A widely used algorithm, Sen2Cor, for the atmospheric correction of Sentinel-2 MSI images assumes a constant ratio of red (0.6 μm) and near-infrared (2.2 μm) spectral reflectance, taking ρ2.2/ρ0.6 = 2. In fact, however, this ratio exhibits substantial dependence on vegetation phenology. The discrepancy produces errors in predicted surface reflectance. We analyse the reflectance of 92,230 forest stands, using full time series of Landsat-8/9 OLI and Sentinel-2 MSI images taken over Estonia. We find the ρ2.2/ρ0.6 value for evergreen needleleaf forests to be rather stable over time, with a mean value around 2.25. In broadleaf deciduous forests, on the other hand, we find ρ2.2/ρ0.6 to increase rapidly in the spring, to remain on average close to 3 or greater, and then to decrease in the autumn. We offer parametric and tabulated models of ρ2.2/ρ0.6 for eventual integration with Sen2Cor.

The amount of gaps in forest canopy is related to the radiation interception for photosynthesis and visibility through the canopy. The dependence of forest canopy gap fraction determination on view zenith angle was calculated from polar-transformed sparse ($ \approx {\rm{4}}{{\rm{m}}<^>{ - 2}}$approximate to 4m-2) airborne laser scanning (ALS) point clouds for a Scots pine (Pinus sylvestris L.) stand growing on Kiriku Bog, Estonia. Visibility of ground targets was estimated from video image frames taken during drone (UAV) overpass at low altitude (40 m). Below-canopy digital hemispherical images (DHP) were taken in zenith direction as reference measurements. Angular grids of 3$<^> \circ $ degrees and 5$<^> \circ $ degrees were used to match the three data sources so as to decrease uncertainties in measurement geometries. The linear relationship between DHP and UAV data had ${R<^>2}$R2 = 0.67, with most of the deviations occurring at gap boundaries. Relationships over individual targets between DHP and polar-transformed ALS data had $0.3 \lt {R<^>2} \le 0.8$0.3

Species' traits and environmental conditions determine the abundance of tree species across the globe. The extent to which traits of dominant and rare tree species differ remains untested across a broad environmental range, limiting our understanding of how species traits and the environment shape forest functional composition. We use a global dataset of tree composition of >22,000 forest plots and 11 traits of 1663 tree species to ask how locally dominant and rare species differ in their trait values, and how these differences are driven by climatic gradients in temperature and water availability in forest biomes across the globe. We find three consistent trait differences between locally dominant and rare species across all biomes; dominant species are taller, have softer wood and higher loading on the multivariate stem strategy axis (related to narrow tracheids and thick bark). The difference between traits of dominant and rare species is more strongly driven by temperature compared to water availability, as temperature might affect a larger number of traits. Therefore, climate change driven global temperature rise may have a strong effect on trait differences between dominant and rare tree species and may lead to changes in species abundances and therefore strong community reassembly.

AimEcological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species?LocationGlobal.Time period1990-2017.Major taxa studiedTrees.MethodsWe used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated.ResultsCommunity dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large-scale land degradation.Main conclusionsBy linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large-scale management and conservation practices.

