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Adapting forests to climate change is a major challenge for forest ecology and forestry. Among the management options available, encouraging the use of mixtures is a promising way forward. However, this practice requires a thorough understanding of how species respond to mixing. In this article, we analyzed species dominant height responds to mixing and how species ontogeny and traits drive this response. We compared species observed dominant height in mixed even-aged stands with the expected dominant height of the same species in a monospecific stand under the same environmental conditions. We then related this dominant height variation due to mixing to between-species dominant height difference and to species traits linked to competition (shade tolerance, wood density, specific leaf area). We focused our analyses on 76 pairs of forest tree species. We used data from the French National Forest Inventory to calculate species dominant height in 1368 mixed stands. We then used previously developed models to estimate the expected dominant height in virtual monospecific stands with the same environmental conditions. We found that mixture had a significant impact on species dominant height for 15 out of 50 species-combination considered. Dominant height of a given species was higher in mixture than in pure stands when this species had a lower dominant height in pure stands, a lower shade tolerance, a lower specific leaf area or a higher wood density than its companion species. Our results suggest that species dominant height response to mixing depends on how mixture influences the competition for light. Our results will help inform strategies aiming to diversify species in forests, and will be especially useful in anticipating a given species' behavior in response to competition for light when it is mixed with other species. • We assessed the mixture effect on species dominant height in even-aged forests. • Species mixture enhanced height dynamics for 12 combinations out of 50. • Species mixture reduced height dynamics for 3 combinations out of 50. • The bigger the height difference between species, the stronger the mixture effect. • Species traits has significant but weak impact on the mixture effect. [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]

• Age structures of Norway spruce and Scots pine stands in Norway were studied. • Regional age class distributions reflected the historical utilization of the forest. • Stand age and biological age of dominant trees did not differ in stands established after 1940. • The age of dominant trees was often much higher than the stand age in older stands. • Increasing within-stand age heterogeneity with increasing stand age was observed. 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. [ABSTRACT FROM AUTHOR]

High Nature Value (HNV) farmland and forest systems play a vital role in supporting biodiversity and delivery of ecosystem services. Estimates of HNV forest area and distribution in the European Union (EU) are rarely conducted, despite having been a requirement of Rural Development Programmes. This work represents the first attempt to identify and estimate the area of HNV forest in the Republic of Ireland in a repeatable and transparent way. Relevant geo-datasets available for Ireland were collated and analysed. We investigated whether the datasets contained information on the indicators used in a recently-developed Nature Value (NV) index, and explored the potential of proxy indicators to determine the likelihood of a mapped area of forest being HNV. Based on these analyses, a likelihood map of the distribution of forest in different NV categories was produced and an accuracy assessment conducted. Results from this study suggest that HNV forest accounts for approximately 1 % of the Irish land area, or 8 % of the total forest area. Accuracy assessments indicated substantial agreement between the likelihood map classifications and the calculated NV status of National Forest Inventory plots. The methodology presented here could also be applied to existing similar datasets to estimate the extent and distribution of HNV forest in other regions. The mapped output provides a likelihood of a forest area being HNV and can provide evidence to inform the development of forest conservation policies. • High Nature Value forests support biodiversity and multiple ecosystem services. • High Nature Value forest accounts for 1 % of the Irish land area. • Mapping approaches support the development and targeting of conservation policies. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality in the face of climate change, accurately estimating forest carbon uptake is crucial. Environmental changes, such as temperature increases or precipitation fluctuations caused by climate change, can alter the growth responses of each species and act as a driver for changes in forest productivity. In particular, using a single-species growth model increases uncertainty in predicting stand growth, given the high species diversity in temperate forests in East Asia. Therefore, in this study, we identified the growth curve of 17 species-specific distributed in Korean temperate forest as well as the growth trend over the last 30 years (1976–2005). For this purpose, the species distribution and growth characteristics of 17 major Korean species of trees were analyzed using 13,808 tree-ring series from the 5th National Forest Inventory, which was conducted between 2006 and 2010. The growth characteristics of each species were analyzed using the average growth curve specific to each species, and the yearly growth trend was examined through residual analysis of the average growth curve. The distribution range of the 17 major tree species varied by species, as did the growth rate, ranging from 7 cm² · year⁻¹ to 17 cm² · year⁻¹ for the 30-year-old average basal area increment (BAI). Over time, fast-growing tree species exhibited initially high BAI, followed by a sharp decline from the 2000s. In contrast, broadleaf trees showed a continuous increase. Furthermore, a comparison of the total volume of individual trees and the volume of the yield table revealed that accurately estimating the actual forest volume was challenging. This study contributes to improving the accuracy of productivity predictions for temperate forests, underscoring the necessity to investigate the interaction between climate and forest succession in the future. • Species-specific growth models were developed using Inventory data of Korea. • The growth trends of species were influenced by their successional stage. • The tree growth was observed to be greater than that predicted. [ABSTRACT FROM AUTHOR]

• Combining Sentinel-1/-2 and a DTM improved classification in complex terrain. • Sentinel-2 predictors were more contributive than Sentinel-1. • Higher accuracies were achieved compared to Copernicus HRL 2018 DLT. • UNET outperformed random forest by single usage of Sentinel-1 predictors. • Map accuracy assessment using independent NFI plot data. Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free summer Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) and a forest mask were used to model and subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved and coniferous trees for the whole of Switzerland. A novel workflow was developed that is robust, cost-efficient and highly automated using reference data from aerial image interpretation. Two machine learning approaches based on Random Forest (RF) and deep learning (UNET) for the whole country with three sets of predictor variables were applied. 24 subareas based on aspect and slope categories were applied to explore effects of the complex mountainous topography on model performances. The reference data split into training, validation and test data sets was spatially stratified using a 25 km regular grid. Model accuracies of both RF and UNET were generally highest with Kappa (K) around 0.95 when predictors were included from both S1/S2 and the topographic variables aspect, elevation and slope from the DTM. While only slightly lower accuracies were obtained when using S2 and DTM data, lowest accuracies were obtained when only predictors from S1 and DTM were included, with RF performing worse than UNET. While on countrywide level RF and UNET performed overall similarly, substantial differences in model performances, i.e. higher variances and lower accuracies, were found in subareas with northwest to northeast orientations. The combined use of S1/S2 and DTM predictors mitigated these problems related to topography and shadows and was therefore superior to the single use of S1 and DTM or S2 and DTM data. The comparison with independent National Forest Inventory (NFI) plot data demonstrated precisions of K around 0.6 in the predictions of DLT and indicated a trend of increasing deviations in mixed forests. A comparison with the Copernicus High Resolution Layer (HRL) DLT 2018 revealed overall higher map accuracies with the exception of pure broadleaved forest. Although, spatial patterns of DTL were overall similar, UNET performed better than RF in areas with a distinct DLT on forest stand level, with the largest differences occurring when only S1 and DTM data was used. In contrast, predictions obtained from RF were more accurate in mixed stands. This study goes beyond the case study level and meets the requirements of countrywide data sets, in particular regarding repeatability, updating, costs and characteristics of training data sets. The 10 m countrywide DLT maps add complementary and spatially explicit information to the existing NFI estimates and are thus highly relevant for forestry practice and other related fields. [ABSTRACT FROM AUTHOR]

