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Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests' behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region. [ABSTRACT FROM AUTHOR]

China is reported as the leading country in the Earth's greening. However, it is a challenge to capture the gradual recovery in forest cover and distinguish the contribution of trees from herbaceous vegetation using remote sensing data. We developed a new fractional tree cover product (GLOBMAP FTC China) from MODIS time series data to investigate change patterns of China's forests during 2000–2022. This annual product showed high consistency with China's National Forest Inventory. We found a significant increase (∼4 Mha/year) in the annual forest area in China from ∼154.47 Mha in 2000 to ∼236.01 Mha in 2015. This rate then slowed by 50% in 2015–2022 (∼2 Mha/year). The forest recovery primarily started in 2000–2004, and reached saturation in 2015. It was primarily contributed by the tree cover gain (92%) from forest conservation and restoration programs. Our findings can support forest management and carbon neutrality achievement for the country. Plain Language Summary: As the largest carbon reservoir in terrestrial ecosystems, forests are an indispensable part of China's carbon sink. Explicitly monitoring when, where, and how the forest recovery happening in China is crucial. In this work, a fractional tree cover product (named GLOBMAP FTC China) is generated, providing the coverage of trees within pixels. Compared to other remote sensing products, this product is proved to have the best consistency with China's National Forest Inventory (NFI). Applying the product for analysis, we found that China's forests have been recovering since 2000, and the increasing rate then slowed by 50% after 2015. Forest area in southwestern China shows the fastest increasing rate during 2000–2022, which is more than 0.2 Mha per year. The changes primarily show a stepwise transition from forests with a low fractional tree cover to forests with a higher fractional tree cover, which mainly thanks to the forest conservation and restoration programs. This study underscores that trees' growth dominates the greening in China's forests, highlighting the importance of the new fractional tree cover product for future accurate forest change studies and implications for forest management. Our findings are crucial for climate change mitigation as proposed by the Paris Agreement. Key Points: This work generates a fractional tree cover product GLOBMAP to separate the mixed effect of herbaceous vegetation from woody coverUsing GLOBMAP, we can obtain detailed and nuanced forest recovery in China, which is consistent with China's National Forest InventoryDuring 2000–2022, China's forests primarily presented as a stepwise transition from low tree cover forests to higher ones [ABSTRACT FROM AUTHOR]

The sources of uncertainty in wall-to-wall AGB maps propagate from the tree to pixel, but uncertainty due to forest cover mapping is rarely incorporated into the error propagation process. This study aimed to (1) elaborate an analytical procedure to incorporate forest-mapping-related uncertainty into the error propagation from plot and pixel predictions; (2) develop a stratified estimator with a model-assisted estimator for small and large areas; and (3) estimate the effect of ignoring the mapping uncertainty on the confidence intervals (CIs) for totals. Data consist of a subset of the Brazilian national forest inventory (NFI) database, comprising 75 counties that, once aggregated, served as strata for the stratified estimator. On-ground data were gathered from 152 clusters (plots) and remotely sensed data from Landsat-8 scenes. Four major contributions are highlighted. First, we describe how to incorporate forest-mapping-related uncertainty into the CIs of any forest attribute and spatial resolution. Second, stratified estimators perform better than non-stratified estimators for forest area estimation when the response variable is forest/non-forest. Comparing our stratified estimators, this study indicated greater precision for the stratified estimator than for the regression estimator. Third, using the ratio estimator, we found evidence that the simple field plot information provided by the NFI clusters is sufficient to estimate the proportion forest for large regions as accurately as remote-sensing-based methods, albeit with less precision. Fourth, ignoring forest-mapping-related uncertainty erroneously narrows the CI width as the estimate of proportion forest area decreases. At the small-area level, forest-mapping-related uncertainty led to CIs for total AGB as much as 63% wider in extreme cases. At the large-area level, the CI was 5–7% wider. [ABSTRACT FROM AUTHOR]

Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R2 of 0.63 and an RMSE of 125.39 m3/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R2 of 0.73 and an RMSE of 128.86 m3/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R2 of 0.64 and an RMSE of 133.77 m3/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R2 of 0.70 and an RMSE of 21,120.72 m3/plot. These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy. [ABSTRACT FROM AUTHOR]

Tropical forests play an important role in mitigating global climate change, emphasizing the need for reliable estimates of forest carbon stocks at regional and global scales. This is essential for effective carbon management, which involves strategies like emission reduction and enhanced carbon sequestration through forest restoration and conservation. However, reliable sample-based estimations of forest carbon stocks require accurate allometric equations, which are lacking for the rainforests of the Atlantic Forest Domain (AFD). In this study, we fitted biomass equations for the three main AFD forest types and accurately estimated the amount of carbon stored in their above-ground biomass (AGB) in Rio de Janeiro state, Brazil. Using non-destructive methods, we measured the total wood volume and wood density of 172 trees from the most abundant species in the main remnants of rainforest, semideciduous forest, and restinga forest in the state. The biomass and carbon stocks were estimated with tree-level data from 185 plots obtained in the National Forest Inventory conducted in Rio de Janeiro. Our locally developed allometric equations estimated the state's biomass stocks at 70.8 ± 5.4 Mg ha−1 and carbon stocks at 35.4 ± 2.7 Mg ha−1. Notably, our estimates were more accurate than those obtained using a widely applied pantropical allometric equation from the literature, which tended to overestimate biomass and carbon stocks. These findings can be used for establishing a baseline for monitoring carbon stocks in the Atlantic Forest, especially in the context of the growing voluntary carbon market, which demands more consistent and accurate carbon stock estimations. [ABSTRACT FROM AUTHOR]

Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests’ behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.

Forests play a crucial role in storing much of the world's carbon (C). Accurately estimating C sequestration is essential for addressing and mitigating the impacts of global warming. While many studies have used machine learning models to estimate carbon storage (CS) in forests based on remote sensing data, this research further examines C sequestration (i.e., the annual carbon uptake by trees; CSE). The objectives of this study are two-fold: firstly, to identify the best models for estimating CSE and CS by testing various methods, and secondly, to examine the effect of climatic data and the canopy height model (CHM) on the estimation of CSE. To achieve the first objective, we will compare the performance of fourteen models, including twelve machine learning models, one deep learning model, and an ensemble model that combines the top four independent models. For the second objective, we study the effect of four input configurations: the first is a baseline configuration based solely on attributes extracted from satellite images (Sentinel-2) and geomorphology; the second combines satellite features with climatic data; the third uses a CHM derived from LiDAR instead of climatic data; and the fourth combines all available features: satellite images, climatic data, and CHM. The results show that adding climatic data does not improve the estimation of CSE and CS. However, adding CHM features significantly improves the models' performance for both targets. The implemented ensemble model demonstrated the best performance across all configurations.

