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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]

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 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]

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]

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]

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]

We report a relatively rare study of a national forest inventory in a megadiverse country with the systematic collection of herbarium specimens. The taxonomic identification of 22,659 herbarium collections from 6942 sites of Mexico's national forest inventory resulted in 1464 native tree species (approximately half of Mexico's estimated total), in 470 genera and 117 plant families. We compared visual tree-species identifications in the field by hired crews, with much more rigorous identification of submitted (mostly sterile) herbarium specimens by experienced taxonomists and specialists at the National Herbarium: for 40% of the 22,659 collections, the identification of species names from the field was confirmed, for 32% it was corrected at the herbarium, and 27% had been sent without any identification. The most commonly collected plant families were Fagaceae (oak family, 21.7% of all collections), Fabaceae (legumes, 17.7%), and Pinaceae (pine family, 13.3%). The most commonly collected tree species in six major forest-vegetation types were Pinus leiophylla in "coniferous forest", Quercus magnoliifolia in "highland broadleaf forest", Liquidambar styraciflua in "mountainous cloud forest", Bursera simaruba in "lowland evergreen forest", Lysiloma divaricatum in "lowland dry forest", and Parkinsonia microphylla in "xerophilous scrub". We overlapped the six major forest-vegetation types with Mexico's 15 mainland floristic provinces, as circumscribed by Rzedowski. This resulted in 75 so-called forest-vegetation provinces, of which 35 had at least 20 collection sites. The similarity of species composition among these 35 forest-vegetation provinces was only 17–34% with the Jaccard community index, and 15–42% with the Morisita-Horn community index. The number of physically undetected species was estimated statistically for the 35 forest-vegetation provinces, which indicates that there are forest-vegetation provinces, where the number of species could be up to 8.8-fold higher than those detected in the present work. Finally, we suggest a method to distribute sites optimally among the country in future forest inventories, such as to minimize the average area represented by the sites in each forest-vegetation province. [ABSTRACT FROM AUTHOR]

Domestic wood production in Japan is low, and more than 60% of the wood consumed is imported. This is surprising because two-thirds of Japan's land area is covered by forests. The dominant explanations for this low wood self-sufficiency rate are the lack of forest road infrastructure and the small-scale forest ownership structure. Austria is a country that is similar in topography and ownership structure but has a high wood self-sufficiency rate. Therefore, previous research has compared Japan to Austria. However, these studies did not address basic forest properties in much detail. This study uses national forest inventory data from both countries to assess the forest structures and utilization percentages of the annual wood increment. In contrast to the hypothesis, the results show that the two countries have similar increment rates. In contrast to former studies, the findings indicate that Japanese plantation forests have a higher timber stocking volume than Austrian forests. In Japan, the proportion of the standing volume in the 40–60-year-old age class is much higher compared to the other age classes, indicating an unbalanced growing stock. The results show that the utilization percentage is much higher in Austria (88%) than in Japan (53%). Therefore, the Japanese forest sector has a high potential to increase the harvest of wood. [ABSTRACT FROM AUTHOR]

Prediction of forest carbon sink in the future is important for understanding mechanisms concerning the increase in carbon sinks and emission reduction, and for realizing the climate goals of the Paris Agreement and global carbon neutrality. Based on stand volume data of permanent monitoring plots of the successive national forest inventories from 2004 to 2018, and combined with multiple variables, such as climatic factors, soil properties, stand attributes, and topographic features, the random forest algorithm was used to predict the stand volume growth-loss and then calculated the forest biomass and its carbon sink potential between 2015 to 2060 in the Yangtze River Economic Belt of China. From 2015 to 2060, the predicted forest biomass carbon storage and density increased from 3053.27 to 6721.61 Tg C and from 33.75 to 66.12 Mg C hm−2, respectively. The predicted forest biomass carbon sink decreased from 90.58 to 73.98 Tg C yr−1, and the average forest biomass carbon storage and sink were ranked in descending order: Yunnan, Sichuan, Jiangxi, Hunan, Guizhou, Hubei, Zhejiang, Chongqing, Anhui, Jiangsu, and Shanghai. The forest biomass carbon storage in the Yangtze River Economic Belt will increase by 3.67 Pg C from 2015 to 2060. The proportion of forest C sinks on the regional scale to C emissions on the national scale will increase from 2.9% in 2021–2030 to 4.3% in 2041–2050. These results indicate higher forest carbon sequestration efficiency in the Yangtze River Economic Belt in the future. Our results also suggest that improved forest management in the upper and middle reaches of the Yangtze River will help to enhance forest carbon sink in the future. [ABSTRACT FROM AUTHOR]

