 angular rings, standard deviation of the LAI value as determined by the inversion of gap fraction data are derived. The results of simulation are compared with those obtained by LAI-2000 instrument and digital hemispheric photographs in three mature forests from Jarvselja, Estonia. The comparison shows an acceptable agreement between the simulated and measured second order statistical characteristics. The distance from the observation height to the crowns and crown dimensions appear to be important in determining the magnitude of variance. When the LAI-2000 instrument is used together with a view-cap, the angular size of the view-cap has an effect on the second order statistics and on the value of apparent clumping index. A computer program has been written to calculate the second-order gap statistics.

International audience; Reliable measurements of the 3D distribution of Leaf Area Density (LAD) in forest canopy are crucial for describing and modelling microclimatic and eco-physiological processes involved in forest ecosystems functioning. To overcome the obvious limitations of direct measurements, several indirect methods have been developed, including methods based on Terrestrial LiDAR scanning (TLS). This work focused on various LAD estimators used in voxel-based approaches. LAD estimates were compared to reference measurements at branch scale in laboratory, which offered the opportunity to investigate in controlled conditions the sensitivity of estimations to various factors such as voxel size, distance to scanner, leaf morphology (species), type of scanner and type of estimator. We found that all approaches to retrieve LAD estimates were highly sensitive to voxel size whatever the species or scanner and to distance to the FARO scanner. We provided evidence that these biases were caused by vegetation heterogeneity and variations in the effective footprint of the scanner. We were able to identify calibration functions that could be readily applied when vegetation and scanner are similar to those of the present study. For different vegetation and scanner, we recommend replicating our method, which can be applied at reasonable cost. While acknowledging that the test conditions in the laboratory were very different from those of the measurements taken in the forest (especially in terms of occlusion), this study revealed existence of strong biases, including spatial biases. Because the distance between scanner and vegetation varies in field scanning, these biases should occur in a similar manner in the field and should be accounted for in voxel-based methods but also in gap-fraction methods. This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry.

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.

The gap fractions of three mature hemi-boreal forest stands in Estonia were estimated using the LAI-2000 plant canopy analyzer ( LI-COR Biosciences, Lincoln, NE, USA), the TRAC instrument (Edgewall, Miami, FL, USA), Cajanus’ tube, hemispherical photos, as well as terrestrial (TLS) and airborne (ALS) laser scanners. ALS measurements with an 8-year interval confirmed that changes in the structure of mature forest stands are slow, and that measurements in the same season of different years should be well comparable. Gap fraction estimates varied considerably depending on the instruments and methods used. None of the methods considered for the estimation of gap fraction of forest canopies proved superior to others. The increasing spatial resolution of new ALS devices allows the canopy structure to be analyzed in more detail than was possible before. The high vertical resolution of point clouds improves the possibility of estimating the stand height, crown length, and clumping of foliage in the canopy. The clumping/regularity of the foliage in a forest canopy is correlated with tree height, crown length, and basal area. The method suggested herein for the estimation of foliage clumping allows the leaf area estimates of forest canopies to be improved.

Four alternative airborne laser scanning (ALS) canopy penetration variables were compared for their suitability for mapping of gap fraction, leaf area index and disturbances in a Scots pine forest. The variables were based on either echo counting or intensity, and on either first or first and last echoes. ALS data and field-measured gap fraction and effective leaf area index (LAIe) were gathered before and after a severe insect defoliation by pine sawflies. LAIe is a commonly used form of leaf area index that is mathematically derived from gap fraction, and includes the areas of foliage, branches and trunks, and which is not corrected for the clumping of foliage. The ALS penetration variables were almost equally strongly related to field-measured gap fraction and LAIe. The estimated slopes in the LAIe models varied from 0.94 to 2.71, and had coefficient of determination R2 values of 0.92-0.94. They were strongly correlated to each other (R2 values of 0.95-0.98) and agreed fairly well for temporal changes of LAIe during the summer and the insect defoliation (R2 values of 0.82-0.95). Counting of first and last echoes produced penetration rates close to the gap fraction, and this penetration variable was able to penetrate tree crowns. Ground-only echoes represented mostly between-tree gaps, and canopy-first-ground-last pulses represented mostly within-canopy gaps. However, the penetration variables based on first and last echoes suffered from the problem that a second echo might be impaired both in low and in tall canopies. In low canopies, two adjacent echoes from the same pulse would be too close in time to be separated by the sensor, while in tall canopies the pulse might apparently be fragmented down through the canopy. The intensity-based penetration variables needed to be supplemented with reflectance values, or at least the ratio between reflectance of the canopy and the ground, and this ratio was estimated from the data. The study demonstrated that one might be able to distinguish between disturbance types, e.g. between defoliation and cutting, by comparing alternative ALS penetration variables. Insect defoliation was dominated by an increase in within-canopy gaps and, correspondingly, the fraction of partly penetrating canopy-first-ground-last pulses. Tree removals from cutting were dominated by increases in between-tree gaps and the corresponding fraction of ground-only pulses.

