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Leaf area index (LAI) is a critical parameter that influences many biophysical processes within forest ecosystems. Collecting in situ LAI measurements by forest canopy hemispherical photography is however costly and laborious. As a result, there is a lack of LAI data for calibration of forest ecosystem models. Citizen science has previously been tested as a solution to obtain LAI measurements from large areas, but simply asking citizen scientists to collect forest canopy images does not stimulate enough interest. As a response, this study investigates how gamified citizen science projects could be implemented with a less laborious data collection scheme. Citizen scientists usually have only mobile phones available for LAI image collection instead of cameras suitable for taking hemispherical canopy images. Our simulation results suggest that twenty directional canopy images per plot can provide LAI estimates that have an accuracy comparable to conventional hemispherical photography with twelve images per plot. To achieve this result, the mobile phone images must be taken at the 57° hinge angle, with four images taken at 90° azimuth intervals at five spread-out locations. However, more images may be needed in forests with large LAI or uneven canopy structure to avoid large errors. Based on these findings, we propose a gamified solution that could guide citizen scientists to collect canopy images according to the proposed scheme.

Environmental monitoring networks such as the Integrated Carbon Observation System (ICOS) in Europe, the National Ecological Observatory Network (NEON) in the U.S., or the Terrestrial Ecosystem Research Network (TERN) in Australia deploy different sampling schemes for in situ measurements. We report on the intercomparison of measurements of the canopy gap fraction with different digital hemispherical photography setups adopting ICOS, NEON, and TERN sampling schemes. The test was carried out at the Järvselja Radiation Transfer Model Intercomparison (RAMI) birch stand. Results show that spreading out sampling points which cover more of the plot is important for a good representation of the forest as a whole. The NEON tower plot layout scheme may be more prone to errors in overall canopy properties estimation than ICOS or TERN due to its compact sampling layout and should always be used in conjunction with its distributed plots. Different camera setups involving different camera operators, camera bodies, lenses and settings yield slightly varied results, and it is important to ensure that the images are taken in such a way that they would not be over or underexposed, or out of focus. As a conclusion we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded. Our study contributes towards establishing the uncertainty and evaluating potential error budget stemming from collecting in situ measurements using different sampling schemes and camera setups.

Leaf area index (LAI) is a canopy structure parameter used to characterize vegetation growth. It is increasingly important to obtain continuous and accurate ground LAI measurement data for the validation of remote-sensing products. In this study, direct destructive sampling method and indirect optical measurement including LAI-2200 plant canopy analyser (PCA) and Tracing Radiation and Architecture of Canopies (TRAC) were used for long-term continuous observation of paddy fields in southern China, to explore the variation of LAI during the growth period of paddy, and to quantitatively analyse the errors of indirect optical measurement. The results show that (1) LAI and Plant area index (PAI) increased first and then decreased in the growing period. The proportion of green leaf area gradually decreased at reproductive growth stage (less than 50%). There was a linear relationship between the proportion of green leaf area and PAI at this stage, with a good correlation (R2 = 0.73). This linear relationship can be used to convert PAI into LAI more conveniently. (2) Effective PAI (PAIe) calculated by the two methods (2000 method and Lang method) from PCA showed good consistency between each other in the growing season (the maximum value of R2 is 0.99). With the increase of zenith angle, the consistency between 2000 method and Lang method decreases (R2 = 0.83). Multiple scattering has an effect on PCA measurement, and this effect is more obvious at high zenith angle. Lang method is more sensitive to the change of zenith angle. (3) Compared to the destructive values, both 2000 method and Lang method are able to compute accurate LAI using four ring sensors (4 R), while TRAC underestimates the LAI and PAI from destructive sampling method. This study provided a new idea for reducing the impact of ground reference data errors on LAI product validation. [ABSTRACT FROM AUTHOR]

Environmental monitoring networks such as the Integrated Carbon Observation System (ICOS) in Europe, the National Ecological Observatory Network (NEON) in the U.S., or the Terrestrial Ecosystem Research Network (TERN) in Australia deploy different sampling schemes for in situ measurements. We report on the intercomparison of measurements of the canopy gap fraction with different digital hemispherical photography setups adopting ICOS, NEON, and TERN sampling schemes. The test was carried out at the Järvselja Radiation Transfer Model Intercomparison (RAMI) birch stand. Results show that spreading out sampling points which cover more of the plot is important for a good representation of the forest as a whole. The NEON tower plot layout scheme may be more prone to errors in overall canopy properties estimation than ICOS or TERN due to its compact sampling layout and should always be used in conjunction with its distributed plots. Different camera setups involving different camera operators, camera bodies, lenses and settings yield slightly varied results, and it is important to ensure that the images are taken in such a way that they would not be over or underexposed, or out of focus. As a conclusion we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded. Our study contributes towards establishing the uncertainty and evaluating potential error budget stemming from collecting in situ measurements using different sampling schemes and camera setups. [ABSTRACT FROM AUTHOR]

