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Modelling of radiation transfer through natural multilayer media is relevant for many climatological, hydrological and ecological issues. The possibility of using the models is determined by the comprehensiveness of the information on the properties and structure of certain layers: ground, vegetation, atmosphere. Three-dimensional spatial organisation of tundra vegetation cover is understudied compared to vegetation structure of the boreal zone. In the scope of the research, the structure of sedge-tundra vegetation cover in the Nenets Autonomous Area and the Murmansk Region was investigated using specialised hardware-software system which allows to take photos of the cover from different angles, construct virtual three-dimensional models and obtain the values of parameters characterising the structure. The field survey of the sites was carried out in August 2016 and 2017. Phytoelement angle distributions obtained differ from standard erectophile distribution frequently used for modelling orientation of phytoelements in cover formed by grasses/sedges. The shape of phytoelement angle distribution varies from site to site depending on the dominant species. Experiments on fitting real distributions by different functions established that the generalised shape of the distribution in the studied cover is best described by a rotated-ellipsoidal function with a parameter equal to 2.69. Information obtained on the structure of the vegetation cover can be used in modelling microwaves and solar radiation propagation.

Photosynthesis is a critical component to understanding how CO2 uptake and vegetation productivity drives the terrestrial carbon cycle. Light use efficiency (LUE) is a key indicator of photosynthesis, which is determined by the most limiting/co-limiting environmental stresses such as nutrition, water, and temperature, restraining the photochemical reaction process across both space and time due to remote sensing offers a capability to relate changes in LUE with changes in the spectral characteristics of leaves through the photochemical reflectance index (PRI). The PRI is a narrow waveband index that responds with changes in xanthophyll-induced absorption feature at 531 nm. It remains challenging, however, to upscale PRI from leaf to canopy and ultimately to the landscape level due to physiological and structural factors such as leaf angle distribution, leaf area, canopy structure and pigment pool size as well as observational considerations such as sun-view geometry and background reflectance. As a result, understanding patterns of PRI and LUE across different climate-induced stress conditions and within age-induced vertical stand structures is helpful for improving our understanding how these relationships change over forested landscapes in anticipation of future airborne and ultimately satellite-based observations. In this study, we report on the use of the automated multi-angular spectro-radiometer (AMSPEC) and eddy-covariance techniques to examine how PRI and LUE varies at young (HDF88) and older (DF49) Douglas-fir sites. Results indicate that a strong relationship between bidirectional reflectance distribution function (BRDF) corrected PRI and LUE exists (DF49: R2 = 0.73; HDF88: R2 = 0.79). The overall LUE at the older, more structurally complex site was higher than at the younger, structurally simpler stand under a range of air temperature and light conditions. We used the standard deviation (SD) of light detection and ranging (LiDAR) return pulses to characterize canopy vertical structure of the site, and found that complex vertical structures (i.e., high SD) may contribute to a higher long-term averaged LUE (R2 = 0.87). While interesting we conclude the mechanism of the relationship between LUE and vertical forest structures needs to be further explored because of the key role internal and external shadowing has on light regimes with stands and thus vertical patterns of LUE varying across forest stands of different age.

Bi-hemispherical reflectance (BHR), in the land surface research community also known as “white-sky albedo”, is independent of the directions of incidence and viewing. For vegetation canopies, it is also nearly independent of the leaf angle distribution, and therefore it can be considered an optical quantity that is only dependent on material properties. For the combination leaf canopy and soil background, the most influential material properties are the canopy LAI (leaf area index), optical properties of the leaves, and soil brightness. When the leaf and soil optical properties are known or assumed, one may estimate the canopy LAI from its white-sky spectral albedo. This is also because a simple two-stream radiative transfer (RT) model is available for the BHR of the leaf canopy and soil combination. In this contribution, crown clumping and lateral linear mixing effects are incorporated in this model. A new procedure to estimate soil brightness is introduced here, even under a moderate layer of green vegetation. The procedure uses the red and NIR spectral bands. A MODIS white-sky albedo product at a spatial resolution of 0.05° is used as a sample input to derive global maps of LAI, soil brightness, and fAPAR at the local moments of minimum and maximum NDVI over a 20-year period. These maps show a high degree of spatial coherence and demonstrate the possible utility of products that can be generated with little effort by using a direct LUT technique.

Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf-wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.

In recent years, digital repeat photography has received increasing attention as an inexpensive means by which seasonal changes in vegetation canopies can be monitored. Offering automation and an increased measurement frequency, colour indices derived from above-canopy digital repeat photography have proven a popular alternative to traditional observations of forest phenology. Nevertheless, previous work has demonstrated several features in time-series of colour indices that are unrelated to canopy structure, limiting their utility to track specific biophysical properties such as leaf area index (LAI). Whilst techniques such as digital cover photography and the use of radiometric sensors are better suited to this task, they are restricted by the need for careful calibration of above- and below-canopy reference sensors, ancillary information on canopy leaf angle distribution, and smaller measurement footprints. Using data collected at a deciduous broadleaf forest site in Southern England, we investigated a new method to derive continuous measurements of LAI, making use of automated digital hemispherical photography (DHP) from below the canopy. After applying simple data screening procedures, the LAI observations derived from our automated DHP system demonstrated very close agreement with those obtained from manually acquired DHP images, which were collected under optimal illumination conditions over the surrounding forest plot (r2 = 0.99, RMSE = 0.20, NRMSE = 13%). By combining our automated DHP system with an above-canopy time-lapse digital camera, we then investigated the relationship between the green chromatic coordinate (GCC) and LAI. Distinct hysteresis effects were observed, as were substantial differences between phenological transition dates derived from the GCC and LAI, particularly in the case of the onset of senescence. Our results indicate that phenological transition dates derived from colour indices cannot easily be linked to any one biophysical property. We recommend further investigation of the automated DHP approach, which provides time-series of LAI whose physical interpretation is straightforward, as an alternative to above-canopy digital repeat photography.

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.

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO2-saturated photosynthesis rate, Vmax,27), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, Vmax,27, Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R2 = 0.73) and LAI (R2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R2 = 0.79), nitrogen concentration (R2 = 0.83), nitrogen content (R2 = 0.77), and Vmax,27 (R2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R2 = 0.80), nitrogen (R2 = 0.85) and Vmax,27 (R2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy Vmax,27, have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

Vegetation composition and water surface area are changing in many tundra regions due to climate warming, which is twice as strong in the Arctic as compared to the global mean. Such land cover changes feed back to climate and permafrost thaw through altering the surface energy budget. We quantified the influence of vegetation type, canopy characteristics, and patchiness on the tundra shortwave radiation components. We used in situ measurements and vegetation mapping to parametrise a 3D radiative transfer model (DART) for summer conditions at the Kytalyk test site in northeast Siberia. We analysed model results assessing the most important drivers of canopy albedo, transmittance, and absorptance of photosynthetically active radiation (PAR). Tundra albedo was strongly influenced by the fractional cover of water surfaces. Albedo decreased with increasing shrub cover. However, plant area index effects on albedo were not statistically significant. Canopy transmittance and PAR absorptance (f APAR ) were almost entirely controlled by plant area index at the landscape scale. Only about one half of the total plant area index consisted of green leaves, while wood and standing dead leaves contributed equally to the other half. While spatial patterns and patch sizes of vegetation types and open water did not significantly influence the radiation budget at the landscape scale, it contributed to the large variability at the local scale. Such local variability of shortwave radiation may impact evapotranspiration and primary productivity at a range of scales. Therefore, the variation of radiation fluxes within single vegetation types potentially affects larger scale energy, water, and carbon fluxes.