Using Korean National Forest Inventory (NFI) data, our study developed a model to estimate stand mean diameter at breast height (DBH) reflecting the influence of site and climate factors on forest growth for the major tree species in South Korea. A DBH estimation model was developed using stand-level variables (stand age, site index and number of trees per hectare) as independent factors. The spatial autocorrelation of residuals of the model was identified using semi-variogram analysis for each tree species. Further, a residual model, in which residuals were estimated by climatic factors (mean temperature, sum temperature in the growing season and precipitation), was developed assuming that the spatial autocorrelation of residuals reflects the differences in regional climatic conditions. Linear regression analysis showed that residuals of all tree species were significantly correlated with temperature and precipitation. The DBH and residual models were integrated to estimate the current DBH under different climatic factors (temperature and precipitation) and stand-level variables. This model had high reliability (R² = 0.74-0.79), and no obvious dependencies or patterns in residuals were noted. Our results indicated that temperature increases caused by climate change would negatively affect the DBH estimate of coniferous trees, but not of oak species. [ABSTRACT FROM AUTHOR]

Forest growth and wood supply projections are increasingly used to estimate the future availability of woody biomass and the correlated effects on forests and climate. This research parameterizes an inventory-based business-as-usual wood supply scenario, with a focus on southwest Germany and the period 2002-2012 with a stratified prediction. First, the Classification and Regression Trees algorithm groups the inventory plots into strata with corresponding harvest probabilities. Second, Random Forest algorithms generate individual harvest probabilities for the plots of each stratum. Third, the plots with the highest individual probabilities are selected as harvested until the harvest probability of the stratum is fulfilled. Fourth, the harvested volume of these plots is predicted with a linear regression model trained on harvested plots only. To illustrate the pros and cons of this method, it is compared to a direct harvested volume prediction with linear regression, and a combination of logistic regression and linear regression. Direct harvested volume regression predicts comparable volume figures, but generates these volumes in a way that differs from business-as-usual. The logistic model achieves higher overall classification accuracies, but results in underestimations or overestimations of harvest shares for several subsets of the data. The stratified prediction method balances this shortcoming, and can be of general use for forest growth and timber supply projections from large-scale forest inventories. [ABSTRACT FROM AUTHOR]

The alien invasive pathogen Hymenoscyphus fraxineus causes large-scale decline of European ash (Fraxinus excelsior). We assessed ash dieback in Germany and identified factors that were associated with this disease. Our assessment was based on a 2015 sampling of national forest inventory plots that represent a supra-regional area. In the time from 2012 to 2015, the number of regrown ash trees corresponded to only 42% of the number of trees that had been harvested or died. Severe defoliation was recorded for almost 40% of the living trees in 2015, and more than half of the crowns mainly consisted of epicormic shoots. Necroses were present in 24% of root collars. A total of 14% of the trees were in sound condition, which sum up to only 7% of the timber volume. On average, trees of a higher social status or with a larger diameter at breast height were healthier. Collar necroses were less prevalent at sites with a higher inclination of terrain, but there was no evidence for an influence of climatic variables on collar necroses. The disease was less severe at sites with smaller proportions of the basal area of ash compared to the total basal area of all trees and in the north-eastern part of the area of investigation. The regeneration of ash decreased drastically. [ABSTRACT FROM AUTHOR]

Reducing uncertainty in forest carbon estimates at local and regional scales has become increasingly important due to the centrality of the terrestrial carbon cycle in issues of climate change. In Victoria, Australia, public natural forests extend over 7.2 M ha and constitute a significant and important carbon stock. Recently, a wide range of approaches to estimate carbon stocks within these forests have been developed and applied. However, there are a number of data and estimation limitations associated with these studies. In response, over the last five years, the State of Victoria has implemented a pragmatic plot-based design consisting of pre-stratified permanent observational units located on a state-wide grid. Using the ground sampling grid, we estimated aboveground and belowground carbon stocks (including soil to 0.3 m depth) in both National Parks and State Forests, across a wide range of bioregions. Estimates of carbon stocks and associated uncertainty were conducted using simple design based estimators. We detected significantly more carbon in total aboveground and belowground components in State Forests (408.9 t ha-1, 95% confidence interval 388.8-428.9 t ha-1) than National Parks (267.6 t ha-1, 251.9-283.3 t ha-1). We were also able to estimate forest carbon stocks (and associated uncertainty) for 21 strata that represent all of Victoria's bioregions and public tenures. It is anticipated that the lessons learnt from this study may support the discussion on planning and implementing low cost large area forest carbon stock sampling in other jurisdictions. [ABSTRACT FROM AUTHOR]

