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Standing deadwood is an important structural component of forest ecosystems. Its occurrence and dynamics influence both carbon fluxes and the availability of habitats for many species. However, deadwood is greatly reduced in managed, and even in many currently unmanaged temperate forests in Europe. To date, few studies have examined how environmental factors, forest management and changing climate affect the availability of standing deadwood and its dynamics. Data from five periods of the Austrian National Forest Inventory (1981-2009) were used to (I) analyse standing deadwood volume in relation to living volume stock, elevation, eco-region, forest type, ownership and management intensity, (II) investigate the influence of forest ownership and management intensity on snag persistence and (III) define drivers of standing deadwood volume loss for seven tree genera (Abies, Alnus, Fagus, Larix, Picea, Pinus and Quercus) using tree-related, site-related and climate-related variables, and predict volume loss under two climate change scenarios. Standing deadwood volume was mainly determined by living volume stock and elevation, resulting in different distributions between eco-regions. While forest type and management intensity influenced standing deadwood volume only slightly, the latter exhibited a significant effect on persistence. Snag persistence was shorter in intensively managed forests than in extensively managed forests and shorter in private than in public forests. Standing deadwood volume loss was driven by a combination of diameter at breast height, elevation, as well as temperature, precipitation and relative humidity. Volume loss under climate change predictions revealed constant rates for moderate climate change (RCP2.6) by the end of the 21st century. Under severe climate change conditions (RCP8.5), volume loss increased for most tree genera, with Quercus, Alnus and Picea showing different predictions depending on the model used as the baseline scenario. We observed trends towards faster volume loss at higher temperatures and lower elevations and slower volume loss at high precipitation levels. The tree genera most susceptible to climate change were Pinus and Fagus, while Abies was least susceptible. Synthesis and applications. We recommend to protect standing dead trees from regular harvesting to ensure the full decomposition process. The consequences for decomposition-dependent species must be taken into account to evaluate the influences of management and climate change on standing deadwood dynamics., Journal of Applied Ecology, 60 (4), ISSN:0021-8901, ISSN:1365-2664

When conducting a forest inventory, sometimes portions of plots cannot be measured due to inaccessibility. Two primary methods have been presented to account for partial nonresponse in the estimation phase: (i) use a ratio-to-size estimator or (ii) apply an adjustment factor to all plot observations in proportion to the missing area. Both approaches provide identical estimates of the population mean, but the estimates of variance differ when partial nonresponse is present. The performance of variance estimators was examined for a range of population forest area and partial nonresponse proportions in the sample. The ratio-to-size variance estimator performed unbiasedly with respect to simulation results, but the adjustment factor variance estimates were biased, with magnitude and direction dependent upon the forest area proportion and amount of partial nonresponse. The bias is relatively small when the partial nonresponse is small, which is often the case; however, the ratio-to-size method is preferred to ensure accurate variance estimation for a wide range of circumstances.

Post-stratification is often used to increase the precision of estimates arising from large-area forest inventories with plots established at permanent locations. Remotely sensed data and associated spatial products are often used for developing the post-stratification, which offers a mechanism to increase precision for less cost than increasing the sample size. While important variance reductions have been shown from post-stratification, it remains unknown where observed gains lie along the continuum of possible gains. This information is needed to determine whether efforts to further improve post-stratification outcomes are warranted. In this study, two types of optimal post-stratification were compared with typical production-based post-stratifications to estimate the magnitude of remaining gains possible. Although the optimal post-stratifications were derived using methods inappropriate for operational usage, the results indicated that substantial further increases in precision for estimates of both forest area and total tree biomass could be obtained with better post-stratifications. The potential gains differed by the attribute being estimated, the population being studied, and the number of strata. Practitioners seeking to optimize post-stratification face challenges, such as evaluation of numerous auxiliary data sources, temporal misalignment between plot observations and remotely sensed data acquisition, and spatial misalignment between plot locations and remotely sensed data due to positional errors in both data types.

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

An ex-ante impact assessment of land use plans is required in the preparation or revision phase to inform planners and other stakeholders about the possible or likely impacts of the plans. This stu...

The USDA Forest Service uses satellite imagery, along with a sample of national forest inventory field plots, to monitor and predict changes in forest conditions over time throughout the United Sta...

Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system.

espanolEl calculo de volumenes forestales es una de las aplicaciones mas comunes de la teledeteccion para apoyar la gestion sostenible de los ecosistemas forestales. En este estudio, se utilizan datos del 4o Inventario Forestal Nacional (IFN-4) y de la 1a y 2a encuesta nacional de Exploracion Laser Aerotransportada (ALS) para desarrollar modelos de rendimiento predictivo y estimaciones directas para las tres principales especies forestales comerciales (Eucalyptus globulus, Pinus pinaster y Pinus radiata) cultivadas en la provincia de Lugo. La integracion de ambos tipos de datos requirio una armonizacion previa debido a las diferencias en el tiempo de adquisicion de los datos y las dificultades para geolocalizar con precision las parcelas del IFN-4. Los datos armonizados de E. globulus, P. pinaster y P. radiata se han utilizado para desarrollar modelos predictivos para determinar el volumen total con corteza (VCC) en un punto t diferente del de captura de los datos PNOA-LiDAR. Se ha calculado el incremento anual de volumen con corteza (IAVC) mediante la relacion entre las variables de las parcelas IFN-4, con las metricas derivadas de los datos de PNOA-LiDAR, mediante el metodo de regresion lineal Random Forest (RF) y varias tecnicas de aprendizaje automatico. Estos datos tambien se utilizan para obtener valores VCC de forma directa mediante datos PNOA-LiDAR multitemporales, siendo estos ultimos capturados en el tiempo t de proyeccion del otro metodo. EnglishIn this study, data from the 4th National Forest Inventory (IFN-4) and from the 1st and 2nd National Airborne Laser Exploration Survey (ALS) are used to develop predictive performance models and direct estimates for the three main commercial forest species (Eucalyptus globulus, Pinus pinasterand Pinus radiata) grown in the province of Lugo. The integration of both types of data required prior harmonization due to the differences in the time of data acquisition and the difficulties to accurately geolocate the IFN-4 plots. The harmonized data of E. globulus, P.pinaster and P.radiata have been used to develop predictive models to determine the total volume with cortex (VCC) at a point t different from the capture of the PNOA-LiDAR data. The annual increase in volume with cortex (IAVC) was calculated by means of the relationship between the variables of the IFN4 plots, with the metrics derived from the PNOA-LiDAR data, by the Random Forest (RF) linear regression method and several techniques of machine learning. These data are also used to obtain VCC values directly using multi-temporal PNOA-LiDAR data, the latter being captured at the projection time t of the other method.

We developed a methodology for extending estimates of the presence-absence of trees and several tree species contained in the Canadian National Forest Inventory using nationally consistent Landsat ...

Instituto de Ciencias Forestales (ICIFOR), National forest inventories, in which trees are often mapped within the plots, provide a tool for the quantification of large-scale forest structure since they cover all forest areas. Many National Forest Inventories follow a nested design in order to reduce the sampling effort for smaller trees. We propose and test a methodology that allows the spatial pattern of trees, species mingling and size differentiation to be characterized using the nearest neighbour indices and second-order moment functions from nested plot data. The nearest neighbour indices and second-order moment functions for the actual distribution are compared with simulations of the appropriate null model: spatial randomness for spatial pattern characterization or spatial independence for species mingling and size differentiation. The proposed method consists of constraining the null model to fit the nested plot design. For the purposes of the study, we simulated 120 plots and used 26 real plots located in pure and mixed stands in Central Spain, for which a complete census with detailed information about trees was available. The nested design used in the Spanish National Forest Inventory (SNFI) plots was simulated to test the performance, taking the complete census as reference. Despite of the limited accuracy for some structural measures, the proposed method based on nested design data performed better for most of the nearest neighbour indices and second-order moment functions than the strategy currently used in the SNFI for structure assessment in a subsample of SNFI plots, consisting of mapping the 20 trees closest to the plot centre. Nearest neighbour indices provided greater accuracy for species mingling assessment than second-order moment functions, whereas the opposite occurred when describing spatial pattern and size differentiation. The methodology proposed provides the first insight into the characterization of forest structure in nested designs although more evaluations are required for different forest types.

