Search

Average leaf inclination angle (θ¯L) is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants. θ¯L can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect optical instruments for quantifying θ¯L remain largely unquantified. In this study, we evaluated and compared the performances of three major indirect optical instruments: (1) LAI-2200, (2) 30°-tilted camera, and (3) digital hemispherical photography (DHP), in different crop fields over a growing season, benchmarked with direct measurements. LAI-2200 and 30°-tilted camera showed higher agreement with direct θ¯ measurements (R2 = 0.54, RMSE = 7.37°; R2 = 0.58, RMSE = 8.08°) than DHP (R2 = 0.14, RMSE = 13.96°). Different performances of indirect optical instruments could be attributed to the accuracy of gap fraction measurement and the performance of the θ¯L quantification algorithms. When using the LAI-2200 algorithm, larger gap fraction gradients over view zenith angles led to larger θ¯L values, and smaller gap fraction gradients led to smaller θ¯L values. Such error propagation was larger in sparse canopy than in dense canopy. The Wilson G function of the LAI-2200 algorithm performed better in estimating θ¯L than the G function based on the ellipsoidal LAD function used by the CAN_EYE algorithm. We also proposed a modification of the LAI-2200 algorithm, which further improved the performance of LAI-2200 and 30°-tilted cameras in estimating θ¯L. We envision that the low-cost 30°-tilted cameras provide a promising sensor solution to continuously monitor canopy structure for various ecosystems.

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]

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.

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.)

During the restoration of secondary forests in Northeast China, it is critically important to promote seed recruitment of Korean pine (Pinus koraiensis), which is highly dependent on rodents and significantly affected by spatially heterogeneous understory vegetation of forest gaps. Understanding the link between understory microhabitats and rodent-mediated seed dispersal, including post-dispersal seed predation and scatter-hoarding seed escaped predation, is essential to improve seed survival. Previous studies have focused on their qualitative relationships by roughly classifying understory vegetation characteristics, which cannot precisely predict regeneration direction. Quantifying the spatial heterogeneity of understory vegetation parameters (e.g., canopy cover, CC; gap fraction, GF) using terrestrial laser scanning (TSL) provides a vital way to determine quantitative relationships. In this study, we collected point cloud data in forest gaps with three sizes and six within-gap positions through TSL to quantify the understory microhabitats using CC and GF and integrated these parameters with spatial site data of Korean pine seeds scatter-hoarding and post-dispersal predation by rodents. The results showed that the influence of the within-gap position on the understory microhabitats was insignificant, and the spatial site selection of post-dispersal seed predation and seed storage was concentrated in the range of 0–22 m away from the seed station, irrelevant to the gap size. Considering the risk of predation by enemies and seed pilferage by competitors in forest gaps, rodents scatter-hoarded or post-dispersal predated seeds in the CC range of 0.2–0.3, and post-dispersal predated seeds less often in the CC range > 0.8. The ratio of post-dispersal seed predation to scatter-hoarding escaped predation firstly increased and then decreased along with the increase in CC and GF. Therefore, the spatial heterogeneity in understory microhabitats caused by gap treatment had an uncertain effect on rodent-mediated seed dispersal, depending on the relative ratio of post-dispersal seed predation to seed scatter-hoarding.