The diffuse and specular components of leaf reflection are both important to determine the leaf optical properties as well as to describe the leaf bidirectional reflectance distribution function (BRDF). However, the specular component is usually ignored in practice in numerous canopy reflectance models that describe the interaction between solar light and vegetated scene components. To evaluate the impact of leaf-specular reflection on canopy bidirectional reflectance factor (BRF) characteristics, we introduce a leaf BRDF model into the radiosity-graphics combined model (RGM; a 3-D scene model) to calculate canopy BRFs with nondiffuse leaves. The modified RGM is validated by comparing simulated BRFs against in situ measured BRFs over real maize canopies. The results show that ignorance of leaf-specular reflection can result in up to 50% of relative error in the blue band (435.8 nm). A series of maize canopies with different leaf angle distributions (LADs) is reconstructed to investigate the effect of five major biophysical/geometrical parameters such as leaf area index, LAD, leaf surface property, view direction, and solar zenith angle on leaf-specular reflection contributions to the canopy BRF. It is demonstrated that increasing the incident solar zenith angle and decreasing the mean leaf angle impact the angular distribution of the canopy BRF more significantly than other factors. The cumulative hemispherical relative and absolute errors of canopy BRF caused by the leaf-specular reflection are often too large to be ignored, even for canopies with rough surface leaves. Moreover, the relative error of BRF in visible waveband shows that, in general, leaf-specular reflection has a large impact than that in near-infrared waveband. However, such impact can be sufficiently accounted for by even just consideration of the first-order leaf-specular reflection in canopy reflectance calculation, leading to a substantial improvement in simulation accuracy for most vegetation canopies.

Marsh canopy structure was mapped yearly from 2009 to 2012 in the Barataria Bay, Louisiana coastal region that was impacted by the 2010 Deepwater Horizon (DWH) oil spill. Based on the previously demonstrated capability of NASA's UAVSAR polarimetric synthetic aperture radar (PolSAR) image data to map Spartina alterniflora marsh canopy structure, structure maps combining the leaf area index (LAI) and leaf angle distribution (LAD, orientation) were constructed for yearly intervals that were directly relatable to the 2010 LAI-LAD classification. The yearly LAI-LAD and LAI difference maps were used to investigate causes for the previously revealed dramatic change in marsh structure from prespill (2009) to postspill (2010, spill cessation), and the occurrence of structure features that exhibited abnormal spatial and temporal patterns. Water level and salinity records showed that freshwater releases used to keep the oil offshore did not cause the rapid growth from 2009 to 2010 in marsh surrounding the inner Bay. Photointerpretation of optical image data determined that interior marsh patches exhibiting rapid change were caused by burns and burn recovery, and that the pattern of 2010 to 2011 LAI decreases in backshore marsh and extending along some tidal channels into the interior marsh were not associated with burns. Instead, the majority of 2010 to 2011 shoreline features aligned with vectors displaying the severity of 2010 shoreline oiling from the DWH spill. Although the association is not conclusive of a causal oil impact, the coexistent pattern is a significant discovery. PolSAR marsh structure mapping provided a unique perspective of marsh biophysical status that enhanced detection of change and monitoring of trends important to management effectiveness.

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a suitable remote sensing proxy of photosynthesis at multiple scales. However, the relationship between fluorescence and photosynthesis observed at the leaf level cannot be directly applied to the interpretation of retrieved SIF due to the impact of canopy structure. We carried out a SIF modelling study for a heterogeneous forest canopy considering the effect of canopy structure in the Discrete Anisotropic Radiative Transfer (DART) model. A 3D forest simulation scene consisting of realistic trees and understory, including multi-scale clumping at branch and canopy level, was constructed from terrestrial laser scanning data using the combined model TreeQSM and FaNNI for woody structure and leaf insertion, respectively. Next, using empirical data and a realistic range of leaf-level biochemical and physiological parameters, we conducted a local sensitivity analysis to demonstrate the potential of the approach for assessing the impact of structural, biochemical and physiological factors on top of canopy (TOC) SIF. The analysis gave insight into the factors that drive the intensity and spectral properties of TOC SIF in heterogeneous boreal forest canopies. DART simulated red TOC fluorescence was found to be less affected by biochemical factors such as chlorophyll and dry matter contents or the senescent factor than far-red fluorescence. In contrast, canopy structural factors such as overstory leaf area index (LAI), leaf angle distribution and fractional cover had a substantial and comparable impact across all SIF wavelengths, with the exception of understory LAI that affected predominantly far-red fluorescence. Finally, variations in the fluorescence quantum efficiency (Fqe) of photosystem II affected all TOC SIF wavelengths. Our results also revealed that not only canopy structural factors but also understory fluorescence should be considered in the interpretation of tower, airborne and satellite SIF datasets, especially when acquired in the (near-) nadir viewing direction and for forests with open canopies. We suggest that the modelling strategy introduced in this study, coupled with the increasing availability of TLS and other 3D data sources, can be applied to resolve the interplay between physiological, biochemical and structural factors affecting SIF across ecosystems and independently of canopy complexity, paving the way for future SIF-based 3D photosynthesis models. publishedVersion

This work proposes a new approach to estimate two canopy structure properties: leaf area index (LAI) and leaf angle distribution (LAD) using terrestrial LiDAR system (TLS) data. Our methodology consists of two steps. First, a forward model was developed to simulate TLS observations of a vegetation scene having known structure variables (i.e. LAI and LAD) which permit obtaining 3D point cloud representing the studied scene. Second, a backward model was designed to retrieve LAI and LAD based on the relationship between light transmittance and foliage density. Our approach was validated with results obtained with different homogenous vegetation covers.

Global change is affecting biodiversity, with consequences on carbon, water, and energy fluxes between the atmosphere, vegetation, and soil. The interaction of shortwave radiation with vegetation drives basic processes of the biosphere, such as primary productivity or species interactions through light competition. In our study, we aim to understand the effects of biodiversity on canopy light absorption. We focus on the diversity of three key functional traits that influence the light-canopy interaction: leaf area index, leaf angle distribution and leaf optical properties. Our study is based on the in-silico combination of process-based modelling of radiation (3D radiative transfer model DART) with the well-established design of biodiversity experiments. We used this novel method to study the effects of leaf functional diversity on a light proxy for productivity (the fraction of absorbed photosynthetically active radiation (FAPAR)) and net radiation (shortwave albedo). We found that diverse canopies had lower albedo and higher FAPAR than the average of the corresponding monoculture values. In mixtures, FAPAR was unequally re-distributed between trees with distinct leaf traits and the net biodiversity effect on absorptance was greater when combining plant functional types with more distinct traits. Our results support the mechanistic understanding of overyielding effects in functionally diverse canopies and may partially explain some of the growth-promoting mechanisms in biodiversity-ecosystem functioning experiments. They can further help to account for biodiversity effects in dynamic vegetation and climate models.We would like to present this study in the BG3.22 session, because it 1) contributes to the understanding of fundamental ecosystem functions related to light interaction 2) describes a novel in-silico combination of process-based modelling of radiation with the well-established design of biodiversity experiments.