In 2019–2023 the 13th Finnish National Forest Inventory (NFI) was implemented by measuring a total of 62 266 sample plots across the country. The methodology of the sampling and measurements was similar as in the previous inventory, but the proportion and number of remeasured permanent plots was increased to improve the monitoring of annual increment and other changes in the forests. Only 6.2 M ha (14%) of Finland’s total land area (30.4 M ha) is other land than forestry land. Productive and poorly productive forests cover 22.9 M ha (75%) of the total land area. The forest area has remained stable in recent decades but the forest area available for wood supply (FAWS) has decreased due to increased forest protection – 23% of the forestry land and 10% of the productive forest are not available for wood supply. Compared to the previous inventory, forest resources have continued to increase but the average annual increment has declined from 107.8 M m3 to 103.0 M m3. The quality of forests from the timber production point of view has remained relatively good or improved slightly. The area of observed forest damage on FAWS is 8.4 M ha (46% of FAWS area), half of these minor damages with no impact on stand quality. Although the area of forest damage has not increased, the amount of mortality has continued to increase, and is now 8.8 M m3 year–1. The amount of dead wood has continued to increase in South Finland, while in North Finland the declining trend has turned into a slight increase. Since the 1920s, the area of forestry land has remained stable, but the area of productive forest has increased due to the drainage of poorly productive or treeless peatlands. The total volume of growing stock has increased by 84% and annual increment has more than doubled.

Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity. [ABSTRACT FROM AUTHOR]

Forests provide essential ecosystem services that range from the production of timber to the mitigation of natural hazards. Rapid environmental changes, such as climate warming or the intensification of disturbance regimes, threaten forests and endanger forest ecosystem services. In light of these challenges, it is essential to understand forests' demographic processes of regeneration, growth, and mortality and their relationship with environmental conditions. Specifically, understanding the regeneration process in present‐day forests is crucial since it lays the foundation for the structure of future forests and their tree species composition. We used Swiss National Forest Inventory (NFI) data covering vast bio‐geographic gradients over four decades to achieve this understanding. Trees that reached a diameter at breast height of 12 cm between two consecutive NFI campaigns were used to determine regeneration and were referred to as ingrowth. Employing three independent statistical models, we investigated the number, species, and diameter of these ingrowth trees. The models were subsequently implemented into a forest simulator to project the development of Swiss forests until the mid‐21st century. The simulation results showed an ingrowth decrease and a shift in its species composition, marked by a significant reduction in Norway spruce Picea abies and concurrent increases in broadleaves. Nevertheless, the pace of this change towards climatically better adapted species composition is relatively slow and is likely to slow down even further as ingrowth declines in the future, in contrast to the fast‐changing climatic conditions. Hence, support through adaptive planting strategies should be tested in case ingrowth does not ensure the resilience of forests in the future. We conclude that since the regeneration of forests is becoming increasingly challenging, the current level at which ecosystem services are provided might not be ensured in the coming decades. [ABSTRACT FROM AUTHOR]

Longleaf pine (Pinus palustris Mill.) is a conifer historically associated with an open forest ecosystem that extended across much of the coastal plain of the Southeastern United States. It now exists mainly in isolated fragments following the conversion of forests and the long-term disruption of the low-intensity fire regime upon which the species depends. Recent decades have seen efforts to restore longleaf pine forests by government and private landowners. This was reflected in analyses of national forest inventory data during two time periods, ca. 2009–2015 and 2016–2021, that showed increases in the estimated number of longleaf pine trees, the area of the longleaf pine forest type, and the number and area of planted longleaf pine, along with growth in mean plot-level longleaf pine carbon and importance value. At the same time, we found a decrease in the overall forest area containing longleaf pine, manifested across a variety of other forest types. These results point to a dynamic through which forests dominated by longleaf pine are becoming more widespread via restoration, while forests in which the species is a less important component are transitioning to other forest types or land uses. We also detected a decrease over time in the estimated number of longleaf seedlings across most states and forest types and a decline in naturally regenerated longleaf pine. To further assess regeneration trends in longleaf pine, we calculated the estimated proportion of small trees (seedlings and saplings) for the entire species and for seed zone sub-populations. We found a species-wide decrease in the proportion of small trees, from 82.1 percent to 75.1 percent. This reduction was most pronounced along the edges of the species distribution and could indicate less sustainable levels of regeneration in some areas. These results underscore the challenges of facilitating natural regeneration in this important species. [ABSTRACT FROM AUTHOR]

In forests, the amount and diversity of structural features with high value for biodiversity, such as large trees and dead wood, are affected by productivity, stand age, and forest management. For efficient conservation of forest biodiversity, it is essential to understand the combined effects of these drivers. We used data from the Swedish National Forest Inventory to study the combined effects of productivity, stand age, and management for wood production on structures with high value for biodiversity: tree species richness, large living trees, dead wood volume, and specific dead wood types. Forest management changed the relationship between productivity and amount or diversity of some of the structures. Most structures increased with productivity and stand age, but decreased due to management. The negative effect of management was greatest for structures occurring mainly in high-productivity forests, such as deciduous dead wood. Thus, biodiversity conservation should target high-productivity forests to preserve these structures. [ABSTRACT FROM AUTHOR]

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Forest age structure is one of the most important ecological indicators of forest sustainability in terms of biodiversity, forest history, harvesting potentials, carbon storage, and recreational values. The available information on the forest age is most often stand age from forest management plans or national forest inventories. Depending on the definition, stand age is often not a good indicator for the biological age of the dominant trees in a stand. Here, we used 6,998 increment cores from dominant Norway spruce (Picea abies L.) and Scots pine (Pinus sylvestris L.) sampled on National Forest Inventory (NFI) plots throughout Norway to gain a better understanding of the age structure of Norway spruce and Scots pine stands in Norway, and on the relationship between the recorded stand age and the biological age of dominant trees on the NFI plots. In forest with stand ages indicating that the stand was established after the abandonment of selective harvesting in favor of even-aged management dominated by clear-cutting methods (ca.1940 C.E.), we found no systematic difference between the biological age of the sampled trees and the stand age assessed by the NFI. In older stands, there was a large difference between the stand age and the age of the overstory trees with the sampled age trees occasionally being hundreds of years older than the stand age. Our study also reveals that the area of forest with old Norway spruce and Scots pine trees ≥ 160 years old is considerably higher than the corresponding area estimate based on information derived from the stand age only. These results are important as the stand age is often used to characterize status with respect to forest naturalness, biodiversity, guide protection efforts, and describe the appropriate and allowed management activities.