In horticultural leafy vegetable production, continuously monitoring crop size indicators such as the leaf area index (LAI), leaf fresh weight (LFW), and leaf length (LL) is of practical value because these indicators are related to crop yields and harvest timing. The aim of this study was to develop a method that enables the continuous, automatic estimation of the LAI, LFW, and LL of a Chinese chive (Allium tuberosum) canopy by combining timelapse photography with allometric equations. LAI was estimated based on the gap fractions of nadir photographs (i.e., the fractions of nonleaf area), which were retrieved using the deep learning framework DeepLabv3+ with satisfactory accuracy (mean intersection over union, 0.71). This photographically estimated LAI (LAIphoto) corresponded well with the destructively measured LAI (LAIdest) (LAIphoto = 0.96LAIdest, R² = 0.87). LAIphoto was then used as the input of allometric regression equations relating LAIphoto with LFW and LL. A power function (y = axb) fit the observed LAIphoto–LFW and LAIphoto–LL relationships well (R² = 0.89 and 0.74, respectively). By combining nadir timelapse photography with the allometric equations, changes in the LFW and LL of a Chinese chive canopy were estimated successfully for a 9-month cultivation period. Our approach can replace time-consuming, labor-intensive manual measurements of these crop size indicators for Chinese chive and may be applicable to other crops with different parameter sets. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a widely used plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly important role in providing the data source needed for LAI extraction. This study proposed a UAV-derived 3-D point cloud-based method to automatically calculate crop-effective LAI (LAIe). In this method, the 3-D winter wheat point cloud data filtered out of bare ground points was projected onto a hemisphere, and then the gap fraction was calculated through the hemispherical image obtained by projecting the sphere onto a plane. A single-angle inversion method and a multi-angle inversion method were used, respectively, to calculate the LAIe through the gap fraction. The results show a good linear correlation between the calculated LAIe and the field LAIe measured by the digital hemispherical photography method. In particular, the multi-angle inversion method of stereographic projection achieved the highest accuracy, with an R2 of 0.63. The method presented in this paper performs well in LAIe estimation of the main leaf development stages of the winter wheat growth cycle. It offers an effective means for mapping crop LAIe without the need for reference data, which saves time and cost.

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]

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.

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.

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.)

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 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.

Mangrove forest represented a coastal ecosystem in Indonesia. Theoretical validation and in-field measurement by calculating the number of trees and the density data that was validated through remote sensing would not be appropriate because the remote sensing recorded canopy density and not tree stands. New method canopy photography or gap fraction method was the technique to predict sun radiation using the photograph taken upward through extremely wide lens and classification object image. The objectives of the study were (1) to examine the acuracy of the estimation of the mangrove forest density using vegetation index transformation, and (2) to map the mangrove forest condition. The location of the study was Alas Purwo Resort Grajagan National Park area. The material of the study was Landsat-8 OLI image recorded on January 19th, 2016 using SAVI vegetation index transformation method. Gap fraction filed measurement method was a new method in Indonesia. The results of the study showed that the regression of the SAVI index between index transformation value and in-field condition (R2) was 0.566, the forest density estimation resulting from the SAVI index transformation had the RMSE of 2.334178 and the density of the mangrove forest in Grajagan Bay of the Alas Purwo National Park included low density of 0-12.5% (30.42 ha), medium density of 12.6-25% (116.55 ha), and high density of 25.1-37.6% (463.68 ha).