Roofs with full or partial coverage of vegetation, also known as green roofs, are perceived to restore ecosystem services in urban areas. Research investigations have been carried out to evaluate green roof performance on reducing stormwater runoff, improving runoff water quality, decreasing urban heat island effects, and several other benefits. The leaf area index (LAI) or an estimate of vegetation coverage on a roof may aid in evaluating these benefits. This study evaluates the leaf area index and plant area index as well as spatial coverage of two dominant species of vegetation on a 5550 m2 extensive green roof in downtown Syracuse, NY. Stems with attached leaves within individual patches of Phedimus takesimensis and Sedum floriferum were picked to estimate leaf area index using geometric models, and a ceptometer was used to provide an approximate range of values for the plant area index (PAI). Patches were picked in the summer of 2020 when plants were flowering, but the surface area of the flowers was not included in measurements. Sizes were measured for 8522 leaves from four patches of Phedimus takesimensis and 7751 leaves from five patches of Sedum floriferum. The estimated LAI for a patch of Phedimus takesimensis is 4.97 +/- 0.52 cm2 leaf area cm−2 ground area, while the LAI for a patch of Sedum floriferum is 2.70 +/- 0.37 cm2 leaf area cm−2 ground area. Furthermore, 154 photos were taken in the fall of 2020 and late spring and summer of 2021, representing 7.1% of the roof area in several categories. Results show the following fractions of area: 0.226 (Phedimus takesimensis), 0.381 (Sedum floriferum), 0.074 (moss), 0.029 (other vegetation), and 0.290 (bare soil). Using a weighted average, the LAI of the entire area of this green roof is 4.03 cm2 leaf area cm−2 ground area if the other vegetation has an LAI of 0.92 cm2 leaf area cm−2 ground area and the moss has an LAI of 25 cm2 leaf area cm−2 ground area. Excluding moss from the weighted average, the LAI of the roof is 2.18 cm2 leaf area cm−2 ground area.

Digital hemispherical photography (DHP) is widely used to derive forest biophysical variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the fraction of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making programmatic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties.To address these limitations, we present HemiPy, an open‐source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time‐series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN‐EYE software package.Multiannual time‐series of PAI, FIPAR and FCOVER demonstrate HemiPy's outputs realistically represent expected temporal patterns. Comparison against litterfall data reveals reasonable accuracies are achievable, with RMSE values close to the error of ~1 unit typically attributed to optical LAI measurement approaches. HemiPy's PAI, FIPAR and FCOVER outputs demonstrate good agreement with CAN‐EYE. Consistent with previous studies, when compared to simulated hemispherical images, better agreement is observed for PAI derived using gap fraction near the hinge angle of 57.5° only, as opposed to values derived using gap fraction over a wider range of zenith angles.HemiPy should prove a useful tool for processing DHP images, and its open‐source nature means that it can be adopted, extended and further refined by the user community. [ABSTRACT FROM AUTHOR]

We examined the performance of airborne light detection and ranging (LiDAR) data obtained in 2011 for leaf area estimation in deciduous broad-leaved forest using the Beer–Lambert law in Takayama, Gifu, Japan. We estimated leaf area index (LAI, allometry-LAI) and vertical leaf area density (LAD) using field survey data by applying allometric equations to estimate leaf-area of trees and a Weibull distribution equation to estimate vertical leaf distribution. We then estimated extinction coefficients (Ke) of LiDAR data for three height layers from the ground to the canopy top using the vertical LAD and vertical laser pulse distribution. The estimated PAI (LiDAR-PAI) using the Beer–Lambert law and Ke, when treating the canopies as three height layers, showed a significant linear relationship with allometry-LAI (p < 0.001). However, LiDAR-PAI when treating the canopies as single layer saturated at a PAI of six. It was similar to the lesser PAI estimation by hemispherical photography or relative photosynthetic photon flux density which treated the canopy as a single layer, compared to LAI measurements by litter traps. It is therefore important to allocate distinct Ke values to each of the multiple height layers for an accurate estimation of PAI and vertical PAD when applying the Beer–Lambert law to airborne LiDAR data. [ABSTRACT FROM AUTHOR]

Ceptometry is a technique used to measure the transmittance of photosynthetically active radiation through a plant canopy using multiple light sensors connected in parallel on a long bar. Ceptometry is often used to infer properties of canopy structure and light interception, notably leaf area index (LAI) and effective plant area index (PAIeff). Due to the high cost of commercially available ceptometers, the number of measurements that can be taken is often limited in space and time. This limits the usefulness of ceptometry for studying genetic variability in light interception, and precludes thorough analysis of, and correction for, biases that can skew measurements depending on the time of day. We developed continuously logging ceptometers (called PARbars) that can be produced for USD $75 each and yield high quality data comparable to commercially available alternatives. Here we provide detailed instruction on how to build and calibrate PARbars, how to deploy them in the field and how to estimate PAI from collected transmittance data. We provide representative results from wheat canopies and discuss further considerations that should be made when using PARbars.

The leaf area index (LAI) is an important structural parameter of plant canopies used in terrestrial biosphere models. Optical methods are commonly used for measuring LAI due to their non-destructive nature, convenience, and rapidity. In the present study, a novel instrument, named the Automated Hemispherical Scanner (AHS), was developed to measure plant area index (PAI) for monitoring daily changes in LAI in forest ecosystems. In the AHS, an optical sensor driven by a pair of servomotors is used to observe hemispherical light transmission continuously at adjustable intervals, and a blue filter is used to reduce the multiple scattering effect of light on the measured transmission. A set of algorithms was developed to screen the direct radiation transmitted through the canopy and to compute the transmissions from the diffuse radiation at seven zenith (0–60) and seven azimuth (0–150) angles for calculating PAI. Field experiments were conducted to verify the reliability of the AHS in three forests of Northeast China against an existing instrument named the LAI-2200 Plant Canopy Analyzer. The PAI values obtained using the AHS agreed well (R2 = 0.927, root mean square error = 0.41) with those from the LAI-2200. Since both instruments use the same gap fraction theory for calculating the PAI from diffuse radiation transmissions obtained from multiple angles, the agreement of these two instruments means that the AHS can reliably measure the transmittance of diffuse radiation and the theory has been implemented correctly. Compared with LAI-2200, the AHS has the advantage of automated and continuous measurements, and therefore it is suitable for monitoring variations in PAI over extended periods, such as the whole growing season. Compared with widely used digital photographic techniques, the AHS also avoids the requirement of determining a suitable photographic exposure, which is often problematic in the field with variable sky conditions. With these advantages, the AHS could be deployed in forest growth monitoring networks. [ABSTRACT FROM AUTHOR]