Leaf angle distribution (LAD) is an important parameter affecting the biophysical interaction of sunlight and forest canopies. But, difficulty in measuring LAD has limited exploration of its species-specific phenology and variation across environmental gradients. To evaluate whether digital photographs from unpersoned aerial vehicles (UAVs) could be used to measure LAD, we directly compared UAV-based measurements of leaf angle against those made from conventional leveled digital photographs taken from towers, ladders, buildings, or poles. We used two different UAV and camera systems, and found that both systems provided statistically similar results to the conventional measurements of LAD on five common broadleaf tree species of Europe and North America. In addition to overcoming challenges of UAV airspace regulation and piloting UAVs within complex forest canopies, we recommend potential users of this method should identify, minimize, and correct for any image distortion effects created by their UAV and camera system. With these considerations in mind, our results indicate that UAVs can be used to measure LAD in virtually any broadleaf forest environment, which opens the new possibility for obtaining accurate, species-specific information on the variability of LAD through time and along broad environmental gradients.

Leaf inclination angles are of critical importance in determining the transmission of radiation by vegetation canopies. In this short communication we address the seasonal and vertical changes in leaf inclination angles for eight deciduous broadleaf tree species common to temperate and hemiboreal ecoclimatic regions. The greatest changes in leaf inclination angles are observed in spring at the top of the canopy. Tree species with more vertical leaf inclination angles are more sensitive to light exposure than tree species with more horizontal inclination angles. We also test the sensitivity to user subjectivity of the leveled digital camera method for measuring leaf inclination angles and show that the method provides comparable results irrespective of the user.

Leaf angle distribution (LAD) is an important canopy structure metric. It controls the flux of radiation, carbon and water, and has therefore been used in many radiative transfer, meteorological and hydrological models. However, LAD is too tedious to measure using conventional manual methods. Terrestrial laser scanning (TLS) has recently been proposed to estimate LAD due to its ability to record unprecedented detailed plant 3D structure. However, previous research was restricted to a controlled environment with simple canopy structure. In this research, TLS was used in a natural deciduous European beech forest to estimate LAD. Digital hemispherical photograph (DHP) was also used as a reference. The results demonstrated that both TLS and DHP could capture a variation of LAD in beech plots at different succession stages. Compared to DHP, TLS has the advantage of resolving foliar and woody materials, as well as deriving the 3D distribution of leaf angles.

Empirical relationships between field-derived Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) and polarimetric synthetic aperture radar (PolSAR) based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were acquired in 2009. Regression analyses showed that LAI correspondence with the PolSAR biophysical indicator variables equaled or exceeded those of vegetation water content (VWC) correspondences. In the final six regressor model, the ratio HV/VV explained 49% of the total 77% explained LAI variance, and the HH-VV coherence and phase information accounted for the remainder. HV/HH dominated the two regressor LAD relationship, and spatial heterogeneity and backscatter mechanism followed by coherence information dominated the final three regressor model that explained 74% of the LAD variance. Regression results applied to 2009 through 2012 PolSAR images showed substantial changes in marsh LAI and LAD. Although the direct cause was not substantiated, following a release of freshwater in response to the 2010 Deepwater Horizon oil spill, the fairly uniform interior marsh structure of 2009 was more vertical and dense shortly after the oil spill cessation. After 2010, marsh structure generally progressed back toward the 2009 uniformity; however, the trend was more disjointed in oil impact marshes.

In urban areas, leaf area index (LAI) is a key ecosystem structural attribute with implications for energy and water balance, gas exchange, and anthropogenic energy use. In this study, we estimated LAI spatially using airborne lidar in downtown Santa Barbara, California, USA. We implemented two different modeling approaches. First, we directly estimated effective LAI (LAIe) using scan angle- and clump-corrected lidar laser penetration metrics (LPM). Second, we adapted existing allometric equations to estimate crown structural metrics including tree height and crown base height using lidar. The latter approach allowed for LAI estimates at the individual tree-crown scale. The LPM method, at both high and decimated point densities, resulted in good linear agreement with estimates from ground-based hemispherical photography (r(2) = 0.82, y = 0.99x) using a model that assumed a spherical leaf angle distribution. Within individual tree crown segments, the lidar estimates of crown structure closely paralleled field measurements (e.g., r(2) = 0.87 for crown length). LAI estimates based on the lidar crown measurements corresponded well with estimates from field measurements (r(2) = 0.84, y = 0.97x + 0.10). Consistency of the LPM and allometric lidar methods was also strong at 71 validation plots (r(2) = 0.88) and at 450 additional sample locations across the entire study area (r(2) = 0.72). This level of correspondence exceeded that of the canopy hemispherical photography and allometric, ground-based estimates (r(2) = 0.53). The first-order alignment of these two disparate methods may indicate that the error bounds for mapping LAI in cities are small enough to pursue large scale, spatially explicit estimation. (C) 2015 Elsevier Inc All rights reserved.

Leaf area index LAI is one descriptor of forest canopy structure and can be linked to vegetation productivity, carbon cycling, and several other ecosystem services. Airborne lidar light detection and ranging provides proxies of canopy gap fraction GF in the near-vertical direction, which can be related to LAI using a logarithmic model derived from Beer’s Law. The approach has been successful in LAI mapping in boreal and temperate forests. In this study, we evaluated the logarithmic model and several GF proxies in tropical montane forests in southeastern Kenya. We used two discrete-return lidar datasets max. scan angle ~16° with different flying heights and pulse densities 5.4 and 2.6 pulses m–2. GF for the 0–15° zenith angle range GF15 and effective LAI Le were estimated for 29 sample plots using digital hemispherical photography. Twenty-one plots were located in indigenous forests and eight plots in plantation forests. According to the results, GF15 was best approximated by the proxies that included all canopy and ground return types all echo cover index, ACI, root mean square error, RMSE = 0.050, bias = –0.003; Solberg’s cover index, SCI, RMSE = 0.057, bias = 0.002 although some saturation occurred when using data from the higher flight altitude. The results of the Le modelling propose that the logarithmic model needs to be fit separately for indigenous forest and plantations. Furthermore, the slope parameters of the models based on SCI suggest planophile β ≈ 1.6 and spherical β ≈ 2 leaf angle distribution for indigenous forests and plantations, respectively. We conclude that lidar cover indices based on all returns can estimate GF15 in closed-canopy tropical forests but the detection of the smallest gaps can be limited by the scanner or scanning parameters. The application of the logarithmic model requires stratification in the structurally heterogeneous and multi-species forest areas as β should be estimated separately for the different forest types.

This paper presents the development of an accurate and simple scattering model for radar backscatters of agricultural fields. We modified the water-cloud model (WCM) by adding new parameters (the average and standard deviation of leaf angle distribution) to accurately estimate the backscattering coefficients with the angular effect of scattering particles in a vegetation canopy. A relatively accurate radiative transfer model (RTM) and field measurements were used in this modification. The accuracy of the RTM was verified with the C-band ground-based scatterometer data of a cornfield, the X-band synthetic aperture radar data and ground-based scatterometer data of a bean field, and the in situ measured ground-truth data of those fields. The newly modified WCM (MWCM) was also verified with the measurement data. It was found that the root-mean-square errors between the MWCM and the measurements were less than 1.5 dB for all backscatter data from the agricultural vegetation fields.

