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Leaf angle distribution (LAD) is an important plant structural trait that determines radiation interception, biomass production, rainfall interception, and evapotranspiration. Assessment of LAD is a challenging task and a significant source of uncertainty in ecological models. So far, the information on leaf inclination angle distributions of different plant species is scarce in literature and databases (e.g. TRY). Approximate quantification of LAD is often made by means of modeling.The aim of this study is to find a user-friendly and accessible method to estimate leaf angle distribution type. We used Google’s TensorFlow convolutional neural network (CNN) to test for the first time the possibility of using machine learning for automatically classifying LAD types from leveled digital photographs. We used different combinations of five LAD distribution types (planophile, erectophile, spherical, plagiophile and uniform). The highest training accuracy of 95% and validation accuracy of 91% were achieved by using the two most distinct leaf angle distribution types – planophile and erectophile. As expected, the accuracy subsequently decreased with the addition of other leaf angle distribution types (spherical, plagiophile, uniform). However, our results indicate that the involvement of machine learning may indeed hold the potential to remove the current bottleneck in retrieving the information on leaf angle distribution and its better quantification in ecosystem modeling.

Leaves form the primary interface between plant physiology, CO2 in the atmosphere and energy (light). Plant productivity (i.e. carbon (C) gain) is primarily determined by the amount of leaf area, leaf orientation and distribution in space. Not much attention has been paid to possible changes in leaf orientation and distribution with elevated CO2 (eCO2), but its effect on plant growth could alter the proportions of sunlit and shaded leaf areas and feedback on carbohydrate available for further growth. We report on first measurements of leaf inclination angle distribution, foliage clumping in a native evergreen Eucalyptus woodland - the EucFACE experiment in Western Sydney, New South Wales, Australia - in ambient CO2 and exposed to +150 ppm elevated CO2 (eCO2). We found that a spherical leaf angle distribution, a common assumption in ecosystem modeling, was not an appropriate supposition for present species (Eucalyptus tereticornis Sm.; Eucalyptus amplifolia Naudin) at this site. Our measurements of leaf inclination angles from imagery indicated an erectophile, highly skewed unimodal leaf inclination angle distribution function. We conclude that despite the measured steeper angles under eCO2 concentrations, the leaf angle change is not significant and falls within the expected natural variability and uncertainties connected with the measurement method. The lack of a clear response of leaf orientation and foliage clumping to eCO2 concentration indicates that the previously produced datasets of leaf inclination angles and foliage clumping maps with Earth observation data may be suitable while modelling carbon and water cycles under climate change.

As Earth's climate has varied strongly through geological time, studying the impacts of past climate change on biodiversity helps to understand the risks from future climate change. However, it remains unclear how paleo-climate shapes spatial variation in biodiversity. Here, we assessed the influence of Quaternary climate change on spatial dissimilarity in taxonomic, phylogenetic, and functional composition among neighboring 200-kilometer cells (beta-diversity) for angiosperm trees worldwide. We found that larger glacial-interglacial temperature change was strongly associated with lower spatial turnover (species replacements) and higher nestedness (rich-ness changes) components of beta-diversity across all three biodiversity facets. Moreover, phylogenetic and functional turnover was lower and nestedness higher than random expectations based on taxonomic beta -di-versity in regions that experienced large temperature change, reflecting phylogenetically and functionally se-lective processes in species replacement, extinction, and colonization during glacial-interglacial oscillations. Our results suggest that future human-driven climate change could cause local homogenization and reduction in taxonomic, phylogenetic, and functional diversity of angiosperm trees worldwide. W.-B.X. and A.O. acknowledge support from the Aarhus University Research Foundation (AUFF) Starting Grant (AUFF-F-2018-7-8). J.-C.S. acknowledges support from the Danish Council for Independent Research|Natural Sciences (grant 6108-00078B) to the TREECHANGE Project. J.-C.S. also considers this work a contribution to the Center for Ecological Dynamics in a Novel Biosphere (ECONOVO) funded by Danish National Research Foundation (grant DNRF173) and the VILLUM Investigator project “Biodiversity Dynamics in a Changing World” funded by VILLUM FONDEN (grant 16549). W.L.E. was supported by the VILLUM FONDEN (grant 00025354). M.A. was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grants program. C.B. was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1A2C1003504). A.G.G. was supported by the ANID PIA/BASAL (grant FB210006) and FONDECYT (grant 1200468). R.E.O. was supported by the German Research Foundation to German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG-FZT 118, 202548816). J.Pe. was supported by the Fundación Ramón Areces project (CIVP20A6621) and Spanish Government project (PID2019-110521GB-I00). J.Pi. was supported by the Estonian Research Council Grants (PUT1355, PRG 1405) and Mobilitas Pluss (MOBERC11). V.D.P. was supported by the National Council of Scientific and Technological Development (CNPq) of Brazil (grant 307689/2014-0).