We examined the performance of airborne light detection and ranging (LiDAR) data obtained in 2011 for leaf area estimation in deciduous broad-leaved forest using the Beer–Lambert law in Takayama, Gifu, Japan. We estimated leaf area index (LAI, allometry-LAI) and vertical leaf area density (LAD) using field survey data by applying allometric equations to estimate leaf-area of trees and a Weibull distribution equation to estimate vertical leaf distribution. We then estimated extinction coefficients (Ke) of LiDAR data for three height layers from the ground to the canopy top using the vertical LAD and vertical laser pulse distribution. The estimated PAI (LiDAR-PAI) using the Beer–Lambert law and Ke, when treating the canopies as three height layers, showed a significant linear relationship with allometry-LAI (p < 0.001). However, LiDAR-PAI when treating the canopies as single layer saturated at a PAI of six. It was similar to the lesser PAI estimation by hemispherical photography or relative photosynthetic photon flux density which treated the canopy as a single layer, compared to LAI measurements by litter traps. It is therefore important to allocate distinct Ke values to each of the multiple height layers for an accurate estimation of PAI and vertical PAD when applying the Beer–Lambert law to airborne LiDAR data.

Vegetation regulates microclimate stability through biophysical mechanisms such as evaporation, transpiration and shading. Therefore, thermal conditions in tree-dominated habitats will frequently differ significantly from standardized free-air temperature measurements. The ability of forests to buffer temperatures nominates them as potential sanctuaries for tree species intolerant to the increasingly challenging thermal conditions established by climate change. Although many factors influencing thermal conditions beneath the vegetation cover have been ascertained, the role of three-dimensional vegetation structure in regulating the understory microclimate remains understudied. Recent advances in remote sensing technologies, such as terrestrial laser scanning, have allowed scientists to capture the three-dimensional structural heterogeneity of vegetation with a high level of accuracy. Here, we examined the relationships between vegetation structure parametrized from voxelized laser scanning point clouds, air and soil temperature ranges, as well as offsets between field-measured temperatures and gridded free-air temperature estimates in 17 sites in a tropical mountain ecosystem in Southeast Kenya. Structural diversity generally exerted a cooling effect on understory temperatures, but vertical diversity and stratification explained more variation in the understory air and soil temperature ranges (30%–40%) than canopy cover (27%), plant area index (24%) and average stand height (23%). We also observed that the combined effects of stratification, canopy cover and elevation explained more than half of the variation (53%) in understory air temperature ranges. Stratification's attenuating effect was consistent across different levels of elevation. Temperature offsets between field measurements and free-air estimates were predominantly controlled by elevation, but stratification and structural diversity were influential predictors of maximum and median temperature offsets. Moreover, stable understory temperatures were strongly associated with a large offset in daytime maximum temperatures, suggesting that structural diversity primarily contributes to thermal stability by cooling daytime maximum temperatures. Our findings shed light on the thermal influence of vertical vegetation structure and, in the context of tropical land-use change, suggest that decision-makers aiming to mitigate the thermal impacts of land conversion should prioritize management practices that preserve structural diversity by retaining uneven-aged trees and mixing plant species of varying sizes, e.g., silvopastoral, or agroforestry systems. • We examined the microclimate impacts of 3D plant structure using terrestrial LiDAR. • Variation in temperature ranges was primarily determined by structural diversity. • Stratification, canopy cover, and elevation explained >50% of thermal variability. • Structural diversity regulates thermal stability by attenuating daytime temperatures. • Late-succession forests could reach thermal properties similar to natural vegetation. [ABSTRACT FROM AUTHOR]

Hemispherical photography provides permanent records of forest canopy structure. We analysed digital hemispherical images taken during the period of 2007–2018 in a mature silver birch stand located in Järvselja, Estonia. The stand was thinned in 2004. Understory trees were removed in the spring of 2018. Images were processed using the LinearRatioSC method. Effective plant area index Leff during the leafless phenophase increased as a result of tree growth from 0.92 to 1.24 and understory cutting was not detectable. During the full foliage condition Leff increased from 3.6 in 2008 to 5.8 in 2017. After removal of understory trees from the stand Leff decreased, and repeated measurements in the summer of 2018 estimated the plant area index range 4.5 < Leff < 4.8. The results are in agreement with the expected changes following forest growth and demonstrate that LinearRatioSC is a suitable method for the estimation and long-term monitoring of forest canopy properties from digital hemispherical images.

