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The forestry sector of Serbia, other users, as well as international organizations and conventions express the need for an increasing volume, specific structure and high reliability of information on the growing stock of Serbia. To meet these requirements, NFI-2 has undergone significant changes compared to NFI-1. In methodological terms, the changes are reflected in the fact that NFI-2 is a two-phase inventory. In the phase of photo-interpretation of the images, land categories are identified, changes occurred in the period 2006-2019 and field works are planned. Unlike NFI-1, all four circles on the cluster have a permanent character, the centers of the circles are better secured, which ensures their easy findings in NFI-3. The field phase is realized using high-precision instruments, digital records of measured or estimated values and online transfer to the database. The control of field works is performed through three levels, which significantly reduces the possibility of the appearance of rough and systematic errors. The scope of information has been significantly expanded, especially in the field of biodiversity assessment, nature protection, carbon storage, bioenergy, etc. Conceived in this way, NFI-2 is compatible with the inventories of most European countries, except in the area of its institutional organization in Serbia, which has not been resolved.

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.

National Forest Inventories are important primary data sources for large-scale forest resource surveys, in which volume estimates of sampled trees are essential for quantitative analysis. Volume prediction models in natural forests are scarce in Brazil due to legal restrictions for cutting trees, especially in the Atlantic Forest. This study aimed to fit volume models for the main forest types and timber species of the Atlantic Forest in Rio de Janeiro state, considering two hypotheses: (I) generic volume models provide greater generalizability of estimates; however, (II) they may reduce the accuracy of forest type- and species-specific predictions. Four linear models with logarithmic transformation of variables were evaluated to fit volume models for generic and specific datasets, which correspond to the main forest types and timber species. Goodness-of-fit statistics were calculated to compare the accuracy and efficiency of the models, and selected models were validated through leave-one-out cross-validation procedures. The estimates obtained by generic and specific models were compared by non-parametric hypothesis tests. Generic models showed similar predictions to the specific models for forest types and timber species, with similar potential for stem and total volume predictions. Therefore, generic models can be used for Atlantic Forests in Rio de Janeiro state, while specific models are recommended to obtain more detailed local estimates.

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 ...

Background The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design. Results The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally. Conclusions LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

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.

Background The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

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.