In 2007, three mature hemi-boreal stands were selected from Järvselja forest district, South-East Estonia to establish one-hectare-large test plots for the international inter-comparison experiment of radiation models (RAMI). All trees with a stem diameter at breast height greater than 4 cm were mapped and measured in the field. In summer 2019, the forests were inventoried again. Here we present a summary of changes that occurred in the forest structure – mainly growth and mortality. In the birch stand basal area G has increased from 23.3 m2 ha-1 to 28.2 m2 ha-1 in the upper layer and the number of trees N has decreased from 654 to 565 ha-1. In the upper layer of spruce stand G has increased from 30.9 m2 ha-1 to 35.4 m2 ha-1 and N has decreased from 774 to 724 ha-1 and N substantially decreased in the lower layers from 912 to 577 ha-1. In the pine stand G has increased from 28.3 m2 ha-1 to 29.1 m2 ha-1 and N decreased from 1116 to 971 ha-1. The three test stands can be used now for validating remote sensing data-based estimates of forest inventory variables at single tree level.

Gross Primary Productivity (GPP) is the core component of the terrestrial and global carbon cycle and Earth’s climate research. In this study, GPP estimation was performed with the Boreal Ecosystem Productivity Simulator (BEPS) model to check its performance for hemi-boreal forests on the example of the Soontaga area in Estonia. The model was run by using a combination of remote sensing (leaf area index (LAI), clumping index) and meteorological data inputs (air temperature, global radiation, air humidity, precipitation and wind speed). The results were validated against GPP derived from the available flux tower measurements. The spatial representativeness of the site was evaluated using multiple spatial thresholds (500 m–2 km), as well. We found that the BEPS model can track the GPP changes with the season and inter-annual variation very well in a coniferous hemi-boreal forest, given that good quality input data are provided.

How leaves are presented affects interaction of atmospheric CO2, energy (light), and plant physiology. Plant productivity is primarily determined by the amount of leaf area, leaf orientation and distribution in space. Not much attention has been paid to possible changes in leaf orientation and distribution with elevated CO2, but its effect on plant growth could alter the proportions of sunlit and shaded leaf areas and feedback on carbohydrate available for further growth. We report on first measurements of leaf inclination angle distribution and foliage clumping in a native evergreen Eucalyptus woodland in ambient CO2 and under +150 ppm elevated CO2. We found that a spherical leaf angle distribution was not an appropriate supposition for present species (Eucalyptus tereticornis Sm.) at this site. Our measurements of leaf inclination angles from imagery indicated an erectophile, highly skewed unimodal leaf inclination angle distribution function. We conclude that despite the measured steeper angles under elevated CO2 concentrations, the leaf angle change is not significant and falls within the expected natural variability and uncertainties connected with the measurement method. The lack of a clear response of leaf orientation and foliage clumping to elevated CO2 concentration indicates that the previously produced datasets of leaf inclination angles and foliage clumping maps with Earth observation data may be suitable while modelling carbon and water cycles under climate change.

Safeguarding Earth?s tree diversity is a conservation priority due to the importance of trees for biodiversity and ecosystem functions and services such as carbon sequestration. Here, we improve the foundation for effective conservation of global tree diversity by analyzing a recently developed database of tree species covering 46,752 species. We quantify range protection and anthropogenic pressures for each species and develop conservation priorities across taxonomic, phylogenetic, and functional diversity dimensions. We also assess the effectiveness of several influential proposed conservation prioritization frameworks to protect the top 17% and top 50% of tree priority areas. We find that an average of 50.2% of a tree species? range occurs in 110-km grid cells without any protected areas (PAs), with 6,377 small-range tree species fully unprotected, and that 83% of tree species experience nonnegligible human pressure across their range on average. Protecting highpriority areas for the top 17% and 50% priority thresholds would increase the average protected proportion of each tree species? range to 65.5% and 82.6%, respectively, leaving many fewer species (2,151 and 2,010) completely unprotected. The priority areas identified for trees match well to the Global 200 Ecoregions framework, revealing that priority areas for trees would in large part also optimize protection for terrestrial biodiversity overall. Based on range estimates for >46,000 tree species, our findings show that a large proportion of tree species receive limited protection by current PAs and are under substantial human pressure. Improved protection of biodiversity overall would also strongly benefit global tree diversity. Made available in DSpace on 2022-06-21T15:23:55Z (GMT). No. of bitstreams: 1 Guo-et-al.-2022-PNAS-High-exposure-of-global-tree-diversity-to-human-pressure.pdf: 2523289 bytes, checksum: 245be3df3fb487ed152d25bf50f4e984 (MD5) Previous issue date: 2022 Proceedings of the National Academy of Sciences of the United States of America.

