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Hemisphere photos are now widely applied to provide information about solar radiation dynamics, canopy structure and their contribution to biophysical processes, plant productivity and ecosystem properties. The present study aims to improve the original 'edge detection' method for binary classification between sky and canopy, which works not well for closed canopies. We supposed such inaccuracy probably is due to the influence of sky pixels on their neighbor canopy pixels. Here, we introduced a new term 'neighbor distance', defined as the distance between pixels participated in the calculation of contrast at the edges between classified canopy and sky, into the 'edge detection' method. We showed that choosing a suitable neighbor distance for a photo with a specific gap fraction can significantly improve the accuracy of the original 'edge detection' method. We developed an ND-IS (Neighbor Distance-Iteration Selection) method that can automatically determine the threshold values of hemisphere photos with high accuracy and reproductivity. It combines the modified 'edge detection' method and an iterative selection method, with the aid of an empirical power function for the relationship between neighbor distance and manually verified gap fraction. This procedure worked well throughout a broad range of gap fractions (0.019–0.945) with different canopy compositions and structures, in five forest biomes along a broad gradient of latitude and longitude across Eastern China. Our results highlight the necessity of integrating neighbor distance into the original 'edge detection' algorithm. The advantages and limitations of the method, and the application of the method in the field were also discussed. [ABSTRACT FROM AUTHOR]

This study focused on developing a novel semi-empirical model for maize's light extinction coefficient (kp) by integrating multiple remotely sensed vegetation features from several different remote sensing platforms. The proposed kp model's performance was independently evaluated using Campbell's (1986) original and simplified kp approaches. The Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and the Irrigation Innovation Consortium (IIC) in Fort Collins, Colorado, USA, served as experimental sites for developing and evaluating the novel maize kp model. Data collection involved multiple remote sensing platforms, including Landsat-8, Sentinel-2, Planet CubeSat, a Multispectral Handheld Radiometer, and an unmanned aerial system (UAS). Ground measurements of leaf area index (LAI) and fractional vegetation canopy cover (fc) were included. The study evaluated the novel kp model through a comprehensive analysis using statistical error metrics and Sobol global sensitivity indices to assess the performance and sensitivity of the models developed for predicting maize kp. Results indicated that the novel kp model showed strong statistical regression fitting results with a coefficient of determination or R2 of 0.95. Individual remote sensor analysis confirmed consistent regression calibration results among Landsat-8, Sentinel-2, Planet CubeSat, the MSR, and UAS. A comparison with Campbell's (1986) kp models reveals a 44% improvement in accuracy. A global sensitivity analysis identified the role of the normalized difference vegetation index (NDVI) as a critical input variable to predict kp across sensors, emphasizing the model's robustness and potential practical environmental applications. Further research should address sensor-specific variations and expand the kp model's applicability to a diverse set of environmental and microclimate conditions. [ABSTRACT FROM AUTHOR]

Copyright of Biotropica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Several smartphone applications have been developed for the purpose of low-cost and convenient assessments of vegetation canopy structure and understorey illumination. Like standard hemispherical photography, most of these applications require user decisions about image processing, posing challenges for repeatability of measurements. Here I report a test of CanopyCapture, an application that instantaneously estimates percentage canopy gap fraction without any input from the user, and has the added advantage of an intuitive levelling mechanism. Methods: Gap fraction estimates by CanopyCapture were compared with gap fraction values computed by the LAI-2200C Canopy Analyzer, in two contrasting evergreen temperate forests in New Zealand: an even-aged southern beech (Nothofagus) stand and an old-growth podocarp/broadleaf forest. These comparisons were repeated using a wide-angle adapter to enhance the smartphone camera's field of view from 45 to 65°. I also asked if CanopyCapture results depended on sky condition (sunny vs. overcast) and on the type of smartphone used. Results: CanopyCapture output was significantly correlated with gap fraction computed by the LAI-2200C (R² = 0.39), and use of the wide-angle adapter lifted this value to 0.56. However, CanopyCapture output was not significantly correlated with LAI-2200C output in the even-aged Nothofagus stand, where there was less spatial variation in canopy structure. Despite being much less sensitive to variation in gap fraction than the LAI-2200C, CanopyCapture was nevertheless able to detect differences in average gap fraction between the two forests studied. CanopyCapture results beneath intact canopies were not significantly affected by sky condition, but reflection of direct light off tree trunks in sunny weather caused slight overestimation of gap fraction beneath broken canopies and gaps. Uneven or patchy cloud cover can also cause erroneous readings beneath large canopy openings. Three different models of smartphone gave different results. Conclusions: CanopyCapture offers a rapid and repeatable proxy for comparisons of average canopy gap fraction in multiple stands/forests, provided large sample sizes are used. Measurement under even overcast skies is recommended, and studies involving multiple operators will need to standardize smartphones to ensure comparability of results. Although wide-angle adapters can improve performance, CanopyCapture's low sensitivity prevents high-resolution comparisons of the light environments of individual understorey plants within a stand. [ABSTRACT FROM AUTHOR]