The sampling intensity of a national forest inventory is usually low. Forest dynamics models can be used to update plots from past inventory campaigns to enhance the precision of the estimate on smaller areas. By doing this, however, the inference relies not only on the sampling design, but also on the model. In this study, the contribution of model predictions to the variance of enhanced small-area estimates was assessed through a case study. The French national forest inventory provided different annual campaigns for a particular region and department of France. Three past campaigns were updated using a forest dynamics model, and estimates of the standing volumes were obtained through two methods: a modified multiple imputation and the Bayesian method. The update greatly increased the precision of the estimate, and the gain was similar between the two methods. The sampling-related variance represented the largest share of the total variance in all cases. This study suggests that plot updating provides more precise estimates as long as (i) the forest dynamics model exhibits no systematic lack of fit and was fitted to a large data set and (ii) the sampling-related variance clearly outweighs the model-related variance.

The objective of this study is to estimate the effects of forest management activities on mortality rates and fuel characteristics of coarse woody debris for Pinus densiflora stands in South Korea. This study used 40 managed plots and 14 unmanaged plots from the 6th National Forest Inventory plots. The results indicated that mortality rates of the managed stands were about 1.6% while those of the unmanaged stands were about 5.3%. The amount of coarse woody debris affecting forest fire spread was 2.2TOE ha-1 for the unmanaged stands, while 1.6TOE ha-1 for the managed stands. According to the results of decay class analysis, the ratio of decay class I, the most vulnerable to forest fires, was 22% for the managed stands while 29% for the unmanaged stands. Therefore, the unmanaged pine stands had higher fuel loads for forest fire in terms of coarse woody debris and higher decay rates than the managed Pinus densiflora stands. This information could be very useful to understand forest fire fuels in coarse woody debris for Pinus densiflora stands in South Korea.

The forest in India have a long history of human induced modifications through intensive forest management that began in the colonial period but continued afterwards to meet the wood-based demand until the forestry goals shifted towards conservation efforts. All this greatly influenced the plant community structure. We attempt to describe the abundance and distribution pattern of trees in recorded forestland of Western Himalaya using the National Forest Inventory database. A total of 3549 sample plots laid across different forest types were analyzed to reveal the abundance, frequency and distribution pattern of 226 tree species/genus using various indices i.e. abundance by frequency ratio, Morisita index and variance mean ratio. Twelve tree species and Eucalyptus genus were found to be most abundantly found tree species in recorded forest area of Western Himalaya. Ten tree species were found to have regular distribution pattern, one hundred ninety-two were contagiously distributed while twenty-five were randomly distributed. Results derived from huge datasets helps in establishing firm statements with quoted significance value. Myrica esculenta and Aegle marmelos were found among the most abundantly occurring tree species whereas Myrica esculenta, Syzygium cumini and Pyrus pashia, were among the most relatively frequently occurring wild fruit yielding tree species. These species (apart from fodder tree species) can be considered in social forest and joint forest management programs in forest fringe villages/areas to enhance and sustain the provisional ecosystem services in Western Himalaya.

National forest inventories (NFIs) are designed to provide accurate information on forest resources at the national and regional levels, but there is also a demand for such information at smaller spatial scales. Auxiliary data such as satellite imagery have been used to facilitate small-area estimation. The commonly used method, k-nearest neighbour (k-NN), provides a model-based estimator for small areas, but a design-unbiased estimator for the mean square error is not available. Post-stratification (PS) is an alternative approach to using auxiliary information that allows for design-based variance estimation. In a case study using real inventory data of the Finnish NFI, we applied this method to the municipality level to explore the lower limit to the area for which the key forest parameters, forest area and growing stock volumes, can be estimated with sufficient precision. For PS, we employed exogenous forest resources maps based on the previous NFI round. In the municipalities of the two study provinces, the relative standard errors of total volume estimates ranged from 2.3% to 26.9%. They were smaller than 10% for municipalities with an area of 390 km2 or larger, corresponding to approximately 100 or more sample plots on forestland. We also demonstrated the usefulness of design-unbiased variance estimation in showing discrepancies between design-based PS and model-based k-NN estimates.