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system. A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters.

© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to- biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g., RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and for modelling energy exchange between atmosphere and biosphere. While LAI of the forest overstory can be accurately assessed over large spatial scales via remote sensing, LAI of the forest understory (LAIu) is still largely ignored in ecological studies and ecosystem modelling due to the fact that it is often too complex to be destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made to retrieve understory LAI via remote sensing, because dense canopies with high LAI are often hindering retrieval algorithms to produce meaningful estimates for understory LAI. Consequently, the forest understory still constitutes a poorly investigated research realm impeding ecological studies to properly account for its contribution to the energy absorption capacity of forest stands. This study aims to compare three conceptually different indirect retrieval methodologies for LAIu over a diverse panel of forest understory types distributed across Europe. For this we carried out near-to-surface measurements of understory reflectance spectra as well as digital surface photography over the extended network of Integrated Carbon Observation System (ICOS) forest ecosystem sites. LAIu was assessed by exploiting the empirical relationship between vegetation cover and light absorption (Beer-Lambert- Bouguer law) as well as by utilizing proposed relationships with two prominent vegetation indices: normalized difference vegetation index (NDVI) and simple ratio (SR). Retrievals from the three methods were significantly correlated with each other (r = 0.63–0.99, RMSE = 0.53–0.72), but exhibited also significant bias depending on the LAI scale. The NDVI based retrieval approach most likely overestimates LAI at productive sites when LAIu > 2, while the simple ratio algorithm overestimates LAIu at sites with sparse understory vegetation and presence of litter or bare soil. The purely empirical method based on the Beer-Lambert law of light absorption seems to offer a good compromise, since it provides reasonable LAIu values at both low and higher LAI ranges. Surprisingly, LAIu variation among sites seems to be largely decoupled from differences in climate and light permeability of the overstory, but significantly increased with vegetation diversity (expressed as species richness) and hence proposes new applications of LAIu in ecological modelling.

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 L eff 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 L eff increased from 3.6 in 2008 to 5.8 in 2017. After removal of understory trees from the stand L eff decreased, and repeated measurements in the summer of 2018 estimated the plant area index range 4.5 < L eff < 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.

The thermal domain (TIR; 2.5–15 μm) delivers unique measurements of plant characteristics that are not possible in other parts of the electromagnetic spectrum. However, these TIR measurements have largely been restricted to laboratory leaf level or coarse spatial resolutions due to the lack of suitable data from airborne and spaceborne instruments. The airborne Hyperspectral Thermal Emission Spectrometer (HyTES) provides an opportunity to retrieve high spectral resolution emissivity and land surface temperature (LST) that can be exploited for canopy level vegetation research. This study is a high spatial resolution analysis of plant species' emissivity and LST using HyTES imagery acquired in the Huntington Botanical Gardens on 2014 July 5 and 2016 Jan 25. Leaf and canopy emissivity variation was identified among 24 plant species and used to determine leaf to canopy scaling capabilities. HyTES LST patterns among species and dates were quantified and correlated to LiDAR derived tree canopy attributes. At the leaf scale, one third of the species showed distinct spectral separation from other species. However, at the canopy scale most species were not spectrally separable. Random forest classification demonstrates the high level of confusion between species with overall accuracies

A calibration target for the support of airborne and satellite measurements was built at the Jarvselja site for ecological and remote sensing studies located in southeastern Estonia. The calibration target is a 10 × 10 m concrete panel which is protected by a removable roof. Optical properties of the panel are carefully studied in order to serve as a reference in spectroscopic remote sensing measurements. In this study we report the spectral distribution of reflectance in the wavelength range of 350–2500 nm, the angular distribution of directional reflectance and the linear polarization of the reflected radiation. The reflectance spectrum of the panel is smooth in the visible and NIR wavelengths but with steep decrease at wavelengths less than 400 nm. Variations of reflectance over the panel surface are less than 1%. The radiation is partly polarized in forward scattering direction. No temporal changes of optical properties have been observed. The calibration target improves the metrological quality of airborne spectral measurements at the Jarvselja test site and neighboring areas. It can also be used for ground truth to aid the validation of adjacency correction algorithms for data from high spatial resolution satellite images, as well as a reference for the registration of airborne and satellite images. It can also serve as an elevation reference for both lidar and radar altimeter measurements.