While a large sum of timber stock in private forests, especially broadleaved forests, has been ignored by their owners, a rising global concern about climate change and ecosystems has led to a renewed interest in natural broadleaved forest management strategies. This study establishes the forest growth model for the natural broadleaved forest of Gangwon-do based on the matrix model developed by Buongiorno and Michie. The matrix model by Buongiorno and Michie has been widely applied to study forest population dynamics, especially for uneven-aged forests. To develop an existing matrix model, our approach applies transitional probabilities of forest stands which are calibrated using National Forest Inventory data. Both long and short-term predicted simulation results show that the predicted average tree density and diameter distribution from our model are very close to the stand density and diameter distribution from observed data. Although the model simplifies reality, the results from our study confirm that our models are valid enough to predict the average stand status of the broadleaved forests in Gangwon-do. [ABSTRACT FROM AUTHOR]

Information on the ecological functions of forests is important for sustainable forest management. In this study, we introduced the national monitoring program which has been used in China to evaluate the overall health status and ecological functions of forests. We also compared it to similar monitoring programs operating in Europe and the United States of America. We revealed the strength and drawbacks of China's monitoring program by analyzing the initial evaluation results. Our analysis showed that among the three programs, the European program gives the most detailed measurements of conditions of forests while the U.S. program generates the most detailed information on individual trees. In comparison, China's monitoring program has a higher spatial resolution but is narrowly focused on trees and uses coarse classifications of indicators. The health status of forests in China suggested that more resources should be invested to improve the health of existing forests, especially plantations. The limitations in China's monitoring program need to be addressed to improve the accuracy of future assessments. [ABSTRACT FROM AUTHOR]

While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors. [ABSTRACT FROM AUTHOR]

The prediction of tree growth is key to further understand the carbon sink role of forests and the short-term forest capacity on climate change mitigation. In this work, we used large-scale data available from three consecutive forest inventories in a Euro-Mediterranean region and the Bertalanffy–Chapman–Richards equation to model up to a decade's tree size variation in monospecific forests in the growing stages. We showed that a tree-level fitting with ordinary differential equations can be used to forecast tree diameter growth across time and space as function of environmental characteristics and initial size. This modelling approximation was applied at different aggregation levels to monospecific regions with forest inventories to predict trends in aboveground tree biomass stocks. Furthermore, we showed that this model accurately forecasts tree growth temporal dynamics as a function of size and environmental conditions. Further research to provide longer term prediction forest stock dynamics in a wide variety of forests should model regeneration and mortality processes and biotic interactions. [ABSTRACT FROM AUTHOR]

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but requires capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service's (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA's experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs. [ABSTRACT FROM AUTHOR]

Research Highlights: The estimation of soil and litter carbon stocks by the Land Use, Land-Use Changes, and Forestry (LULUCF) sectors has the potential to improve reports on national greenhouse gas (GHG) inventories. Background and Objectives: Forests are carbon sinks in the LULUCF sectors and therefore can be a comparatively cost-effective means and method of GHG mitigation. Materials and Methods: This study was conducted to assess soil at 0–30 cm and litter carbon stocks using the National Forest Inventory (NFI) data and random forest (RF) models, mapping their carbon stocks. The three main types of forest in South Kora were studied, namely, coniferous, deciduous, and mixed. Results: The litter carbon stocks (t C ha−1) were 4.63 ± 0.18 for coniferous, 3.98 ± 0.15 for mixed, and 3.28 ± 0.13 for deciduous. The soil carbon stocks (t C ha−1) were 44.11 ± 1.54 for deciduous, 35.75 ± 1.60 for mixed, and 33.96 ± 1.62 for coniferous. Coniferous forests had higher litter carbon stocks while deciduous forests contained higher soil carbon stocks. The carbon storage in the soil and litter layer increased as the forest grew older; however, a significant difference was found in several age classes. For mapping the soil and litter carbon stocks, we used four random forest models, namely RF1 to RF4, and the best performing model was RF2 (root mean square error (RMSE) (t C ha−1) = 1.67 in soil carbon stocks, 1.49 in soil and litter carbon stocks). Our study indicated that elevation, accessibility class, slope, diameter at breast height, height, and growing stock are important predictors of carbon stock. Soil and litter carbon stock maps were produced using the RF2 models. Almost all prediction values were appropriated to soil and litter carbon stocks. Conclusions: Estimating and mapping the carbon stocks in the soil and litter layer using the NFI data and random forest models could be used in future national GHG inventory reports. Additionally, the data and models can estimate all carbon pools to achieve an accurate and complete national GHG inventory report. [ABSTRACT FROM AUTHOR]