Airborne lidar is often used to calculate forest metrics about trees, but it may also provide a wealth of information about the space between trees. Forest canopy gaps are defined by the absence of vegetative structure and serve important roles for wildlife, such as facilitating animal movement. Forest canopy gaps also occur around snags, keystone structures that provide important substrates to wildlife species for breeding, roosting and foraging.We wanted to test a method for quantifying canopy gaps around individual snags and live trees, with the working hypothesis that snags would have more gaps surrounding them overall than live trees. We evaluated canopy gaps around individual snags (n = 270) and live trees (n = 2,186) and evaluated correlations between canopy structure and snag occurrence in dense conifer stands of the Idaho Panhandle National Forest, United States. We paired airborne lidar with ground reference data collected at fixed‐radius plots (n = 53) to evaluate local gap structure. The r package ForestGapR was used to quantify canopy gaps throughout the canopy to determine where the differences were greatest. A canopy space profile was created for each tree by mapping gaps (a) vertically every 2 m in height (2–50 m above‐ground), and (b) horizontally across small (16 m2), medium (36 m2) and large (64 m2) footprint sizes.Our results suggest that this method is robust for quantifying canopy gaps around individual trees. The canopy space profiles were distinctly different for snags and live trees, with more canopy gaps within the area surrounding snags relative to live trees. The greatest differences occurred at mid‐canopy heights (~20 m above‐ground) and at the smallest footprint size (16 m2).These results show potential to improve the understanding of gap dynamics in closed‐canopy conifer forests, and we suggest that snag modelling could be improved by incorporating lidar‐derived canopy gap analyses alongside existing methodologies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Abstract: Topography is a key driver of tropical forest structure and composition, as it constrains local nutrient and hydraulic conditions within which trees grow. Yet, we do not fully understand how changes in forest physiognomy driven by topography impact other emergent properties of forests, such as their aboveground carbon density (ACD). Working in Borneo – at a site where 70‐m‐tall forests in alluvial valleys rapidly transition to stunted heath forests on nutrient‐depleted dip slopes – we combined field data with airborne laser scanning and hyperspectral imaging to characterise how topography shapes the vertical structure, wood density, diversity and ACD of nearly 15 km2 of old‐growth forest. We found that subtle differences in elevation – which control soil chemistry and hydrology – profoundly influenced the structure, composition and diversity of the canopy. Capturing these processes was critical to explaining landscape‐scale heterogeneity in ACD, highlighting how emerging remote sensing technologies can provide new insights into long‐standing ecological questions. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil moisture, soil CO2 efflux (RS), and isotopic composition of RS (δR) were studied in three sessile oak forest stands with different management practice: a high forest (HF), a high forest converted from coppice stand (CHF), and a coppice forest (CF). Measurements were taken monthly, with each position described by light parameters. RS was driven by temperature and moisture. RS increased dramatically with moisture in relatively dry soil, while they remained on the same level when moisture exceeded 13%. Differences in mean soil moisture and RS normalized for 10 °C (R10) among the stands usually followed the pattern HF > CF > CHF. The δR was highest in CHF, but during drought the pattern was HF > CHF > CF. To test the effect of stand structure on soil moisture and R10, we analyzed relationship of these variables to light parameters calculated for the angles of 60° and 180°. Subsequently, we analyzed data from all measured positions divided according to gap fraction into six groups. The results showed that forest floor shading by the canopy decreases moisture regardless of stand type, but no significant relationship was found between the light parameters and R10. We may conclude that soil moisture is affected by canopy structure and RS by stand type. [ABSTRACT FROM AUTHOR]