Leaf inclination plays a crucial role in regulating the radiation, carbon and water fluxes in plant canopies. Accurate measurement of the probability density function of leaf inclination (i.e., the leaf angle distribution or leaf inclination distribution function (LIDF)), is very important for modelling photosynthesis as well as measuring leaf area index. In spite of its importance, in situ measurement of LIDF is very challenging. Both digital hemispherical photography (DHP) and terrestrial LiDAR (TLS) have been used to measure LIDF. However, the consistency and relative accuracy of these two techniques has never been evaluated. In this research, we aimed to evaluate which in situ technique, either DHP or TLS, could measure LIDF more accurately, with respect to both field-based and synthetic datasets. The field-based datasets were collected from 36 natural European beech stands covering a range of forest structures. The synthetic datasets were generated from 44 virtual forest scenes using TLS and DHP simulators. Due to the inability of differing leaf and woody materials in DHP, the average plant inclination angle (θ ¯) from DHP and TLS was selected for LIDF comparison. The average inclination angle (θ ¯) was retrieved from TLS point clouds using a geometric method, and from DHP using three gap fraction inversion methods including the NC method (Norman and Campbell, 1989), as well as the CAN-EYE and Hemisfer software. Results from the field-based datasets showed a significant difference and inconsistency between the average inclination angle (θ ¯) retrieved from TLS and DHP (respectively θ ¯ ∈ (40°, 58°) from TLS, (15°, 86°) from DHP NC, (36°, 78°) from DHP CAN-EYE, (0°, 67°) from DHP Hemisfer). Results from the synthetic datasets demonstrated that the accuracy of θ ¯ from TLS was considerably higher than that obtained from DHP (R 2: 0.90 > 0.74; RMSE: 5.38° < 13.30°). This study demonstrated that the LIDF estimated from TLS and DHP were not coherent. Based on the experimental results as well as deduction from theoretical arguments, we recommended using TLS when measuring leaf inclination in broadleaf forests, especially for stands with a heterogeneous structure. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key parameter that affects the surface fluxes of energy, mass, and momentum over vegetated lands, but observational measurements are scarce, especially in remote areas with complex canopy structure. In this paper we present an indirect method to calculate the LAI based on the analyses of histograms of hemispherical photographs. The optimal threshold value (OTV), the gray-level required to separate the background (sky) and the foreground (leaves), was analytically calculated using the entropy crossover method (Sahoo, P.K., Slaaf, D.W., Albert, T.A., 1997. Threshold selection using a minimal histogram entropy difference. Optical Engineering 36(7) 1976–1981). The OTV was used to calculate the LAI using the well-known gap fraction method. This methodology was tested in two different ecosystems, including Amazon forest and pasturelands in Brazil. In general, the error between observed and calculated LAI was ∼6%. The methodology presented is suitable for the calculation of LAI since it is responsive to sky conditions, automatic, easy to implement, faster than commercially available software, and requires less data storage.

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., 2 = 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.

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]

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.

This paper develops an algorithm to estimate vegetation canopy gap fraction (GF), taking advantage of the full Terrestrial Laser Scanner (TLS) resolution. After calculating the TLS angular resolution, the algorithm identifies the missing laser hits (gaps) within an angular grid in the azimuthal and zenithal directions. The algorithm was first tested on angular data simulations with random (R), cluster (C) and random and cluster together (RC) gap pattern distributions. Noise introduced in the simulations as a percentage of the resolution accounted for the effect of TLS angular uncertainty. The algorithm performs accurately if angular noise is Quercus ilex L. trees, the algorithm rendered a GF between 0.26 and 0.40. TLS had an angular noise

The leaf area index (LAI) is a crucial structural parameter of forest canopies. Light Detection and Ranging (LiDAR) provides an alternative to passive optical sensors in the estimation of LAI from remotely sensed data. However, LiDAR-based LAI estimation typically relies on empirical models, and such methods can only be applied when the field-based LAI data are available. Compared with an empirical model, a physically-based model—e.g., the Beer−Lambert law based light extinction model—is more attractive due to its independent dataset with training. However, two challenges are encountered when applying the physically-based model to estimate LAI from discrete LiDAR data: i.e., deriving the gap fraction and the extinction coefficient from the LiDAR data. We solved the first problem by integrating LiDAR and hyperspectral data to transfer the LiDAR penetration ratio to the forest gap fraction. For the second problem, the extinction coefficient was estimated from tiled (1 km × 1 km) LiDAR data by nonlinearly optimizing the cost function of the angular LiDAR gap fraction and simulated gap fraction from the Beer−Lambert law model. A validation against LAI-2000 measurements showed that the estimates were significantly correlated to the reference LAI with an R2 of 0.66, a root mean square error (RMSE) of 0.60 and a relative RMSE of 0.15. We conclude that forest LAI can be directly estimated by the nonlinear optimization method utilizing the Beer−Lambert model and a spectrally corrected LiDAR penetration ratio. The significance of the proposed method is that it can produce reliable remotely sensed forest LAI from discrete LiDAR and spectral data when field-measured LAI are unavailable.

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.

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.