Vegetation foliage clumping significantly alters the radiation environment and affects vegetation growth as well as water, carbon cycles. The clumping index (CI) is useful in ecological and meteorological models because it provides new structural information in addition to the effective leaf area index. Previously generated CI maps using a diverse set of Earth Observation multi-angle datasets across a wide range of scales have all relied on the single approach of using the normalized difference hotspot and darkspot (NDHD) method. We explore an alternative approach to estimate CI from space using the unique observing configuration of the Deep Space Climate Observatory Earth Polychromatic Imaging Camera (DSCOVR EPIC) and associated products at 10 km resolution. The performance was evaluated with in situ measurements in five sites of the Australian Terrestrial Ecosystem Research Network comprising a diverse range of canopy structure from short and sparse to dense and tall forest. The DSCOVR EPIC data can provide meaningful CI retrievals at the given spatial resolution. Independent but comparable CI retrievals obtained with a completely different sensor and new approach were encouraging for the general validity and compatibility of the foliage clumping information retrievals from space. We also assessed the spatial representativeness of the five TERN sites with respect to a particular point in time (field campaigns) for satellite retrieval validation. Our results improve our understanding of product uncertainty both in terms of the representativeness of the field data collected over the TERN sites and its relationship to Earth Observation data at different spatial resolutions.

The leaf inclination angle distribution is an important parameter in models useful for understanding forest canopy processes of photosynthesis, evapotranspiration, radiation transmission, and spectral reflectance. Yet, despite the strong sensitivity of many of these models to variability in leaf inclination angle distribution, relatively few measurements have been reported for different tree species in literature and databases such as TRY, and various assumptions about leaf inclination angle distribution are often made by modellers. Here we provide a dataset of leaf inclination angles for 71 different Australia-native Eucalyptus species measured in 13 botanical gardens around the world. Leaf inclination angles were measured using a leveled digital camera approach. The leaf angle measurements were used to estimate corresponding Beta distribution parameters and to assign the appropriate classic type of leaf inclination angle distribution. The data can be used to parameterize leaf angle distributions in e.g., physically-based reflectance models, land surface models, and regional carbon cycle models.

Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of normalized difference vegetation index (NDVI) of forest understory with multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 meter daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were hence tested across diverse forest conditions and phenological phases during the growing season, to examine its applicability on ecosystems other than boreal forests. Here we report the method can deliver good retrievals especially over different forest types with open canopies (low foliage cover). The performance of the method was found limited over forests with closed canopies (high foliage cover), where the signal from understory gets much attenuated. The spatial heterogeneity of individual field sites as well as the limitations and documented quality of the MODIS BRDF product are shown to be important for correct assessment and validation of the retrievals obtained with remote sensing.