Abstract The vegetation structure is a good indicator of the conservation condition of an ecosystem, since it reflects alterations caused by anthropic action. This study proposes to analyze the phytosociological aspects of the Caatinga domain under anthropic disturbance and their correlations with hydrological variables. Twenty-five 400 m 2 plots were sampled in the municipality of Floresta - PE, Brazil. Phytosociological parameters such as density, frequency and dominance were calculated. The seasonality of plant area index was analyzed for six species. The association between structural characteristics and hydrological variables (throughfall, stemflow and interception loss) in the species was evaluated by multivariate analysis. A total of 930 individuals, six families and 10 species were recorded. The abundance of the species Cenostigma pyramidale in the area may be an indicator of the degree of change in the vegetation. The structural characteristics of the species revealed little association with rainfall partitioning.

Aim: Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full‐waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness. Location: Tropics. Time period: Present. Major taxa studied: Trees. Methods: First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large‐footprint full‐waveform lidar data (22 m diameter) and related these findings to in‐situ tree species information. Then, we developed structure–richness models at the local (within 25–50 ha plots), regional (biogeographical regions) and pan‐tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression. Results: The results showed a weak structure–richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R2 ranging from.44–.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23–61%]. Modelling the relationship pan‐tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R2 =.39 and RMSD% = 43%, 0.25‐ha resolution). Main conclusions: Our results may serve as a basis for the future development of a set of structure–richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics. [ABSTRACT FROM AUTHOR]

The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R2 > 0.8, RMSD < 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R2 = 0.88 > 0.72, 0.49; RMSE = 0.45 < 0.7, 0.94; nRMSE = 15.7% < 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP.

Hemispherical photography provides permanent records of forest canopy structure. We analysed digital hemispherical images taken during the period of 2007–2018 in a mature silver birch stand located in Järvselja, Estonia. The stand was thinned in 2004. Understory trees were removed in the spring of 2018. Images were processed using the LinearRatioSC method. Effective plant area index Leff during the leafless phenophase increased as a result of tree growth from 0.92 to 1.24 and understory cutting was not detectable. During the full foliage condition Leff increased from 3.6 in 2008 to 5.8 in 2017. After removal of understory trees from the stand Leff decreased, and repeated measurements in the summer of 2018 estimated the plant area index range 4.5 < Leff < 4.8. The results are in agreement with the expected changes following forest growth and demonstrate that LinearRatioSC is a suitable method for the estimation and long-term monitoring of forest canopy properties from digital hemispherical images. [ABSTRACT FROM AUTHOR]

In a context of urban warming, the effects of trees on outdoor thermal stress are important even during the increasingly hot autumn season. This study examines the effects of a deciduous tree species (Tilia x europaea L) on surface temperature over different ground materials and in turn on human thermal comfort, with a particular focus on tree shade variation due to leaf fall. Grass, asphalt, and gravel-covered ground surfaces, both sun-exposed and under the Tilia, were monitored in Florence, Italy, during the summer (2014) and autumn (2017) seasons. The Index of Thermal Stress (ITS) was used to gauge the micrometeorological effects of the changing tree canopy, with tree defoliation quantified by the Plant Area Index. On clear summer days, thermal discomfort was especially pronounced over exposed asphalt, and even more intense above exposed gravel due to its higher reflectivity—while shaded surfaces showed large reductions in thermal stress. Even though incoming solar radiation decreases over the course of the fall season, the direct radiation under the gradually defoliating tree canopy actually increases. Due to this diminished shading effect, the differences in surface temperature between exposed and shaded asphalt shrink dramatically from about 20 to 3 °C. However, since ambient conditions become milder as the season progresses, the Tilia demonstrated a double benefit in terms of ITS: providing thermal comfort under its full canopy at the beginning of autumn and maintaining comfort even as its canopy thins out. At the same time, tree species with earlier defoliation may be unable to replicate such benefits. [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.

Mapping the microclimate effect of forest canopies on understory temperature requires spatially explicit predictors at very fine spatial resolutions. Light Detection And Ranging (LiDAR) offers promising prospects in that regard, as it allows capturing the vertical dimension of vegetation structure at a very high resolution over large areas. To explore the potential of airborne LiDAR-derived metrics to predict understory temperature, we focused on the forest of Blois (France), a 2740-ha lowland managed forest dominated by oak (Quercus petraea). We installed HOBO sensors measuring microclimate air temperature at one-metre height in 53 stands of contrasting vegetation structure, from open to very dense and from young regeneration to mature stages. Using a nearby weather station as the macroclimate temperature reference, we calculated the slope (log scale) coefficient of the linear regression between microclimate and macroclimate, as a simple parameter describing the microclimatic buffering (log(slope) < 0) or amplification (log(slope) > 0) capacity of the habitat. An airborne LiDAR flight was conducted during summer 2021, matching the timing of our temperature measurements. From the resulting 3D point cloud, three complementary metrics of forest structure were derived: the maximum height, the Plant Area Index and the Vertical Complexity Index. They were calculated for circular buffers of different radii (1 m to 100 m) centred on each HOBO sensor. We found that the 5-m radius combining the three metrics into a single multivariate model explained the greatest proportion of variance in the microclimate effect of each stand (R2 = 0.91). We mapped the buffering or amplification effect of vegetation structure on understory temperatures over the entire forest of Blois at a 10-m resolution. 91.4% of the surface of the forest was significantly buffered relative to macroclimate temperature, while 2.7% was amplified, especially in road verges, clear-cut and regeneration areas. Based on our simple linear model, we were able to derive understory air temperature maps for any temporal resolution (i.e. hourly, daily, or seasonal). The results highlight the great capacity of airborne LiDAR to retrieve forest structure parameters and generate high-resolution maps of the thermal environment. Applications for mapping the buffering or amplification of microclimate temperature are plentiful, especially in the context of climate change. They include improving the understanding of physiological processes such as thermoregulation or phenology, modelling the thermal connectivity of the landscape, improving species redistribution models, spotting microrefugia for conservation, informing forest management for tree regeneration or wildfire control, or even prioritising cooling recreational areas for humans to escape heatwaves in urban forests. [Display omitted] • Airborne LiDAR is ideal to predict forest thermal environment at a high resolution. • We used Maximum height, Plant Area & Vertical Complexity Index as structure metrics. • Forest structure explained 91% of microclimate variability at a 10-m resolution. • We mapped the buffering or amplification effect of forest on microclimate temperature. • Ecologists, conservationists and forest managers would highly benefit from such maps. [ABSTRACT FROM AUTHOR]