The future biomass carbon sequestration (BCS) in forests were often predicted by the theoretical stand age (TSA, based on the aging of the stand). Due to tree regeneration and various disturbances, however, the real stand age (RSA, calculated by averaging the age of single individuals in the stand) is often inconsistent with TSA in a given forest, and its effect on BCS prediction was poorly understood. Here, this study analysed the variations in RSA in three forest types (i.e., coniferous, broadleaf, and mixed broadleaf forests) of two forest origins (i.e., planted and natural forests) using the National Forest Inventory dataset of China between 1999 and 2018, and evaluated their effects on BCS between 2020 and 2060 using a random forest model. The ratio of RSA to TSA differed in forest origins and age groups. For all forest types, the ratio was higher and increased with an increase of age group in planted forests, while it showed opposite trends in natural forests, indicating a high variability in stand age in natural forests. The differences in predicted biomass carbon (C) storage between RSA and TSA varied over time. The enhanced C storage from RSA in natural forests in 2060 was characterized by a trend of mixed broadleaf forests (45.61 TgC) > broadleaf forests (17.62 TgC) > coniferous forests (8.16 TgC). Moreover, the predicted BCS in all forests were higher in the RSA scenario than in the TSA scenario and showed various trends between 2020 and 2060. Especially in natural forests, broadleaf forests showed a high and stable BCS (from 15.14 TgC yr−1 to 15.44 TgC yr−1) and mixed broadleaf forests exhibited an increased BCS (from 60.18 TgC yr−1 to 63.90 TgC yr−1) during this period. Our results confirmed the widespread phenomenon of inconsistency between RSA and TSA in China's forests and underlined their various effects on future forest BCS. More importantly, these results suggested that considering the RSA rather than the TSA is more scientific for forest C accounting. • Inconsistency between theoretical and real stand age is common in a given forest. • Ratio of real age to theoretical age differed in forest origins and age groups. • The predicted annual biomass C sinks were higher in the real stand age scenario. • Considering real stand age is more scientific for forest C accounting. [ABSTRACT FROM AUTHOR]

Effective and comprehensive monitoring of the quantity of deadwood has become an important aspect of forest inventories for studies on structural and demographic dynamics, biodiversity and carbon stocks. Assessing dead wood quantity, however, is challenging and time consuming due to the structural complexity of deadwood. To monitor coarse woody debris (CWD) in a 10-year remeasuring cycle, we propose to combine full area sampling, to assess and position large-diameter CWD, with line intersect sampling, to estimate the volume of small-diameter CWD. The aim was to simultaneously I) lower the work load in the field, II) ensure low variability in average CWD volume and III) enable remeasurement of a high share of individual CWD objects to study dead wood dynamics and related biodiversity. Using data from 1601 circular plots measured with full area sampling in 16 temperate forest reserves (Flanders, northern Belgium), we simulated line intersect transects and tested threshold diameters between 10 and 130 cm to subdivide CWD to be measured with full area or line intersect sampling. The work load of the combined sampling with a threshold diameter of 20 to 40 cm was about 50 to 90% of the full area sampling work load respectively. Yet, no significant increase in the coefficient of variation (CV) of the average CWD volume was registered for threshold diameters up to 30 cm (133 %) compared to the full area sampling (125 %). The probability to relocate a 30 cm diameter CWD object after 10 years was 50 % and the probability for remeasuring increased with diameter. Using full area sampling with 10 cm threshold results in only 32 % of the objects relocated and remeasured after 10 years; using the combined sampling, only positioning logs over 30 cm increases this figure to 67 %, thus avoiding idle work. We conclude that combining full area and line intersect sampling has the advantage of lowering the work load, increasing the share of relocated objects over time, while not significantly increasing the variability in average CWD volume. An optimal threshold diameter between both methods was comprised between 20 and 40 cm but might further depend on relative importance of work load, need for relocation, CV, the study setup (e.g. number and size of plots), stand characteristics (e.g. CWD volume, dominant tree species) and decay rate. We propose to use a 30 cm threshold diameter for measuring CWD in temperate forest reserves. • 1601 Full Area Sampling forest reserve plots with simulated Line Intersect Sampling. • Threshold diameter 30 cm to split-up Line Intersect to Full Area Sampling. • Reduced work load in the field of 17–33 %. • Little effect on the variability in average Coarse Woody Debris volume. • Increased share of Coarse Woody Debris objects re-measured after 10 years. [ABSTRACT FROM AUTHOR]

• Five vegetation structure metrics are derived from airborne laser scanning data. • The metrics describe the 3D forest edge structure, particularly the shelterbelt. • Our approach allows to retrieve area-wide, continuous edge structure information. • The information is used to assess the structural complexity of forest edges. 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. [ABSTRACT FROM AUTHOR]

Analyzing how climate change has affected forest growth is crucial for predicting future dynamics and adapting forest management to future climate change. In this paper, we investigate how climate change has modified stand dominant height dynamics and site index of 20 European tree species. We used an innovative method based on an annual height increment equation to model stand dominant height as a function of climate back to 1872 and of other stand environmental conditions. We used these models to simulate stand dominant height dynamics and site index under two different climates (prior to climate change and actual recent climate) to analyze the impact of climate change over the past century. To build our models, we combined the recently published FYRE long-term climate database, which provides daily data since 1871, with data from more than 17,000 forest stands of the French National Forest Inventory network. Higher temperature, precipitation and climatic water balance generally favor stand dominant height dynamics when the variables are considered separately. However, the positive effects often saturate at the higher end of the variable distribution. Over the past century, the effect of climate change on the site index has varied widely among species, ranging from a decrease of less than 3% to an increase of more than 5%. The effect of climate change has also varied within species, with more positive effects on initially temperature-limited stands for some species. For the species and environmental conditions considered, our results highlight a positive response of site index to past climate change for most species, albeit with between- and within-species differences. Our results also suggest that this positive response could become negative under continued climate change. These conclusions, as well as the quantitative relationships we provide between climate and stand dominant height dynamics or site index, will help design management strategies to adapt forests to climate change. • We modeled stand dominant height as a function of annual climate for 20 species. • On average, site index increased over the past century for most species. • Site index varied widely between and within species with climate change. • Site index increased mainly on temperature-limited sites. [ABSTRACT FROM AUTHOR]

Highlights • Mediterranean Pinus species showed a dissimilar increase in greening during summer. • P. nigra has heterogeneous behaviour that suggests the existence of different ecotypes. • Phenology and physical variables allowed a robust discrimination between species. • Start and end of the growing season, amplitude, and physical variables were important in discriminating species. Abstract Land surface phenology (LSP) can improve the monitoring of forest areas and their change processes. The aim of this study was to characterize the temporal dynamics in Mediterranean pines and evaluate the potential of LSP for species discrimination. We used 661 mono-specific plots for five different Pinus species (Pinus halepensis , P. pinea, P. pinaster ; P. sylvestris , P. nigra) and the MOD13Q1-NDVI time series (2000–2016) to perform the analyses. The time series were smoothed to extract the phenological parameters and calculate multi-temporal metrics, to synthesize the inter-annual variability. The potential of LSP for discriminating between Pinus species was evaluated by the application of the Random Forest (RF) classifier from different subsets of explanatory variables: i) the smooth time series; ii) the multi-temporal metrics; and iii) the multi-temporal metrics plus the auxiliary physical variables. This latter subset was also used as input to a Classification and Regression Tree (CART) algorithm to better explain the differences between Pinus species regarding LSP parameters and other environmental drivers. The analysis showed two different patterns: an important NDVI decrease during the summer for P. halepensis , P. pinea , and P. pinaster ; and lower NDVI variation along the year for P. sylvestris. P. nigra showed a heterogeneous intra-specific behavior, having locations with different patterns. We distinguished Pinus species plots with a global accuracy of 0.82, when we used multi-temporal metrics of LSP and auxiliary physical variables. More generally, the Mediterranean Pinus species could be differentiated considering the 23rd of July as the start of season and 179 km and 1100 m as distance to the coastline and elevation, respectively. [ABSTRACT FROM AUTHOR]