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

National Forest Inventory (NFI) data are the main source of information on forest resources at country and subcountry levels. This chapter explores the strengths and limitations of NFI-derived indicators to assess forest development with respect to adaptation to and mitigation of climate change, that is, the criteria of Climate-Smart Forestry (CSF). We reflect on harmonizing NFI-based indicators across Europe, use literature to scrutinize available indicators to evaluate CSF, and apply them in 1) Switzerland, where CSF is evaluated for NFI records and simulation model projections with four management scenarios; 2) 43 selected European countries, for which the indicators for Sustainable Forest Management (SFM) are used. The indicators were aggregated to composite indices for adaptation and mitigation and to an overall CSF rating. The Swiss NFI records showed increased CSF ratings in mountainous regions, where growing stocks increased. Simulations under business-as-usual management led to a positive CSF rating, whereas scenarios of increased harvesting decreased either only adaptation or both mitigation and adaptation. European-level results showed increases in CSF ratings for most countries. Negative adaptation ratings were mostly due to forest damages. We discuss the limitations of the indicator approach, consider the broader context of international greenhouse gas reporting, and conclude with policy recommendations.

Effective modelling of forest susceptibility to defoliating insect outbreaks requires a better understanding of outbreak dynamics, which includes detailed knowledge of the pre- and post-out...

There are significant inter-annual fluctuations of growing stock volume changes of living trees estimated by the Lithuanian National Forest Inventory (NFI). In the current study, we compared two sources of information on forest productivity: conventional NFI data and dendrochronological data based on tree cores collected in parallel with the measurements of the fourth Lithuanian NFI cycle during 2013–2017 on the same permanent plots (total number of cores was 4967). The main finding is that the dendrochronological basal area increment data confirmed the depression of gross stand volume increment around 2006–2007 (based on Lithuanian NFI measurements in 2008–2009), followed by a steep increase during 2008–2011 (NFI from 2010–2013). The findings explain the differences between projected growing stock volume change, which have been used for forest reference level estimation according to land use, land-use change and forestry sector regulation, and the one recently provided in National Greenhouse Gas Inventory Reports. Key words: Growing stock volume change, basal area increment, forest reference level, greenhouse gas reporting

Planning a forest inventory comprises making decisions related to the sampling strategy: cluster configuration, sample size and sample allocation within the survey area. Cluster configurati...

Forest site quality assessment refers to the judgment and prediction of the appropriate forestry or potential productivity of the site. It is an extremely important basic work in the forest managem...

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.

Made available in DSpace on 2020-12-10T19:47:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-10-01 The aim of this study was to explore methods to: (1) enhance coarse-scale estimates of wood volume from National Forest Inventories (NFIs) data and (2) map them at finer scales. The 'standard' coarse-scale estimation extrapolates wood volume from clusters to the grid cell they represent, using the cluster's represented forested area (RFA) to predict the cell's forested area. Data from a subset of Brazil's NFI clusters were combined with Landsat-8 imagery to explore a new coarse-scale method, where forested area derived from image classification (FADIC) is used instead of RFA. The RFA- and FADIC-derived estimates of total volume were, respectively, 197.4 million m(3) and 116.3 million m(3). For fine-scale methods, volume was estimated and mapped at pixel level using: (i) surface reflectance-based models (SRMs), and (ii) regression-kriging (RK) and a RK model (RKM) whose inputs were latitude and longitude of pixels. The SRM-based method captured the mean and the general spatial trend of the volume well. The RK-based method also estimated the mean well, but it failed to predict higher and lower volumes. The SRM- and RK-based estimates of total volume were 211.7 million m(3) and 203.3 million m(3), an overestimate of 7 per cent and 3 per cent, respectively, of the 'standard' NFI estimate (197.4 million m(3)), though both estimates were within the 95 per cent confidence interval, meaning that both fine-scale methods yield total volume statistically similar to the 'standard' coarse-scale method. Rural Fed Univ Amazonia, Dept Forestry, Presidente Tancredo Neves Ave 2501, Belem, PA, Brazil Michigan State Univ, Dept Forestry, 480 Wilson Rd, E Lansing, MI 48824 USA Univ Fed Parana, Dept Forestry, Pref Lothario Meissner Ave 900, BR-80210170 Curitiba, PR, Brazil Fed Univ South Bahia, Dept Sci & Agroforestry Technol, Highway 415,Km 22, BR-45653919 Ilheus, BA, Brazil Embrapa Forests, Rd Ribeira,Km 111, BR-83411000 Colombo, PR, Brazil Mato Grosso State Univ, Dept Forestry, Perimetral Deputado Rogerio Silva Ave, BR-78580000 Alta Floresta, MT, Brazil State Univ Sao Paulo, Dept Cartog, Roberto Simonsen St 305, BR-19060900 Presidente Prudente, SP, Brazil State Univ Sao Paulo, Dept Cartog, Roberto Simonsen St 305, BR-19060900 Presidente Prudente, SP, Brazil