A rapid rise of urban population is making cities denser. Consequently, the proportion of impervious surface cover has enlarged, increasing the amount and speed of run‐off reaching urban catchment areas, which may cause flash flooding. Trees play a key role to reduce run‐off in the city, as they intercept rainfall and store part of it on their leaves and branches, reducing the amount and speed of water running onto impervious surfaces. Storage capacity will depend on the rainfall event, the climate conditions and tree characteristics and canopy density. These canopy characteristics vary greatly among different species and their phenology. Furthermore, these canopy characteristics can vary greatly among individual trees of the same age, size, and species. This study tested how canopy density and leaf characteristics of three different tree species affect storage capacity under simulated rainfall conditions. Three species were selected (Ulmus procera, Platanus × acerifolia, and Corymbia maculata), each being of the same height and similar canopy dimensions. Storage capacity was measured using a mass balance approach during a 15‐min indoor, simulated rainfall event (2.5 mm/hr). Canopy metrics were estimated using a terrestrial laser scanner. Canopy surface area was measured through destructive harvest and leaf/twig/branch scanning. To investigate variations in the canopy leaf density, leaves were systematically removed to create four treatments: full, half, quarter, and woody. Canopy storage capacity was well correlated to plant surface area (m2), plant area index (m2/m2), and plant area density (m2/m3). All analyses indicated U. procera as the most efficient species for storing rainfall water within a canopy of equal volume or area index. Results reveal the complexity of evaluating interception of rainfall by tree canopies. This study contributes to the discipline and practice by distinguishing how variation in the leaf density is important to consider when selecting urban tree species to be planted. [ABSTRACT FROM AUTHOR]

Forest understory may be strongly affected by silvicultural practices such as thinning, which simultaneously modulates the overstory canopy cover and influences the availability of light. However, the understory layer is rarely considered in management decisions, partly because methods to estimate understory leaf area index are poorly developed. In this study we used two different restricted view angle photographic methods to estimate overstory plant area index LO (zenith cover photography), understory leaf area index LU (nadir cover photography) and their related canopy attributes (foliage clumping, foliage cover, crown cover, crown porosity). These measurements were performed in beech stands under different management regime. Results from photography indicated that not only overstory but also understory canopy attributes were significantly influenced by forest management. In addition, a significant negative correlation was found between LO and LU. We conclude that the photographic methods are effective for monitoring (overstory and understory) canopy status in managed stands, on account of their rapid and not destructive procedures, which allows large scale implementation of the methods.

Plant phenology is a sensitive indicator of the effects of global change on terrestrial ecosystems and controls the timing of key ecosystem functions including photosynthesis and transpiration. Aerial drone imagery and photogrammetric techniques promise to advance the study of phenology by enabling the creation of distortion-free orthomosaics of plant canopies at the landscape scale, but with branch-level image resolution. The main goal of this study is to determine the leaf life cycle events corresponding to phenological metrics derived from automated analyses based on color indices calculated from drone imagery. For an oak-dominated, temperate deciduous forest in the northeastern USA, we find that plant area index (PAI) correlates with a canopy greenness index during spring green-up, and a canopy redness index during autumn senescence. Additionally, greenness and redness metrics are significantly correlated with the timing of budburst and leaf expansion on individual trees in spring. However, we note that the specific color index for individual trees must be carefully chosen if new foliage in spring appears red, rather than green-which we observed for some oak trees. In autumn, both decreasing greenness and increasing redness correlate with leaf senescence. Maximumredness indicates the beginning of leaf fall, and the progression of leaf fall correlates with decreasing redness. We also find that cooler air temperature microclimates near a forest edge bordering a wetland advance the onset of senescence. These results demonstrate the use of drones for characterizing the organismic-level variability of phenology in a forested landscape and advance our understanding of which phenophase transitions correspond to color-based metrics derived from digital image analysis. [ABSTRACT FROM AUTHOR]