Highlights • In a span of seven years Texas experienced three major disturbance events. • National forest inventory data are useful for assessing disturbance effects. • Masking of trends was observed in dead tree estimates using the full set of data. • Estimates using specific subsets of data were helpful in assessing effects. • Adding new estimation capabilities to online analytical tools is recommended. Abstract Understanding the impacts of large-scale disturbances on forest conditions is necessary to support forest management, planning, and policy decision making. National forest inventories (NFIs) are an important information source that provide consistent data encompassing large areas, covering all ownerships, and extending through time. Here we compare how temporal aggregation approaches with NFI data affects estimates of standing dead trees as these respond to extreme disturbance events. East Texas was selected for this study owing to the occurrence of three significant disturbance events in a short span: Hurricane Rita in 2005, Hurricane Ike in 2008, and a historic drought in 2011. Wide-spread tree damage and mortality were reported after each disturbance and estimates of standing dead trees were used as the inventory variable for assessment. In the NFI of the US, the plot network is systematically divided into panels and one panel is measured each year. A measurement cycle is completed when all panels have been measured, which varies between 5 and 10 years depending on the region. Using the standard estimation approach of the US NFI, we computed population estimates using data from the full set of panels (FSP), multiple sets of panels (MSP), and single set of panels (SSP). For estimation, a single plot observation is computed from the most recent measurement of the plot that does not exceed the estimate year. Because one panel is measured per year, FSP and MSP estimates will necessarily consist of plot observations whose measurements were collected over a number of years. The SSP estimate is computed from one panel and thus all the plot observations are based on measurements collected over one year. We found that interpretations of disturbance event impacts varied depending on which sets of estimates were considered. All sets of estimates suggested a large and significant drought impact. However, differences existed among the estimates in the timing and magnitude of the impacts. The FSP estimates showed clear lag bias and smoothing of trends relative to the SSP estimates. MSP estimates were intermediate between FSP and SSP estimates. Differences in Hurricane Rita impacts were also observed between sets of estimates. Evidence of a net impact on standing dead trees following Hurricane Ike was weak among all sets of estimates. Given the potential for lag bias and smoothing, we recommend that SSP and MSP estimates be considered along with FSP estimates in assessments of large-scale disturbance impacts on forest conditions. [ABSTRACT FROM AUTHOR]

Abstract Forest ecosystems contribute substantially to carbon (C) storage. The dynamics of litter decomposition, translocation and stabilization into soil layers are essential processes in the functioning of forest ecosystems, as these processes control the cycling of soil organic matter and the accumulation and release of C to the atmosphere. Therefore, the spatial distribution of litter and soil C stocks are important in greenhouse gas estimation and reporting and inform land management decisions, policy, and climate change mitigation strategies. Here we explored the effects of spatial aggregation of climatic, biotic, topographic and soil variables on national estimates of litter and soil C stocks and characterized the spatial distribution of litter and soil C stocks in the conterminous United States (CONUS). Litter and soil variables were measured on permanent sample plots (n = 3303) from the National Forest Inventory (NFI) within the United States from 2000 to 2011. These data were used with vegetation phenology data estimated from LANDSAT imagery (30 m) and raster data describing environmental variables for the entire CONUS to predict litter and soil C stocks. The total estimated litter C stock was 2.07 ± 0.97 Pg with an average density of 10.45 ± 2.38 Mg ha−1, and the soil C stock at 0–20 cm depth was 14.68 ± 3.50 Pg with an average density of 62.68 ± 8.98 Mg ha−1. This study extends NFI data from points to pixels providing spatially explicit and continuous predictions of litter and soil C stocks on forest land in the CONUS. The approaches described illustrate the utility of harmonizing field measurements with remotely sensed data to facilitate modeling and prediction across spatial scales in support of inventory, monitoring, and reporting activities, particularly in countries with ready access to remotely sensed data but with limited observations of litter and soil variables. Graphical abstract Unlabelled Image Highlights • Spatial patterns found in the estimated litter and soil carbon stocks in forests • Including Normalized Difference Vegetation Index facilitated the model predictions. • Forest disturbances caused statistically significant differences in litter carbon. • Estimates of litter and soil carbon stocks were 2.07 Pg and 14.68 Pg, respectively. [ABSTRACT FROM AUTHOR]

• Initial estimate of United States National Forest System (NFS) old-growth forest area. • Applied existing NFS old growth definitions to Forest Inventory and Analysis data. • We estimate there are ∼ 10 million hectares of National Forest System (NFS) old growth. • Estimates and methods were designed respond to Executive Order #14072 in co-production with NFS officials. Old-growth forests are globally valued for their ecological attributes, cultural significance, and in many cases their rarity. Yet, defining and quantifying these forests has been a difficult task. This study developed an approach to consistently estimate extent of old-growth forest on United States Department of Agriculture (USDA) Forest Service National Forest System (NFS) lands, using NFS regional old-growth definitions applied to the US national forest inventory (conducted by the USDA Forest Service Forest Inventory and Analysis [FIA] program). This method was developed in response to a presidential order (EO#14072, April 22, 2022) and federal laws (e.g., Infrastructure Investment and Jobs Act, 2021; Inflation Reduction Act, 2022). We worked with NFS experts to obtain regionally approved criteria for establishing old growth status based on NFS definitions, assessments, and related documents. NFS regional old growth definitions focus on structural characteristics of forests with criteria for old growth status commonly including minimum abundance of large live trees (in eight of nine regions), tree or stand age (in eight of nine regions), and dead large tree density (in three of nine regions). Determining the regional criteria to use was straightforward for some NFS regions where old-growth forest definitions were specific, and in some cases, had already been applied to FIA data to quantify old-growth forest area. In other NFS regions, such as where definitions have never been applied in an operational manner, or where there were merely assessments of remnant old-growth forest conditions, determining exact criteria was more difficult. We estimate that there are approximately 10 million ha of old growth across NFS forests, as defined by NFS criteria, with the preponderance in the western US states. This study produces the first old-growth forest assessment at the national scale based on NFS definitions and FIA's statistically-rigorous national forest inventory of the US. These methods can be repeated with future inventories or modified when definitions change to produce updated estimates of old-growth forest attributes, and such work is already underway. [ABSTRACT FROM AUTHOR]

• We explored the potential of bi-temporal ALS data for site index estimation. • Species-specific site index models were developed using NFI and ALS data. • ALS estimated height and height increment were key predictors of site index. • Validation checks indicate that the functions seem suitable for their purpose. • Nationwide site index maps are provided at a resolution of 12.5 × 12.5 m. Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even-aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper-parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 – 2.11 m, and the relative RMSE was less than 10 % (7.68 – 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden. [ABSTRACT FROM AUTHOR]

Graphical abstract Modulation in basal area growth with climate for Quercus petraea according to three IPCC scenarios for the Orleans Forest area. Black dots correspond to IPCC historical climate values, colored dots correspond to modulation in basal area growth for three IPCC climate projections. The dashed line corresponds to smoothed model extrapolations for scenario RCP 8.5. Highlights • Large-scale NFI data provide growth models including silvicultural effects. • Tree rings data provide models for annual modulation of growth by climate. • Coupling both models allowed to develop climate-dependent stand growth models. Abstract Climate change has swept away the former general principles of long-term stability in forest productivity. New types of models are needed to predict growth and to plan forest management under future climate conditions. These models must remain robust for silvicultural practices and variations in climate. In this study, we present a new type of model development to achieve these goals. Our study focused on pure and mixed stands of Quercus petraea and Pinus sylvestris in central France. We used National Forest Inventory (NFI) data: respectively, 525 and 548 pure plots of Quercus petraea and Pinus sylvestris , and 68 plots of mixed species. We also used 108 tree cores from an experimental site of the same species. The cores cover the period from 1971 to 2013, making a total of 4572 individual annual increments. We coupled two types of models. One was developed with NFI data (transversal data). This model takes into account mean diameter and stand density effects on stand growth. It includes a set of biophysical factors accounting for stand fertility. The other one was developed with the data from tree cores (longitudinal data), and provides a climate modulation thanks to the correlation between ring width and yearly climate. The model with tree core data reveals the influence of December to July rainfalls on yearly variability in stand growth for Quercus petraea and of May to August rainfalls for Pinus sylvestris. We obtained a coupled model that allowed us to project growth up to 2100 for all the different IPCC scenarios but one; the model was outside its area of validity beyond 2060 for the RCP 8.5 scenario. [ABSTRACT FROM AUTHOR]

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009–2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k -nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10–20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher. [ABSTRACT FROM AUTHOR]