Key message: 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. Abstract: 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 Järvselja 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. [ABSTRACT FROM AUTHOR]

A study was carried out during two growing seasons to evaluate the performance of a digital photography method to estimate the leaf area index (LAID) of cherry tree cultivars. The trial comprised 10 ‘Bing’ and 10 ‘Sweetheart’ trees where actual leaf area index (LAIA) was obtained by defoliation. Estimations ofLAIDwere obtained by the batch processing of images of the canopy of the same trees which were obtained using a conventional digital RGB camera. Comparisons ofLAIAandLAIDaverages resulted in a good level of agreement for ‘Sweetheart’ for the two growing seasons (percentage of mean absolute error: MAE% = 10.4%). For ‘Bing’,LAIDwas accurate in the first growing season (MAE% = 17.7%), but underestimated by 44% (MAE%) in the second growing season, presumably due to differences observed in the clumping index and the light extinction coefficient. Results evidenced the robustness of this simple method for determining the leaf area index of cherry trees. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Key message : A hypergeometric model is proposed explicitly instead of two previous stochastic models (the Poisson model and Neyman-A model) to describe the topological relationship of trees and the influence of the exclusion distance on gap fraction and clumping index of forest plantation canopies. Abstract: Gap fraction (GF) and clumping index (CI) play key roles in plant light interception, and therefore they have strong impacts on plant growth and canopy radiative transfer processes. Trees are usually assumed to be randomly distributed in natural forests in many previous studies. However, few studies have shown how trees are distributed in forest plantations and how these distribution patterns affect GF and CI in these forests. In this paper, a simple and general distance factor defined as relative allowable shortest distance between centers of two adjacent crowns divided by the mean diameter of the crowns (RASD) is proposed to describe quantitatively the degree of mutual exclusion among trees in forest plantations of various tree distribution patterns. A hypergeometric model is proposed instead of two previous stochastic tree distribution models (the Poisson model and Neyman-A model) to describe the topological relationship of trees and the influences of the exclusion distance on the GF and CI of the forest plantation canopies. The results show that: (1) the hypergeometric model is more suitable than the Poisson model and Neyman-A model for describing the topological relationship of trees in forest plantations; (2) the exclusion distance has strong impacts on GF and CI: there are significant differences between the results of the hypergeometric model and the Poisson model. Larger RASD causes lower GF and larger CI. The simulations are verified by field measurements in four forest plantation stands. Similarly, impacts of RASD on GF and CI are also found for other two crown shapes (prolate and oblate ellipsoids). [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Crown porosity influences radiation interception, air movement through the fruit orchard, spray penetration, and harvesting operation in fruit crops. The aim of the present study was to develop an accurate and reliable methodology based on transmitted radiation measurements to assess the porosity of traditional olive trees under different pruning treatments. Transmitted radiation was employed as an indirect method to measure crown porosity in two olive orchards of the Picual and Hojiblanca cultivars. Additionally, three different pruning treatments were considered to determine if the pruning system influences crown porosity. This study evaluated the accuracy and repeatability of four algorithms in measuring crown porosity under different solar zenith angles. From a 14 to 30 solar zenith angle, the selected algorithm produced an absolute error of less than 5% and a repeatability higher than 0.9. The described method and selected algorithm proved satisfactory in field results, making it possible to measure crown porosity at different solar zenith angles. However, pruning fresh weight did not show any relationship with crown porosity due to the great differences between removed branches. A robust and accurate algorithm was selected for crown porosity measurements in traditional olive trees, making it possible to discern between different pruning treatments. [ABSTRACT FROM AUTHOR]

The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products. [ABSTRACT FROM AUTHOR]