Fuel moisture is a key driver of forest flammability as it influences ignition likelihood, fire intensity and resultant fire severity. Changes to forest canopy cover following disturbances like wildfire or logging may alter forest flammability by changing the microclimatic conditions that influence fine fuel moisture. Wet forests dominated by Mountain Ash ( Eucalyptus regnans ) are highly valued for their flora and fauna, timber, carbon and water. Wildfires are an important part of the lifecycle of these forests, but too frequent fire can threaten post-fire regeneration. With large tracts of Mountain Ash forest recovering from recent wildfires (in 2009 and 1983) there is a need to understand the mechanisms driving flammability in these forests, particularly as the forest structure changes following fire. This study sought to understand the effects of fire history on the flammability of Mountain Ash forests by specifically considering fuel moisture for different times since fire and fire severities. We measured canopy cover (plant area index) and fuel moisture within 8 forest sites last burnt between 7 and 200 years ago by wildfires of low or high severity. Fuel moisture and fuel availability (i.e. number of days when fine fuels are dry enough to ignite and sustain spreading fire) were strongly associated with canopy cover; with denser canopied forests having higher fuel moisture. The largest differences in canopy cover occurred between the recently burnt high and low severity forests. For the longer-unburnt forests there were no systematic differences evident in canopy cover with time since fire or fire severity. The fuel moisture was higher and fuels only available to burn on one day in the forest recently burnt by high severity fire (in association with a dense canopy). In contrast, fuels were drier and available to burn on 238 days in the forest recently burnt by low severity fire (in association with a sparser canopy). For the longer-unburnt forests (33 or more years since fire) there were no clear trends between fuel moisture and time since fire and fire severity suggesting that fires do not have a lasting impact on fuel moisture within these wet forests. Overall, this study shows that wildfires have immediate impacts on fuel moisture in Mountain Ash forests but as the time since fire increases, moisture appears to be more a function of canopy properties than fire history. [ABSTRACT FROM AUTHOR]

Digital hemispherical photography is a simple, non-destructive method for estimating canopy biophysical parameters for ecological applications and validation of remote sensing products. Determination of optimum and repeatable acquisition procedures is well documented in the literature but so far this has not focused on evaluating the levelling procedure used to align the camera. In this paper, the standard recommendation that tripod levelling is a necessity is tested by comparing it with a hand-levelled procedure. The results show that the average difference between the two procedures is < 2% for effective plant area index and < 1% for gap fraction at the VALERI plot scale, which generally falls within the variance. Users implementing the hand-levelled technique can expect large reductions in data acquisition time, allowing many more samples to be collected without compromising the overall quality of the data retrieved. [ABSTRACT FROM AUTHOR]

The 3D distribution of plant material is a key parameter to describe vegetation structure, which influences several processes such as radiation interception and ecosystem functioning. Vegetation covers are often described using Leaf Area Index (LAI) or Plant Area Index (PAI) for monitoring or modeling purposes. Characterizing vegetation 3D structure at fine scale is increasingly required, notably in order to be able to apply radiative transfer simulations at scales consistent with the spatial resolution of recent remote sensing sensors. To assess 3D PAI of a vegetation plot, this paper evaluates the potential of a voxelization method using Terrestrial Laser Scanning (TLS) data, based on the Beer-Lambert transmittance computation law. The theoretical validation was performed using a simulation framework based on a radiative transfer model (DART). The framework allowed simulating TLS acquisition on a theoretical distribution of leaves and a realistic representation (single tree), for which all characteristics are well known. Hence, a sensitivity analysis was performed to study the influence of instrument parameters (i.e. single- or multi-echo, beam divergence), scanning configuration (scan angle step), vegetation characteristics (leaf size and density, leaf angle distribution), and voxel parameters (cubic versus spherical geometry, at different resolutions, with and without occlusion) on the estimation of PAI. For a theoretical distribution of leaves, results showed good accuracy of the voxelization method ( R 2 = 0.91 and RMSE = 20 % for a mean case, at voxel level) with a high resolution multi-echo TLS scan, cubic voxels over 0.5-m resolution, low inter-voxel occlusion, small leaves, and up to a surface density of 2 m 2 . m −3 . Error increased with a larger scan angle resolution, single echo TLS systems, and vegetation density. Also, without clumping, error increased with smaller voxels or larger leaves. Best results were obtained with multi-echo TLS scans (angular resolution of 0.05°), cubic voxels at 1-m resolution when occlusion is low (voxel sampling higher than 50% of maximum sampling at 15 m) and small leaves (e.g. 10 cm 2 ), which provided very good agreement ( RMSD = 7.6 % , R 2 = 0.98, p = 0.99). On a realistic isolated tree, PAI was correctly assessed with cubic voxels at 0.25 m resolution. A method to merge voxelized scans was proposed to deal with inter-voxel occlusion effects. [ABSTRACT FROM AUTHOR]

The northeastern United States is currently experiencing a decline in forested lands, primarily due to the expansion of human development. Of particular concern is the loss of “core areas” or the areas within forests that are not influenced by other land cover types. Core areas are of significant importance to native flora and fauna, since they generally are less vulnerable to invasion by exotics and are more resilient to the effects of climate change. However, the exact reduction of core area in the northeast is still unknown. Current methods of estimation are not particularly precise, since areas of edge influence are quite variable and situational. Therefore, the purpose of this study is to devise a new method for identifying edge influence areas using remote sensing techniques. Eight transects were sampled perpendicular to the edge of an abandoned golf course within Harvard Forest in Petersham, MA. Vegetation inventories as well as Photosynthetically Active Radiation (PAR) at different heights within the canopy were used to determine edge depth in the field. Small-footprint discrete aerial LiDAR datasets were comparatively used to identify edge depths. LiDAR returns were binned and transformed into canopy height profiles before the sum of squared differences was used to determine where edge influence diminished, creating a LiDAR derived edge depth. LiDAR estimated edge depths were not significantly different from the field identified edge depths, indicating it might be a low-cost method for estimating edge depths. [ABSTRACT FROM AUTHOR]