Rapid, reliable and meaningful estimates of forest canopy are essential to the characterization of forest ecosystems. In this paper the accuracy of digital hemispherical (DHP) and cover (DCP) photography for the estimation of canopy properties in deciduous forests was evaluated. Leaf area index (LAI) estimated from both these photographic methods and from light transmittance data derived from DHP were compared with direct measurements obtained by litter traps (LAILT) and an AccuPAR ceptometer. Also, comparison with different gap fraction methods used to calculate LAI in DHP and LAI-2000 PCA were performed. We applied these methods in four forest stands of Quercus cerris, two stands of Castanea sativa and four stands of Fagus sylvatica, the most common deciduous species in Italy, where LAILT ranged from 3.9 to 7.3. Both photographic methods provided good indirect estimates of LAILT. The DCP method provided estimates of crown porosity, crown cover, foliage cover and the clumping index at the zenith, but required assumptions about the light extinction coefficient at the zenith (k), to accurately estimate LAI. Cover photography provided good indirect estimates of LAI assuming a spherical leaf angle distribution, even though k appeared to decrease as LAI increased, thus affecting the accuracy of LAI estimates in DCP. In contrast, the accuracy of LAI estimates in DHP appeared insensitive to LAILT values, but the method was sensitive to photographic exposure and more time-consuming than DCP. The studied stands were characterized by higher within-crown clumping than between-crowns clumping; only the segmented analysis of gap fraction for each ring of the fisheye images was found to provide reliable and useful clumping index in DHP. The 1-azimuth segment method employed in PCA poorly detected clumping in dense canopies. The correlation between transmittance estimates by DHP with values measured at noon with the AccuPAR ceptometer was linear and significant, although the variability observed in reference measures suggested that results obtained with the ceptometer should be treated with caution. We conclude both photographic methods are suitable for dense deciduous forests. Cover photography holds great promise as a means to quickly obtain inexpensive estimates of LAI over large areas. However, in situations where no direct reference measurements of k are available, we recommend using both DHP and DCP, in order to cross-calibrate the two methods; DCP could then be used for more routinely indirect measurement and monitoring of LAI.

The leaf inclination angle distribution (LAD) is an important characteristic of vegetation canopy structure affecting light interception within the canopy. However, LADs are difficult and time consuming to measure. To examine possible global patterns of LAD and their implications in remote sensing, a model was developed to predict leaf angles within canopies. Canopies were simulated using the SAIL radiative transfer model combined with a simple photosynthesis model. This model calculated leaf inclination angles for horizontal layers of leaves within the canopy by choosing the leaf inclination angle that maximized production over a day in each layer. LADs were calculated for five latitude bands for spring and summer solar declinations. Three distinct LAD types emerged: tropical, boreal, and an intermediate temperate distribution. In tropical LAD, the upper layers have a leaf angle around 35° with the lower layers having horizontal inclination angles. While the boreal LAD has vertical leaf inclination angles throughout the canopy. The latitude bands where each LAD type occurred changed with the seasons. The different LADs affected the fraction of absorbed photosynthetically active radiation (fAPAR) and Normalized Difference Vegetation Index (NDVI) with similar relationships between fAPAR and leaf area index (LAI), but different relationships between NDVI and LAI for the different LAD types. These differences resulted in significantly different relationships between NDVI and fAPAR for each LAD type. Since leaf inclination angles affect light interception, variations in LAD also affect the estimation of leaf area based on transmittance of light or lidar returns.

It is important to properly understand the light environment for improving plant production efficiency in a plant factory. Plant canopy structure affects the photosynthetic photon flux density (PPFD) distribution, thereby strongly affecting plant growth. Therefore, it is important to understand the canopy structure and control it in some cases for plant production. Indices for evaluating the canopy structure include extinction coefficient, leaf area index (LAI) or leaf area density (LAD) distribution, and leaf angle distribution. LAI is an important index not only for plant growth but also for evaluating the light environment of the canopy. Several indirect but nondestructive ways for LAI estimation have been proposed, including methods using plant canopy analyzer (PCA), hemispherical photography, terrestrial laser scanner (TLS), spectral reflectance, and image analysis. Moreover, the canopy surface has a PPFD distribution because of its variation in leaf inclination and orientation. A simple method for evaluating PPFD distribution on plant canopy surface using the reflection image of canopy was proposed and successfully constructed PPFD histograms of the canopy surface of several plant species under both natural and artificial lights.

Solar radiation (short wave) is the energy source for photosynthesis, warming and evaporation in agricultural systems. Its value can be calculated as a function of latitude, time of year and cloud cover. Fifty percent of solar radiation is available to photosynthesis and is called photosynthetically active radiation (PAR), although only a very small fraction is actually used in this process. Net radiation is obtained by discounting the reflected solar radiation (which depends on the albedo) and longwave losses that depend on air temperature, humidity and cloud cover. Plants intercept all the radiation fluxes. Shortwave radiation interception is modulated by leaf angle distribution that varies with LAI and plant type. The fraction of radiation intercepted by trees can be calculated assuming simple geometrical forms (spheroids).

International audience; The effects of leaf inclination on plant light capture, growth, and water balance of monospecific canopies are well documented, but we still lack information on such effects in the case of multispecific canopies. We investigated the effects of leaf inclination on the absorption of photosynthetically active radiation (PAR) of a mixed forest. We ran a 3D mechanistic radiation transfer model for a Mediterranean forest where Pinus halepensis makes the upper strata while Quercus ilex occupies the lower strata. As factors, we included (1) the distributions of leaf inclinations that ranged from vertical to horizontal (including the actual inclinations), (2) the fraction of diffuse light, sun position, and leaf area index (LAI), and (3) the Pinus/Quercus LAI ratio. Simulated PAR absorption was more than twice as sensitive to leaf inclination in oaks than in pines because oaks depended on PAR transmitted below the pine layer. The extent of the effect depended on season, fraction of diffuse light, LAI, and vegetation spatial structure. None of the observed inclinations maximized PAR absorption, suggesting a trade-off with water economy. Erroneous assumptions about leaf inclination lead to larger errors when modelling heterogeneous, mixed canopies. This also highlights potential caveats when using models that do not account for the spatial structure of canopies.

Hyperspectral remote sensing for monitoring horticultural production systems requires the understanding of how plant physiology, canopy structure, management and solar elevation affect the retrieved canopy reflectance during different stages of the phenological cycle. Hence, the objective of this study was to set up and to interpret a hyperspectral time series for a mature and healthy citrus orchard in the Western Cape province of South Africa considering these effects. Based on the remotely sensed data, biophysical parameters at the canopy level were derived and related to known observed physiological and phenological changes at the leaf level and to orchard management. Fractions of mature fruit, flowers, and sunburnt leaves were considered, and indices related to canopy structure chlorophyll content and canopy water status were calculated. Results revealed small cover fractions of mature fruit, flowers and sunburnt leaves of respectively 2.1%, 3.1% and 7.0%, but the high spectral contrast between flowers and leaves allowed a successful classification of flowering intensity. Furthermore, it was shown that canopy level time series of vegetation indices were sensitive to changes in solar elevation and soil reflectance which could be reduced by applying an empirical soil line correction for the most affected indices. Most trends in vegetation indices at the canopy level could be explained by a combination of changes at the leaf level (chlorophyll, carotenoids, dry matter), changes in canopy structure (leaf area index and leaf angle distribution) and changes in cover fractions of vegetative flushes, flowers and sunburnt leaves. The transformed chlorophyll absorption ratio index over the optimised soil adjusted vegetation index (MCARI/OSAVI) was best related to leaf level trends in chlorophyll content. Seasonal changes in the photochemical reflectance index (PRI) were linked to inverse changes in the carotenoid-to-chlorophyll ratio. Canopy structure indices (the modified triangular vegetation index or MTVI2 and the standardized leaf area index determining index or sLAIDI) were sensitive to changes in leaf area index, average leaf angle as well to management interactions (pruning and harvest). Canopy water status was highly impacted during the spring flush due to expanding leaves that concealed trends in the underlying mature leaves. Seasonal trends in soil and weeds reflectance were related to changes in volumetric soil water content and to the earlier and reduced growth period of non-irrigated weeds.