• High Nature Value (HNV) forest are characterised by high levels of naturalness. • A framework for identifying HNV forest is proposed; six indicators were selected for constructing a Nature Value (NV) index. • The NV index was determined for 1676 NFI plots in the ROI and c. 11% of the forest plots were identified as HNV forest. • The framework presented can serve as a guide for HNV forest identification in other biogeographical regions. High Nature Value (HNV) forests contribute to maintaining European biodiversity and public good supply. This study aimed to a) develop an objective and quantitative Nature Value (NV) index for the identification of HNV forests in the Republic of Ireland; and b) apply and validate the index using available data from the Irish National Forest Inventory (NFI). Following recent European definitions of HNV forest, a six-step framework was adapted from literature and used for assessing forest naturalness. The reference forest (in an Irish context) and its naturalness traits were first described. Six indicators were selected to construct a NV index and three categories (low, medium and high NV) were defined based on the range of NV scores. Using data from the Irish NFI, the approach was implemented by calculating the indicators' values and the NV score for 1,676 forest plots. The selected indicators were tested for redundancy and the NV index was validated with available floristic variables and with forest sub-type classes. A sensitivity analysis was conducted on the weighting values of the indicators. Approximately 11% of the NFI plots were categorised as HNV. There was no redundancy between the selected indicators. The NV index was significantly positively correlated with data from the floristic variables collated by the NFI. The averages of the floristic variables per NV category were significantly different. NFI plots classified as HNV had a higher percentage of natural/semi-natural forest types than medium NV and low NV plots. The sensitivity analyses showed little effect of changes to the indicators' weighting values on a) the correlation coefficients between the floristic variables' data and the new NV scores obtained and b) on the proportion of natural/semi-natural forests in HNV plots. This work provides an approach for the development of a NV index to identify HNV forests in a European country following the naturalness concept exclusively. The selected indicators and their weighting should be tailored to each country's particular conditions, especially due to potential differences in the reference level of naturalness of forests and differences within NFIs. [ABSTRACT FROM AUTHOR]

• Evaluating methods to derive stand descriptions from large-scale sampling data. • Multi-scale approach to improve initialisation of dynamic forest models. • Simultaneous parameter prediction method best to predict tree diameter distributions. • Random Forest approach best to predict tree species composition. Most strategic and operational forest management decisions are taken based on stand-level information, and quantitative models of forest dynamics are key for developing sustainable management strategies. However, data on forest stands for the initialisation of such models that are representative at large spatial scales, e.g., countries or ecoregions, are often lacking. National Forest Inventories (NFIs) provide forest data from small sample plots at large spatial scales, yet deriving full stand information based on such data is challenging. Here, we evaluate seven methods of varying complexity for deriving quantitative stand descriptions based on sample data as provided by the Swiss NFI. We selected 271 extensively measured Swiss forests stands with unimodal diameter distributions, classified them as beech- vs. spruce-dominated in five development stages and randomly placed a small sized sample plot in each stand using the Swiss NFI sampling design (i.e., a circular plot of 500 m2). Seven modelling approaches were used to derive diameter distributions and species-specific stem numbers (i.e., tree species composition) from the sample data that are representative for a particular stand (local scale) and for stand types in general (generalised scale). The prediction performance of the modelling approaches was evaluated using 100 random samples per stand to calculate prediction errors. Generalised even-aged diameter distributions were best predicted by the simultaneous parameter prediction method (PPM), i.e. a combined three-step regression approach, with on average 1.3 to 2.5 times lower prediction errors compared to the simple pooling of diameter samples. However, uneven-aged diameter distributions were best predicted by pooling. At the local scale, the simultaneous PPM performed best for data from sample plots with fewer than 17 to 19 trees across all development stages. Prediction performance of the PPMs increased for structurally and spatially diverse local stands with positively skewed diameter distributions. A Random Forest approach was most suitable for predicting species composition at both the generalised and the local scale. Our study evaluates the strengths and weaknesses of methods to model stands based on data from small sample plots. We emphasise terminological pitfalls by consequently distinguishing local accuracy and generalised representativity of the stand descriptions. We demonstrate the feasibility of deriving locally accurate stands using data from small forest sample plots and evaluate the derivation of generalised stands representative at large regions. At both scales, our developments contribute to an improved initialisation of forest models and thus to a more realistic modelling of forest development under future boundary conditions. [ABSTRACT FROM AUTHOR]

Large-scale surveys, such as national forest inventories and vegetation monitoring programs, usually have complex sampling designs that include geographical stratification and units organized in clusters. When models are developed using data from such programs, a key question is whether or not to utilize design information when analyzing the relationship between a response variable and a set of covariates. Standard statistical regression methods often fail to account for complex sampling designs, which may lead to severely biased estimators of model coefficients. Furthermore, ignoring that data are spatially correlated within clusters may underestimate the standard errors of regression coefficient estimates, with a risk for drawing wrong conclusions. We first review general approaches that account for complex sampling designs, e.g. methods using probability weighting, and stress the need to explore the effects of the sampling design when applying logistic regression models. We then use Monte Carlo simulation to compare the performance of the standard logistic regression model with two approaches to model correlated binary responses, i.e. cluster-specific and population-averaged logistic regression models. As an example, we analyze the occurrence of epiphytic hair lichens in the genus Bryoria ; an indicator of forest ecosystem integrity. Based on data from the National Forest Inventory (NFI) for the period 1993–2014 we generated a data set on hair lichen occurrence on >100,000 Picea abies trees distributed throughout Sweden. The NFI data included ten covariates representing forest structure and climate variables potentially affecting lichen occurrence. Our analyses show the importance of taking complex sampling designs and correlated binary responses into account in logistic regression modeling to avoid the risk of obtaining notably biased parameter estimators and standard errors, and erroneous interpretations about factors affecting e.g. hair lichen occurrence. We recommend comparisons of unweighted and weighted logistic regression analyses as an essential step in development of models based on data from large-scale surveys. [ABSTRACT FROM AUTHOR]

Accurate carbon-balance accounting in forest soils is necessary for the development of climate change policy. However, changes in soil organic carbon (SOC) occur slowly and these changes may not be captured through repeated soil inventories. Simulation models may be used as alternatives to SOC measurement. The Yasso07 model presents a suitable alternative because most of the data required for the application are readily available in countries with common forest surveys. In this study, we test the suitability of Yasso07 for simulating SOC stocks and stock changes in a variety of European forests affected by different climatic, land use and forest management conditions and we address country-specific cases with differing resources and data availability. The simulated SOC stocks differed only slightly from measured data, providing realistic, reasonable mean SOC estimations per region or forest type. The change in the soil carbon pool over time, which is the target parameter for SOC reporting, was generally found to be plausible although not in the case of Mediterranean forest soils. As expected under stable forest management conditions, both land cover and climate play major roles in determining the SOC stock in forest soils. Greater mean SOC stocks were observed in northern latitudes (or at higher altitude) than in southern latitudes (or plains) and conifer forests were found to store a notably higher amount of SOC than broadleaf forests. Furthermore, as regards change in SOC, an inter-annual sink effect was identified for most of the European forest types studied. Our findings corroborate the suitability of Yasso07 to assess the impact of forest management and land use change on the SOC balance of forests soils, as well as to accurately simulate SOC in dead organic matter (DOM) and mineral soil pools separately. The obstacles encountered when applying the Yasso07 model reflect a lack of available input data. Future research should focus on improving our knowledge of C inputs from compartments such as shrubs, herbs, coarse woody debris and fine roots. This should include turnover rates and quality of the litter in all forest compartments from a wider variety of tree species and sites. Despite the limitations identified, the SOC balance estimations provided by the Yasso07 model are sufficiently complete, accurate and transparent to make it suitable for reporting purposes such as those required under the UNFCCC (United Nations Framework Convention on Climate Change) and KP (Kyoto Protocol) for a wide range of forest conditions in Europe. [ABSTRACT FROM AUTHOR]