Beavers are known as ecosystem engineers and the habitats that they create can change the landscape greatly. When a new beaver colony establishes itself by building dams, digging canals and burrows and felling trees, the resulting changes are often large enough to be studied with medium resolution earth observation sensors like Sentinel-2 MSI. In this study we compare the spectral signatures of 405 sample areas disturbed by beaver-induced flooding with 261 healthy forest sample areas surrounding the area. We used 28 Sentinel-2 MSI multispectral images from years 2015 to 2018 over the southern part of Estonia in Military Grid Reference System map tile T35VME. In summer the average pixel values for areas that are affected by beavers show the largest differences in reflectance values for shortwave infrared (SWIR) and the visible parts of the spectrum compared to areas that are not affected by beavers. Some smaller differences are present also in the near-infrared (NIR) parts of the spectrum, but sometimes they are not significant according to p-values, being over the 0.05 threshold, of the t-test. In autumn and spring, when deciduous trees do not have their leaves and ground vegetation is not thriving, the differences in visible parts of the spectrum disappear. Multispectral vegetation indices, like the Normalized Difference Vegetation Index (NDVI) and others, also show clear differences in the beaver-disturbed forests and surrounding healthy forests throughout all vegetation phenology stages.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Canopy hemispherical photography (HP) is a technology for vegetation surveying that usually employs consumer-oriented devices. Variations on those devices are caused by technological advances and design. These variations, despite providing the potential for more reliable surveying, make the measurements challenging. The knowledge about how to standardize devices output is available, what is missing is a simple and inexpensive methodology to accomplish it. This is needed since the use of consumer-oriented devices creates an expectation of simplicity and affordability. To fill this methodology gap, we developed an easy-to-assemble calibration board of near-zero material cost (simple board) and outlined how to use it to develop device-specific functions: the projection function (fp) and the vignetting function (fv). The former relates view zenith angle (θ) with distance from image center, while fv models the radial decreasing of brightness known as vignetting effect. Explanations are accompanied by tailored R code. Altogether, we call it “the simple method”. Three devices were used to test it: a Nikon Coolpix 5700 with a FC-E9 converter (D1), a Nikon D610 with a Nikkor 8 mm fisheye lens (D2), and an iPhone 6 Plus with an Olloclip fisheye converter (D3). The reference data was taken with D1, corrected for vignetting effect with a photometric sphere and reprojected to the standard equidistant projection with a fv developed by Delta-T Devices (www.delta-t.co.uk). The root mean square error (RMSE) of the simple method were 0.55° θ and 0.021 relative radiance (RR) for D1; 1.8° θ and 0.1 RR for D2; and 2.1° θ and 0.066 RR for D3. This level of standardization will help to simplify the classification of canopy gaps, a key processing step in HP. And, if it is broadly adopted, will facilitate comparisons between independent studies and large time series.

The density of wood is a key indicator of trees’ carbon investment strategies, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here, we analyze information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially-explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical dry forests being up to twice as dense as that in boreal forests. In both angiosperms and gymnosperms, temperature and water availability emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

The study analysed 2019–2022 summertime canopy height predictions (HICESat) given in ICESat-2 ATLAS dataset ATL08 for hemiboreal forests growing on an area of 40,000 km2 in Estonia around 25.6° E, 58.8° N. In total 12,711 ATL08 20×20 m pixel observations were used from 3,065 forest stands with homogenous canopy structure. Regression modelling was used to explain variability in ground surface elevation estimates, and relationships of HICESat to basal area weighted mean tree height given in the forest inventory database (HFI) and to the 95th percentile of the vertical distribution of airborne laser scanning pulse return (HALS). The other explanatory variables were the ICESat-2 ATLAS observation geographic location, ICESat-2 ATLAS track and beam energy indicators, forest canopy cover, evergreen coniferous tree dominance indicator, and deep peat soil indicator. The linear model between the Estonian digital terrain model elevation and ATL08 ground elevation had a determination coefficient of R2=99.97% and residual standard error of δ=0.51 m when a geographic location was included. The HFI can be predicted from HICESat with R2=85% and δ=2.7 m. A comparison of means indicated that, on average, HICESat was about 0.3 m greater than HFI. All the predictive variables (except the geographic location) were significant in canopy height models, and the best models fitted HICESat with R2=95% and δ=1.6 m, however, there was no notable increase in R2 if more predictors than HALS were added in the models. In practical applications using ATL08 data for forest inventories, the inclusion of weak energy beam observations increases the number of observations, but the beam energy indicator has to be included in the models.