openaire: EC/H2020/771049/EU//FREEDLES Funding Information: We would like to thank Tomáš Fabiánek for significant contributions in airborne data processing. Thanks also to Lukáš Fajmon, Petri Forsström, Karel Holouš, Mihkel Kaha, Bijay Karki, Lauri Korhonen, Nea Kuusinen, Andres Kuusk, Titta Majasalmi, Matti Mõttus, Ville Ranta, and the staff of Hyytiälä forestry field station for assistance and comments in various stages of field work, data processing, and interpretation. This study has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 771049). The text reflects only the authors' view and the Agency is not responsible for any use that may be made of the information it contains. This study was also funded by the Academy of Finland grants BOREALITY (286390) and DIMEBO (323004). This study was also supported by the Ministry of Education, Youth and Sports of the Czech Republic within the CzeCOS program, grant number LM2018123 . Petr Lukeš acknowledges support by the project LTAUSA18154 awarded by the Ministry of Education, Youth and Sports of the Czech Republic. Jan Pisek acknowledges support by the Eesti Teadusagentuur (grant no. PUT1355). Publisher Copyright: © 2021 The Authors We report a new version and an empirical evaluation of a forest reflectance model based on photon recollision probability (p). For the first time, a p-based approach to modeling forest reflectance was tested in a wide range of differently structured forests from different biomes. To parameterize the model, we measured forest canopy structure and spectral characteristics for 50 forest plots in four study sites spanning from boreal to temperate biomes in Europe (48°–62°N). We compared modeled forest reflectance spectra against airborne hyperspectral data at wavelengths of 450–2200 nm. Large overestimation occurred, especially in the near-infrared region, when the model was parameterized considering only leaves or needles as plant elements and assuming a Lambertian canopy. The model root mean square error (RMSE) was on average 80%, 80%, 54% for coniferous, broadleaved, and mixed forests, respectively. We suggest a new parameterization that takes into account the nadir to hemispherical reflectance ratio of the canopy and contribution of woody elements to the forest reflectance. We evaluated the new parameterization based on inversion of the model, which resulted in average RMSE of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests. The model requires only few structural parameters and the spectra of foliage, woody elements, and forest floor as input. It can be used in interpretation of multi- and hyperspectral remote sensing data, as well as in land surface and climate modeling. In general, our results also indicate that even though the foliage spectra are not dramatically different between coniferous and broadleaved forests, they can still explain a large part of reflectance differences between these forest types in the near-infrared, where sensitivity of the reflectance of dense forests to changes in the scattering properties of the foliage is high.

Trees are of vital importance for ecosystem functioning and services at local to global scales, yet we still lack a detailed overview of the global patterns of tree diversity and the underlying drivers, particularly the imprint of paleoclimate. Here, we present the high-resolution (110 km) worldwide mapping of tree species richness, functional and phylogenetic diversities based on ∼7 million quality-assessed occurrences for 46,752 tree species (80.5% of the estimated total number of tree species), and subsequent assessments of the influence of paleo-climate legacies on these patterns. All three tree diversity dimensions exhibited the expected latitudinal decline. Contemporary climate emerged as the strongest driver of all diversity patterns, with Pleistocene and deeper-time (>107 years) paleoclimate as important co-determinants, and, notably, with past cold and drought stress being linked to reduced current diversity. These findings demonstrate that tree diversity is affected by paleoclimate millions of years back in time and highlight the potential for tree diversity losses from future climate change.

Although trees are key to ecosystem functioning, many forests and tree species across the globe face strong threats. Preserving areas of high biodiversity is a core priority for conservation; however, different dimensions of biodiversity and varied conservation targets make it difficult to respond effectively to this challenge. Here, we (i) identify priority areas for global tree conservation using comprehensive coverage of tree diversity based on taxonomy, phylogeny, and functional traits; and (ii) compare these findings to existing protected areas and global biodiversity conservation frameworks. We find that ca. 51% of the top-priority areas for tree biodiversity are located in current protected areas. The remaining half top-priority areas are subject to moderate to high human pressures, indicating conservation actions are needed to mitigate these human impacts. Our findings emphasize the effectiveness of using tree conservation priority areas for future global conservation planning.

Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions.Liu Y. et al. (2017): Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.

Gap fraction measurements with digital a hemispherical camera were carried out in 2007–2016 in a Scots pine (Pinus sylvestris L.) stand growing on transitional Sphagnum bog in Jarvselja, Estonia. The measurements were done at the time when (1) the pines had maximum foliage, or (2) after autumn needle fall or before bud burst. Data for gap fraction calculation were extracted from camera raw files. For data recording, the sensor output signal maximum was kept according to the image brightness histogram within the interquartile range of the sensor dynamic range. A linear conversion method (LinearRatio) was used for image processing. The relative needle loss in autumn (20–30%) increased gap fraction by 0.02–0.05 at view zenith angles from 10° to 60° and decreased the mean plant area index from 1.86 to 1.68 after autumn litter fall. The measurement results were in good agreement with a theoretical gap fraction model simulation and with gap fraction estimates made from cover photos. The measurement and data processing protocol is appropriate for long-term monitoring in permanent sample plots.

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

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.

Spatial and temporal patterns of forest background (understory) reflectance are crucial for retrieving biophysical parameters of forest canopies (overstory) and subsequently for ecosystem modeling. In this communication, we retrieved seasonal courses of understory normalized difference vegetation index (NDVI) from multiangular Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function (MODIS BRDF)/albedo data. We compared satellite-based seasonal courses of understory NDVI to understory NDVI values measured in different types of forests distributed along a wide latitudinal gradient (65.12°N–31.35°N). Our results indicated that the retrieval method performs well particularly over open forests of different types. We also demonstrated the limitations of the method for closed canopies, where the understory signal retrieval is much attenuated.