Although the annual diameter growth of trees is vital for assessing site suitability in terms of potential timber yield, the effects of climatic and topographic factors on this variable are poorly understood. The main objective of this study was to develop a tree-level radial growth model incorporating topographic and climatic factors for four major temperate tree species [red pine ( Pinus densiflora ), oak ( Quercus spp.), Japanese larch ( Larix kaempferi ), and Korean pine ( Pinus koraiensis )] in South Korea. The model was developed and then validated using increment cores sampled from permanent plots in the Korean National Forest Inventory country wide. The Standard Growth (SG) of each increment core, which eliminated the effect of tree age on radial growth, was derived using a SG model. Spatial autocorrelation was detected for the SGs of every species, but not for the original radial growth data. The results showed that using the SG model to standardize radial growth for age was successful for explaining the impact of topographic and climatic factors on radial growth. The influence of climatic (warmth index and precipitation effectiveness index) and topographic (topographic wetness index) factors on the SG of each species was evaluated by the estimated SG (eSG) model. Results show that for all species each variable was correlated to SG. The mean R 2 of the final radial growth model for red pine, oak, Japanese larch, and Korean pine during 2001–2009 were estimated to be 0.71, 0.73, 0.67, and 0.65, respectively. In addition, for every tree species the time sequence of estimated annual radial growth exhibited similar characteristics to that of the observed annual radial growth on an individual tree scale. Thus, this growth model can contribute to an understanding of the impacts of topographic and climatic factors on tree radial growth and predict the annual growth changes of major tree species in South Korea, given climate change. [ABSTRACT FROM AUTHOR]

The National Mapping Agency in Sweden has conducted an airborne laser scanning (ALS) campaign covering almost the entire country for the purpose of creating a new national Digital Elevation Model (DEM). The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegl, and Trimble scanners and have a point density of 0.5–1.0 pulses/m 2 . A high resolution national raster database (12.5 m × 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2–22.0%, 13.9–18.2%, 5.4–9.5%, and 8.7–13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand-level. An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. Thus, all predicted forest variables can be viewed and downloaded free of charge at the Swedish Forest Agency's homepage ( http://www.skogsstyrelsen.se/skogligagrunddata ). [ABSTRACT FROM AUTHOR]

Climate is an important driver of forest health, productivity, and carbon cycle, but our understanding of these effects is limited for many regions and ecosystems. We present here a large-scale evaluation of climate effects on the productivity of three temperate tree species. We determine whether the National Forest Inventory data (NFI) collected in the Czech Republic (14,000 plots) and Slovakia (1,180 plots) contains sufficient information to be used for designing the regional climate-productivity models. Neural network-based models were used to determine which among 13 tested climate variables best predict the tree species-specific site index (SI). We also explored the differences in climate-productivity interactions between the drier and the moister part of the distribution of the investigated species. We found a strong climatic signal in spruce SI (R 0.45–0.62) but weaker signals in fir and beech (R 0.22–0.46 and 0.00–0.49, respectively). We identified the most influential climate predictors for spruce and fir, and found a distinct unimodal response of SI to some of these predictors. The dominance of water availability-related drivers in the dry-warm part of a species’ range, and vice versa, was not confirmed. Based on our findings, we suggest that (i) the NFI-based SI is responsive to climate, particularly for conifers; (ii) climate-productivity models should consider the differences in productivity drivers along ecological gradients, and models should not be based on a mixture of dry and moist sites; and (iii) future studies might consider the subset of influential climate variables identified here as productivity predictors in climate-productivity models. [ABSTRACT FROM AUTHOR]

Mortality is a key process in forest stand dynamics. However, tree mortality is not well understood, particularly in relation to climatic factors. The objectives of this study were to: ( i ) determine the patterns of maximum stem number per ha (MSN) over dominant tree height from 5-year remeasurements of the permanent sample plots for temperate forests [Red pine ( Pinus densiflora ), Japanese larch ( Larix kaempferi ), Korean pine ( Pinus koraiensis ), Chinese cork oak ( Quercus variabilis ), and Mongolian oak ( Quercus mongolica )] using Sterba’s theory and Korean National Forest Inventory (NFI) data, ( ii ) develop a stand-level mortality (self-thinning) model using the MSN curve, and ( iii ) assess the impact of temperature on tree mortality in semi-variogram and linear regression models. The MSN curve represents the upper boundary of observed stem numbers per ha. The developed mortality model with our results showed a high degree of reliability (R 2 = 0.55–0.81) and no obvious dependencies or patterns in residuals. However, spatial autocorrelation was detected from residuals of coniferous species (Red pine, Japanese larch and Korean pine), but not for oak species (Chinese cork oak and Mongolian oak). Based on the linear regression analysis of residuals, we found that the mortality of coniferous forests tended to increase with the rising seasonal temperature. This is more evident during winter and spring months. Conversely, oak mortality did not significantly vary with increasing temperature. These findings indicate that enhanced tree mortality due to rising temperatures in response to climate change is possible, especially in coniferous forests, and is expected to contribute to forest management decisions. [ABSTRACT FROM AUTHOR]

• Forest biomass and productivity are correlated to tree density and NDVI. • Species models differ on the effect of soil factors on biomass and productivity. • Aridity negatively affects forest biomass and productivity. • Projections of increasing aridity show a decrease on forest biomass and productivity. One of the main challenges under global warming is understanding and predicting the effects of increased aridity on the carbon sink role of forests, particularly in Mediterranean regions. Forest inventories monitor the real state of the forest at a high temporal and financial cost. Cloud computing tools and high spatio-temporal resolution datasets generate fast and low-cost remote sensing data. Our objective is to understand the underlying variables explaining carbon storage (aboveground biomass) and forest productivity of Mediterranean forests using remote and in field-based variables and predict expected future trends. Then, we quantify the potential effects of a hypothetical increase in aridity under climate change on aboveground biomass and forest productivity. We included remote sensing indices (NDVI), abiotic factors (climate, soil and topography) and biotic factors (forest structure) as key variables of forest biomass and productivity in a large and heterogeneous Mediterranean region (Andalusia, southern Spain). We used around 7000 forest plots from the second and the third Spanish National Forest Inventory (1995 and 2006) considering the eight most abundant species (Olea europaea, Pinus pinea, P. pinaster, P. halepensis, P. nigra, P. sylvestris, Quercus ilex subsp ballota, and Q. suber). The variance explained by the models ranged from 25% in Q. ilex forests to 65% in P. sylvestris forests. Aridity affected all-species and Quercus biomass and most productivity models. NDVI and tree density had a strong positive effect on forest biomass and productivity with a significant interaction effect in all-species models, whereas aridity had a negative effect on both. The predicted increase in aridity under future climate change scenarios could seriously reduce forest biomass by 18% and productivity by 16%. Our study suggests that aridity is a key factor determining forest biomass and productivity in Mediterranean forests, that could potentially lead to reductions of their carbon sink role. [Display omitted] [ABSTRACT FROM AUTHOR]

• Modern aspen forest management guides are based on data from nearly a century ago. • Current atmospheric CO2, climate, and management objectives changed over a century. • Mismatched data for guides and forest conditions reduces forest health and management. • Aspen stocking tables recalculated with modern data show that guides are outdated. • New management guides should be dynamic to empower adaptive management. Since the development of contemporary stocking techniques a century ago, the combination of climatic, atmospheric, financial, and social factors that determine forest management strategies have changed, altering aspen stand dynamics in the western Great Lakes, USA. Despite this, aspen management is still informed by 1970s management guides that are based on 1920s inventories; hence, a century exists between the data that underlie current management guidelines and current stand conditions. We hypothesized that current aspen stands may support higher stocking and height growth than nearly a century ago at relatively similar age and site indices, due to increased atmospheric CO 2 concentrations and fertilization, intensive coppice harvests, and other factors. To explore this question, we compared historic aspen observations with comparable contemporary data from the USDA Forest Service's Forest Inventory and Analysis program. The results show increased stand stocking levels as well as increased height growth of aspen throughout the region over the historic inventory data. Although other controlled experimental studies support the hypothesis of increased carbon fertilization altering aspen size-density relationships, our study is the first to examine an empirical application to forest management guides. Our results suggest a comprehensive reevaluation of aspen growth dynamics under contemporary environmental conditions is warranted. We highlight the need to assess the value of current stocking standards in an era of increasingly variable environmental conditions and to reimagine a more dynamic, responsive, and predictive approach to guide forest management for future application as global change may accelerate. [ABSTRACT FROM AUTHOR]