Carbon dioxide fluxes are being measured in three maize-based agroecosystems in eastern Nebraska in an effort to better un derstand the potential for these systems to sequester carbon in the soil. Landscape-level fluxes of carbon, water and energy were measured using tower eddy covariance systems. In order to better understand the landscape-level results, measurements at smaller scales, using techniques promoted by John Norman, were made and scaled up to the landscape-level. Single leaf gas exchange properties (CO 2 assimilation rate and stomatal conductance) and optical properties, direct and diffuse radiation incident on the canopy, and photosynthetically active radiation (PAR) reflected and transmitted by the canopy were measured at regular intervals throughout the growing season. In addition, soil surface CO 2 fluxes were measured using chamber techniques. From leaf measurements, the responses of net CO 2 assimilation rate to relevant biophysical controlling factors were quantified. Single leaf gas exchange data were scaled up to the canopy level using a simple radiative model that considers direct beam and diffuse PAR penetration into the canopy. Canopy level photosynthesis was estimated, coupled with the soil surface CO2 fluxes, and compared to measured net ecosystem CO 2 exchange (NEE) values from the eddy covariance approach. Estimated values of canopy level absorbed PAR was also compared to measured values. The agreement between estimated and observed values increases our confidence in the measured carbon pools and fluxes in these agroecosys tems and enhances our understanding of biophysical controls on carbon sequestration.

Leaf area index (LAI) has traditionally been difficult to estimate accurately at the landscape scale, especially in heterogeneous vegetation with a range in LAI, but remains an important parameter for many ecological models. Several different methods have recently been proposed to estimate LAI using aerial light detection and ranging (LIDAR), but few systematic approaches have been attempted to assess the performance of these methods using a large, independent dataset with a wide range of LAI in a heterogeneous, mixed forest. In this study, four modeling approaches to estimate LAI using aerial discrete-return LIDAR have been compared to 98 separate hemispherical photograph LAI estimates from a heterogeneous mixed forest with a wide range of LAI. Among the four approaches tested, the model based on the Beer–Lambert law with a single parameter (k: extinction coefficient) exhibited highest accuracy (r2 = 0.665) compared with the other models based on allometric relationships. It is shown that the theoretical k value (=0.5) assuming a spherical leaf angle distribution and the zenith angle of vertical beams (=0°) may be adequate to estimate effective LAI of vegetation using LIDAR data. This model was then applied to six 30 m × 30 m plots at differing spatial extents to investigate the relationship between plot size and model accuracy, observing that model accuracy increased with increasing spatial extent, with a maximum r2 of 0.78 at an area of 900 m2. Findings of the present study can provide useful information for selection and application of LIDAR derived LAI models at landscape or other spatial scales of ecological importance.

Modeling and simulation of Earth imaging sensors with large spatial coverage necessitates an understanding of how photons interact with individual land surface processes at an aggregate level. For example, the leaf angle distribution of a deciduous forest canopy has a significant impact on the path of a single photon as it is scattered among the leaves and, consequently, a significant impact on the observed bidirectional reflectance distribution function (BRDF) of the canopy as a whole. In particular, simulation of imagery of heterogeneous scenes for many multispectral/hyperspectral applications requires detailed modeling of regions of the spectrum where many orders of scattering are required due to both high reflectance and transmittance. Radiative transfer modeling based on ray tracing, hybrid Monte Carlo techniques and detailed geometric and optical models of land cover means that it is possible to build effective, aggregate optical models with parameters such as species, spatial distribution, and underlying terrain variation. This paper examines the capability of the Digital Image and Remote Sensing Image Generation (DIRSIG) model to generate BRDF data representing land surfaces at large scale from modeling at a much smaller scale. We describe robust methods for generating optical property models effectively in DIRSIG and present new tools for facilitating the process. The methods and results for forest canopies are described relative to the RAdiation transfer Model Intercomparison (RAMI) benchmark scenes, which also forms the basis for an evaluation of the approach. Additional applications and examples are presented, representing different types of land cover.

Accurate estimates of leaf area index (L) are strongly required for modelling ecophysiological processes within urban forests. The majority of methods available for estimating L is ideally applicable at stand scale and is therefore poorly suitable in urban settings, where trees are typically sparse and isolated. In addition, accurate measurements in urban settings are hindered by proximity of trees to infrastructure elements, which can strongly affect the accuracy of tree canopy analysis. In this study we tested whether digital photography can be used to obtain indirect estimate of L of isolated trees. The sampled species were Platanus orientalis, Liquidambar styraciflua and Juglans regia. Upward-facing photography was used to estimate gap fraction and foliage clumping from images collected in unobstructed (open areas) and obstructed (nearby buildings) settings; two image classification methods provided accurate estimates of gap fraction, based on comparison with measurements obtained from a high quality quantum sensor (LAI-2000). Leveled photography was used to characterize the leaf angle distribution of the examined tree species. L estimates obtained combining the two photographic methods agreed well with direct L measurements obtained from harvesting. We conclude that digital photography is suitable for estimating leaf area in isolated urban trees, due to its simple, fast and costeffective procedures. Use of vegetation indices allows extending significantly the applicability of the photographic method in urban settings, including green roofs and vertical greenery systems. L'articolo è disponibile sul sito dell'editore www.elsevier.com/locate/ufug

On global and regional scales, earth observation (EO)-based estimates of leaf area index (LAI) provide valuable input to climate and hydrologic modelling, while fraction of absorbed photosynthetically active radiation (fAPAR) is a key variable in the assessment of vegetation productivity and yield estimates. Validation of moderate resolution imaging spectroradiometer (MODIS) LAI and fAPAR products is an important prerequisite to using these variables for global modelling or for local water resource modelling and net primary production (NPP) assessment, as in semi-arid West Africa and Senegal. In situ measurements of LAI and fAPAR from three sites in semi-arid Senegal were carried out in 2001 and 2002 for comparison with remotely sensed MODIS data. The seasonal dynamics of both in situ LAI and fAPAR were captured well by MODIS LAI and fAPAR. MODIS LAI is overestimated by approximately 2–15% and the overall level of fAPAR is overestimated by 8–20%. Both MODIS LAI and fAPAR are characterised by a moderate offset, which is slightly higher than can be explained by model and input data uncertainty. In situ fAPAR and normalised differential vegetation index (NDVI) for three different vegetation types showed a strong linear relationship, suggesting that covariance between fAPAR and NDVI is insensitive to variations in leaf angle distribution (LAD) and vegetative heterogeneity. A strong linear relation also exists between MODIS fAPAR and NDVI but with different regression coefficients than the in situ relation because of MODIS' tendency to overestimate fAPAR. The fAPAR/NDVI relations found here, however, do not apply on a global scale but are only valid for similar sun-sensor view geometry and soil colour.