Gap dynamics theory proposes that treefall gaps provide high light levels needed for regeneration in the understory, and by increasing heterogeneity in the light environment allow light-demanding tree species to persist in the community. Recent studies have demonstrated age-related declines in leaf area index of individual temperate trees, highlighting a mechanism for gradual changes in the forest canopy that may also be an important, but less obvious, driver of forest dynamics. We assessed the prevalence of age-related crown thinning among 12 tropical canopy tree species sampled in lowland forests in Panama and Puerto Rico (total N = 881). Canopy gap fraction of individual canopy tree crowns was positively related to stem diameter at 1.3 m (diameter at breast height) in a pooled analysis, with 10 of 12 species showing a positive trend. Considered individually, a positive correlation between stem diameter and canopy gap fraction was statistically significant in 4 of 12 species, all of which were large-statured canopy to emergent species: Beilschmiedia pendula, Ceiba pentandra, Jacaranda copaia, and Prioria copaifera. Pooled analyses also showed a negative relationship between liana abundance and canopy gap fraction, suggesting that lianas could be partially obscuring age-related crown thinning. We conclude that age-related crown thinning occurs in tropical forests, and could thus influence patterns of tree regeneration and tropical forest community dynamics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 (LAID), which was compared with LAI estimated by the proposed digital photography method (LAIM). Results showed that the LAIM was able to estimate LAID 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 (ff) 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. [ABSTRACT FROM AUTHOR]

Fragmentation poses one of the greatest threats to tropical forests with shortterm changes to the structure of forest canopies affecting microclimate, tree mortality, and growth. Yet the long-term effects of fragmentation are poorly understood because (1) most effects require many decades to materialize, but long-term studies are very rare, (2) the effects of edges on forest canopy structure as a function of fragment size are unknown, and (3) edge effects are often confounded by fragment shape. We quantified the long-term (centennial) effects of fragmentation on forest canopy structure using airborne light detection and ranging (LiDAR) of 1060 Hawaiian rain forest fragments ranging in size from 0.02 to 1000 ha, created more than 130 years ago by flowing lava. Along with distance from edge, we developed a metric, minimum span, to gain additional insight into edge effects on three measures of canopy structure: canopy height, height variation, and gap fraction. Fragment size was a strong determinant of the three structural variables. Larger fragments had greater average height, larger variation in height, and smaller gap fraction. Minimum span had a large effect on the depth and magnitude of edge effects for the three structural variables. Locations associated with high span values (those surrounded by more forest habitat) showed little effect of distance to fragment edge. In contrast, locations with low span values (those more exposed to edges) were severely limited in canopy height, showed lower height variation, and were associated with greater gap fraction values. The minimum span attribute allows for a more accurate characterization of edge as well as fragment-level effects, and when combined with high resolution imagery, can improve planning of protected areas for long-term ecological sustainability and biodiversity protection. [ABSTRACT FROM AUTHOR]

Proximal sensing methods using digital photography have gained wide acceptance for describing and quantifying canopy properties. Digital hemispherical photography (DHP) is the most widely used photographic technique for canopy description. However, the main drawbacks of DHP have been the tedious and time-consuming image processing required and the sensitivity of the results to the image analysis methods. Recently, an alternative approach using vertical photography has been proposed, namely, digital cover photography (DCP). The method captures detailed vertical canopy gaps and performs canopy analysis by dividing gap fractions into large between-crown gaps and small within crown gaps. Although DCP is a rapid, simple and readily available method, the processing steps involved in gap fraction analysis have a large subjective component by default. In this contribution, we propose an alternative simple, more objective and easily implemented procedure to perform gap fraction analysis of DCP images. We compared the performance of the two image analysis methods in dense deciduous forests. Leaf area index (LAI) estimates from the two image analysis methods were compared with reference LAI measurements obtained through the use of litter traps to measure leaf fall. Both methods provided accurate estimates of the total gap fraction and, thus, accurate estimates of the LAI. The new proposed procedure is recommended for dense canopies because the subjective classification of large gaps is most error-prone in stands with dense canopy cover. [ABSTRACT FROM AUTHOR]

Optical properties (OPs) of non-flat narrow plant leaves, i.e., coniferous needles, are extensively used by the remote sensing community, in particular for calibration and validation of radiative transfer models at leaf and canopy level. Optical measurements of such small living elements are, however, a technical challenge and only few studies attempted so far to investigate and quantify related measurement errors. In this paper we review current methods and developments measuring optical properties of narrow leaves. We discuss measurement shortcomings and knowledge gaps related to a particular case of non-flat nonbifacial coniferous needle leaves, e.g., needles of Norway spruce (Picea abies (L.) Karst.). [ABSTRACT FROM PUBLISHER]