For forest ecosystems, studies dealing with diversity-productivity relationships are often based on diameter increment observations. Studying how height growth is affected by species interactions can provide new insights on this issue. We studied the mixture effect on dominant height growth in order to answer two questions. Do species interactions in mixed forest modify the dominant height growth of species? Does the diversity effect on diameter found in previous studies correspond to actual overyielding, or rather to an effect on allocation of growth between diameter and height? We used the French National Forest Inventory (NFI) data to model the mixture effect on dominant height. We included biophysical factors in the models to compare the dominant height of mixed and monospecific stands, all other parameters being equal. We studied five target species – Quercus petraea (Matt.) Liebl., Fagus sylvatica L., Picea abies (L.) Karst., Abies alba Mill., and Pinus sylvestris L. – in association with sixteen other species. Mixture effects on dominant height were weak, though often significant. They were either positive or negative according to species association. We showed that mixture effect on dominant height corresponds to a leveling process between species: the taller one limits its growth while the smaller one’s growth increases. Furthermore, most of the time, mixture effects on dominant height are in the same direction as those found on diameter, though with a lower magnitude. Our results confirm that tree diversity results in overyielding rather than in a different allocation of volume between the parts of the tree. [ABSTRACT FROM AUTHOR]

The relatively small sampling intensities used by national forest inventories are often insufficient to produce the desired precision for estimates of population parameters unless the estimation process is augmented with auxiliary information, usually in the form of remotely sensed data. The k-Nearest Neighbors (k-NN) technique is a non-parametric, multivariate approach to prediction that has emerged as particularly popular for use with forest inventory and remotely sensed data and has been shown to contribute substantially to increasing precision. k-NN predictions are calculated as linear combinations of observations for sample units that are nearest in a space of auxiliary variables to the population unit for which a prediction is desired. Implementation of a nearest neighbors algorithm requires four choices: (i) a distance metric, (ii) specific auxiliary variables to be used with the distance metric, (iii) the number of nearest neighbors, and a (iv) scheme for weighting the nearest neighbors. Regardless of the choices for a distance metric and weighting scheme, emerging evidence suggests that optimization of the technique, including selection of an optimal subset of auxiliary variables, greatly enhances prediction. However, optimization can be computationally intensive and time-consuming. A promising approach that is gaining favor is based on genetic algorithms, a technique that uses search heuristics that mimic natural selection to solve optimization problems. The objective of the study was to compare optimized k-NN configurations with respect to inferences for mean volume per unit area using airborne laser scanning variables as auxiliary information. For two study areas, one in Norway and one in Minnesota, USA, the analyses focused on optimizing k-NN configurations that used the weighted Euclidean and canonical correlation distance metrics and two neighbor weighting schemes. Novel features of the study include introduction of a neighbor weighting scheme that has not previously been used for forestry applications, simultaneous optimization of all four k-NN choices, and basing comparisons on confidence intervals, rather than intermediate products such as prediction accuracies. Two conclusions were primary: (1) optimized selection of feature variables produced greater precision than using all feature variables, and (2) computational intensity necessary to optimize the weighted Euclidean metric was considerably greater than for the canonical correlation analysis metric. Specific findings were that optimization produced pseudo-R 2 as large as 0.87 for the Norwegian dataset and as large as 0.89 for the Minnesota dataset. For the optimized canonical correlation distance metric, widths of approximate 95% confidence intervals as proportions of the estimated means were as small as 0.13 for the Norwegian dataset and as small as 0.15 for the Minnesota dataset. [ABSTRACT FROM AUTHOR]

Dead wood is a critical resource for forest biodiversity and widely used as an indicator for sustainable forest management. Based on data from the Swedish National Forest Inventory we provide baseline information and analyze trends in volume and distribution of dead wood in Swedish managed forests during 15 years. The data are based on ≈30,000 sample plots inventoried during three periods (1994–1998; 2003–2007 and 2008–2012). The forest policy has since 1994 emphasized the need to increase the amount of dead wood in Swedish forests. The average volume of dead wood in Sweden has increased by 25% (from 6.1 to 7.6 m 3 ha −1 ) since the mid-1990s, but patterns differed among regions and tree species. The volume of conifer dead wood (mainly from Picea abies ) has increased in the southern part of the country, but remained stable or decreased in the northern part. Heterogeneity of dead wood types was low in terms of species, diameter and decay classes, potentially negatively impacting on biodiversity. Overall, we found only minor effects of the current forest policy since most of the increase can be attributed to storm events creating a pulse of hard dead wood. Therefore, the implementation of established policy instruments (e.g. legislation and voluntary certification schemes) need to be revisited. In addition to the retention of dead trees during forestry operations, policy makers should consider calling for more large-scale targeted creation of dead trees and management methods with longer rotation cycles. [ABSTRACT FROM AUTHOR]

Forest ecosystems are the largest terrestrial carbon sink on earth, with more than half of their net primary production moving to the soil via the decomposition of litter biomass. Therefore, changes in the litter carbon (C) pool have important implications for global carbon budgets and carbon emissions reduction targets and negotiations. Litter accounts for an estimated 5% of all forest ecosystem carbon stocks worldwide. Given the cost and time required to measure litter attributes, many of the signatory nations to the United Nations Framework Convention on Climate Change report estimates of litter carbon stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, the country-specific model used to predict litter C stocks is sensitive to attributes on each plot in the national forest inventory, but these predictions are not associated with the litter samples collected over the last decade in the national forest inventory. Here we present, for the first time, estimates of litter carbon obtained using more than 5000 field measurements from the national forest inventory of the United States. The field-based estimates mark a 44% reduction (2081 ± 77 Tg) in litter carbon stocks nationally when compared to country-specific model predictions reported in previous United Framework Convention on Climate Change submissions. Our work suggests that Intergovernmental Panel on Climate Change defaults and country-specific models used to estimate litter carbon in temperate forest ecosystems may grossly overestimate the contribution of this pool in national carbon budgets. [ABSTRACT FROM AUTHOR]

Acquiring forest resources information for tropical developing countries is challenging due to financial and logistical constraints. Yet, this information is critical for enhancing management capability and engaging in international initiatives such as Reducing Emissions from Deforestation and forest Degradation (REDD +). The use of multi-source inventories (i.e., remote-sensing, field, and other data) in integrated models has shown increasing promise for accurately estimating forest attributes at lower costs. In this study, we compared the use of Landsat 8 OLI versus RapidEye satellite imagery in four modeling approaches (generalized linear model (GLM), generalized additive model (GAM), k-Nearest Neighbors (k-NN), Random Forests), with and without auxiliary information (e.g., soils characteristics, distance to roads, etc.) to estimate percent canopy cover by pixel for an ~ 1,000,000 ha area in Zambia. We derived plot-level canopy cover as the dependent variable, using field-measured data collected according to current National Forest Inventory (NFI) protocol. Using cross-validation statistics, Landsat 8 OLI exhibited better results than RapidEye across modeling approaches likely due to the additional short-wave infrared bands which consistently improved model performance (average root mean squared prediction error = 10.1% versus 11.0%). The GAM approach was more precise, though more challenging to fit. For both remote sensing data sources and all modeling approaches, other auxiliary information improved the model; soil variables were commonly selected for inclusion using a Genetic Algorithm. Using a binomial GAM with Landsat 8 OLI and soil variables, and by applying the current FAO forest/non-forest definition (i.e., canopy cover > 10% for a 0.5 ha area), we estimated the total forest area as 758,100 ha (95% bootstrapped confidence interval of ± 3,953 ha). Overall, our research indicates that sufficiently accurate forest area estimates for Zambia can be obtained using canopy cover GAM models that incorporate NFI data and freely-available remote sensing imagery and soil information. [ABSTRACT FROM AUTHOR]