Retrievals of LAI from inversion of canopy radiometric measurements, using the Li-Cor LAI-2000 Plant Canopy Analyzer and the Decagon AccuPar Ceptometer (a linear quantum probe) were analyzed and compared. Field data were collected from 34 sites in the boreal forest of interior Alaska, and sensitivity tests were conducted to estimate the effect of a variety of measurement conditions on the LAI retrievals. We also tested the response of estimated LAI to different values of the theoretical parameters in the retrieval algorithms. Uncertainty in the incident radiation level was magnified by the LAI retrieval, meaning that even small errors in this measurement significantly affected the LAI estimates. Changes in solar zenith angle over long data acquisition times also contributed to the errors. The most important quality control factors for accurate retrieval of LAI from field measurements were the incident radiation and solar zenith angle. A series of sensitivity tests showed that extreme values of leaf angle distribution could change LAI estimates, but our multi-angle measurements produced results consistent with a spherical leaf angle distribution. Alternative methods taken from the literature for post-processing of the data from the two instruments produced similar results for the LAI-2000, but widely different results for the Decagon AccuPar. Retrievals from the two instruments had an overall correlation coefficient r = 0.88 ,( P

A two-dimensional, hourly or daily time step model was developed, which takes canopy characteristics and row orientation into account to simulate solar radiation interception in hedgerow orchards. In order to determine the spatial and temporal distribution of soil irradiance across the tree row, the canopy path length through which the radiation must travel to reach a certain point on the soil surface is calculated. The model assumes leaves to be uniformly distributed within an ellipsoid, and radiation penetrating the canopy is attenuated according to Beer’s law. Beam or direct radiation and diffuse radiation for the PAR (photosynthetically active radiation) and NIR (near-infrared radiation) wavebands are calculated separately, as they interact differently with the canopy. The attenuation of beam radiation by the canopy is strongly dependent on canopy dimensions and architecture, zenith and azimuth angle, as well as row orientation. Radiation can penetrate neighbouring rows, so two rows on either side of the simulated row are considered. Validation of the model was carried out for a wide range of conditions (crops, row orientation, canopy density, tree size and shape). Field measurements included solar radiation, soil irradiance at different distances from the tree row with tube solarimeters, leaf area density, as well as canopy size and row orientation. Model predictions of soil irradiance were excellent in orchards with symmetrical and elliptical canopies having a uniform leaf distribution. In orchards where the canopy was non-symmetric and/or had non-uniform leaf distribution, errors in predictions of solar radiation transmittance occurred. As a result of these discrepancies, the overall MAE was 40% of the average measured value of radiant transmittance over the whole day.

Reflectance and polarized reflectance in the visible red band were measured for wheat canopies in a wide range of solar zenith angles to explore the relations among reflectance and polarization, view and illumination geometry, and crop canopy development. The reflected sunlight in a 10˚ field of view was measured with a radiometer at approximately 1.6 m in height. The view zenith angles were set from 0˚ to 75˚ at 15˚ intervals, and the observation azimuth was towards the sun. The relation between the polarization and solar zenith angle depended both on the view zenith angle and the growth stage. Multiple regressions were applied to estimate the polarization and reflectance at solar zenith 40˚. Seasonal profiles of LAI, leaf inclination distribution, reflectance, and polarized reflectance indicate that polarization includes information for canopy structure such as leaf inclination distribution. Observations at solar zenith angles of more or less than 40˚ may also give similar results when the view zenith angle is appropriately set, corresponding to the solar zenith angle at the time of measurement.

A decrease in stratospheric ozone may result in a serious threat to plants, since biologically active short-wavelength ultraviolet-B (UV-B 280–320 nm) radiation will increase even with a relatively small decrease in ozone. Numerous investigations have demonstrated that the effect of UV-B enhancements on plants includes reduction in grain yield, alteration in species competition, susceptibility to disease, and changes in plant structure and pigmentation. To determine the physiological effects on plants of any increases in UV-B radiation, the irradiances at the potential sensitive plant surface need to be known. A number of radiative transfer models exist but because of the importance of sky diffuse radiation to the global UV-B irradiance, models designed to estimate photosynthetically active radiation or total solar radiation may not accurately model the UV-B. This paper compares spatially and temporally averaged measurements of the UV-B canopy transmittance of a relatively dense maize canopy (sky view: 0.27°) to the estimations of two one-dimensional models differing mainly in the handling of sky radiance. The model that considered the distribution of sky radiance tended to underestimate the canopy transmittance, the model that assumed an isotropic sky radiance distribution tended to overestimate the canopy transmittance. However, the assumption concerning the sky radiance distribution accounted for only about 0.01 of the model error. Consequently, the sky radiance distribution is probably not important in modeling such dense crop canopies. The model that overestimated transmittance and had the generally larger errors, a modified Meyers model, used the assumption of uniform leaf angle distribution, whereas in the other model, designated the UVRT model, leaf angle distributions were estimated by sample measurements. Generally this model would be satisfactory in describing the statistically average UV-B irradiance conditions in the canopy. This model may also be applied to other dense plant canopies including forests.

Poplars (Populus spp.) have a particular petiole morphology that enhances leaf flutter even in light winds. Previous studies have shown that this trait enhances whole canopy carbon gain through changes in the temporal dynamics and spatial distribution of light in the lower canopy. However, less is known about the effects of flutter for leaves at the top of the canopy ("sun leaves"). A computer simulation model was developed that uses latitude, time of day, day of year, azimuth and a slope component, which was varied at a 3 Hz frequency over an arc of rotation to create the flutter motion, and generate data on light interception for both surfaces of a fixed or fluttering leaf. The light files generated (10 Hz) were input into a dynamic model of photosynthesis to estimate the carbon gain for both fluttering and fixed leaves. As compared to leaves fixed at various angles and azimuths, fluttering leaves had a more uniform light interception. Depending on their angle and azimuth, fixed leaves may not always be intercepting high photon flux density (PFD) even when exposed to full sun. Leaf flutter continuously randomizes leaf angles creating uniform PFD inputs for photosynthetic reactions regardless of variation in leaf orientation and solar position. These effects on light interception could have positive impacts on carbon gain for leaves at the top of the canopy.

© 2017 IOP Publishing Ltd. The rapid evolution of high performance computing technology has allowed for the development of extremely detailed models of the urban and natural environment. Although models can now represent sub-meter-scale variability in environmental geometry, model users are often unable to specify the geometry of real domains at this scale given available measurements. An emerging technology in this field has been the use of terrestrial LiDAR scanning data to rapidly measure the three-dimensional geometry of trees, such as the distribution of leaf area. However, current LiDAR methods suffer from the limitation that they require detailed knowledge of leaf orientation in order to translate projected leaf area into actual leaf area. Common methods for measuring leaf orientation are often tedious or inaccurate, which places constraints on the LiDAR measurement technique. This work presents a new method to simultaneously measure leaf orientation and leaf area within an arbitrarily defined volume using terrestrial LiDAR data. The novelty of the method lies in the direct measurement of the fraction of projected leaf area G from the LiDAR data which is required to relate projected leaf area to total leaf area, and in the new way in which radiation transfer theory is used to calculate leaf area from the LiDAR data. The method was validated by comparing LiDAR-measured leaf area to (1) 'synthetic' or computer-generated LiDAR data where the exact area was known, and (2) direct measurements of leaf area in the field using destructive sampling. Overall, agreement between the LiDAR and reference measurements was very good, showing a normalized root-mean-squared-error of about 15% for the synthetic tests, and 13% in the field.