The sources of uncertainty in wall-to-wall AGB maps propagate from the tree to pixel, but uncertainty due to forest cover mapping is rarely incorporated into the error propagation process. This study aimed to (1) elaborate an analytical procedure to incorporate forest-mapping-related uncertainty into the error propagation from plot and pixel predictions; (2) develop a stratified estimator with a model-assisted estimator for small and large areas; and (3) estimate the effect of ignoring the mapping uncertainty on the confidence intervals (CIs) for totals. Data consist of a subset of the Brazilian national forest inventory (NFI) database, comprising 75 counties that, once aggregated, served as strata for the stratified estimator. On-ground data were gathered from 152 clusters (plots) and remotely sensed data from Landsat-8 scenes. Four major contributions are highlighted. First, we describe how to incorporate forest-mapping-related uncertainty into the CIs of any forest attribute and spatial resolution. Second, stratified estimators perform better than non-stratified estimators for forest area estimation when the response variable is forest/non-forest. Comparing our stratified estimators, this study indicated greater precision for the stratified estimator than for the regression estimator. Third, using the ratio estimator, we found evidence that the simple field plot information provided by the NFI clusters is sufficient to estimate the proportion forest for large regions as accurately as remote-sensing-based methods, albeit with less precision. Fourth, ignoring forest-mapping-related uncertainty erroneously narrows the CI width as the estimate of proportion forest area decreases. At the small-area level, forest-mapping-related uncertainty led to CIs for total AGB as much as 63% wider in extreme cases. At the large-area level, the CI was 5–7% wider.

Abstract Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity.

Every year New Zealand measures 1/5th of the permanent sample plots of the National Planted Forest Inventory administered by the Ministry for the Environment (MFE). This means that all plots are measured once during a five-year cycle to provide the full inventory, while each measurement year in its own right provides a random nationally-representative sample of New Zealand's plantation forests.Land-use change and planted forest area are highly dynamic in New Zealand compared to forests in many northern hemisphere countries and every year new plots are added to the inventory due to new afforestation areas. Over time, old plot data representing stands that have been recently harvested will be replaced with measurements of the new forests growing on these sites or will drop out altogether due to improvements in mapping or deforestation. This means that estimates from a single year's plot measurements can give different results from the previous year due to the changes that the plantation forest estate experiences from year to year.In this paper we analyse results for the latest measurement cycle (Panels 2016 - 2020) and identify any issues or trends in carbon stocks. We use all panel data plus data from previous periodic forest inventories to generate a fully representative yield table for all plantation forests in the sub-categories of Kyoto Protocol-compliant afforestation and other planted forests (“non-Kyoto”). Furthermore, we use an imputation approach to provide a statistically accurate representation of stock changes over time. The yield tables generated are used in MFE's Land Use Carbon Analysis System (LUCAS) to generate estimates of carbon stocks and stock changes for international reporting.The estimated mean carbon sequestration rate over the typical rotation for New Zealand's dominant plantation species is higher than the default range provided by the IPCC (Intergovernmental Panel on Climate Change). Predicted carbon sequestration in non-Kyoto planted forests (10.5 tC ha−1 yr−1) exceeds the estimated carbon sequestration in Kyoto-compliant planted forests (9.8 tC ha−1 yr−1) with both at the upper range of estimates for temperate plantation forests (IPCC). Current mean carbon stocks are significantly larger in Kyoto-compliant plantation forests (169 tC ha−1) than in non-Kyoto planted forests (108 tC ha−1) across New Zealand, largely because they are on average five years older. Estimated Mean annual increment, m3 ha−1 yr−1 expressed as a comparable index (“300Index”) indicates that Kyoto-compliant forest land has slightly higher productivity than non-Kyoto planted forests, but because the latter are composed of younger stands, greater carbon sequestration rates but lower current stocks are estimated. Plots with slower growing tree species such as Douglas-fir, cypresses and coastal redwood are now an increasing part of the Kyoto-compliant estate, reducing the overall sequestration rates for this forest type.

Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018.

Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia’s geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R2 of 0.63 and an RMSE of 125.39 m3/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R2 of 0.73 and an RMSE of 128.86 m3/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R2 of 0.64 and an RMSE of 133.77 m3/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R2 of 0.70 and an RMSE of 21,120.72 m3/plot. These results underscore RF’s precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.

Understanding species’ elevational distributions in mountain ecosystems is needed under climate change, but remote biodiverse mountain areas may be poorly documented. National Forest Inventories (NFIs) offer a potential source of data. We used NFI records from Bhutan to ask three questions about elevational richness patterns of Himalayan woody plant species. First, does the mean elevation for all species differ from those species whose entire elevational distribution is recorded in the survey? Second, how does the elevation of maximum richness differ when combining species originating from temperate and tropical regions vs. analyzing them separately? And third, do the highest species turnover rates adjoin elevation zones of maximum species richness? We used 32,198 species records from 1685 forest plots along a 7570 m gradient to map species elevation ranges. Species whose entire range was documented were those whose lowest records are located above 400 m, while bare rock defined all species’ upper limits. We calculated species richness and turnover using 400 m elevation bands. Of 569 species, 79% of temperate and 61% of tropical species’ elevation ranges were fully sampled within the NFI data. Mean elevation of tree and shrub species differed significantly for temperate and tropical species. Maximum combined species richness is from 1300 to 1700 m (277 species), differing significantly from maximum tropical (900–1300 m, 169) and temperate species richness (2500–2900 m, 92). Temperate tree turnover rate was highest in the bands adjoining its maximum species richness (2500–2900 m). But turnover for tropical trees was highest several bands above their maximum species richness, where turnover and decrease in richness interact. Shrub species turnover patterns are similar, but rates were generally higher than for trees. Bhutan’s NFI records show that woody plant species are arrayed on the Himalaya in part according to floristic origins, and that combining temperate- and tropical-originating floras for gradient-based studies such as species richness and turnover obscures actual elevational patterns. In addition, species whose ranges extend below the Himalayan elevation gradient should be accounted for in future studies that correlate climate and environment factors with elevational species richness patterns.