Canopy gap fraction has been estimated from hemispherical images using a thresholding method to separate sky and canopy pixels. The optimal objective thresholding rule has been searched by many authors without satisfactory results due to long list of reasons. Some recent studies have shown that unprocessed readings of camera CCD or CMOS sensor (raw data) have linear relationship with incident radiation. This allows a pair of cameras used in similar to a pair of plant canopy analyzers and canopy gap fraction can be calculated as the ratio of below canopy image and above canopy image. We tested new freeware program HemiSpherical Project Manager (HSP) for the restoration of the above canopy image from below canopy image which allows making field measurements with single below canopy operated camera. Results of perforated panel image analysis and comparison of plant area index (PAI) estimated independently by three operators from real canopy hemispherical images showed high degree of reliability of the new approach. Determination coefficients of linear regression of the PAI estimations of the three operators were 0.9962, 0.9875 and 0.9825. The canopy gap fraction data obtained from HSP were used to validate Nobis-Hunziker automatic thresholding algorithm. The results indicated that the Nobis-Hunziker algorithm underestimated PAI from out of camera JPEG images and overestimated PAI from raw data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) - remotely estimated from LiDAR - control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function. [ABSTRACT FROM AUTHOR]

Quantifying the 3-D forest canopy structure and leaf area index of an individual tree or a forest stand is challenging. The canopy structural information implicitly contained within point cloud data (PCD) generated from terrestrial laser scanning (TLS) makes it possible to characterize directly the spatial distribution of foliage elements. In this paper, a new voxel-based method titled “point cloud slicing” is presented to retrieve the biophysical characteristics of the forest canopy including extinction coefficient, gap fraction, overlapping effect, and effective leaf area (ELA) from PCD. These extractions were performed not only from the whole canopy but also from layers of the canopy to depict the distribution patterns of foliage elements within the canopy. The results showed that the TLS-based ELA estimation method could explain 88.7% (rmse = 0.007, p < 0.001, and n = 30) variation of the destructive-sample-based leaf area measurement results. It was found that the sampling resolution was a key parameter in defining the dimension of a single voxel. Furthermore, the TLS-based method can also serve as a calibration tool for airborne laser scanning application with ground sampling. [ABSTRACT FROM PUBLISHER]

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, mm) for branches and an opaque cone for the trunk. We introduced a clumping parameter (μ) 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. [ABSTRACT FROM AUTHOR]

The effectiveness of using leveled digital camera for measuring leaf inclination angles was investigated in this study as an inexpensive and convenient alternative to existing approaches. The new method is validated with manual leaf angle measurements for various broadleaf tree species common to hemi-boreal region of Estonia and the tropical forests of Hawai'i Islands. The acquired leaf angle distributions suggest that planophile case might be more appropriate than the commonly assumed spherical as the general approximation of leaf orientation while modeling the radiation transmission through the canopies of (hemi)-boreal broadleaf stands. However, direct leaf inclination measurements should be obtained whenever possible, as there will always exist a large variety of leaf orientation, both among different species and in the space-time domain within a single species. The camera method tested in this study provides a new robust and affordable tool to obtain this information. [ABSTRACT FROM AUTHOR]