Clumping index quantifies the level of foliage aggregation, relative to a random distribution, and is a key structural parameter of plant canopies and is widely used in ecological and meteorological models. In this study, the inter- and intra-annual variations in clumping index values, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product, are investigated at six forest sites, including conifer forests, a mixed deciduous forest and an oak-savanna system. We find that the clumping index displays large seasonal variation, particularly for the deciduous sites, with the magnitude in clumping index values at each site comparable on an intra-annual basis, and the seasonality of clumping index well captured after noise removal. For broadleaved and mixed forest sites, minimum clumping index values are usually found during the season when leaf area index is at its maximum. The magnitude of MODIS clumping index is validated by ground data collected from 17 sites. Validation shows that the MODIS clumping index can explain 75% of variance in measured values (bias = 0.03 and rmse = 0.08), although with a narrower amplitude in variation. This study suggests that the MODIS BRDF product has the potential to produce good seasonal trajectories of clumping index values, but with an improved estimation of background reflectance.

Terrestrial laser scanner (TLS) measurements were carried out in mature birch, pine, and spruce stands in Järvselja, Estonia. The structure of stands has been previously studied. A simple special clustering procedure is developed for automatic detection of stem positions and estimation of tree stem dimensions from the TLS point cloud. Tree stem diameter at breast height (DBH), vertical profile of stem and size distribution of DBH is estimated with high precision in the pine stand where the second growth tree layer is almost missing. The presence of second growth limits estimating stem dimensions accurately in the birch stand, and the procedure fails in the dense spruce stand of rich undergrowth and low dead branches. In such stands the TLS measurements should be done in dense grid with short-range sounding setup only. The developed procedure is undemanding for computers and can be applied on simple PC-s.

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.

Due to the growing demand on more accurate prediction of biophysical properties (e.g., leaf area index) or carbon balance models based on remotely sensed data, the understory effect needs to be separated from the overstory. Reflectance models can provide possibility to model and retrieve understory reflectance over large scales, but ground truth data is needed to validate such models and algorithms. In this study, we documented the seasonal variation (April–September) and spectral changes occurring in understory layers of a typical European hemi-boreal forest. The understory composition was recorded and its spectra measured with an ASD FieldSpec Hand-Held UV/VNIR Spectroradiometer eight times at four site types during the growing period (from May to September) in 2013. The collected dataset presented within this study would be of much use to improve and validate algorithms or models for extracting spectral properties of understory from remote sensing data. It can be also further used as a valuable input in radiative transfer simulations that are used to quantify the roles of forest tree layer and understory components in forming a seasonal reflectance course of a hemi-boreal forest, and the upcoming phases of the RAdiation Model Intercomparison (RAMI) experiment.

Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. In this paper, overstory and understory LAI values were estimated separately for global needleleaf and deciduous broadleaf forests by fusing MISR and MODIS observations. Monthly forest understory LAI was retrieved from the forest understory reflectivity estimated using MISR data. After correcting for the background contribution using monthly mean forest understory reflectivities, the forest overstory LAI was estimated from MODIS observations. The results demonstrate that the largest extent of forest understory vegetation is present in the boreal forest zones at northern latitudes. Significant seasonal variations occur for understory vegetation in these zones with LAI values up to 2–3 from June to August. The mean proportion of understory LAI to total LAI is greater than 30 %. Higher understory LAI values are found in needleleaf forests (with a mean value of 1.06 for evergreen needleleaf forests and 1.04 for deciduous needleleaf forests) than in deciduous broadleaf forests (0.96) due to the more clumped foliage and easier penetration of light to the forest floor in needleleaf forests. Spatially and seasonally variable forest understory reflectivity helps to account for the effects of the forest background on LAI retrieval while compared with constant forest background. The retrieved forest overstory and understory LAI values were compared with an existing dataset for larch forests in eastern Siberia (40–75° N, 45–180° E). The retrieved overstory and understory LAI is close to that of the existing dataset, with an absolute error of 0.02 (0.06), relative error of 1.3 % (14.3 %) and RMSE of 0.93 (0.29) for overstory (understory). The comparisons between our results and field measurements in eight forest sites show that the R2 values are 0.52 and 0.62, and the RMSEs are 1.36 and 0.62 for overstory and understory LAI, respectively.