Forest edges represent the transition zone (ecotone) between the forest interior and the surrounding open land. Due to their great ecological importance, the value of assessing the structure of forest edges has been recognized. In Switzerland, for example, forest edge structure is assessed during field surveys of the Swiss National Forest Inventory (NFI). However, these assessments are time consuming and limited to sample plots. Publicly available countrywide airborne laser scanning (ALS) data, in contrast, offers possibilities to retrieve forest edge structure information over large spatial extents. In this study, we derived five metrics from ALS point clouds, namely the canopy height variability; ratios of the areas of the three edge components, i.e. shrub belt, shelterbelt and forest layer; the sky-view fraction; the shelterbelt slope; and the front density of the forest edge. These metrics describe the three-dimensional edge structure and therefore could enhance existing NFI edge metrics, which focus on two-dimensional structure characteristics. An expert assessment of the ALS edge metrics demonstrated the ability of the defined set of edge metrics to capture ecologically relevant and indicative characteristics of forest edges. Understanding this relationship between edge metrics and ecological functions is a prerequisite if ALS metrics are to be integrated into NFIs. We subsequently used the ALS edge metrics to group 284 forest edge into three classes with respect to their structural complexity using k-means clustering. The results indicated that the structual complexity was low for 173, medium for 46 and high for 65 forest edges, respectively. Applied to countrywide ALS data sets, our approach allows to retrieve area-wide, spatially continuous information on the forest edge conditions, and, if multitemporal ALS data is available, to monitor the development of the forest edges.

In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained. [ABSTRACT FROM AUTHOR]

Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018. The study compares stratified field-based data from the National Forest Inventory (NFI) with remote sensing data on forest variables, at the pixel level, in order to identify suitable methods for accuracy and area uncertainty estimation. Additionally, the paper introduces and presents a variety of accuracy metrics applicable to assess overall uncertainties in GHG inventories. The results indicate that the High-Resolution Layer Forest Type (HRL FTY) product (part of the broader Copernicus Land Monitoring Service [CLMS] portfolio), assessed using NFI field-based information, achieved an overall accuracy (OA) of 69.2%. This metric varies among particular nature protection forms, with the highest observed ones in Natura 2000 sites of 70.45%. The primary source of map errors was associated with distinguishing between broad-leaved and coniferous forest areas. Improving future maps necessitates more precise differentiation between species to better support national forest monitoring systems for the purpose of greenhouse gas (GHG) inventories where information on the spatial distribution and variability of forests sources, biodiversity assessment, threat prevention, estimation of carbon content is becoming an important part of the associated reporting system. [ABSTRACT FROM AUTHOR]

The Ellenberg quotient (EQ) is a climate index defined as a ratio of the hottest month's temperature and the average annual precipitation sum. The quotient indirectly expresses the relationship between climate and vegetation, and its application is related to the ecological niche of Fagus sp. Although the quotient was curated on the grounds of field research primarily on German vegetation, the possibilities of its utilisation are not limited to the Central European region. The objective of this study is (i) to compare the EQ values calculated for the forest vegetation zones in the Czech Republic with the published data using the ecological niche of Fagus sylvatica; and (ii) to compare the new EQ-based vertical model with field empirical mapping. The study area is the Czech Republic, Central Europe. We used climate data from 1970-2000 and the data of the National Forest Inventory, 2nd cycle (2011-2015), representing an objective data design. Geospatial analytic methods, machine learning (boosting), and verification through statistical testing were performed. The results indicate higher EQ values between the two most substantial spatial frames - the Hercynicum and Carpaticum regions. By comparing empirical mapped units to their climatic potential (in the EQ), a match was found only within the Carpaticum region. The study presents a concretisation of the general climate index for a specific region, adds to the knowledge about the Fagus ecological niche in context with the Central European vegetation, and also points to the EQ's potential for evaluating the concept of vertical differentiation of forest communities, as well as a possible prediction tool for the vegetation migration in context with climate change.

Abstract Key message This paper proposes a methodology that could be considered as a base for a harmonized protocol for stem-quality reporting in Europe while conducting National Forest Inventories, in order to cost-efficiently obtain a visual wood quality proxy. The importance of the variables selected, the limitations identified, and some improvements to the methodology are suggested. Forest areas with better wood quality, which in turn it would be useful for breeding programs, can be easily detected. Context The establishment of harmonized standards or indicators that allow us to determine the quality of the wood present in a forest prior to its exploitation has long been demanded by the European forestry sector, although agreed methodologies for the evaluation of wood quality in standing trees, which is one of the sector’s most urgent requirements, have not, as yet, been implemented. Aims To develop a protocol that visually characterizes wood quality on standing trees in a cost-effective way for the National Forest Inventory (NFI). After some improvements, it can be considered as a base for a European harmonized protocol. Methods In this article, we analyze the implementation, in the NFI, of a visual wood-quality assessment methodology in forests of Central Spain based on the different European standards as well as on research papers addressing this issue. Results The silvicultural practices employed are of the utmost importance to obtain the best wood quality, regardless of the species. Several areas with higher wood quality were identified as well as areas most affected by specific pests in the studied region. The impact of the variables measured (e.g., branchiness, crookedness, maximum branch diameter) is discussed. Conclusion It is feasible to estimate a proxy for wood quality on standing trees in the NFI. Furthermore, after studying the inventory data provided, several enhancements are proposed, not only to improve wood-quality estimates but also to optimize fieldwork costs. Harmonizing NFIs to assess and map European standing wood quality can be achieved.