Fast and accurate estimates of understory leaf area are essential to a wide range of ecological applications. Indirect methods have mainly been used to estimate leaf area of overstory but their application in understory remains largely unexplored. In this study we described a combination of digital photographic methods to obtain rapid, reliable and non-destructive estimate of leaf area index of understory deciduous trees. Nadir photography was used to estimate foliage cover, vertical gap fraction and foliage clumping index. Leveled photography was used to characterize the leaf angle distribution of the examined tree species. Leaf area index estimates obtained combining the two photographic methods were compared with direct measurements obtained from harvesting ( L ). We applied these methods in Quercus cerris , Carpinus betulus and Fagus sylvatica stands. Foliage cover estimates derived from two nadir image classification methods were significantly correlated with leaf area index measurements obtained from harvesting. The leveled digital photographic method, previously tested in tall trees and field crops, provided reliable leaf angle measurements in understory tree species. Digital photography provided good indirect estimates of L . We conclude that digital photography is suitable for routine estimate and monitoring of understory leaf area, on account of its fast and cost-effective procedure.

Tree fall gaps are widely considered to play a prominent role in the maintenance of species diversity, while the spatiotemporal variability of canopy structure within closed forest stands is largely ignored. In this study we examined the vertical and horizontal components of canopy structure and its seasonal variability in a tropical wet semideciduous rainforest in Panama. Leaf area indices (LAI) were derived from measurements of diffuse radiation and empirically-based leaf angle distribution by mathematical inversion of a light interception model. Vertical distribution of LAI was non-homogeneous with 50% of the leaf area being concentrated in the uppermost 5 m of the canopy. In the wet season, when foliage is most abundant, the horizontal distribution of LAI in a 2100 m 2 plot ranged widely from 3 to 8, with a mean of 5.41. Changes in mean LAI between wet and dry seasons were small but highly significant. While ca 40% of the area was not affected by local changes in LAI, sizeable small scale changes in LAI did occur between wet and dry season in some locations. Local changes in LAI ranged from –2.3 to 2.4. These changes resulted in a 50% or more increase in light reaching the forest floor at 29% of the measuring locations, and a doubling or more at 13% of the location. Our results imply that structural heterogeneity by simple tree fall gaps do not adequately describe the dynamics of forest canopies.

Albedo (the fraction of reflected incoming solar radiation in the 400–3000 nm range) is of critical importance in crop simulation studies because it determines how much solar radiation is available for carbon uptake, crop water use, and ultimately productivity and yield. However, many crop simulation models do not place as much emphasis on albedo as is warranted given its importance in determining these crop processes. In addition, prediction of albedo in crop simulation models dealing with heliotropic crops (their leaves follow the sun) has received relatively little attention. An albedo algorithm based on a spherical leaf angle distribution was modified to predict albedo in two heliotropic crops, alfalfa (Medicago sativa L.) and soybean (Glycine max (L.) Merr.). Since no analytical expression exists for the heliotropic leaf angle distribution, linear relationships were developed to predict the albedo of these heliotropic crops from the albedo of a crop with a spherical leaf angle distribution. These relationships were developed from field data and tested on independent field measurements. Albedo was measured over soybean for a wide range of leaf area indices and leaf optical properties and between two cuttings in alfalfa. The normalized mean absolute error was used to compare predicted and measured values. There was good agreement between predicted and measured values except under foggy conditions when the direct beam fraction was zero and when water droplets on leaf surfaces may have influenced leaf optical properties.

To describe canopy emitting thermal radiance precisely and physically is one of the key researches in retrieving land surface temperature (LST) over vegetation-covered regions by remote sensing technology. This work is aimed at establishing gap probability models to describe the thermal emission characteristics in continuous plant, including the basic model and the sunlit model. They are suitable respectively in the nighttime and in the daytime. The sunlit model is the basic model plus a sunlit correcting item which takes the hot spot effect into account. The researches on the directional distribution of radiance and its relationship to canopy structural parameters, such as the leaf area index (LAI) and leaf angle distribution (LAD), were focused. The characteristics of directional radiance caused by temperature differences among components in canopy, such as those between leaf and soil, and between sunlit leaf or soil and shadowed leaf or soil, were analyzed. A well fitting between experimental data and the theoretical calculations shows that the models are able to illustrate the canopy thermal emission generally.

Many current vegetation–atmosphere models require structural information describing the canopy in order to calculate rates of mass and energy exchange. One of the most important pieces of information is the variation with height in leaf area density, ρ (m2 leaf area per m3 canopy volume), but it is notoriously difficult to measure this in forest canopies. Consequently, very few data on ρ exist for tall tropical forests. We describe a relatively rapid photographic method to make this measurement and we demonstrate its use for a rain forest in Cameroon, and two rain forests in Brazil. The method requires an assumption or knowledge of leaf angle distribution, but is relatively rapid and has the benefit of sampling a relatively large volume of canopy. The technique works adequately, especially if the leaf area index of the site is already known; the results agree quantitatively and qualitatively with previous more laborious determinations.

We describe the calibration and structure of a multi-species, two-scale light transmission model, and demonstrate its effectiveness for predicting instantaneous light availability at the stand scale across a wide range of forest stand compositions. The model, MIXLIGHT, calculates light transmission through the forest overstory at the stand or microsite scale using standard forest inventory data and two other parameters, foliage area density and foliage inclination. Since MIXLIGHT simulations on both scales are based on a list of individual tree characteristics, it allows for simple manipulation of stand structure to study the effects of silvicultural options on light availability. The two scales allow the input of various kinds of data, allowing predictions from data of different sources and completeness. A simple and rapid method of calibrating the foliage parameters for the species of interest is presented, using measurements of direct-beam light transmission measurements made in the shadows of newly isolated trees. In an independent validation, MIXLIGHT predicted light transmission at the stand level closely for 17 forest inventory plots with a wide range of density and species composition, during leaf-on and leaf-off seasons and under sunny and cloudy conditions. A sensitivity analysis indicated that the influence of the parameters in the model on stand level light transmission predictions was (highest to lowest): foliage area density, crown radius, crown length, and foliage inclination. Nonetheless, a test of the common assumption that the foliage is spherically inclined caused significant underestimation of light transmission. With this flexibility and demonstrated accuracy, we believe MIXLIGHT will provide an accessible and effective tool for forest stand management and regeneration modeling.

