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Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95-1.47 and 0.96-1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%., Accepted preprint; Canadian Journal of Forest Research

Using satellite-based maps, Ceccherini et al. (2020) report abruptly increasing harvested area estimates in several EU-countries beginning in 2015. They identify Finland and Sweden as countries with the largest harvest increases and the biggest potential effect on the EU’s climate policy strategy. Using more than 120,000 field reference observations to analyze the satellite-based map employed by Ceccherini et al. (2020) we found that the map’s ability to detect harvested areas abruptly increases after 2015. While the abrupt detected increase in harvest is merely an artifact, Ceccherini et al. (2020) interpret this difference as an indicator of increasing intensity in forest management and harvesting practice. In their response to comments, Ceccherini et al. (2021) revised their estimates to some degree but still used inadequate methods leading to an overestimation of harvested area in Finland and Sweden. Ceccherini, G. et al. (2020). Abrupt increase in harvested forest area over Europe after 2015. Nature 583, 72-77. Ceccherini, G., et al. (2021). Reply to Wernick, IK et al.; Palahí, M. et al. Nature 592(7856): E18-E23., Includes smaller additions to the text compared to version 1.0

Using satellite-based maps, Ceccherini et al. report abruptly increasing harvested area estimates in several EU26-countries beginning in 2015. They identify Finland and Sweden as countries with the largest harvest increases and the biggest potential effect on the EU’s climate policy strategy. Using more than 120,000 field reference observations to analyze the satellite-based map employed by Ceccherini et al. we found that the map’s ability to detect harvested areas abruptly increases after 2015. While the abrupt detected increase in harvest is merely an artifact, Ceccherini et al. interpret this difference as an indicator of increasing intensity in forest management and harvesting practice. Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. Nature 583, 72-77 (2020).

Since the 1990s the invasive fungus Hymenoscyphus fraxineus has been threatening European ash (Fraxinus excelsior), a tree species with high ecological and economic importance. This pathogen is causing severe crown dieback, leading to high mortality rates across Europe and is present in Switzerland since 2008. In this study, we provide a comprehensive overview of the temporal evolution of crown damage and mortality rates in Switzerland over the 2009–2019 period. Harnessing the power of the annualized design of the Swiss national forest inventory (NFI), we show that annual mortality rates (AMRs) of ash increased significantly since the arrival of the fungus, with stronger effects in small trees [, Frontiers in Forests and Global Change, 4, ISSN:2624-893X

Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported (FAO, 2014) almost no changes in growing stock (+1.8%) and biomass (+0.6%). Yet remote sensing products indicate increased vegetation productivity (Guay et al., 2014), tree cover (Song et al., 2018) and above-ground biomass (Liu et al., 2015). Here, we challenge the official national statistics with a combination of recent National Forest Inventory and remote sensing data products to provide an alternative estimate of the growing stock of Russian forests and assess the relative changes in the post-Soviet era. Our estimate for the year 2014 is 118.29±1.3 109 m3, which is 48% higher than the official value reported for the same year in the State Forest Register. The difference is explained by increased biomass density in forested areas (+39%) and larger forest area estimates (+9%). Using the last Soviet Union report (1988) as a reference, Russian forests have accumulated 1163×106 m3 yr-1 of growing stock between 1988–2014, which compensates for forest growing stock losses in tropical countries (FAO FRA, 2015). Our estimate of the growing stock of managed forests is 94.2 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory (National Inventory Report, 2020).Acknowledgement: The research plots data collection was performed within the framework of the state assignment of the Center for Forest Ecology and Productivity of the Russian Academy of Sciences (no. АААА-А18-118052590019-7), and the ground data pre-processing were financially supported by the Russian Science Foundation (project no. 19-77-30015).