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.

The slope effect and correction methods for estimation of canopy gap fraction, leaf area index (LAI), mean leaf angle and clumping index using hemispherical photography, were investigated. The evaluation was carried out in tropical cloud forest and plantations in South-East Kenya in order to consider a range of canopy architecture and slopes up to 65%. The aim was to compare two acquisition techniques and various correction procedures. All estimates assume uniform slope, canopy parallel to ground and homogeneous canopy structure at the photo site level.

The concept of recollision probability originates from the theory of canopy spectral invariants (‘p-theory’) but is a simplification that involves several heuristic assumptions. Nonetheless, the concept has been shown to be a useful tool for incorporating the effects of 3D structure on canopy absorptive and reflective properties in forest reflectance models. A method is presented by which an average value of the canopy recollision probability (pˆ) can be calculated from canopy gap fraction data, which are provided for example by the LAI-2000 plant canopy analyzer or can be extracted from fisheye photographs. The new method was used to calculate the average recollision probabilities (pˆ values) of uniform leaf and shoot canopies, showing good agreement with results from Monte Carlo simulations. Strengths of the method presented here are the explicitly formulated relationship between recollision probability and canopy structure, and its direct applicability in canopy RT studies.

Leaf Area Index (LAI) is an important biophysical parameter necessary to infer vegetation vigour, seasonal vegetation variability and different physiological and biochemical processes of vegetation. Gap fraction analysis has been carried out to estimate plot‐wise LAI. Tropical dry deciduous forests of the study area can be categorized into two prominent phases—growing and senescent phases. Efforts have been made to observe the relationship between ground based LAI values and satellite derived parameters, such as radiance values and also different vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), maximum, minimum, amplitude, sum and radiance NDVI values for both the phases. Multi‐temporal IRS 1C WiFS data have been used. WiFS provides information in two bands: red (0.62–0.68 µm) and near‐infrared (0.77–0.86 µm). All the images from the representative months have been corrected radiometrically and geometrically. NDVI values have been derived for all the representative months. Amo...

A correct exposure is of crucial importance for accurate retrieval of canopy parameters using hemispherical photograph techniques. Digital hemispherical photographs were collected under different sky brightness conditions using a Nikon CoolPix 4500 camera with an FC-E8 fish-eye lens for canopies of different species and openness. Different exposure schemes were employedto investigatethe effects ofphotographic exposure ontheestimations ofthe effective leaf area index(Le)and gap fraction. The contrast between the sky and foliage under each exposure scheme was calculated to determine the correct exposure under different weather conditions. The results demonstrated that digital hemispherical photographs taken with automatic exposure are not reliable, causing Le underestimations by 16‐71% for medium and high density canopies (Le = 3.2‐4.8) and corresponding gap fraction overestimations by 18‐72%. While for open canopies with Le < 1.26, Le was overestimated by 11‐29%, and the corresponding gap fraction was underestimated by 4‐28%. Studies showed that increasing one stop of exposure results in 3‐28% differences inLe for canopies with different openness. Based on the analysis, we determined the optimum exposure and developed a protocol for acquiring digital hemispherical photographs. The protocol requires first measuring reference exposure for the open sky using a built-in camera light meter, and then take photographs inside the canopy using the same camera with two stops of more exposure than the reference exposure in order to make the sky appear white and consequently also maximize the contrast between theskyandfoliage.Thisprotocol isapplicablefordifferentskybrightnessandfordifferentcanopyopenness.Indensecanopies,this procedure requires much less exposure than automatic exposure, but in very open canopies, this procedure requires more exposure than automatic exposure. Using the exposure determined with this procedure rather than the automatic exposure, the comparison of Le values from the LAI-2000 and digital photographs is greatly improved, with R 2 increasing from 0.77 to 0.95, and RMSE

Thresholding is a central part of the analysis of hemispherical images in terms of gap fraction and leaf area index (LAI), and the selection of optimal thresholds has remained a challenge over decades. The need for an objective, automatic, operatorindependent thresholding method has long been of interest to scientists using hemispherical photography. This manuscript deals with the comparison of a wide variety of different well-known automatic thresholding techniques against the subjective manual method, using high-dynamic range digital hemispherical photographs. The performance of the different thresholding methods was evaluated based on: (1) visual inspection by means of a multi-criteria decision system and (2) quantitative analysis of the methods’ sensitivity to an overall performance criterium. The automatic Ridler clustering method proved to be the most robust thresholding method for various canopy structure conditions. This automatic method might be the best solution for a fast, reliable and objective use of hemispherical photographs for gap fraction and LAI estimation in forest stands, given that the threshold setting is no longer manually performed. The fine-tuning potential of local thresholding methods to better address particular photographic limitations (e.g. over-exposure in a certain image region) is also presented. # 2005 Elsevier B.V. All rights reserved.