The link between biodiversity and ecosystem function can depend on environmental conditions. This contingency can impede our ability to predict how biodiversity‐ecosystem function (BEF) relationships will respond to future environmental change, causing a clear need to explore the processes underlying shifts in BEF relationships across large spatial scales and broad environmental gradients. We compiled a dataset on five functional traits (maximum height, wood density, specific leaf area [SLA], seed size, and xylem vulnerability to embolism [P50]), covering 78%–90% of the tree species in the National Forest Inventory from Italy, to test (i) how a water limitation gradient shapes the functional composition and diversity of forests, (ii) how functional composition and diversity of trees relate to forest annual increment via mass ratio and complementarity effects, and (iii) how the relationship between functional diversity and annual increment varies between Mediterranean and temperate climate regions. Functional composition varied with water limitation; tree communities tended to have more conservative traits in sites with higher levels of water limitation. The response of functional diversity differed among traits and climatic regions but among temperate forest plots, we found a consistent increase of functional diversity with water limitation. Tree diversity was positively associated with annual increment of Italian forests through a combination of mass ratio and niche complementarity effects, but the relative importance of these effects depended on the trait and range of climate considered. Specifically, niche complementarity effects were more strongly associated with annual increment in the Mediterranean compared to temperate forests. Synthesis: Overall, our results suggest that biodiversity mediates forest annual increment under water‐limited conditions by promoting beneficial interactions between species and complementarity in resource use. Our work highlights the importance of conserving functional diversity for future forest management to maintain forest annual increment under the expected increase in intensity and frequency of drought. [ABSTRACT FROM AUTHOR]

Masson pine natural forests are ecologically and economically valuable forest ecosystems extensively distributed across China. However, they have been subject to deforestation due to human disturbance. Moreover, climate change affects the growth, mortality, and recruitment of forests, yet available forest growth models do not effectively analyze the impacts of climate. A climate-sensitive transition matrix model (CM) was developed using data from 330 sample plots collected during the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventories in Hunan Province. To assess model robustness, two additional models were created using the same data: a non-climate-sensitive transition matrix model (NCM) and a fixed probability transition matrix model (FM). The models were compared using tenfold cross-validation and long-term predictive performance analysis. The cross-validation results did not show any significant differences among the three models, with the FM performing slightly better than the NCM. However, the application of the CM for long-term prediction (over a span of 100 years) under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) revealed distinct dynamics that demonstrated enhanced reliability. This is attributed to the consideration of climate variables that impact forest dynamics during long-term prediction periods. The CM model offers valuable guidance for the management of Masson pine natural forests within the context of changing climatic conditions. [ABSTRACT FROM AUTHOR]

Abstract Key message Authors have analyzed the possible correlation between measurements/indicators of forest structure and species richness of many taxonomic or functional groups over three regions of Germany. Results show the potential to use structural attributes as a surrogate for species richness of most of the analyzed taxonomic and functional groups. This information can be transferred to large-scale forest inventories to support biodiversity monitoring. Context We are currently facing a dramatic loss in biodiversity worldwide and this initiated many monitoring programs aiming at documenting further trends. However, monitoring species diversity directly is very resource demanding, in particular in highly diverse forest ecosystems. Aims We investigated whether variables applied in an index of stand structural diversity, which was developed based on forest attributes assessed in the German National Forest Inventory, can be calibrated against richness of forest-dwelling species within a wide range of taxonomic and functional groups. Methods We used information on forest structure and species richness that has been comprehensively assessed on 150 forest plots of the German biodiversity exploratories project, comprising a large range of management intensities in three regions. We tested, whether the forest structure index calculated for these forest plots well correlate with the number of species across 29 taxonomic and functional groups, assuming that the structural attributes applied in the index represent their habitat requirements. Results The strength of correlations between the structural variables applied in the index and number of species within taxonomic or functional groups was highly variable. For some groups such as Aves, Formicidae or vascular plants, structural variables had a high explanatory power for species richness across forest types. Species richness in other taxonomic and functional groups (e.g., soil and root-associated fungi) was not explained by individual structural attributes of the index. Results indicate that some taxonomic and functional groups depend on a high structural diversity, whereas others seem to be insensitive to it or even prefer structurally poor stands. Conclusion Therefore, combinations of forest stands with different degrees of structural diversity most likely optimize taxonomic diversity at the landscape level. Our results can support biodiversity monitoring through quantification of forest structure in large-scale forest inventories. Changes in structural variables over inventory periods can indicate changes in habitat quality for individual taxonomic groups and thus points towards national forest inventories being an effective tool to detect unintended effects of changes in forest management on biodiversity.

Forest parameter estimation is required to support the sustainable management of forest ecosystems. Currently, forest resource assessment is increasingly linked to auxiliary information obtained from remote sensing (RS) technologies. In forest parameter estimation, airborne laser scanning (ALS) data have been demonstrated to be an invaluable source of information. However, ALS data are often not available for the entire forest area, whereas images from multiple satellite systems offer new opportunities for large-scale forest surveys. This study aims to assess and estimate the contribution of field plot data and ALS data along with Landsat data to the precision of growing stock volume (GSV) estimates. We compared different approaches for model-assisted estimation of mean forest GSV per unit area using different proportions of field sample data, ALS cover data, and wall-to-wall Landsat data. Model-assisted estimators were used with NFI sample data in an Italian study area using 10 RS predictors, specifically the seven Landsat 7 ETM+ bands and three fine-resolution metrics based on ALS-derived canopy height. We found that relative to the standard simple expansion estimator, the model-assisted estimators produced relative efficiency of 1.16 when using only Landsat data and relative efficiencies as great as 1.33 when using increasing levels of ALS coverage.

In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late‐successional stages. Most forests are distant from these late‐successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short‐term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size‐structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two‐"species" system from temperate European forests, that is, the shade‐tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short time series from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade tolerance of F. sylvatica saplings is key for its long‐term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities. [ABSTRACT FROM AUTHOR]

The Ellenberg quotient (EQ) is a climate index defined as a ratio of the hottest month's temperature and the average annual precipitation sum. The quotient indirectly expresses the relationship between climate and vegetation, and its application is related to the ecological niche of Fagus sp. Although the quotient was curated on the grounds of field research primarily on German vegetation, the possibilities of its utilisation are not limited to the Central European region. The objective of this study is (i) to compare the EQ values calculated for the forest vegetation zones in the Czech Republic with the published data using the ecological niche of Fagus sylvatica; and (ii) to compare the new EQ-based vertical model with field empirical mapping. The study area is the Czech Republic, Central Europe. We used climate data from 1970–2000 and the data of the National Forest Inventory, 2nd cycle (2011–2015), representing an objective data design. Geospatial analytic methods, machine learning (boosting), and verification through statistical testing were performed. The results indicate higher EQ values between the two most substantial spatial frames – the Hercynicum and Carpaticum regions. By comparing empirical mapped units to their climatic potential (in the EQ), a match was found only within the Carpaticum region. The study presents a concretisation of the general climate index for a specific region, adds to the knowledge about the Fagus ecological niche in context with the Central European vegetation, and also points to the EQ's potential for evaluating the concept of vertical differentiation of forest communi)ties, as well as a possible prediction tool for the vegetation migration in context with climate change. [ABSTRACT FROM AUTHOR]