International audience; Stem biomass ratio (SBR, kg m(-3)) is a forest state variable that converts forest volume of growing stock into biomass. However, huge intraspecific variation in wood density (WD) driven by biotic and abiotic environments of tree growth remains ignored in C budgets. The aims of this study were (i) to identify variations in SBR along water, soil nutrition and elevation gradients, (ii) to test for differences between broadleaved and conifer tree species in SBR variations, and (iii) to weight the contributions of interspecific and intraspecific diversity in SBR variations. Analyses were based on massive X-ray WD measurements performed on 54,700 tree cores collected in 2016 and 2017 on the spatially systematic plot sampling design of the French national forest inventory (NFI) programme. Stem biomass ratio (SBR) variations along the three gradients were found to be significant, with differences between botanical classes found on the water gradient only. SBR hence decreased by 73 kg m(-3) (conifers) and 126 kg m(-3) (broadleaves) along a 180 mm gradient of soil water holding capacity (SWHC), increased by 153 kg m(-3) along a full gradient of soil basicity index (SBI), and decreased by 155 kg m(-3) from 200 to 2000 m of elevation asl. Species distribution, not intraspecific variation in WD, was found to be the main cause of SBR variation along these gradients. SBR was found to be efficient for analysing large scale vegetation changes along environmental gradients, with interspecific variability being well explained by the three gradients. Mean WD species values were found to suffice to assess these SBR variations at the scale of the French forest resources.

Since the 1990s the invasive fungus Hymenoscyphus fraxineus has been threatening European ash (Fraxinus excelsior), a tree species with high ecological and economic importance. This pathogen is causing severe crown dieback, leading to high mortality rates across Europe and is present in Switzerland since 2008. In this study, we provide a comprehensive overview of the temporal evolution of crown damage and mortality rates in Switzerland over the 2009–2019 period. Harnessing the power of the annualized design of the Swiss national forest inventory (NFI), we show that annual mortality rates (AMRs) of ash increased significantly since the arrival of the fungus, with stronger effects in small trees [

The changing forest disturbance regimes emphasize the need for improved damage risk information. Here, our aim was to (1) improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on trees, versus forest properties in predicting the probability of snow damage, (2) produce a snow damage probability map for Finland. We also compared the results for winters with typical snow load conditions and a winter with exceptionally heavy snow loads. To do this, we used damage observations from the Finnish national forest inventory (NFI) to create a statistical snow damage occurrence model, spatial data layers from different sources to use the model to predict the damage probability for the whole country in 16 x 16 m resolution. Snow damage reports from forest owners were used for testing the final map. Our results showed that best results were obtained when both abiotic and forest variables were included in the model. However, in the case of the high snow load winter, the model with only abiotic predictors performed nearly as well as the full model and the ability of the models to identify the snow damaged stands was higher than in other years. The results showed patterns of forest adaptation to high snow loads, as spruce stands in the north were less susceptible to damage than in southern areas and long-term snow load reduced the damage probability. The model and the derived wall-to-wall map were able to discriminate damage from no-damage cases on a good level. The damage probability mapping approach identifies the drivers of snow disturbances across forest landscapes and can be used to spatially estimate the current and future disturbance risks in forests, informing practical forestry and decision-making and supporting the adaptation to the changing disturbance regimes.

Avoiding deforestation in the United States cools in some places but warms in others., Storing carbon in forests is a leading land-based strategy to curb anthropogenic climate change, but its planetary cooling effect is opposed by warming from low albedo. Using detailed geospatial data from Earth-observing satellites and the national forest inventory, we quantify the net climate effect of losing forest across the conterminous United States. We find that forest loss in the intermountain and Rocky Mountain West causes net planetary cooling but losses east of the Mississippi River and in Pacific Coast states tend toward net warming. Actual U.S. forest conversions from 1986 to 2000 cause net cooling for a decade but then transition to a large net warming over a century. Avoiding these forest conversions could have yielded a 100-year average annual global cooling of 0.00088°C. This would offset 17% of the 100-year climate warming effect from a single year of U.S. fossil fuel emissions, underscoring the scale of the mitigation challenge.