Research Highlights: This study developed the first remote sensing-based forest cover map of Baden-Württemberg, Germany, in a very high level of detail. Background and Objectives: As available global or pan-European forest maps have a low level of detail and the forest definition is not considered, administrative data are often oversimplified or out of date. Consequently, there is an important need for spatio-temporally explicit forest maps. The main objective of the present study was to generate a forest cover map of Baden-Württemberg, taking the German forest definition into account. Furthermore, we compared the results to NFI data; incongruences were categorized and quantified. Materials and Methods: We used a multisensory approach involving both aerial images and Sentinel-2 data. The applied methods are almost completely automated and therefore suitable for area-wide forest mapping. Results: According to our results, approximately 37.12% of the state is covered by forest, which agrees very well with the results of the NFI report (37.26% ± 0.44%). We showed that the forest cover map could be derived by aerial images and Sentinel-2 data including various data acquisition conditions and settings. Comparisons between the forest cover map and 34,429 NFI plots resulted in a spatial agreement of 95.21% overall. We identified four reasons for incongruences: (a) edge effects at forest borders (2.08%), (b) different forest definitions since NFI does not specify minimum tree height (2.04%), (c) land cover does not match land use (0.66%) and (d) errors in the forest cover layer (0.01%). Conclusions: The introduced approach is a valuable technique for mapping forest cover in a high level of detail. The developed forest cover map is frequently updated and thus can be used for monitoring purposes and for assisting a wide range of forest science, biodiversity or climate change-related studies. [ABSTRACT FROM AUTHOR]

Research Highlights: The informed decisions on land use require assessment of the impacts of these decisions on the supply of different ecosystem services. Background and Objectives: Scenario modeling can be used to provide harmonized and quantitative information on the impacts of various zoning decisions on the provision of various ecosystem services. In this study, we explored the effects of land-use zoning decisions on the provision of roundwood and energy wood, carbon stock of living tree components, berry yields, scenic beauty and recreation. Materials and Methods: Three alternative forest scenarios were formulated in terms of restrictions on forest management for already established land-use zones by the provinces in Finland. These data were integrated with the National Forest Inventory (NFI) plot data for forest dynamics modeling. Results: In Finland, 9% of the forest land was protected and 9% was under restricted forest management due to legislative decisions, forest owners' decisions, or regional land use plans in 2016. These established zoning decisions resulted in an estimated 17% smaller roundwood and energy wood removals per year in 2016–2025 compared to the estimated wood removals in the scenario without any restrictions. The decrease in annual gross stumpage earnings was EUR 775 million per year. The carbon stock of living tree components in 2025 was 12% larger than in the scenario without any restrictions. Bilberry yield increased due to the restrictions considered in our study, while the restrictions had only a slight effect on cowberry yield at province and country levels. The restrictions increased the recreation value, particularly in Lapland, but only slightly affected scenic beauty at province and country levels. Conclusions: Scenario analyses support informed decision making and the balancing of different forest uses. Harmonized translation of restrictions and the use of standard indicators to model the impacts also support comparisons between the regions. [ABSTRACT FROM AUTHOR]

In many countries, National Forest Inventory (NFI) data is used to assess the variability of forest growth across the country. The identification of areas with similar growths provides the foundation for development of regional models. The objective of the present study is to identify areas with similar diameter and basal area growth using increment cores acquired by the NFI for the three main Romanian species: Norway spruce (Picea abies L. Karst), European beech (Fagus sylvatica L.), and Sessile oak (Quercus petraea (Matt.) Liebl.). We used 6536 increment cores with ages less than 100 years, a total of 427,635 rings. The country was divided in 21 non-overlapping ecoregions based on geomorphology, soil, geology and spatial contiguousness. Mixed models and multivariate analyses were used to assess the differences in annual dimeter at breast height and basal area growth among ecoregions. Irrespective of the species, the mixed models analysis revealed significant differences in growth between the ecoregions. However, some ecoregions were similar in terms of growth and could be aggregated. Multivariate analysis reinforced the difference between ecoregions and showed no temporal grouping for spruce and beech. Sessile oak growth was separated not only by ecoregions, but also by time, with some ecoregions being more prone to draught. Our study showed that countries of median size, such as Romania, could exhibit significant spatial differences in forest growth. Therefore, countrywide growth models incorporate too much variability to be considered operationally feasible. Furthermore, it is difficult to justify the current growth and yield models as a legal binding planning tool. [ABSTRACT FROM AUTHOR]