A method that applies the terrestrial laser scanning to estimate leaf areas of individual trees in a mature conifer forest is presented. It is based on the theory of conventional optical LAI determinations, but refined for the inclusion of 3D depth information from the laser scanner. For each objective tree, we first used a single scan to measure local gap fractions beyond determined crown depths and combined this scan with other scans to delineate the geometrical dimensions of the crown. Then, through integrating the information from both aspects, the local leaf area density and the corresponding volume were derived. Finally, the total leaf area was obtained as the scalar product of these two variables. As most procedures were implemented on segmented 2D range images, the method possesses high efficiency. Additionally, through using gap fraction beyond determined crown depths, it solved the zero gap fraction problem encountered in segmented hemispherical photograph analysis. The method was tested on 11 trees in a 39 years old Norway spruce ( Picea abies [L.] Karst.) stand located in southern Bavaria, Germany. Through correlation of the results with the estimates obtained with allometric equations, the accuracy was validated. The influence of the crown depth, for measuring gap fraction, and the segment size on estimation were also analyzed. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) systems can be used to estimate both vertical and horizontal forest structure. Woody components, the leaves of trees and the understory can be described with high precision, using geo-registered 3D-points. Based on this concept, the Effective Plant Area Indices (PAI) for areas of Korean Pine ( Pinus koraiensis), Japanese Larch ( Larix leptolepis) and Oak ( Quercus spp.) were estimated by calculating the ratio of intercepted and incident LIDAR laser rays for the canopies of the three forest types. Initially, the canopy gap fraction ( G) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area, using k-means statistics. The LiDAR-derived PAI was then estimated by using G with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAI revealed the coefficients of determination for Korean Pine, Japanese Larch and Oak to be 0.82, 0.64 and 0.59, respectively. These differences between field-based and LIDAR-based PAI for the different forest types were attributed to the amount of leaves and branches in the forest stands. The absence of leaves, in the case of both Larch and Oak, meant that the LiDAR pulses were only reflected from branches. The probability that the LiDAR pulses are reflected from bare branches is low as compared to the reflection from branches with a high leaf density. This is because the size of the branch is smaller than the resolution across and along the 1 meter LIDAR laser track. Therefore, a better predictive accuracy would be expected for the model if the study would be repeated in late spring when the shoots and leaves of the deciduous trees begin to appear. [ABSTRACT FROM AUTHOR]

The quantification of forest carbon sequestration is helpful to understand the carbon storage on the Earth. The estimation of forest carbon sequestration can be achieved by the use of leaf area index (LAI), which is derived from forest gap fraction. The hemispherical image-based technique is the most popular non-destructive means for obtaining such information. However, only the gap fraction of the top canopy is derived due to the limitation of imaging technique. The gap fraction information of understory is thus neglected. In this study, we evaluate the use of a terrestrial laser scanner (TLS) to obtain the forest canopy and understory gap fraction. The forest TLS data were manually classified as the top canopy and understory layers to facilitate the estimation of top canopy and understory gap fraction, respectively. [ABSTRACT FROM AUTHOR]

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning of forest structure is used increasingly in place of traditional technologies; however, deriving physical parameters from point clouds remains challenging because LiDAR returns do not have defined areas or volumes. While voxelization methods overcome this challenge, estimation of canopy gaps and other structural attributes are often performed by reducing the point cloud to two-dimensions, thus decreasing the fidelity of the data. Furthermore, relatively few studies have evaluated voxel-size effects on estimation accuracy. Here, we show that voxelized laser-scanning data can be used for canopy-gap estimation without performing dimensionality reduction to the point cloud. Both airborne and terrestrial LiDAR were used to estimate canopy gaps along six vertical transects and four height intervals. Voxel-based estimates were evaluated against hemispherical photography and a sensitivity analysis was performed to identify an optimal voxel size. While the results indicate that our approach can be used with both airborne and terrestrial LiDAR, voxel size has a considerable influence on canopy-gap estimation. Results from our sensitivity analysis indicate that TLS estimation performs best when using 10 cm voxels, yielding canopy gaps ranging from 32–78%. The optimal voxel size for ALS estimation was obtained with 25 cm voxels, yielding estimates ranging from 25–68%. [ABSTRACT FROM AUTHOR]