Various tree species contribute differently to total biomass stock, making the development of species-specific stand-level equations critical for better estimation of forest biomass and quantification of carbon stocks. Previously derived dry weight biomass models did not assess the effect of dominant species composition according to stand growing stock. Growing stock definitions and forest species composition differ by country, justifying the need for national stand-level biomass equations. We explored the relationship between growing stock volume and stand biomass density of above- and below-ground components in six common forest categories in Latvia using plot-level data from the National Forest Inventory from 2016 to 2020. Additionally, we explored model dependence on region, forest type, and species composition index. Models that considered growing stock and dominant species composition index performed better than models with growing stock as the only variable, especially for heterogeneous deciduous forests with greater species diversity. The elaborated models are a useful alternative to individual-level assessment for estimating forest biomass stocks in circumstances where individual tree data are not available.

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

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

The files refer to the data used in Scherrer et al. (2021) "Canopy disturbances catalyse tree species shifts in Swiss forests" in _Ecosystems_. The two data files contain information about site factors (e.g. disturbance events, dominant tree species, elevation) and species-specific biomass of 5521 plots of the Swiss National Forest Inventory visited during the second (NFI2 1993-1995) and fourth (NFI4 2009-2017) inventory. In addition, we provide all the R-scripts necessary to reproduce the Figures and data tables of the related publication. For more detailed information about the data files please check the ReadMe.docx file.

Preliminary assessment of carbon in forest soils of Papua New Guinea (PNG) was done for 53 sites. Simple soil-landscape model was constructed to explain how soil carbon relates to landscape position and landform processes. Despite limitations of the soil survey, sufficient data have been obtained to indicate that the amount of carbon in forest soils surveyed so far is related primarily to their carbon-complexing capacity (CCC) which, in turn, depends on their geological parent material and parent rock. Soils were grouped into six categories based on their carbon content. The six categories ranged from very low 0–50 t/ha to extremely high 601–1,000 t/ha. Amalgamating the areas of mapped geological units on the basis of their likely CCC and their median carbon content has enabled the total carbon content of all forest soils in PNG to be estimated as 7,727 × 106 t. This figure is approximate and will be modified as more results are obtained and more sophisticated GIS-based landscape analysis is undertaken. The total carbon in forest biomass, as measured in this National Inventory and estimated for roots, is 4,006 × 106 t. Thus, the total carbon in PNG’s forests at present is estimated to be 11,733 × 106 t, with 66% stored in the soil and 34% in the biomass. The high proportion of ecosystem carbon held in the soil emphasises the importance of implementing soil conservation measures to maintain the total carbon resource in PNG’s forests.

Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate‐related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross‐validated (50%:50% ‐ training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS‐weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak‐stand locations as a training dataset; the national forest inventory (NFI) “published regional reports” of oak woodland area; and an “NFI map” of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from “NFI survey” sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p, The manuscript provides a method to predict a thematic woodland type within broad habitat woodland classes, using oak dominated woodland as a case study. The approach uses different woodland spatial and non‐spatial data with the biomod2 ensemble model. Good results were obtained by the approach, and the resulting oak woodland distribution map will be used to evaluate the interactions between climate, topography and soil conditions under climate change. This will help develop timely adaptation management plans to reduce impacts.