The aim of this study was to quantitatively assess and analyze the dynamic changes and current problems of Chinese forest resources based on the National Forest Inventory (NFI). In this study, a hierarchical model was established using the analytic hierarchy process to assess forest resources quality (FRQ) at the provincial level. Four criteria were used, including forest quantity, forest productivity, forest structure, and forest health and each criterion was further composed of multiple factors. Among these assessment factors, stock volume per unit area was the most important, while canopy structure was the least important. The ranges of FRQ Indies across China during the 6th NFI (1999–2003), 7th NFI (2004–2008), and 8th NFI (2009–2013) were 0.3031–0.6366, 0.3499–0.7186, and 0.3534–0.7555, respectively. From the 6th to 8th NFI, forest quality improved by different degrees for all provinces, whereas the other three criteria presented an increasing or decreasing trend. In general, the implementation of ecological projects has significantly improved the FRQ at provincial and national levels. During the 8th NFI, the FRQ levels were excellent for 3 provinces, good for 15 provinces, medium for 12 provinces, and only one province exhibited an inferior level of FRQ. Based on cluster analysis, Chinese forest resources during the 8th NFI could be grouped into four clusters according to the provincial administrative region, and each cluster had its own advantages and disadvantages. Stock volume increment and forest calamity were in a very good state, while canopy structure was the key factor limiting the FQR for all the clusters. Some relevant measures were proposed to improve the existing conditions of Chinese forest resources. The results of this study are significant in that they can provide theoretical and technical references for future adjustment and sustainable management of forest resources in China. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a strategic forest inventory approach that has been applied on public land in Victoria, Australia. The Victorian Forest Monitoring System is an integral component of a monitoring and reporting program that enables Victoria to critically assess and evaluate its progress towards achieving its sustainable forest management objectives and targets. The inventory approach utilises field measurements in combination with remote sensing data. The approach is novel in that it utilises a relatively small sample size with a stratification scheme designed to examine questions around tenure and land management options. The small sample was dictated by limited resources (capacity, time and budget). The stratification scheme was designed to explore different land management approaches (e.g. National Parks versus State forest), as a more consistent and balanced approach to the management of public forests is currently being considered by most State Governments in Australia. The resultant accuracy of estimates for management and bioregion strata and associated characteristics, like above ground biomass from this small sample size were found to be sufficient for the regional monitoring goals of this study. [ABSTRACT FROM AUTHOR]

Within the Paris Agreement's Enhanced Transparency Framework, consistent data collections are the prerequisite for a successful reporting of GHG emissions. For such purposes, NFIs are usually the primary source of information, even if they are frequently not designed for producing estimations on a yearly basis and in the form of wall-to-wall high-resolution maps. In this framework, we present a new spatial model to produce yearly growing stock volume (GSV), above-ground biomass (AGB), and carbon stock wall-to-wall estimates. We tested the model in Italy for the period 2005–2018, obtaining a time-series of yearly maps at 23 m spatial resolution. Results were validated against the 2015 Italian NFI reaching an average RMSE% of 19% for aggregated areas. Results were also compared against data reported by the Italian GHG inventory, reaching an RMSE% of 28% and 20% for GSV and carbon stock respectively. We demonstrated that the modeling approach can be successfully used for setting up a forest monitoring system to meet the interests of governments in inventories of GHG emissions and private entities in carbon offset investments. • A spatial approach for multitemporal estimation of carbon stock is presented. • The approach is consistent with the IPCC's best guidance and practices. • The aboveground carbon stock of forests in Italy exceeded 566 million tons in 2018. • Results are consistent with official Greenhouse gasses and national forest inventories. [ABSTRACT FROM AUTHOR]

• Trends of basal area growth and mean height were studied in the period 1983–2020. • The study was made for 20 to 60 year old pines and spruces in Sweden. • On average, mean height at a given age of both species has increased by 2 m. • The basal area growth level was stable in the period. • Current trees are becoming taller and slenderer in Swedish forests. Changes over time in annual basal area growth and mean height for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) over the period, 1983–2020 were studied using sample tree data from temporary plots recorded in the Swedish National Forest Inventory. The annual basal area growth was derived from the last measured full ring on increment cores. Using 20 to 60-year-old dominant trees, the mean height and annual basal area growth were examined as functions of tree, stand and site conditions, and trends were assessed mainly using residual analyses over time. A significant increase in mean height at a given age was found for both species, but the annual basal area growth level remained stable over the 38-year period. Currently, at a given age of 50 annual rings at breast height, the mean heights of pines and spruces increased on average by 10.1% (i.e. ∼2 m), compared to 50 year-old pines and spruces in the 1980s, and the increase was similar in the different regions. The results suggest that trees have become taller and slenderer in Swedish forests. Increasing tree height over time at a given age in Northern Europe has been documented in several reports and many causes have been suggested, such as changed forest management, increasing temperatures and nitrogen deposition. We suggest that elevated CO 2 in the air and improved water-use efficiency for the trees might also be strong drivers. [ABSTRACT FROM AUTHOR]

• We tested a rapid assessment for saproxylic insects based on feeding galleries and boreholes. • 3641 standing and lying dead objects were examined in 1,444 NFI plots for insect traces. • We found 2,770 insect traces of the orders Coleoptera (n = 2,624), Hymenoptera (n = 143) and Lepidoptera (n = 3) • All insect abundances increased on trees taller than 18 m and above a living stand volume of 41 m3ha−1. • We recommend a tree species-specific dead wood management already considering living wood. Knowledge of habitat requirements of saproxylic insects and their response to habitat changes is critical for assessing the ecological impacts of forest management. Several studies have demonstrated a positive relationship of tree-species richness, deadwood volume, or structural diversity with saproxylic species diversity, while the relationship with the abundance of potential pest species have often been negative. A better understanding of which factors drive saproxylic insects' occurrence is therefore essential for deriving urgently needed thresholds for key habitat conditions. We tested a rapid assessment method applicable at large scale based on recorded feeding galleries and boreholes assessed during the Austrian National Forest Inventory to investigate the drivers and habitat thresholds of different saproxylic insect families; i.e. Buprestidae, Cerambycidae, Curculionidae, Siricidae, at multiple spatial scales; i.e. at the object, forest stand and landscape level. We modelled the relative abundance of all insects and these families considering nineteen explanatory variables using ordinal logistic regression models. Key habitat characteristics were identified using recursive partitioning. Our results revealed complex interactions among influencing factors at different spatial scales. We showed that deadwood volume was of surprisingly little importance. Instead, individual tree characteristics were of major importance, demonstrating the value of resource quality and variability. The abundance of all saproxylic insect families increased with advancing decomposition, on trees taller than 18 m, and above a living stand volume of 41 m3ha−1. Aiming to guide forest management, not only forest type-specific, but tree species-specific deadwood management is needed, taking into account site-specific conditions, including temperature and precipitation. For assessing temporal trends in insect colonization and habitat dynamics as well as the effects of forest management, we propose a continuous monitoring of insect traces, including living but weakened trees. This will allow for further thresholds that are urgently needed for maintaining biological diversity in forest ecosystems in the face of climate change. [ABSTRACT FROM AUTHOR]

Sustainable forest management (SFM) is many things to many people – yet a common thread is the production of forest goods and services for the present and future generations. The promise of sustainability is rooted in the two premises; first that ecosystems have the potential to renew themselves and second that economic activities and social perceptions or values that define human interaction with the environment are choices that can be modified to ensure the long term productivity and health of the ecosystem. SFM addresses a great challenge in matching the increasing demands of a growing human population while maintaining ecological functions of healthy forest ecosystems. This paper does not seek to define SFM, but rather provides analyses of key indicators for the national-scale enabling environment to gain a global insight into progress in implementing enabling and implementing SFM at the national and operational levels. Analyses of the Global Forest Resources Assessment 2015 (FRA) country report data are used to provide insights into the current state of progress in implementing the enabling conditions for SFM. Over 2.17 billion ha of the world’s forest area are predicted by governments to remain in permanent forest land use, of which some 1.1 billion ha are covered by all of the SFM tools investigated in FRA 2015. At the global scale, SFM-related policies and regulations are reported to be in place on 97% of global forest area. While the number of countries with national forest inventories has increased over that past ten years from 48 to 112, only 37% of forests in low income countries are covered by forest inventories. Forest management planning and monitoring of plans has increased substantially as has forest management certification, which exceeded a total of over 430 million ha in 2014. However, 90% of internationally verified certification is in the boreal and temperate climatic domains – only 6% of permanent forests in the tropical domain have been certified as of 2014. Results show that more work is needed to expand the extent and depth of work on establishing the enabling conditions that support SFM over the long term and suggests where those needs are greatest. [ABSTRACT FROM AUTHOR]