Calibrations of indirect methods for estimating leaf area are usually based on small data sets and are often species-specific and may even be stand-specific. We used the Licor LAI-2000 plant canopy analyser (PCA) as a reference to calibrate leaf area measurements based on hemispherical photography. Ten stands of 6‐8 year-old, plantation grown Tasmanian bluegum (Eucalyptus globulus Labill.) in Western Australia were used to investigate the effects of variations in sampling position, photographic exposure and image processing on leaf area (or leaf area index, L) estimated using hemispherical photography. We also compared our photographic estimates of L with those obtained via destructive sampling (allometry) in both evenly spaced and highly clumped stands of E. globulus. Varying exposure by one stop affected estimated L by approximately 13% but we confirmed that correct exposure can be approximately predicted by metering exposure outside the canopy. In situations where metering exposure outside the canopy is impractical, we recommend the use of empirically derived relationships between L estimated from photographic images made at a constant exposure and actual L derived from other means. Mean tilt angle obtained for E. globulusfrom the photographic method (68.7 2.5 s.e.) agreed well with estimates for Eucalyptusspecies from other studies. Owing to the non-random arrangement of crowns within plantations, sampling position significantly affected mean tilt angle. In highly clumped stands with closely spaced double rows and wide inter-double row gaps, underestimation of L by 16‐30% with the photographic method was probably the result of greater foliage clumping at the crown level. We concluded that, in stands of E. globulus and probably other broadleaf species with evenly distributed crowns, foliage clumping at the shoot or branch level is unlikely to be a significant source of error in indirect estimates of L. Scattering of blue light may result in large underestimates of L when using the PCA. In stands with ‘extreme’ architecture, indirect, light interception-based methods are likely to greatly underestimate L, although, positioning the sensor so as remove large gaps from view may allow accurate estimates of L even in these stands. ©2000 Published by Elsevier Science B.V. All rights reserved.

Knowledge of single leaf boundary layer conductance ( g b ) within extensive canopies is required to scale stomatally regulated gas exchange from leaf to canopy. Fluxes of terpenes and heat, formation of dew, and other processes occurring at the leaf surface may be controlled by g b . We develop, test, and deploy at five canopy heights, a set of paired, heated and unheated brass surrogate leaves, with realistic 3-dimensional shape and characteristic dimension ( d ) for cotton ( Gossypium hirsutum L.) in California. An equation describing heat transfer from a flat plate, enhanced by a factor of 1.15 for complex leaf shape, adequately described g b of the surrogate leaves. In the field, values of g b measured with the surrogate leaves near the top of the canopy were closely related to wind speed, as expected from theory. The dependence of g b on leaf size was apparent at all heights in the canopy using surrogate leaves differing in d . The profile of within-canopy wind speed ( u ) was determined using the profile of g b and d of the surrogate leaves. This profile of effective u in the leaf environment was well described by a conventional exponential decay model with an empirically derived wind extinction coefficient ( K = 1.5). This profile of u was combined with the directly measured profile of d of biological leaves to determine the actual profile of g b in this canopy. The increasing leaf size toward the middle of the canopy caused actual g b to decline more steeply than predicted from constant d . With depth in the canopy sunflecks increasingly contributed to unequal radiation interception between paired surrogate leaves and therefore to anomalies in calculated g b . A simple data filtering protocol, rejecting data outside the range observed at the top of the canopy, is introduced. Data rejection increased appropriately with increasing sunfleck frequency and depth in the canopy. These results extend the applicability of the heated, paired surrogate leaf technique to sheltered positions in dense canopies, and to large-leafed species, allowing direct measurement of an important canopy parameter ( g b ) that has typically been estimated or calculated as a residual.

Hyperspectral bidirectional reflectance distribution function (BRDF) data of Konza prairie grassland acquired in the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) on the ground with two SE-590 instruments and remotely with the airborne advanced solid-state array spectroradiometer (ASAS) are analyzed and compared to BRDF data of dense ryegrass obtained in the laboratory and field with the European goniometric facility (EGO) and the Swiss field-goniometer system (FIGOS). The soil underlying the relatively sparse Konza prairie grass disturbed the spectral BRDF effects of the vegetation components. After a correction of the soil influence based on the bidirectional canopy gap probability, the Konza data from SE-590 and ASAS sensors showed a consistently strong dependence of spectral BRDF effects from nadir reflectance as was observed in the EGO and FIGOS data. BRDF effects were inversely related to reflectance intensities, low reflectances being associated with pronounced BRDF effects and high reflectances with low BRDF effects. This relationship is due to multiple scattering effects and is influenced by the canopy optical properties and architecture parameters such as leaf area index (LAI), leaf angle distribution (LAD), and the gap fraction. The BRDF data of the Konza prairie grass from both ground and aircraft measurements showed a strong relationship between LAI and spectral BRDF variability. Reflectance data with high spectral resolution in the red edge range from 675 to about 900 nm wavelength, acquired from the two viewing directions with maximum and minimum reflectance intensities proved to be useful for deriving vegetation canopy architecture characteristics from hyperspectral BRDF data. BRDF data with high spectral resolution from the airborne ASAS sensor and from planned commercial remote sensing satellites are therefore an ideal testbed for a further exploration of this promising approach.

A new field goniometer system (FIGOS) is introduced that allows in situ measurements of hyperspectral bidirectional reflectance data under natural illumination conditions. Hyperspectral bidirectional reflectance distribution function (BRDF) data sets taken with FIGOS nominally cover the spectral range between 300 and 2450 nm in 704 bands. Typical targets are small-growing, dense, and homogeneous vegetation canopies, man-made surfaces, and soils. Field BRDF data of a perennial ryegrass surface reveal a strong spectral variability. In the blue and red chlorophyll absorption bands, BRDF effects are strong. Less-pronounced bidirectional reflectance effects are observed in the green and in most of the near-infrared range there surface reflectance is high. An anisotropy index (ANIX), defined as the ratio between the maximum and minimum bidirectional reflectance over the hemisphere, is introduced as a surrogate measurement for the extent of spectral BRDF effects. The ANIX data of the ryegrass surface show a very high correlation with nadir reflectance due to multiple scattering effects. Since canopy geometry, multiple scattering, and BRDF effects are related, these findings may help to derive canopy architecture parameters, such as leaf area index (LAI) or leaf angle distribution (LAD) from remotely sensed hyperspectral BRDF data. Furthermore, they show that normalized difference vegetation index (NDVI) data are strongly biased by the spectral variability of BRDF effects.

Analyses of various biophysical and biochemical factors affecting plant canopy reflectance have been carried out over the past few decades, yet the relative importance of these factors has not been adequately addressed. A combination of field and modeling techniques were used to quantify the relative contribution of leaf, stem, and litter optical properties (incorporating known variation in foliar biochemical properties) and canopy structural attributes to nadir-viewed vegetation reflectance data. Variability in tissue optical properties was wavelength-dependent. For green foliage, the lowest variation was in the visible (VIS) spectral region and the highest in the near-infrared (NIR). For standing litter material, minimum variation occurred in the VIS/NIR, while the largest differences were observed in the shortwave-IR (SWIR). Woody stem material showed opposite trends, with lowest variation in the SWIR and highest in the NIR. Leaf area index (LAI) and leaf angle distribution (LAD) were the dominant controls on canopy reflectance data with the exception of soil reflectance and vegetation cover in sparse canopies. Leaf optical properties (and thus foliar chemistry) were expressed most directly at the canopy level in the NIR, but LAI and LAD strongly controlled the relationship between leaf and canopy spectral characteristics. Stem material played a small but significant role in determining canopy reflectance in woody plant canopies, especially those with LAI