Töpper J. & Jakobsson S. 2021. The Index-Based Ecological Condition Assessment (IBECA) - Technical protocol, version 1.0. NINA Report 1967. Norwegian Institute for Nature Research. Planet Earth experiences substantial and rapid losses of ecological values, primarily driven by human impacts such as habitat loss due to human land use, exploitation, pollution, introduction of alien species and anthropogenic climate change. Although science and politics have recognised these imminent threats to the world’s ecosystems, and despite many initiatives for conservation, restoration, and sustainable management, the ecological degradation continues. One of the central challenges towards a sustainable future for our ecosystems is the knowledge basis, where we still fall short of effective and applicable frameworks for evaluating the ecological con-dition of ecosystems. The Norwegian ‘system for assessment of ecological condition’ for terrestrial and marine ecosystems aims to fill such a function, by providing a sound and measurable representation of ecosystem condition. This should function as an ecological foundation for authorities when deciding management goals, and assist in designing measures to reach these goals. One of the methods developed within this system is the Index-Based Ecological Condition Assessment (IBECA). A key aspect of IBECA is the quantitative aggregation of scaled indicators into ecological condition indices. The scaling procedure relies primarily on the concept of a reference condition, representing intact nature, and limit values for what is regarded good ecological condition. This technical report presents the methods for the IBECA framework. We address (i) how reference conditions for indicators in IBECA are defined, including reference values, limit values for good ecological condition, and minimum/maximum values of indicators, (ii) the scaling procedures that make the indicators comparable, (iii) the aggregation procedures for calculating estimates for ecological condition for the ecosystem as a whole as well as for single ecosystem characteristics and ecosystem pressures, (iv) how uncertainty is treated in IBECA, and finally (v) the requirements for indicators and data used in IBECA. Töpper J. & Jakobsson S. 2021. The Index-Based Ecological Condition Assessment (IBECA) - Teknisk protokoll, versjon 1.0. NINA Rapport 1967. Norsk institutt for naturforskning. Planeten vår er utsatt for betydelige og raske tap av økologiske verdier, hovedsakelig drevet av menneskelige påvirkninger som tap av habitat på grunn av menneskelig arealbruk, beskatning, forurensning, spredning av fremmede arter og menneskeskapte klimaendringer. Selv om vitenskap og politikk har anerkjent disse pågående truslene mot verdens økosystemer, og til tross for mange initiativer for bevaring, restaurering og bærekraftig forvaltning, fortsetter tapet av økologiske verdier. En av de sentrale utfordringene mot en bærekraftig fremtid for våre økosystemer er kunnskapsgrunnlaget, der vi fortsatt mangler effektive og anvendbare rammer for å evaluere økologisk tilstand. I Norge tar ‘fagsystemet for vurdering av økologisk tilstand for terrestriske og marine økosystemer’ sikte på å fylle en slik rolle ved å gi en faglig og målbar representasjon av økologisk tilstand i økosystemene. Dette skal danne et faglig grunnlag for myndighetene når det skal fastsettes forvaltningsmål og for å innrette den samlede virkemiddelbruken for å nå slike mål. En av metodene som er utviklet i dette systemet er ‘Indeksmetoden’ (Index-Based Ecological Condition Assessment, IBECA). Et sentralt aspekt ved IBECA er den kvantitative aggregeringen av skalerte indikatorer til økologiske tilstandsindekser. Skaleringsprosedyren relaterer i hovedsak til begrepet referansetilstand, som representerer intakt natur, og grenseverdier for det som anses som god økologisk tilstand. Denne tekniske protokollen presenterer metodene for IBECA-rammeverket. Vi tar for oss (i) hvordan referansetilstanden for indikatorer i IBECA er definert, inkludert referanseverdier, grenseverdier for god økologisk tilstand og minimum/maksimumsverdier for indikatorer, (ii) skaleringsprosedyrene som gjør indikatorene sammenlignbare, (iii) aggregeringsprosedyrer for beregning av indekser for økologisk tilstand for økosystemet som helhet, samt for enkelte økosystemegenskaper og påvirkninger, (iv) hvordan usikkerhet behandles i IBECA, og til slutt (v) krav til indikatorer og data brukt i IBECA.