The future dynamics of forest species and ecosystems depend on the effects of climate change and are related to forest management strategies. The expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequencies, intensities, durations and timings of extreme events. The main aim of this work is to assess and describe the climatic requirements for Italian forest tree species. We used 7.272 field observations from Italian National Forest Inventory plots and average annual temperatures and precipitation as interpolated from raster maps with 1 km spatial resolution. On this basis we evaluated the current observed distributions of the 19 most important tree species in Italy with respect to potential climatic limits based on expert knowledge and the available literature. We found that only 46% of the observations fall within the potential joint temperature and precipitation limits as defined by expert knowledge. For precipitation alone, 70% of observations were within the potential limits, and for temperature alone, 80% of observations were within the potential limits. Similarity between current observed and potential limits differ from species-to-species with broadleaves in general more frequently distributed within the potential climatic limits than conifers. We found that ecological requirements and potential information should be revised for some species, particularly for the Pinus genus and more frequently for precipitation. The results of the study are particularly relevant given the threat of climate change effects for Italian forests which are broadly acknowledged to be a biodiversity hotspot. Further investigations should be aimed at modelling the effects of climate changes on Italian forests as a basis for development of mitigation and adaptation forest management strategies.

To gauge, whether variances in density estimates computed under an assumption of a simple random sampling (SRS) are inflated – when the actual design ensures a spatial balance, we obtained design-c...

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates.

SLU Forest Map are maps of the Swedish forest state, produced by the Swedish University of Agricultural Sciences (SLU) from satellite images using the Swedish National Forest Inventory sample plots as reference data. Until now, four maps have been produced, in raster format (12.5 × 12.5 m2 to 25 × 25 m2 cell sizes), with estimates of basal area-weighted mean tree height, basal area-weighted mean stem diameter, stand age, total as well as species-specific stem volume, for the years 2000, 2005, 2010, and 2015. These maps provide publicly available data, free of charge, supporting a wide range of applications; scientific research as well as operational uses in forest management planning, biodiversity assessment, and monitoring. This paper presents SLU Forest Map, the data and methods utilised in the production, and a new consistent evaluation of the estimation accuracy for each variable and mapped year.

4 Pág. Centro de Investigación Forestal (CIFOR)., In 2019, 100 years had elapsed since the first National Forest Inventory (NFI) was established in Norway. Motivated by a fear of over-exploitation of timber resources, NFIs today enable informed policy making by providing data vital to decision support at international, national, regional, and local scales. This Collection of articles celebrates the 100th anniversary of NFIs with a description of past, present, and future research aiming at improving the monitoring of forest and other terrestrial ecosystems., The Beijing Forestry University waived the Open Access fees of all studies published in this Collection.

Key message Large-scale forest resource maps based on national forest inventory (NFI) data and airborne laser scanning may facilitate synergies between NFIs and forest management inventories (FMIs). A comparison of models used in such a NFI-based map and a FMI indicate that NFI-based maps can directly be used in FMIs to estimate timber volume of mature spruce forests. Context Traditionally, FMIs and NFIs have been separate activities. The increasing availability of detailed NFI-based forest resource maps provides the possibility to eliminate or reduce the need of field sample plot measurements in FMIs if their accuracy is similar. Aims We aim to (1) compare a timber volume model used in a NFI-based map and models used in a FMI, and (2) evaluate utilizing additional local sample plots in the model of the NFI-based map. Methods Accuracies of timber volume estimates using models from an existing NFI-based map and a FMI were compared at plot and stand level. Results Estimates from the NFI-based map were similar to or more accurate than the FMI. The addition of local plots to the modeling data did not clearly improve the model of the NFI-based map. Conclusion The comparison indicates that NFI-based maps can directly be used in FMIs for timber volume estimation in mature spruce stands, leading to potentially large cost savings.

Background The Norwegian forest resource map SR16 combines national forest inventory (NFI) and airborne laser scanning (ALS) data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Material and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and ⅔ of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) strata by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) a stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35, 34, 31, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2-7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0-4 pp smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0 – 3.9 pp, depending on the main tree species. RSMEs for timber volume ranged between 19 – 59% among individual ALS projects.Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

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).

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.