Land use change (LUC), disturbances, and their interactions play an important role in regional forest carbon (C) dynamics. Here we quantified how these activities and events may influence future aboveground biomass (AGB) dynamics in forests using national forest inventory (NFI) and Landsat time series data in the Northern United States (US). Total forest AGB predictions were based on simulations of diameter growth, mortality, and recruitment using matrix growth models under varying levels of LUC and disturbance severity (low (L), medium (M), and high (H)) every five years from 2018 to 2098. Land use change included the integrated effects of deforestation and reforestation/afforestation (forest [F]→agriculture [A], settlements [S, urbanization/other], and A&S→F), specifically, conversion from F→A, F→S, F→A&S, A→F, S→F, and A&S→F. Disturbances included natural and anthropogenic disturbances such as wildfire, weather, insects and disease, and forest harvesting. Results revealed that, when simultaneously considering both medium LUC and disturbances, total forest AGB predictions of LUC + fire, LUC + weather, LUC + insect & disease, and LUC + harvest indicated substantial increases in regional C stocks (± standard deviation) from 1.88 (±0.13) to 3.29 (±0.28), 3.10 (±0.24), 2.91 (±0.19), and 2.68 (±0.17) Pg C, respectively, from 2018 to 2098. An uncertainty analysis with fuzzy sets suggested that medium LUC under disturbances would lead to greater forest AGB C uptake than undisturbed forest C uptake with high certainty, except for LUC + harvest. The matrix models in this study were parameterized using NFI and Landsat data from the past few decades. Thus, our results imply that if recent trends persist, LUC will remain an important driver of forest C uptake, while disturbances may result in C emissions rather than undisturbed forest C uptake by 2098. The combined effects of LUC and disturbances may serve as an important driver of C uptake and emissions in the Northern US well into the 21st century. [ABSTRACT FROM AUTHOR]

Forests play an important role in regulating the carbon (C) cycle. The main objective of this study was to quantify the effects of South Korean national reforestation programs on carbon budgets. We estimated the changes in C stocks and annual C sequestration in the years 1961–2014 using Korea-specific models, a forest cover map (FCM), national forest inventory (NFI) data, and climate data. Furthermore, we examined the differences in C budgets between Cool forests (forests at elevations above 700 m) and forests in lower-altitude areas. Simulations including the effects of climate conditions on forest dynamics showed that the C stocks of the total forest area increased from 6.65 Tg C in 1961 to 476.21 Tg C in 2014. The model developed here showed a high degree of spatiotemporal reliability. The mean C stocks of the Cool forests and other forests increased from 4.03 and 0.43 Mg C ha−1, respectively, to 102.43 and 73.76 Mg C ha−1 at a rate of 1.82 and 1.36 Mg C ha−1 yr−1 during the same period. These results imply that, although the total Cool forest area of South Korea occupied only about 12.3% (772,788 ha) of the total forest area, the Cool forests play important roles in C balances and forest ecosystems in South Korea. Annual C sequestration totals are projected to decrease at a low rate in the near future because the overall growth rate of a mature forest decreases as the stand ages. Our results quantified forest C dynamics in South Korean forests before and after national reforestation programs. Furthermore, our results can help in development of regional and national forest management strategies to allow for sustainable development of society and to cope with climate change in South Korea. [ABSTRACT FROM AUTHOR]

National Forest Inventories (NFIs) are the primary source of information to fulfill international requirements, such as growing stock volume. However, NFI cycles may be "out of phase" in terms of the information required, so prediction techniques are needed. To disentangle the effects of climate and competition on stand productivity and to estimate the volume of stocks at national scale, it is important to recognize that growth and competition are species-specific and vary along climatic gradients. In this study, we estimate the productivity of five pine species (Pinus sylvestris, Pinus pinea, Pinus halepensis, Pinus nigra and Pinus pinaster), growing in monospecific stands or in mixtures along an aridity gradient in the Iberian Peninsula, based on Spanish NFI data. We study the stand volume growth efficiency (VGE), since it allows the comparison of volume growth in monospecific and mixed stands. The results reveal the importance of considering the aridity when assessing VGE. Moreover, it was found that, in general, admixture among pine species leads to modifications in the VGE, which can vary from negative to positive effects depending on species composition, and that this is always influenced by the aridity. Finally, we provide simple growth efficiency models for the studied pines species which are valid for both monospecific and mixed stands along the aridity gradient of the Iberian Peninsula. [ABSTRACT FROM AUTHOR]