Question: To what extent do small-scale disturbances in the forest canopy, created by natural disturbance agents, affect stand development? Doubts exist as to whether small canopy openings have any real effect on the understory tree recruitment, especially in boreal forests. Location: Conifer and mixed stands in the Gaspesian region in eastern Québec. The main natural disturbance agents are recurring outbreaks of Choristoneura fumiferana (eastern spruce budworm) and winds. Methods: Linear transects in 27 sites were used to describe the gap (< 0.1 ha) regime parameters, including gap fraction, gap size and change in disturbance severity through time. Three stand types were distinguished, based on a gradient of abundance of tree host species for the eastern spruce budworm. The impact of gaps was evaluated on the basis of changes in the number, the period of recruitment, and the composition of tree saplings present within gap areas. Changes were measured along the gap size gradient, and according to the pattern of recent budworm epidemics. Results: The gap fraction is highly variable (18%–64%) and is on average relatively high (42%). Gap sizes have a positively skewed distribution. In most cases the growth rate among gap filling saplings increased sufficiently to date disturbance events. The composition and the structure of understory trees were affected by gap formation. The number of shade-intolerant tree species did increase during or following periods of particularly severe canopy disturbances. However, the establishment or survival of shade intolerant species was not restricted to larger gaps or more intensely disturbed periods. Conclusions: In sub-boreal forests of Eastern Canada, small scale disturbances in the tree canopy influence stand regeneration dynamics, but not to the extent that parameters such as sapling composition and recruitment patterns depend on gap regime characteristics. [ABSTRACT FROM AUTHOR]

The Brazilian Amazon forest and cerrado savanna encompasses a region of enormous ecological, climatic, and land-use variation. Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout this region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The methodology of the CLAS approach is presented along with a study of its sensitivity to atmospheric correction errors. CLAS is then applied to a mosaic of Landsat images spanning the years 1999–2001 as a proof of concept and capability for large-scale, very high resolution mapping of the Amazon and bordering cerrado savanna. A total of 197 images were analyzed for fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate covers using a probabilistic spectral mixture model. Results from areas without significant land use, clouds, cloud shadows, and water bodies were compiled by the Brazilian state and vegetation class to understand the baseline structural typology of forests and savannas using this new system. Conversion of the satellite-derived PV data to woody canopy gap fraction was made to highlight major differences by vegetation and ecosystem classes. The results indicate important differences in fractional photosynthetic cover and canopy gap fraction that can now be accounted for in future studies of land-cover change, ecological variability, and biogeochemical processes across the Amazon and bordering cerrado regions of Brazil. [ABSTRACT FROM AUTHOR]

The article presents a study which combines a field study of canopy gap fraction with spectral mixture analyses of satellite imagery and Landsat 7 ETM to evaluate landscape and dynamics of canopy damage after selective logging in an eastern Amazon forest. The use of Monte Carlo analysis and a spectral mixture model to estimate high spatial resolution cover of forest canopies is stated. The usefulness of the method for quantifying forest canopy cover fraction in intact forest areas is cited.

Selective logging is a dominant form of land use in the Amazon basin and throughout the humid tropics, yet little is known about the spatial variability of forest canopy gap formation and closure following timber harvests. We established chronosequences of large-area (14–158 ha) selective logging sites spanning a 3.5-year period of forest regeneration and two distinct harvest methods: conventional logging (CL) and reduced-impact logging (RIL). Our goals were to: (1) determine the spatial characteristics of canopy gap fraction immediately following selective logging in the eastern Amazon; (2) determine the degree and rate of canopy closure in early years following harvest among the major landscape features associated with logging – tree falls, roads, skid trails and log decks; and (3) quantify spatial and temporal differences in canopy opening and closure in high- and low-damage harvests (CL vs. RIL). Across a wide range of harvest intensities (2.6–6.4 felled trees ha−1), the majority of ground damage occurred as skid trails (4–12%), whereas log decks and roads were only a small contributor to the total ground damage (<2%). Despite similar timber harvest intensities, CL resulted in more ground damage than RIL. Neither the number of log decks nor their individual or total area was correlated with the number of trees removed or intensity of tree harvesting (trees ha−1). The area of skids was well correlated with the ground area damaged (m2) per tree felled. In recently logged forest (0.5 years postharvest), gap fractions were highest in log decks (mean RIL=0.83, CL=0.99) and lowest in tree-fall areas (RIL: 0.26, CL: 0.41). However, the small surface area of log decks made their contribution to the total area-integrated forest gap fraction minor. In contrast, tree falls accounted for more than two-thirds of the area disturbed, but the canopy gaps associated with felled trees were much smaller than for log decks, roads and skids. Canopy openings decreased in size with distance from each felled tree crown. At 0.5 years postharvest, the area initially affected by the felling of each tree was approximately 100 m in radius for CL and 50 m for RIL. Initial decreases in gap fraction during the first 1.5 years of regrowth diminished in subsequent years. Throughout the 3.5-year period of forest recovery, tree-fall gap fractions remained higher in CL than in RIL treatments, but canopy gap closure rates were higher in CL than in RIL areas. During the observed recovery period, the canopy gap area affected by harvesting decreased in radius around each felled tree from 100 to 40 m in CL, and from 50 to 10 m in RIL. The results suggest that the full spatial and temporal dynamics of canopy gap fraction must be understood and monitored to predict the effects of selective logging on regional energy balance and climate regimes, biogeochemical processes including carbon cycling, and plant and faunal population dynamics. This paper also shows that remote sensing of log decks alone will not provide an accurate assessment of total forest area impacted by selective logging, nor will it be closely correlated to damage levels and canopy gap closure rates. [ABSTRACT FROM AUTHOR]