There is a well-documented opportunity and need to incorporate biodiversity conservation priorities into REDD+ (Reducing Emissions from Deforestation and Forest Degradation) initiatives. This requires thorough monitoring of changes to biodiversity at appropriate temporal and spatial scales. A national forest inventory is one of the essential tools used to monitor carbon stock changes but can also be expanded to include biodiversity indicators. Here we analyse the progress and potential of 70 countries in monitoring primarily non-tree biodiversity using national forest inventories. Progress on national forest inventories among countries participating in REDD+ is variable: 11 countries have not started; 26 have started but do not include non-tree biodiversity indicators; the remaining 33 countries do include non-tree biodiversity indicators but use various methodological approaches, levels of detail and taxonomic groups. Very few of these provide comprehensive and accessible manuals or results, highlighting a need for greater transparency. The capacity of countries to fund ongoing national forest inventories is a constraining factor. Remote sensing technologies can help reduce costs for countries with limited monitoring capacity but the need to understand biodiversity variation at finer scales often limits the utility of such methods.

This article aims to identify and realize opportunities for Reducing Emissions from Deforestation and Forest Degradation (REDD) through consideration of gender issues during the development and implementation of Papua New Guinea’s first National Forest Inventory (NFI). To improve the planning and management of forests, the agenda is to achieve sustainability of ecosystem services, uncover economic and social values, and contribute to combating climate change and biodiversity loss at the global level. In the context of the NFI, we canvass three thematic areas of forest practice which, in assimilating women’s roles, knowledge, and interests, promise a positive impact on economic efficiency and development. They are (1) women fostering the reduction of emissions from deforestation, forest degradation, and other management objectives, which include conservation, sustainable management of forests, and enhancement of forest carbon stocks or REDD+; (2) women and sustainable forest management; and (3) women in community forestry. The conclusion gauges the general role of women in forestry and summarizes future research directions, through policy recommendations, and technological and institutional interventions.

Biogenic Volatile Organic Compounds (BVOC) are well-known ozone and secondary organic aerosol precursors. Consequently, the estimation of their emissions is crucial for air quality modeling, and specifically for photochemical models. We have adapted the Spanish National Forest Inventory creating a new landcover database that can be used into the new Model of Emissions of Gases and Aerosols from Nature (MEGANv3) in order to improve air quality simulations in Spain. Preliminary results show that spatial distributions of emission potentials compared to the MEGANv3’s default database are closer to reality.

Widely observed inertia of forest communities contrasts with climate change projections that suggest dramatic alterations of forest composition for the coming decades. Disturbances might be a key process to catalyse changes in tree species composition under environmental change by creating opportunities for ‘new’ species to establish. To test this assumption, we compared two assessments (1993–1995, 2009–2017) from the Swiss National Forest Inventory to evaluate which forests were opened by natural canopy disturbance (that is, wind, insect outbreaks, fire and drought) and if these disturbances altered tree species composition both in terms of species-specific basal area and recruitment densities. Natural disturbances affected 14% of the Swiss forests within 25 years, with wind and insect outbreaks being the most frequent (75%) and fire and drought being rare (, Ecosystems, 25 (1), ISSN:1432-9840, ISSN:1435-0629

This chapter presents the forest ecosystems of the Montenegrin coast. The characteristic of the Montenegrin coast is according to the data of the First National Forest Inventory the forest cover of as much as 49.9% of total area. These are mainly low forests, as well as degradation stages of these forests in the form of shrubs and macchia, which have a distinct protective function. The significance of these ecosystems is manifold. It can be seen through protection, soil, water, habitat and biodiversity protection, as well as through aesthetic function and landscape characteristics. This chapter also reviews the greatest risks to these ecosystems, fires and soil erosion processes.