Key message: This paper proposes a methodology that could be considered as a base for a harmonized protocol for stem-quality reporting in Europe while conducting National Forest Inventories, in order to cost-efficiently obtain a visual wood quality proxy. The importance of the variables selected, the limitations identified, and some improvements to the methodology are suggested. Forest areas with better wood quality, which in turn it would be useful for breeding programs, can be easily detected. Context: The establishment of harmonized standards or indicators that allow us to determine the quality of the wood present in a forest prior to its exploitation has long been demanded by the European forestry sector, although agreed methodologies for the evaluation of wood quality in standing trees, which is one of the sector's most urgent requirements, have not, as yet, been implemented. Aims: To develop a protocol that visually characterizes wood quality on standing trees in a cost-effective way for the National Forest Inventory (NFI). After some improvements, it can be considered as a base for a European harmonized protocol. Methods: In this article, we analyze the implementation, in the NFI, of a visual wood-quality assessment methodology in forests of Central Spain based on the different European standards as well as on research papers addressing this issue. Results: The silvicultural practices employed are of the utmost importance to obtain the best wood quality, regardless of the species. Several areas with higher wood quality were identified as well as areas most affected by specific pests in the studied region. The impact of the variables measured (e.g., branchiness, crookedness, maximum branch diameter) is discussed. Conclusion: It is feasible to estimate a proxy for wood quality on standing trees in the NFI. Furthermore, after studying the inventory data provided, several enhancements are proposed, not only to improve wood-quality estimates but also to optimize fieldwork costs. Harmonizing NFIs to assess and map European standing wood quality can be achieved. [ABSTRACT FROM AUTHOR]

Abstract The link between biodiversity and ecosystem function can depend on environmental conditions. This contingency can impede our ability to predict how biodiversity‐ecosystem function (BEF) relationships will respond to future environmental change, causing a clear need to explore the processes underlying shifts in BEF relationships across large spatial scales and broad environmental gradients. We compiled a dataset on five functional traits (maximum height, wood density, specific leaf area [SLA], seed size, and xylem vulnerability to embolism [P50]), covering 78%–90% of the tree species in the National Forest Inventory from Italy, to test (i) how a water limitation gradient shapes the functional composition and diversity of forests, (ii) how functional composition and diversity of trees relate to forest annual increment via mass ratio and complementarity effects, and (iii) how the relationship between functional diversity and annual increment varies between Mediterranean and temperate climate regions. Functional composition varied with water limitation; tree communities tended to have more conservative traits in sites with higher levels of water limitation. The response of functional diversity differed among traits and climatic regions but among temperate forest plots, we found a consistent increase of functional diversity with water limitation. Tree diversity was positively associated with annual increment of Italian forests through a combination of mass ratio and niche complementarity effects, but the relative importance of these effects depended on the trait and range of climate considered. Specifically, niche complementarity effects were more strongly associated with annual increment in the Mediterranean compared to temperate forests. Synthesis: Overall, our results suggest that biodiversity mediates forest annual increment under water‐limited conditions by promoting beneficial interactions between species and complementarity in resource use. Our work highlights the importance of conserving functional diversity for future forest management to maintain forest annual increment under the expected increase in intensity and frequency of drought.

Abstract Key message Fifteen species are most susceptible to require vegetation control during tree regeneration in the range of our study. Among these 15 species, Rubus fruticosus, Pteridium aquilinum, and Molinia caerulea cover each more than 300,000 ha of open-canopy forests. Context Vegetation control, i.e., the reduction of competitive species cover, is often required to promote tree seedling establishment during the forest regeneration stage. The necessity to control understory vegetation largely depends on the species to be controlled. In order to plan forest renewal operations, it is critical to identify which species require vegetation control during the regeneration stage and to quantify the forest area affected by these species. Aims We aimed at identifying the main species requiring vegetation control and at estimating the forest area they cover at the national level. Methods Using National Forest Inventory data, we created four indicators based on two levels of plant cover, cross-referenced with two levels of canopy opening, and compared them to the outcome of a survey of forest manager practices. Results The best indicator was the one that represented the proportion of forests with open canopy where the species was present with a large cover in the understory. In non-Mediterranean France, according to the indicator, a total of 15 species were found to frequently require vegetation control during the tree regeneration stage. Pteridium aquilinum, Molinia caerulea, and Rubus fruticosus were the main species, and each covered more than 300,000 ha of forest with open canopies, representing about 13% of the total forest area with open canopies outside of the Mediterranean area. Conclusions Forests covered by species requiring vegetation control according to forest managers represent a large share of the forest area undergoing regeneration. This study provides the first list of species that require vegetation control based on a methodological protocol that makes it possible to calculate the area associated with each species.

Abstract Key message Dead wood in forests is an important resource due to its role for nutrient cycles, carbon budgets, and biodiversity, among other. While standing and downed dead wood are typically monitored in National Forest Inventories (NFI), stumps have not received comparable attention. Based on the detailed stump inventory in the current Swiss NFI, this study demonstrates the important contribution of stumps to the dead wood pool. Context Dead wood (DW) in forests is an important resource due to its role for nutrient cycles, carbon budgets, and biodiversity, among other. NFIs provide representative DW estimates focusing primarily on standing and downed DW. Little is known on stumps as a DW pool. Aims The aim of this study is to obtain an accurate assessment of the stump volume and biomass in the Swiss NFI to identify its significance for the DW pool, to evaluate the development over the last 30 years, and to examine the need for additional measurements for improving estimates compared to commonly applied assumptions for stump height such as a constant stump height or a fraction of tree height. Methods The current NFI includes a detailed stump inventory to improve accuracy and completeness of the aboveground DW pool estimate. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW pool over time. Results Based on the extended stump inventory in the NFI5, the contribution of stumps to the total DW pool is approximately 25%. The effect of simplifying assumptions or limited measurements to estimate stump volume can result in a significant underestimation of up to $$2/3$$ 2 / 3 of the more accurate and comprehensive assessment of this pool. Conclusion This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.