Urban trees deliver many ecological services to the urban environment, including reduced runoff generation in storms. Trees intercept rainfall and store part of the water on leaves and branches, reducing the volume and velocity of water that reaches the soil. Moreover, trees modify the spatial distribution of rainwater under the canopy. However, measuring interception parameters is a complex task because it depends on many factors, including environmental conditions (rainfall intensity, wind speed, etc.) and tree characteristics (plant surface area, leaf and branch inclination angle, etc.). In the few last decades, remotely sensed data have been tested for retrieving tree metrics, but the use of this derived data for predicting interception parameters are still being developed. In this study, we measured the minimum water storage capacity (Cmin) and throughfall under the canopies of 12 trees belonging to three different species. All trees had their plant surface metrics calculated: plant surface area (PSA), plant area index (PAI), and plant area density (PAD). Trees were scanned with a mobile terrestrial laser scan (TLS) to obtain their individual canopy point clouds. Point clouds were used to calculate canopy metrics (canopy projected area and volume) and TLS-derived surface metrics. Measured surface metrics were then correlated to derived TLS metrics, and the relationship between TLS data and interception parameters was tested. Additionally, TLS data was used in analyses of throughfall distribution on a sub-canopy scale. The significant correlation between the directly measured surface area and TLS-derived metrics validates the use of the remotely sensed data for predicting plant area metrics. Moreover, TLS-derived metrics showed a significant correlation with a water storage capacity parameter (Cmin). The present study supports the use of TLS data as a tool for measuring tree metrics and ecosystem services such as Cmin; however, more studies to understand how to apply remotely sensed data into ecological analyses in the urban environment must be encouraged.

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

Plant phenology is a sensitive indicator of the effects of global change on terrestrial ecosystems and controls the timing of key ecosystem functions including photosynthesis and transpiration. Aerial drone imagery and photogrammetric techniques promise to advance the study of phenology by enabling the creation of distortion-free orthomosaics of plant canopies at the landscape scale, but with branch-level image resolution. The main goal of this study is to determine the leaf life cycle events corresponding to phenological metrics derived from automated analyses based on color indices calculated from drone imagery. For an oak-dominated, temperate deciduous forest in the northeastern USA, we find that plant area index (PAI) correlates with a canopy greenness index during spring green-up, and a canopy redness index during autumn senescence. Additionally, greenness and redness metrics are significantly correlated with the timing of budburst and leaf expansion on individual trees in spring. However, we note that the specific color index for individual trees must be carefully chosen if new foliage in spring appears red, rather than green—which we observed for some oak trees. In autumn, both decreasing greenness and increasing redness correlate with leaf senescence. Maximum redness indicates the beginning of leaf fall, and the progression of leaf fall correlates with decreasing redness. We also find that cooler air temperature microclimates near a forest edge bordering a wetland advance the onset of senescence. These results demonstrate the use of drones for characterizing the organismic-level variability of phenology in a forested landscape and advance our understanding of which phenophase transitions correspond to color-based metrics derived from digital image analysis.

This study analyzed how the variations of plant area index (PAI) and weather conditions alter the influence of urban green infrastructure (UGI) on microclimate. To observe how diverse UGIs affect the ambient microclimate through the seasons, microclimatic data were measured during the growing season at five sites in a local urban area in The Netherlands. Site Awas located in an open space; sites B, C, and D were covered by different types and configurations of green infrastructure (grove, a single deciduous tree, and street trees, respectively); and site E was adjacent to buildings to study the effects of their façades on microclimate. Hemispherical photography and globe thermometers were used to quantify PAI and thermal comfort at both shaded and unshaded locations. The results showed that groves with high tree density (site B) have the strongest effect on microclimate conditions. Monthly variations in the differences of mean radiant temperature (ΔTmrt) between shaded and unshaded areas followed the same pattern as the PAI. Linear regression showed a significant positive correlation between PAI and ΔTmrt. The difference of daily average air temperature (ΔTa) between shaded and unshaded areas was also positively correlated to PAI, but with a slope coefficient below the measurement accuracy (±0.5 °C). This study showed that weather conditions can significantly impact the effectiveness of UGI in regulating microclimate. The results of this study can support the development of appropriate UGI measures to enhance thermal comfort in urban areas. [ABSTRACT FROM AUTHOR]

The demographic performance of avian predators is influenced by climate variations. The Plant Area Index (PAI) is considered a key descriptor of the (evolving) vegetation sensitive to climate and soil aridity and drought stress. To assess if the increased aridity regime in the mediterranean region affects the reproduction of a forest predator, the breeding success of the Tawny Owl Strix aluco was studied over 7 years (1997-2003) in Castelporziano, central Italy. The inter-annual fluctuations in owl demographic output observed in neighbor forest and agricultural sites were compared with climatic variables (rainfall, minimum and maximum air temperature, aridity index) and changes in the PAI estimated by satellite imagery. Out of 326 breeding attempts, the percentage of successful pairs per year ranged between 4 and 28 %, the number of fledglings per successful pair ranged between 1.0 and 1.8 and breeding rate (i.e. the number of fledglings per breeding pair) ranged between 0.1 and 0.4. Demographic, climatic and remotely sensed indexes showed coherent variations among years. Rainfall and lower aridity index are associated with a higher breeding success. The relationship between PAI and breeding rate was found significant only in forest sites. Results suggest that Tawny Owls may positively respond to drier weather conditions by reducing the breeding rate. [ABSTRACT FROM AUTHOR]