Accurate quantification of forest loss is required to meet international reporting requirements, even in countries where deforestation rates are low. In Ireland, recent evidence has suggested the rate of gross annual deforestation is increasing. However, no spatially explicit data on the extent and character of contemporary deforestation exists. Here, we quantify deforestation in a region where forest loss is rare. Deforestation estimates derived from wall-to-wall photointerpretation, official records (e.g. felling licences), the CORINE land-use/land cover changes dataset and a combined approach (hereafter termed “the Deforestation Map”) are compared in two regions in Ireland for the period 2000 to 2012. Deforestation area based on the Deforestation Map (1497 ha) was greater than estimates derived from using photo-interpretation (730 ha), official records (908 ha) and CORINE (139 ha) alone. Independent accuracy assessment highlighted high errors of omission for photo-interpretation (68.9%), official records (66.7%) and CORINE (91.84%) estimates compared to the Deforestation Map (20%). No general increase in the deforestation rate during the study period was recorded, despite regional variations. Post deforestation land-use transitions were principally to wetland, grassland and settlement although the magnitude and proportion of change varied regionally. Gross annual deforestation was higher in older broadleaf forests than in conifer plantation forests, a surprising finding considering the small area and conservation status of many broadleaf forests in Ireland. For countries with small forest area and/or low rates of deforestation, the use of methodologies employed herein can provide a valuable record of forest loss and be used to validate sample-based or remotely sensed deforestation estimates. [ABSTRACT FROM AUTHOR]

The growing awareness of biodiversity by forest managers has fueled the demand for information on abiotic and biotic factors that determine spatial biodiversity patterns. Detailed and area-wide environmental data on potential predictors and site-specific habitat characteristics, however, are usually not available across large spatial extents. Recent developments in environmental data acquisition such as the advent of Light Detection And Ranging (LiDAR) remote sensing provide opportunities to characterize site-specific habitat conditions at a high level of detail and across large areas. Here, we used a dataset of regularly distributed local-scale records of vascular plant, bryophyte and snail (Gastropoda) species to model richness patterns in forests across an environmentally heterogeneous region in Central Europe (Switzerland). We spatially predicted species richness based on a set of area-wide environmental factors representing climate, topography, soil pH and remotely sensed vegetation structure. Additionally, we investigated the relationship between species richness and field measures of forest stand structure and composition obtained from National Forest Inventory (NFI) data to identify potential target variables for habitat management. The predictions for species richness were most accurate for snails, followed by bryophyte and vascular plants, with R 2 values ranging from 0.37 to 0.07. Besides climate, site-specific factors such as soil pH, indices of topographic position and wetness as well as canopy structure were important for predicting species richness of all three target groups. Several NFI variables were identified as potential target variables for managing snail species richness. Stands with tree species from the genera Fraxinus , Tilia , Ulmus and Acer , for example, showed a positive relationship with snail species richness, as did an increasing overstory cover or higher volumes of deadwood. However, only weak relationships were found between NFI variables and species richness of vascular plants, and none for bryophytes. Our findings support the assumption that besides climate, site-specific habitat factors are important determinants of spatial variation of species richness at the local scale. The strength and direction of the determinants vary with taxa, thus indicating a functional relationship between site conditions and the respective species community. [ABSTRACT FROM AUTHOR]

Abstract: Estimation of forest aboveground biomass (AGB) is informative of the role of forest ecosystems in local and global carbon budgets. There is a need to retrospectively estimate biomass in order to establish a historical baseline and enable reporting of change. In this research, we used temporal spectral trajectories to inform on forest successional development status in support of modelling and mapping of historic AGB for Mediterranean pines in central Spain. AGB generated with ground plot data from the Spanish National Forest Inventory (NFI), representing two collection periods (1990 and 2000), are linked with static and dynamic spectral data as captured by Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors over a 25year period (1984–2009). The importance of forest structural complexity on the relationship between AGB and spectral vegetation indices is revealed by the analysis of wavelet transforms. Two-dimensional (2D) wavelet transforms support the identification of spectral trajectory patterns of forest stands that in turn, are associated with traits of individual NFI plots, using a flexible algorithm sensitive to capturing time series similarity. Single-date spectral indices, temporal trajectories, and temporal derivatives associated with succession are used as input variables to non-parametric decision trees for modelling, estimation, and mapping of AGB and carbon sinks over the entire study area. Results indicate that patterns of change found in Normalized Difference Vegetation Index (NDVI) values are associated and relate well to classes of forest AGB. The Tasseled Cap Angle (TCA) index was found to be strongly related with forest density, although the related patterns of change had little relation with variability in historic AGB. By scaling biomass models through small (∼2.5ha) spatial objects defined by spectral homogeneity, the AGB dynamics in the period 1990–2000 are mapped (70% accuracy when validated with plot values of change), revealing an increase of 18% in AGB irregularly distributed over 814km2 of pines. The accumulation of C calculated in AGB was on average 0.65tha−1 y−1, equivalent to a fixation of 2.38tha−1 y−1 of carbon dioxide. [Copyright &y& Elsevier]

Highlights: [•] CBM-CFS3 evaluated against ground plot data from Canada's National Forest Inventory. [•] Total ecosystem C stock estimation was unbiased with significant correlation. [•] Aboveground biomass error largely attributable to input data quality issues. [•] Soil C stocks are largest contributor to error in ecosystem total estimates. [•] Need exists for standards to judge forest C model reliability. [Copyright &y& Elsevier]

Abstract: Mapping aboveground carbon density (ACD) in tropical forests can enhance large-scale ecological studies and support CO2 emissions monitoring. Light Detection and Ranging (LiDAR) has proven useful for estimating carbon density patterns outside of field plot inventory networks. However, the accuracy and generality of calibrations between LiDAR-assisted ACD predictions (EACDLiDAR) and estimated ACD based on field inventory techniques (EACDfield) must be increased in order to make tropical forest carbon mapping more widely available. Using a network of 804 field inventory plots distributed across a wide range of tropical vegetation types, climates and successional states, we present a general conceptual and technical approach for linking tropical forest EACDfield to LiDAR top-of-canopy height (TCH) using regional-scale inputs of basal area and wood density. With this approach, we show that EACDLiDAR and EACDfield reach nearly 90% agreement at 1-ha resolution for a wide array of tropical vegetation types. We also show that Lorey's Height – a common metric used to calibrate LiDAR measurements to biomass – is severely flawed in open canopy forests that are common to the tropics. Our proposed approach can advance the use of airborne and space-based LiDAR measurements for estimation of tropical forest carbon stocks. [Copyright &y& Elsevier]

Highlights: [•] We investigate problems of spatial autocorrelation for competition indices. [•] We test different search criteria and plot edge bias correction methods. [•] No problems with type I statistical error across measures of stand structure. [Copyright &y& Elsevier]

Highlights: [•] The Carbon Budget Model (CBM) was applied to Italian forests at the national scale. [•] Developed standing and net increment curves based on forest inventory data. [•] Reconstructed past forest age structure for 1995. [•] A novel approach was implemented for uneven-aged forests. [•] Italian forests were a C sink between 2000 and 2009. [•] Demonstrated the impacts of fire and harvest rates on forest C budget in 2020. [Copyright &y& Elsevier]