Forest biomass and biodiversity are the most important elements of forest functions and ecosystem services. In this study, we explore the possibilities and ways to enhance ecosystem functions and services related to biomass and biodiversity. Biotic drivers (i.e., species, phylogenetic and functional diversity, stand structural attributes, and community-weighted mean of trait values), abiotic drivers (i.e., topography and climate), and stand age were extracted as independent variables to explain aboveground biomass (AGB). Using South Korea’s 7th National Forest Inventory data, we analyzed 2,070 plots belonging to the natural forests consisting of 394 plots (19.0%) of coniferous stands, 829 plots (40.0%) of broadleaved stands, and 847 plots (40.9%) of mixed stands. Multimodel inference test and model-averaging approaches were conducted to determine the most significant control variables on AGB in each stand type, and piecewise structural equation modeling was conducted to quantify the relationships and directions between the variables. Abiotic drivers, including stand age and climate moisture index, control tree size variation in all stand types, but biotic drivers control AGB through different mechanisms depending on the stand type. Our results show that there were differences in the composition of variables for controlling AGB among stand types. Across all forest types and total stands, we found that increasing the tree size variation is the key driver of increasing AGB as stand age increases. Our study suggests that forest carbon accumulation by stand type can be enhanced if the key drivers of each stand type are properly managed across forest succession, and different forest management plans that consider different regulation factors among stand types are required. Moreover, it is important to adapt resource use patterns for each stand type with considering environmental conditions to maintain healthy and sustainable forests.

Abstract In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late‐successional stages. Most forests are distant from these late‐successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short‐term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size‐structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two‐“species” system from temperate European forests, that is, the shade‐tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short time series from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade tolerance of F. sylvatica saplings is key for its long‐term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.

Over the years, various methods for estimating and projecting forest resources have been developed and are used by countries where the forest sector is important. Therefore, the obligation to report and account for forest resources, including changes in carbon stocks in a forest area, has gained attention. The latest regulations (Land Use, Land Use Change and Forestry—LULUCF) requires European Union (EU) members to annually report and publish national accounting plans estimating emissions and removals from managed forest areas (Regulation EU 2018/841). The major challenge is to choose and adapt a unique tool for this accounting. At the same time, they need to provide reliable estimates that are recognized by regulators and control authorities. This study focuses on comparing the adaptation of two accounting frameworks: the Operational-Scale Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) and the European Forest Dynamics Model (EFDM). Both tools are based on National Forest Inventory (NFI) data. It is assumed that the EFDM can provide similar results to the CBM-CFS3, which is already used in Poland. Implementing the EFDM and adapting it to Polish conditions could facilitate forest management decision-making and the preparation of forest policies. The main objective of this study was to compare and validate the accuracy of the results obtained with the EFDM framework. Metrics compared using both tools included growing stock volume, biomass of growing stock expressed in carbon units and age–class distribution over area. The comparison was based on the agreement of EFDM with CBM-CFS3 results. The volume of logging was taken from the EFDM and compared with the values obtained by Statistics Poland. This study also provides a guide for framework parameterization directly from the Polish National Forest Inventory data from the 2010–2015 cycle. Our main findings are that the results of the two models are reasonably comparable (the extent of deviation is acceptable). Moreover, the first implementation of the EFDM showed that it is an easy-to-use open-source program that allows forest managers to implement their own settings according to their needs. This document elucidates the concept of using both frameworks under Polish conditions and provides an impression of their performance for future modelers, students and researchers. [ABSTRACT FROM AUTHOR]

Forests play a crucial role in storing much of the world's carbon (C). Accurately estimating C sequestration is essential for addressing and mitigating the impacts of global warming. While many studies have used machine learning models to estimate carbon storage (CS) in forests based on remote sensing data, this research further examines C sequestration (i.e., the annual carbon uptake by trees; CSE). The objectives of this study are two-fold: firstly, to identify the best models for estimating CSE and CS by testing various methods, and secondly, to examine the effect of climatic data and the canopy height model (CHM) on the estimation of CSE. To achieve the first objective, we will compare the performance of fourteen models, including twelve machine learning models, one deep learning model, and an ensemble model that combines the top four independent models. For the second objective, we study the effect of four input configurations: the first is a baseline configuration based solely on attributes extracted from satellite images (Sentinel-2) and geomorphology; the second combines satellite features with climatic data; the third uses a CHM derived from LiDAR instead of climatic data; and the fourth combines all available features: satellite images, climatic data, and CHM. The results show that adding climatic data does not improve the estimation of CSE and CS. However, adding CHM features significantly improves the models' performance for both targets. The implemented ensemble model demonstrated the best performance across all configurations. [Display omitted] • New ensemble model outperforms 13 models in carbon sequestration & storage estimation. • Canopy height enhances carbon sequestration & storage estimation. • Climatic data doesn't improve the accuracy of estimation. [ABSTRACT FROM AUTHOR]

Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km ^2 to 675 239 km ^2 . These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national ( n = 26 220 plots) and local scales ( n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range

Key message: Authors have analyzed the possible correlation between measurements/indicators of forest structure and species richness of many taxonomic or functional groups over three regions of Germany. Results show the potential to use structural attributes as a surrogate for species richness of most of the analyzed taxonomic and functional groups. This information can be transferred to large-scale forest inventories to support biodiversity monitoring. Context: We are currently facing a dramatic loss in biodiversity worldwide and this initiated many monitoring programs aiming at documenting further trends. However, monitoring species diversity directly is very resource demanding, in particular in highly diverse forest ecosystems. Aims: We investigated whether variables applied in an index of stand structural diversity, which was developed based on forest attributes assessed in the German National Forest Inventory, can be calibrated against richness of forest-dwelling species within a wide range of taxonomic and functional groups. Methods: We used information on forest structure and species richness that has been comprehensively assessed on 150 forest plots of the German biodiversity exploratories project, comprising a large range of management intensities in three regions. We tested, whether the forest structure index calculated for these forest plots well correlate with the number of species across 29 taxonomic and functional groups, assuming that the structural attributes applied in the index represent their habitat requirements. Results: The strength of correlations between the structural variables applied in the index and number of species within taxonomic or functional groups was highly variable. For some groups such as Aves, Formicidae or vascular plants, structural variables had a high explanatory power for species richness across forest types. Species richness in other taxonomic and functional groups (e.g., soil and root-associated fungi) was not explained by individual structural attributes of the index. Results indicate that some taxonomic and functional groups depend on a high structural diversity, whereas others seem to be insensitive to it or even prefer structurally poor stands. Conclusion: Therefore, combinations of forest stands with different degrees of structural diversity most likely optimize taxonomic diversity at the landscape level. Our results can support biodiversity monitoring through quantification of forest structure in large-scale forest inventories. Changes in structural variables over inventory periods can indicate changes in habitat quality for individual taxonomic groups and thus points towards national forest inventories being an effective tool to detect unintended effects of changes in forest management on biodiversity. [ABSTRACT FROM AUTHOR]