Forest understory may be strongly affected by silvicultural practices such as thinning, which simultaneously modulates the overstory canopy cover and influences the availability of light. However, the understory layer is rarely considered in management decisions, partly because methods to estimate understory leaf area index are poorly developed. In this study we used two different restricted view angle photographic methods to estimate overstory plant area index LO (zenith cover photography), understory leaf area index LU (nadir cover photography) and their related canopy attributes (foliage clumping, foliage cover, crown cover, crown porosity). These measurements were performed in beech stands under different management regime. Results from photography indicated that not only overstory but also understory canopy attributes were significantly influenced by forest management. In addition, a significant negative correlation was found between LO and LU. We conclude that the photographic methods are effective for monitoring (overstory and understory) canopy status in managed stands, on account of their rapid and not destructive procedures, which allows large scale implementation of the methods. [ABSTRACT FROM AUTHOR]

Abstract: Understanding how trees influence water movement in an urban landscape is important because in an ‘engineered xeriscape’ small changes in rainfall frequency or magnitude have significant implications to plant water availability and mortality at one extreme, and stormwater runoff and flooding at the other. This study relates direct measures of tree canopy interception and discusses their implication for catchment hydrology in different urban landscape contexts. We measured canopy throughfall and stemflow under two eucalypt tree species in an urban street setting over a continuous five month period. Eucalyptus nicholii has a dense canopy and rough bark, whereas Eucalyptus saligna has a less-dense canopy and smooth bark. E. nicholii, with the greater plant area index, intercepted more of the smaller rainfall events, such that 44% of annual rainfall was intercepted as compared to 29% for the less dense E. saligna canopy (2010). Stemflow was less in amount and frequency for the rough barked E. nicholii as compared to the smooth barked E. saligna. However, annual estimates of stemflow to the ground surface for even the smooth barked E. saligna would only offset approximately 10mm of the 200mm intercepted by its canopy (2010). Tree canopy and bark characteristics should be considered when planting in pervious green space, or impervious streetscapes, because of their profound impact upon tree and surrounding water availability, soil water recharge or runoff. This study provides an evidence base for tree canopy impacts upon urban catchment hydrology, and suggests that rainfall and runoff reductions of up to 20% are quite possible in impervious streetscapes. Street tree canopies can function as a cost-effective compliment to water sensitive urban design for stormwater reduction benefits. [Copyright &y& Elsevier]

Highlights: [•] This is the first comparison of different methods of LAI estimation in the tropics. [•] The optical method that best estimated the direct harvest was the LICOR LAI-2000. [•] Wood area and leaf clumping correction improved estimations of indirect methods. [•] Corrected fisheye photographs represent the most practical approach to estimate LAI. [Copyright &y& Elsevier]

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]

Abstract: Leaf area index (LAI; the single-sided leaf area per unit area of ground) is a measure of plant standing crop in terrestrial ecosystems and is a key parameter in meteorological, climate and hydrological models. While the LAI of individual species can be measured by destructive harvesting and LAI of whole landscapes can be estimated by remote sensing, estimates of species-specific LAI at the landscape-scale are lacking. Working on a 125ha farm in Somerset, UK during the summers of 2007 and 2008, we developed a method to identify sources of variation in total leaf area over time, between habitats and between species. We measured LAI for all vegetation types on the farm, namely: crops, unmanaged non-woody vegetation, hedgerows and trees. We used established methods to estimate LAI, mostly by gap fraction analysis with a LI-COR LAI-2000 Plant Canopy Analyzer and we divided LAI into its constituent species based on the data from field surveys. We found that the total LAI of the farm was 4.1, although this varied substantially between habitats from 3.3 for cereal fields to 8.0 for woods. Each month we measured LAI and found that, depending on the month, 61–81% of the leaf area was comprised of crop species (including grass and clover in pastures and short-term leys), 7–10% was woody vegetation and the remainder was non-woody semi-natural vegetation. We developed predictive models of LAI, based on our measurements of LAI in each habitat, and found that, for non-woody vegetation, variation in LAI was a function of vegetation type and vegetation height, while for hedgerows LAI was a function of hedge height and width. From our data and our predictive models we predicted changes in LAI due to five likely farm management scenarios. Changes that would have a huge impact on the visual landscape (loss of woods, reduction in hedge size) were predicted to have a smaller effect on farm-scale LAI than changes that would be visually more subtle (changes in vegetation length of grass fields). This type of modelling could be combined with regional land use data to predict the effect of regional agricultural changes (changes in land use or the intensity of land management) on LAI, and hence on ecosystems, biogeochemical cycling, carbon sequestration and hydrology. [Copyright &y& Elsevier]

Abstract: Thresholding methods for estimating canopy indices from gamma corrected digital hemispherical images are influenced both by the image interpreter and applicability of the automatic interpretation algorithm. The matrix output data of camera sensors (i.e., raw data) can be used to avoid gamma correction. Nevertheless, the impact of an interpreter or a thresholding algorithm is still present. In this paper, we present a new methodology which applies a previously published linear conversion method of raw data to a single below-canopy image to obtain unbiased estimates of canopy gap fraction. The new methodology derives an above-canopy reference image from a below-canopy image using sky radiance information available in gaps. A sky radiance model and interpolation are used to create the above-canopy image which could alternatively be obtained with a second sensor. To evaluate the new method, we conducted test measurements in three mature forest stands. The comparison of image processing output to LAI-2000 Plant Canopy Analyzer data showed high agreement and repeatability. The proposed method can be applied in versatile future versions of hemispherical image analysis programs to create unbiased comparison data for other manual or automatic image processing methods designed for plant canopies. [Copyright &y& Elsevier]