Leaf area index (LAI) is an important forest canopy variable that is related to various biophysical processes of forest ecosystems. Airborne laser scanning (ALS) has shown promise in modelling and mapping LAI using different types of ALS metrics. The most common ways of modelling LAI with ALS data are multivariate empirical models and the semi-physical model shape derived from the Beer-Lambert law of radiation attenuation. We tested the utility of ALS-based empirical and semi-physical models in the estimations of effective LAI (LAIe), canopy clumping index (ΩE) and clumping-corrected LAI at three boreal forest sites in Finland. In semi-physical models, the All echo Penetration Index (API) showed consistently the best performance in predicting LAIe. It is therefore a robust and potentially the most transferable predictor using this model shape. Empirical models overall yielded slightly better model fits compared to the semi-physical models, yet they are also more prone to overfitting. In addition, empirical models had constantly lower accuracies when predicting LAI than LAIe. We also tested the utility of ALS-based multi-angular canopy gap fraction metrics that were derived from polar transformed ALS point clouds. Images derived from polar transformed point clouds can be analysed similar to digital hemispherical photographs to obtain canopy gap fractions. The results showed that polar metrics derived from polar transformed ALS data can provide supporting information to empirical models in the estimation of LAIe, LAI and especially ΩE. In particular, a combination of ALS penetration indices and polar metrics yielded positive results in ΩE estimation.

Airborne laser scanning (ALS) is used to predict different forest inventory parameters; however, the ALS point cloud properties depend on various parameters such as the type of ALS scanner employed, flight altitude and scanning angle, forest stand structure, forest tree species composition, vegetation season, etc. This study used national coverage high-resolution ALS data with minimum point density of 4 points per square meter in combination with field data from the National Forest Inventory (NFI) to build forest stand height models for forest stands dominated by 6 most common tree species in Latvian mixed forest stands, viz. Pinus sylvestris L., Betula pendula Roth, Picea abies (L.) Karst., Populus tremula L., Alnus incana (L.) Moench and Alnus glutinosa (L.) Gaertn. for the various ALS scanners employed and at different growing seasons. The selected NFI plots are divided into modelling and validation datasets in a ratio of 3 : 1. The results show that for a universal forest stand height model, the RMSE value is 1.91 m and the MAE is 1.41 m. For the forest stand height models, which are stratified by scanner, individual tree species and seasons, the RMSE value is within the limits of 1.4 m for forest stands dominated by Scots pine in leaf-on canopy condition to 3.8 m for birch in leaf-off canopy condition. Key words: forest inventory, airborne laser scanning, phenology, large scale forest mapping

1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale.2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness-productivity relationship.3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive.4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity-ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions.

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1 . Remote-sensing estimates to quantify carbon losses from global forests 2 – 5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2 , 7 , 8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2 , 3 , 9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

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

Abstract The view angle range of Sentinel-1 SAR in the Interferometric Wide swath (IW) measurement mode is 29.1° – 46.0°. The dependence of backscatter on the arbitrary local incidence angle is usually corrected using a linear regression model where the incidence angle is a predictor variable. We analysed the whole time series of Sentinel-1 SAR VV-polarised backscatter over the flat 15×15 km test site in Laeva, Estonia (26° 26′ 43″ E; 58° 31′ 56″ N). Time series containing measurements from three different orbits were constructed for 3,159 stands from nighttime data and for 1,105 stands from daytime data. We can confirm that daytime backscatter is systematically greater than nighttime backscatter. We found a significant deviation from linearity in the backscatter dependence on local incidence angle. The empirical finding may be caused by the microwave scattering dependence on local incidence angle or by the influence of Sentinel-1 SAR sub-swath configuration in the Terrain Observation with Progressive Scans SAR (TOPSAR) method that is used for the measurements.

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 degrees -62 degrees 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.

Significance Tree diversity is fundamental for forest ecosystem stability and services. However, because of limited available data, estimates of tree diversity at large geographic domains still rely heavily on published lists of species descriptions that are geographically uneven in coverage. These limitations have precluded efforts to generate a global perspective. Here, based on a ground-sourced global database, we estimate the number of tree species at biome, continental, and global scales. We estimated a global tree richness (≈73,300) that is ≈14% higher than numbers known today, with most undiscovered species being rare, continentally endemic, and tropical or subtropical. These results highlight the vulnerability of global tree species diversity to anthropogenic changes.