Plant functional traits are highly plastic to changes in climatic factors and nutrient availability. However, the intraspecific plant response to abiotic factors and the overall effect on tree growth and productivity is still under debate. We studied forest productivity for 30 Quercus ilex subsp. ballota forests in Spain along a broad climatic gradient of aridity (mean annual precipitation from 321 to 858 mm). We used linear mixed models to quantify the effect of climatic and edaphic (soil nutrients, topography, and texture) factors on tree functional traits (leaf and branch traits), and subsequently, the effect of such functional traits and abiotic factors on the relative growth rate (RGR) of adult trees. We used piecewise structural equation models (SEMs) to determine the causal effect of intrinsic and extrinsic factors on forest productivity. Our results showed that tree functional traits were mainly explained by climatic and edaphic factors. Functional traits and tree biomass explained forest biomass and RGR, respectively, which ultimately explained forest productivity. In conclusion, intraspecific variability of functional traits has a significant effect on plant biomass and growth, which ultimately may explain forest productivity in Quercus ilex forests., Fondo Europeo de Desarrollo Regional-FEDER, Junta de Andalucía, Ministerio de Educación y Ciencia, Gobierno de España

16 Páginas., Background: National and international institutions periodically demand information on forest indicators that are used for global reporting. Among other aspects, the carbon accumulated in the biomass of forest species must be reported. For this purpose, one of the main sources of data is the National Forest Inventory (NFI), which together with statistical empirical approaches and updating procedures can even allow annual estimates of the requested indicators. Methods: Stand level biomass models, relating the dry weight of the biomass with the stand volume were developed for the five main pine species in the Iberian Peninsula (Pinus sylvestris, Pinus pinea, Pinus halepensis, Pinus nigra and Pinus pinaster). The dependence of the model on aridity and/or mean tree size was explored, as well as the importance of including the stand form factor to correct model bias. Furthermore, the capability of the models to estimate forest carbon stocks, updated for a given year, was also analysed. Results: The strong relationship between stand dry weight biomass and stand volume was modulated by the mean tree size, although the effect varied among the five pine species. Site humidity, measured using the Martonne aridity index, increased the biomass for a given volume in the cases of Pinus sylvestris, Pinus halepensis and Pinus nigra. Models that consider both mean tree size and stand form factor were more accurate and less biased than those that do not. The models developed allow carbon stocks in the main Iberian Peninsula pine forests to be estimated at stand level with biases of less than 0.2 Mg∙ha− 1. Conclusions: The results of this study reveal the importance of considering variables related with environmental conditions and stand structure when developing stand dry weight biomass models. The described methodology together with the models developed provide a precise tool that can be used for quantifying biomass and carbon stored in the Spanish pine forests in specific years when no field data are available., This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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

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

Tree growing conditions are changing rapidly in the face of climate change. Capturing tree-growth response to such changes across environmental contexts and tree species calls for a continuous forest monitoring over space. Based on >10,000 tree-ring measurements sampled across the systematic grid of the continuous French national forest inventory (NFI) over the 2006–2016 period, we evaluated the radial growth trends of eight conifer tree species prevalent in European forests across their native and introduced ranges and various bioclimatic contexts (n = 16 forest systems). For each forest system, radial increments were filtered out from tree, plot, soil and climatic normal influences to isolate environment-driven growth signals and quantify residual time-series. Associated growth trends across forest systems were then confronted against environmental variables (e.g. short-term averages and trends in seasonal climate). Trends for a given species were systematically more positive in cooler contexts (higher elevations or northern distribution margins) than in warmer contexts (plains). Decreases and increases in precipitation regimes were found to be associated with negative and positive tree growth trends, respectively. Remarkably, positive growth trends were mainly observed for native forest systems (7/9) and negative trends for introduced systems (5/7). Native forests showed a more heterogeneous forest structure as compared to introduced forests that, in line with observed positive dependence of tree growth trends onto both water availability and forest heterogeneity, appears to modulate the competitive pressure on water resource with ongoing summer maximum temperature increase. Over a short annually-resolved study period, we were able to capture tree growth responses coherent with climate change across diverse forest ecosystems. With ongoing accumulation of data, the continuous French NFI hence arises as powerful support to monitoring climate change effects on forests.