We present an operational method for estimating the amount of PAR intercepted by a coniferous shoot. Interception of PAR by a shoot is divided into three components: the amount of radiation coming from the sky, the transmission of radiation through the surrounding vegetation, and the shoot's silhouette area facing the direction of the incoming radiation. All three components usually vary with direction. Radiation incident from the sky consists of direct and diffuse radiation. The well-known equation of motion for the sun and Beer's Law for atmospheric transmittance are used to simulate the directional distribution of direct sunlight for any given period of time. The diffuse component is assumed to be uniform. Meteorological field measurements are used to calibrate the absolute amounts of the direct and diffuse components. The gap fraction (proportion of visible sky) in different directions around a shoot is measured by analyzing a hemispherical fish-eye photograph, taken at the location of the shoot, with an image processing program. Similarly, the shoot silhouette area (SSA) is measured by photographing the shoot from many different directions. The measurements of SSA are interpolated by a method called trigonometric interpolation to obtain the directional distribution of SSA over the entire hemisphere. This distribution is then rotated according to the shoot's position in the canopy. Multiplying incoming PAR, canopy gap fraction and SSA in different directions, and summing over all directions, gives an estimate of PAR intercepted by the shoot during the chosen period of time. The method is described step by step, and applied, as an example, to a shoot from a Scots pine (Pinus sylvestris L.) stand in central Finland. Differences in radiation interception properties between sun and shade shoots and their relevance to canopy-scale models are discussed. [ABSTRACT FROM PUBLISHER]

In the past few years, a number of systems for making indirect canopy structural estimates have become commercially available. These include linear sensors, such as the DEMON, line quantum sensors, and hemispherical sensors, such as the LAI-2000, the Leaf Laser, and the Cl-100. The performance of these instruments as reported in the literature is reviewed for forests, row crops, and individual trees. [ABSTRACT FROM PUBLISHER]

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Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally. [ABSTRACT FROM AUTHOR]

Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR's perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season. [ABSTRACT FROM AUTHOR]

Research Highlights: This paper proposes a new method for hemispherical forest canopy image segmentation. The method is based on a deep learning methodology and provides a robust and fully automatic technique for the segmentation of forest canopy hemispherical photography (CHP) and gap fraction (GF) calculation. Background and Objectives: CHP is widely used to estimate structural forest variables. The GF is the most important parameter for calculating the leaf area index (LAI), and its calculation requires the binary segmentation result of the CHP. Materials and Methods: Our method consists of three modules, namely, northing correction, valid region extraction, and hemispherical image segmentation. In these steps, a core procedure is hemispherical canopy image segmentation based on the U-Net convolutional neural network. Our method is compared with traditional threshold methods (e.g., the Otsu and Ridler methods), a fuzzy clustering method (FCM), commercial professional software (WinSCANOPY), and the Habitat-Net network method. Results: The experimental results show that the method presented here achieves a Dice similarity coefficient (DSC) of 89.20% and an accuracy of 98.73%. Conclusions: The method presented here outperforms the Habitat-Net and WinSCANOPY methods, along with the FCM, and it is significantly better than the Otsu and Ridler threshold methods. The method takes the original canopy hemisphere image first and then automatically executes the three modules in sequence, and finally outputs the binary segmentation map. The method presented here is a pipelined, end-to-end method. [ABSTRACT FROM AUTHOR]