Identifying the signs and symptoms of pathogens, insects, and other biotic and abiotic agents provides valuable information about the absolute and relative impacts of different types of damage across the forest landscape. In the USA, damage collection protocols have been included in various forms since the initiation of state-level forest surveys in the early twentieth century; however, changes in the protocols over time have made it difficult for the data to be used to its full potential. This article outlines differences in protocols across inventory regions, changes in protocols over time, and limitations and utility of the data so that those interested in using the US national forest inventory database will better understand what data are available and how they have been and can be used.

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.

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.

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.

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.

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.

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 The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha−1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha−1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha−1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.

Background Forests are an important component of the global carbon (C) cycle and can be net sources or sinks of CO2, thus mitigating or exacerbating the effects of anthropogenic greenhouse gas emissions. While forest productivity is often inferred from national-scale yield tables or from satellite products, forest C emissions resulting from dead organic matter decay are usually simulated, therefore it is important to ensure the accuracy and reliability of a model used to simulate organic matter decay at an appropriate scale. National Forest Inventories (NFIs) provide a record of carbon pools in ecosystem components, and these measurements are essential for evaluating rates and controls of C dynamics in forest ecosystems. In this study we combine the observations from the Swiss NFIs and machine learning techniques to quantify the decay rates of the standing snags and downed logs and identify the main controls of dead wood decay. Results We found that wood decay rate was affected by tree species, temperature, and precipitation. Dead wood originating from Fagus sylvatica decayed the fastest, with the residence times ranging from 27 to 54 years at the warmest and coldest Swiss sites, respectively. Hardwoods at wetter sites tended to decompose faster compared to hardwoods at drier sites, with residence times 45–92 and 62–95 years for the wetter and drier sites, respectively. Dead wood originating from softwood species had the longest residence times ranging from 58 to 191 years at wetter sites and from 78 to 286 years at drier sites. Conclusions This study illustrates how long-term dead wood observations collected and remeasured during several NFI campaigns can be used to estimate dead wood decay parameters, as well as gain understanding about controls of dead wood dynamics. The wood decay parameters quantified in this study can be used in carbon budget models to simulate the decay dynamics of dead wood, however more measurements (e.g. of soil C dynamics at the same plots) are needed to estimate what fraction of dead wood is converted to CO2, and what fraction is incorporated into soil.

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.

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.

Non-native trees enhance services that are fundamental for human well-being. Yet, the extensive use of non-native trees has the potential of causing environmental and socio-economic harm. Eucalyptus and Pinus are the most widely distributed and extensively planted tree genera worldwide, because their rapid growth allows profitable production of timber and pulp. Their naturalization is causing severe effects on the environment, but the relative importance of underlying factors determining their demographic performance is not well known. Thus, our aim was to evaluate the relative importance of biotic, abiotic, and anthropogenic factors driving demographic changes of Eucalyptus and Pinus at the regional scale. We compiled environmental variables and demographic data for Eucalyptus globulus, Eucalyptus camaldulensis, and Pinus radiata across 6388 permanent forestland plots surveyed in the Spanish Forest Inventory (SFI). We used the second (1986?1996), third (1997?2007), and fourth (2008?2017) SFI datasets to quantify annual changes in basal area per plot between consecutive inventories (?BA; m2 ha?1 year?1). We also quantified the components of ?BA: tree ingrowth (transitions from juvenile to adult trees), growth, and mortality. We evaluated juvenile recruitment with in-situ regeneration (No. juvenile trees ha?1) within plots already occupied by the focal species in the previous inventory, and with natural colonization of plots where the focal species was absent at the beginning of the time interval. We found that the structure of the biotic community was especially important to explain demographic performance of non-native trees growing in benign environments (E. globulus and P. radiata), whereas abiotic factors were particularly important regulating basal area increments of E. camaldulensis, which occurs in harsher environments. Basal area increments decreased with species and functional richness, heterospecific density, mean annual temperature, and increased with soil capacity to retain nutrients and water. Colonization of new plots increased with propagule availability in the surrounding landscape. Tree cutting was beneficial for P. radiata. Collectively, our results suggest that non-native trees perform better in forests with high propagule pressure, low biotic resistance, favourable abiotic conditions, and human management., Agencia Estatal de Investigación, Universidad de Alcalá, Ministerio de Ciencia e Innovación, Comunidad de Madrid