International audience; & Key message French forests exhibit the fastest relative changes across Europe. Growing stock increases faster than area,and is greatest in low-stocked private broadleaved forests. Past areal increases and current GS levels show positive effectson GS expansion, with GS increases hence expected to persist.& Context Strong increases in growing stocks (GS) of European forests for decades remain poorly understood and of unknownduration. French forests showing the greatest relative changes across Europe form the investigated case study.& Aims The magnitudes of net area, GS, and GS density (GSD) changes were evaluated across forest categories reflecting forestpolicy and land-use drivers. The roles of forest areal changes, GS and GSD levels on GS changes were investigated.& Methods National Forest Inventory data were used to produce time series of area, GS and GSD across forest categories over1976–2014, and exploratory causal models of GS changes.& Results GS (+ 57%) increased three times faster than area, highlighting an advanced stage in the forest transition. Low-stockedprivate forests exhibited strong changes in GS/GSD, greatest in private broadleaved forests, stressing the contribution of returningforests on abandoned lands. Regression models demonstrated positive effects of both past areal increases and current GS, on GSexpansion.& Conclusion Aerial C-sink in French forests is expected to persist in future decades.

Background and Objectives: Significant progress in developing European national forest inventory (NFI) systems could ensure accurate evaluations of gross annual increment (GAI) and its components by employing direct measurements. However, the use of NFI data is insufficient for increasing the efficiency of forest management and the use of wood, as well as for meeting sustainable forestry needs. Specification of forest characteristics, such as GAI and its components, identification of the main factors that impact forest growth, accumulation of wood, and natural losses are among the key elements promoting the productivity of forest stands and possibilities of rational use of wood in large forest areas. The aims of this research were (a) to validate the quality of forest statistics provided by a standwise forest inventory (SFI) and (b) to reveal the potential benefits of rational wood use at the country level through the analysis of forest management results, which are based on GAI, including its components derived from the NFI. Materials and Methods: SFI and NFI data from 1998&ndash, 2017 were collected from 5600 permanent sample plots and used to evaluate the main forest characteristics. Potential wood use was estimated based on the assumption that 50&ndash, 70% of the total GAI is accumulated for final forest use. Results: Mean growing stock volume (GSV) is underestimated by 7&ndash, 14% on average in the course of SFI. Therefore, continuous monitoring of the yield changes in forest stands, detection of factors negatively affecting yield and its accumulation, and regulation of these processes by silviculture measures could increase potential forest use in Lithuania. Conclusions: Implementation of sample-based NFI resulted in an improvement of forest characteristics and led to an increase in GSV and GAI. Continuously gathered data on GAI and its components are a prerequisite for efficient forest management and control of the use of wood.

Forests and forest industries can contribute to climate change mitigation by sequestering carbon from the atmosphere, by storing it in biomass, and by fabricating products that substitute more greenhouse gas emission intensive materials and energy. The objectives of the study are to specify alternative scenarios for the diversification of wood product markets and to determine how an increasingly diversified market structure could impact the net carbon emissions (NCEs) of forestry in Finland. The NCEs of the Finnish forest sector were modelled for the period 2016-2056 by using a forest management simulation and optimization model for the standing forests and soil and separate models for product carbon storage and substitution impacts. The annual harvest was fixed at approximately 70 Mm(3), which was close to the level of roundwood removals for industry and energy in 2016. The results show that the substitution benefits for a reference scenario with the 2016 market structure account for 9.6 Mt C (35.2 Mt CO2 equivalent [CO2 eq]) in 2056, which could be further increased by 7.1 Mt C (26 Mt CO2 eq) by altering the market structure. As a key outcome, increasing the use of by-products for textiles and wood-plastic composites in place of kraft pulp and biofuel implies greater overall substitution credits compared to increasing the level of log harvest for construction. peerReviewed