The 3D distribution of canopy foliage affects the radiation regime and retrievals of canopy biophysical parameters. The gap fraction is one primary indicator of a canopy structure. Historically the Beer–Lambert–Bouguer law and the linear mixture model have served as a basis for multiple technologies for retrievals of the gap (or vegetation) fraction and Leaf Area Index (LAI). The Beer–Lambert–Bouguer law is a form of the Radiative Transfer (RT) equation for homogeneous canopies, which was later adjusted for a correlation between fitoelements using concept of the clumping index. The Stochastic Radiative Transfer (SRT) approach has been developed specifically for heterogeneous canopies, however the approach lacks a proper model of the vegetation fraction. This study is focused on the implementation of the stochastic version of the Beer–Lambert–Bouguer law for heterogeneous canopies, featuring the following principles: 1) two mechanisms perform photon transport- transmission through the turbid medium of foliage crowns and direct streaming through canopy gaps, 2) the radiation field is influenced by a canopy structure (quantified by the statistical moments of a canopy structure) and a foliage density (quantified by the gap fraction as a function of LAI), 3) the notions of canopy transmittance and gap fraction are distinct. The derived stochastic Beer–Lambert–Bouguer law is consistent with the Geometrical Optical and Radiative Transfer (GORT) derivations. Analytical and numerical analysis of the stochastic Beer–Lambert–Bouguer law presented in this study provides the basis to reformulate widely used technologies for retrievals of the gap fraction and LAI from ground and satellite radiation measurements. [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]

Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI prediction accuracy was comparable with the one of a gap fraction-based handheld device ( R 2 of 0.92 , RMSE of 0.42  m  2 m −2 ) and correlated well with destructive LAI measurements ( R 2 of 0.89 , RMSE of 0.41  m 2  m −2 ). These results indicate that, if respecting the range (LAI ≤ 3 ) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]

Fast and accurate estimates of canopy cover are central for a wide range of agricultural applications and studies. Visual assessment is a traditionally employed method to estimate canopy cover in the field, but it is limited by the costs, subjectivity and non-reproducibility of the produced estimates. Digital photography is a low-cost alternative method. In this study we tested two automated image classification methods, the first one based on a histogram-analysis method (Rosin), the second one based on a combination of a visible vegetation index and the L*a*b* colour space conversion (LAB2), which have both been previously tested in forestry, and a supervised image classification method (Winscanopy), to estimate canopy cover from downward-looking images of agricultural crops. These methods were tested using artificial images with known cover; this allowed exploring the influence of canopy density and object size on canopy cover estimation from photography. The Rosin method provided the best estimates of canopy cover in artificial images, whose accuracy was largely unaffected by variation in canopy density and object size. By contrast, LAB2 systematically overestimated canopy cover, because of the sensitivity of the method to small variations of chromaticity in artificial images. Winscanopy showed good performance when at least two regions per class were manually selected from a representative image. The results were replicated in real images of cultivated aromatic crops. The main findings indicate that digital photography is an effective method to obtain rapid, robust and reproducible measures of canopy cover in downward-looking images of agricultural crops, including aromatic plants. [ABSTRACT FROM AUTHOR]

Shade factors, defined as the percentage of sky covered by foliage and branches within the perimeter of individual tree crowns, have been used to model the effects of trees on air pollutant uptake, building energy use and rainfall interception. For the past 30 years the primary source of shade factors was a database containing values from 47 species. In most cases, values were obtained from measurements on a single tree in one location. To expand this database 11,024 shade factors were obtained for 149 urban tree species through a photometric process applied to the predominant species in 17 U.S. cities. Two digital images were taken of each tree, crowns were isolated, silhouette area defined and shade factors calculated as the ratio of shaded (i.e., foliage and woody material) pixels to total pixels within the crown silhouette area. The highly nonlinear relationship between both age and diameter at breast height (DBH), and shade factor was captured using generalized additive mixed models. We found that shade factors increased with age until trees reached about 20 years or 30 cm DBH. Using a single shade factor from a mature tree for a young tree can overestimate actual crown density. Also, in many cases, shade factors were found to vary considerably for the same species growing in different climate zones. We provide a set of tables that contain the necessary values to compute shade factors from DBH or age with species and climate effects accounted for. This new information expands the scope of urban species with measured shade factors and allows researchers and urban foresters to more accurately predict their values across time and space. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