The development of allometric biomass models is important process in biomass estimation because the reliability of forest biomass and carbon estimations largely depends on the accuracy and precision of such models. National Forest Inventories (NFI) are detailed assessments of forest resources at national and regional levels that provide valuable data for forest biomass estimation. However, the lack of biomass allometric equations for each tree species in the NFI currently hampers the estimation of national-scale forest biomass. The main objective of this study was to develop allometric biomass regression equations for each tree species in the NFI of China based on limited biomass observations. These equations optimally grouped NFI and biomass observation species according to their phylogenetic relationships. Significant phylogenetic signals demonstrated phylogenetic conservation of the crown-to-stem biomass ratio. Based on phylogenetic relationships, we grouped and matched NFI and biomass observation species into 22 categories. Allometric biomass regression models were developed for each of these 22 species categories, and the models performed successfully (R² = 0.97, root mean square error (RMSE) = 12.9 t.ha-1, relative RMSE = 11.5%). Furthermore, we found that phylogeny-based models performed more effectively than wood density-based models. The results suggest that grouping species based on their phylogenetic relationships is a reliable approach for the development and selection of accurate allometric equations. [ABSTRACT FROM AUTHOR]

Efforts to assess and understand changes in plant diversity and ecosystem functioning focus on the analysis of taxonomic diversity. However, the resilience of ecosystems depends not only on species richness but also on the functions (responses and effects) of species within communities and ecosystems. Therefore, a functional approach is required to estimate functional diversity through functional traits and to model its changes in space and time. This study aims to: (i) assess the accuracy of estimates of species richness and tree functional richness obtained from field data and Sentinel-2 imagery in tropical dry forests of the Yucatan Peninsula; (ii) map and analyze the relationships between these two variables. We calculated species richness and functional richness (from six functional traits) of trees from 87 plots of the National Forest Inventory in a semi-deciduous tropical forest and 107 in a semi-evergreen tropical forest. Species richness and functional richness were mapped using reflectance values, vegetation indices, and texture measurements from Sentinel-2 imagery as explanatory variables. Validation of the models to map these two variables yielded a coefficient of determination (R2) of 0.43 and 0.50, and a mean squared relative error of 25.4% and 48.8%, for tree species richness and functional richness, respectively. For both response variables, the most important explanatory variables were Sentinel-2 texture measurements and spectral bands. Tree species richness and functional richness were positively correlated in both forest types. Bivariate maps showed that 44.9% and 26.5% of the forests studied had high species richness and functional richness values. Our findings highlight the importance of integrating field data and remotely sensed variables for estimating tree species richness and functional richness. In addition, the combination of species richness and functional richness maps presented here is potentially valuable for planning, conservation, and restoration strategies by identifying areas that maximize ecosystem service provisioning, carbon storage, and biodiversity conservation. [ABSTRACT FROM AUTHOR]

Forest parameter estimation is required to support the sustainable management of forest ecosystems. Currently, forest resource assessment is increasingly linked to auxiliary information obtained from remote sensing (RS) technologies. In forest parameter estimation, airborne laser scanning (ALS) data have been demonstrated to be an invaluable source of information. However, ALS data are often not available for the entire forest area, whereas images from multiple satellite systems offer new opportunities for large-scale forest surveys. This study aims to assess and estimate the contribution of field plot data and ALS data along with Landsat data to the precision of growing stock volume (GSV) estimates. We compared different approaches for model-assisted estimation of mean forest GSV per unit area using different proportions of field sample data, ALS cover data, and wall-to-wall Landsat data. Model-assisted estimators were used with NFI sample data in an Italian study area using 10 RS predictors, specifically the seven Landsat 7 ETM+ bands and three fine-resolution metrics based on ALS-derived canopy height. We found that relative to the standard simple expansion estimator, the model-assisted estimators produced relative efficiency of 1.16 when using only Landsat data and relative efficiencies as great as 1.33 when using increasing levels of ALS coverage. [ABSTRACT FROM AUTHOR]

Models based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug‐in option to any inventory‐based initial condition. Forty‐year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority. Recommendations for Resource Managers: •Uncovering the potential and limitations of large‐scale forest models are needed when deducing recommendations from forest resource projections under forest management and policy scenarios at regional, national, or continental scales.•Estimating simulation uncertainty in these models is crucial to assess their accuracy. The present study offers a generic methodological strategy for assessing parameter uncertainty in large‐scale forest models.•Users of the MARGOT model should consider that simulation uncertainties proved to be low at a national scale, but decennial wood stock increases as observed in the French forests over the period 1970–2016 were underestimated.•Assessing simulation uncertainty is also major for model bias appraisal. Better accounting for the controls of forest demographic processes (growth, regeneration and mortality) appears to be a priority for the development of MARGOT, and for other large‐scale models. [ABSTRACT FROM AUTHOR]

Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%–86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species‐level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave‐one‐species‐out" cross‐validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available. [ABSTRACT FROM AUTHOR]

In large-area forest inventories, a trade-off between the amount of data to be sampled and the corresponding collection costs is necessary. It is not always possible to have a very large data sample when dealing with sampling-based inventories. It is therefore important to optimize the sampling design with the limited resources. Whereas this sort of inventories are subject to these constraints, the availability of remote sensing (RS) data correlated with the forest inventory variables is usually much higher. For this reason, the RS and sampled field measurement data are often used in combination for improving the forest inventory estimation. In this study, we propose a model-based data sampling method founded on Bayesian optimization and machine learning algorithms which utilizes RS data to guide forest inventory sample selection. We evaluate our method in empirical experiments using real-world volume of growing stock data from the Aland region in Finland. The proposed method is compared against two baseline methods: simple random sampling and the local pivotal method. When a suitable model link is selected, the empirical experiments show on best case on average up to 22% and 79% improvement in population mean and variance estimation respectively over baselines. However, the results also illustrate the importance of model selection which has a clear effect on the results. The novelty of the study is in the application of Bayesian optimization in national forest inventory survey sampling. [ABSTRACT FROM AUTHOR]