Key message National Forest Inventories (NFIs) hold promise for monitoring and valuing of non-productive forest functions, including social and recreational services. European countries use a range of methods to collect social and recreational information within their NFI methodologies. Data collected frequently included general and recreation-specific infrastructure, but innovative approaches are also used to monitor recreational use and social abuse. Context Social and recreational indicators are increasingly valued in efforts to measure the non-productive value of forests in Europe. National Forest Inventories (NFIs) can be used to estimate recreational and social usage of forest land at a national level and relate this use to other biophysical, spatial and topographical features. Nonetheless, there is little information concerning the extent. Aims The study aims to identify the coverage of social and recreational data present in European NFIs including the types of data recorded as part of the NFI methodologies across European countries. It also aims to examine contrasting methods used to record social and recreational data and present recommendations for ways forward for countries to integrate these into NFI practice. Methods A pan-European questionnaire was designed and distributed to 35 counties as part of the EU-funded project Distributed, Integrated and Harmonised Forest Information for Bioeconomy Outlooks (DIABOLO). The questionnaire probed countries on all social and recreational data that was included within NFIs. Qualitative response data was analysed and recoded to measure the extent of social and recreational data recoded in European NFIs both as a function of the number of variable categories per country and the number of countries recording particular variables. Results Thirty-one countries reported at least one social or recreational variable over 12 categories of data. The most frequently recorded variables included ownership, general transport infrastructure and recreation-specific infrastructure. Countries collecting data over many different categories included Switzerland, Great Britain, Czech Republic, Luxemburg and Denmark. Conclusion The study proposes a specific set of indicators, based upon countries with well-developed social and recreational data in their NFIs, which could be used by other countries, and report on the extent to which these are currently collected across Europe. It discusses results and makes a series of recommendations concerning priorities for the inclusion of social and recreational data in European NFIs.

Forest age serves as an essential factor in determining the accuracy of historical and future carbon (C) uptake quantifications, which is especially critical for China since the forest C stock dynamics are sensitive to the fast-growing, young-age plantations. However, a spatially explicit forest age maps with specific focus on forest plantations is not available yet. In this study, we developed a 1-km resolution age and type maps of forest plantations, and quantified their uncertainties spatially using field-measured data, national forest inventory data, digitalized forest maps, and remote sensing-based forest height maps. Simulation results showed forest plantations were 16.5 years old at national scale in 2005, which is close to the age of 16.6 years old derived from the 7th national inventory data using medium age in each forest plantation group with weighted area. Interestingly, we found that human management played an important role in forest age map reconstruction, which has not yet been considered in former studies. We also suggest that forest age and type maps should be used consistently in C stock simulations to avoid biases from mismatch information. Large uncertainty found in this study suggests further endeavors are required for improving the forest age and type maps.

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

Abstract. Calculation of land use and land use change matrix is one of the key elements for the national greenhouse gas (GHG) inventory in land use, land use change and forestry (LULUCF) sector. Main purpose of the land use and land use change matrix is to present comprehensive and harmonized land use and land use change information nationwide over certain time period. Information on land use and land use changes is further used to calculate other parameters important for determination of carbon stock changes and GHG emissions like the stock changes of living and dead biomass, as well as basic information on applied management measures. Aim of this study is to improve methodology for development and maintenance of land use and land use change matrix in the national GHG inventory system using geospatial data information of National forest inventory (NFI) and auxiliary data sources. Creation of land use and land use change matrix is performed in semi-automated way by using GIS tools, which eliminates possible impurities of reported data and have made the calculation process less time consuming than before. New calculation method takes into account present land use data from NFI and land use data from two previous NFI cycles, considerably reducing uncertainty of the estimates, and takes into account land management practices which may alter the land use category in long-term. Auxiliary data, like national land parcel information systems (LPIS), has been introduced to increase certainty, consistency and accuracy for determination of final land-use category. Year-by-year land use change extent detection is carried out by using linear interpolation and extrapolation method is used for the consecutive years for which NFI data are not available.Key words: ERA-GAS INVENT, land use and land use changes, national forest inventory, greenhouse gas inventory.