The aboveground biomass (AGB) of vegetation is of central importance for ecosystem services by providing a measure of productivity. Models have been developed for estimating AGB via canopy structural variables in both fundamental and applied ecological studies. However, the potential of canopy structural variables for describing AGB dynamics throughout a growing season are still unclear. This study focuses on the AGB seasonal dynamics of a pioneer community, Cynodon dactylon (L.) Pers. (Bermuda grass), in a newly-formed riparian habitat at China’s Three Gorges Reservoir. The objectives are (1) to determine the most important structural variable for estimating AGB at different growing stages during the season, and (2) to develop a model that can estimate AGB at the different growing stages and using multiple structural variables. We sampled the C. dactylon community six times during the growing season from May to September 2016. Six variables were engaged in the analysis, including five canopy structural variables, i.e., canopy height (H), canopy cover (CC), leaf area index (LAI), the volume related variables V LAI (H × LAI) and V CC (H × CC), and one seasonal growth effect variable (SV). We conducted univariate linear regression analysis to determine the most important estimator of AGB and the best subset regression analysis were used to develop the AGB estimation model. The detected most important AGB estimator changed with different growing stages throughout a season. Canopy structural characteristics of the community are key factors for determining such changes. Cover was the most important variable for AGB estimation during the early growing season and V LAI was the most important variable in the mid and end of the growing season. The developed best multivariate models explained an additional 11% in AGB variance on average for the different growing stages compared with the univariate models using the most important estimators. SV was found to be useful in developing an acceptance general AGB estimation model appropriate for the entire growing season. The findings of this study are expected to provide knowledge for guiding sampling work and to assist with modeling AGB and understanding the AGB seasonal dynamics in the future. [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.

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.

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.

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

Leaf area index estimates in dense evergreen tropical moist forest almost exclusively rest on indirect methods most of which being of limited accuracy or spatial resolution. In this study we examine the potential of full waveform Aerial Laser Scanning (ALS) to derive accurate spatially explicit estimates of Plant Area Index (PAI). A discrete representation of the forest canopy is introduced in the form of a 3D voxelized space. For each voxel (elementary volume, typically one cubic m) a first estimate of local transmittance of vegetation is computed as the ratio of the sum of energy exiting a voxel to the sum of energy entering the same voxel. A spatially hierarchical model is subsequently applied to refine estimates of individual voxel transmittance. Plant area density (PAD) profiles are then computed from the local transmittance values by applying Beer Lambert's turbid medium approximation. PAI values are obtained from vertical integration of PAD profiles. The model is shown to be robust to low sampling intensity and high occlusion rates. We further compared simulated values of gap fraction obtained by ray tracing for 5 angular sectors with in situ LAI2200 measurements taken at 135 positions in a 0.5 ha forest plot located in the center of the scene. The overall patterns of simulated and measured values (average value per inclination and pattern of variation along a 70 m transect line) were highly consistent. A slight but systematic discrepancy was observed along the inclination gradient, gap fractions derived from ray tracing in the voxelized scene being slightly lower than the measured values. This difference might be the consequence of multiple reflections which have been found to bias gap fractions estimates produced by LAI2200. PAI estimates derived from LAI2200 measurements (either simulated 6.8 or observed 5.9) are much lower than the PAI derived from vertical integration of local PAD (13.6). This large difference reflects the fact that distribution of foliage is strongly spatially structured and that this structural information is not properly accounted for in PAI estimates derived from mean gap fraction per elevation angle. After adjusting local transmittance to match mean LAI 2200 profiles the PAI at plot level was found to be 13.2 m 2 ·m − 2 . We conclude that Aerial Laser Scanning can produce accurate maps of Plant Area Index over large areas with unmatched efficacy, accuracy and ease. This should be of major relevance for many forest ecological studies. [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]

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.