Forest renewal is a key stage in the life of forest stands as it allows the long-term maintenance of the forest and its associated ecosystem services. Among the factors likely to have a negative impact on forest renewal, colonisation by competitive species can slow down tree regeneration, or even block it for several decades.The objective of this thesis is to estimate the impact of competitive vegetation on tree regeneration on a regional to national scale. The thesis is mainly based on the use of French National Forest Inventory (NFI) data.A first step, using the results of a survey of forest managers, was to establish a list of the main competing species for tree regeneration, and to determine the areas where each species is present with a high abundance. Bramble (Rubus fruticosus), Eagle Fern (Pteridium aquilinum) and Purple Moor-grass (Molinia caerulea) are the main competitive species in France, and are each present with a high abundance in more than 300,000 ha of forests with open canopies in France.In order to better understand the ecology of competitive species, the role of canopy openness on the probability of presence and high abundance of competitive species was studied. For a majority of species, canopy cover has a weak effect on the probability of presence of species. Conversely, species abundance was strongly correlated with canopy cover, with high canopy cover being associated with lower abundance for most competitive species.The effect of competitive vegetation on tree regeneration cover was then modelled at the scale of France for the three main species. High abundance of P. aquilinum and M. caerulea is associated with a relative decrease in tree regeneration cover of about 30% and 40% respectively, compared to situations where these species are present at low abundance. Conversely, R. fruticosus has a more ambivalent effect on tree regeneration. Woody regeneration cover increases slightly on average at intermediate R. fruticosus abundances, and decreases significantly at high abundance of this species.Finally, the probability of presence and high abundance of the three main competing species was modelled, in order to identify the conditions of high abundance and to predict the location of the areas potentially impacted by the appearance of these three species when the canopy is opened. The difficulty of accurately modelling the presence and, more importantly, the level of abundance of these species led to models with poor predictive abilities, which did not allow for robust estimates of the conditions at high risk of colonisation by the three species studied.In conclusion, this thesis has shown that tree regeneration problems related to competitive vegetation species are present on significant areas in French forests. P. aquilinum and M. caerulea have a marked negative effect on tree regeneration regardless of their abundance, whereas the effect of R. fruticosus is only negative at high abundances. Finally, this thesis also demonstrated the importance of modelling presence and abundance separately, and identified the main factors that need to be taken into account to better model species abundance., Le renouvellement forestier est une étape clef dans la vie des peuplements forestiers car il permet le maintien à long terme de la forêt, et des services écosystémiques qui lui sont associés. Parmi les facteurs susceptibles d’impacter négativement le renouvellement, la colonisation par des espèces végétales concurrentes peut ralentir la régénération ligneuse, voire la bloquer pendant plusieurs décennies.L’objectif de cette thèse est d’estimer l’impact, à une échelle régionale à nationale, de la végétation concurrente sur la régénération ligneuse. La thèse est principalement basée sur l’utilisation des données de l’inventaire forestier national français (IFN).Une première étape, utilisant les résultats d’une enquête auprès de gestionnaires forestiers, a permis de dresser une liste des principales espèces concurrentes pour la régénération ligneuse, et de déterminer les surfaces sur lesquelles chaque espèce est présente avec une forte abondance. La ronce (Rubus fruticosus), la fougère aigle (Pteridium aquilinum) et la molinie bleue (Molinia caerulea) sont les principales espèces concurrentes en France, et sont chacune présentes à forte abondance dans plus de 300 000 ha des forêts à faible couvert de canopée en France.Afin de mieux comprendre l’écologie des espèces concurrentes, le rôle de l’ouverture de la canopée sur la probabilité de présence et de forte abondance des espèces concurrentes a été étudié. Pour une majorité d’espèces, le couvert de la canopée a un effet faible sur la probabilité de présence des espèces. À l’inverse, l’abondance des espèces est fortement corrélée au couvert de la canopée, un couvert important étant pour la majorité des espèces concurrentes associé à une plus faible abondance.L’effet de la végétation concurrente sur le recouvrement de la régénération ligneuse a ensuite été modélisé à l’échelle de la France pour les trois principales espèces. Une forte abondance de P. aquilinum et M. caerulea est associée à une diminution relative du recouvrement de la régénération ligneuse d’environ 30 % et 40 % respectivement, par rapport à des situations où ces espèces sont présentes à faible abondance. À l’inverse, R. fruticosus a un effet plus ambivalent sur la régénération ligneuse. Le recouvrement de régénération ligneuse augmente en moyenne légèrement pour des abondances de R. fruticosus intermédiaires, et diminue sensiblement pour une forte abondance de cette espèce.Enfin, la probabilité de présence et de forte abondance des trois principales espèces concurrentes a été modélisée, afin d’identifier les conditions de fortes abondances et de prédire la localisation des surfaces potentiellement impactées par l’apparition de ces trois espèces lors de l’ouverture de la canopée. La difficulté de modéliser précisément la présence mais surtout le niveau d’abondance de ces espèces a conduit à des modèles de faibles capacités prédictives, ne permettant pas d’obtenir des estimations robustes des conditions à fort risque d’envahissement par les trois espèces étudiées.En conclusion, cette thèse a montré que les problèmes de régénération ligneuse liés à des espèces de végétation concurrente sont présents sur des surfaces notables dans les forêts françaises. P. aquilinum et M. caerulea ont globalement un effet négatif marqué sur la régénération ligneuse quelle que soit leur abondance, tandis que l’effet de R. fruticosus n’est globalement négatif que pour de fortes abondances. Enfin, cette thèse a également démontré l’importance de modéliser séparément la présence et l’abondance, et identifié les principaux facteurs à mieux prendre en compte pour mieux modéliser l’abondance des espèces.

International audience; Models based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug‐in option to any inventory‐ based initial condition. Forty‐year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority.

Analizirali smo izbrana merila (naklon in nadmorska višina gozdnih sestojev, prirastek, oddaljenost od gozdnih cest), ki lahko vplivajo na razpoložljivost gozdov za oskrbo z lesom v posameznih ekoloških regijah. Z izbranimi merili (naklon, nadmorska višina, prirastek, gozdni rezervati, prvo varstveno območje Triglavskega narodnega parka in varovalni gozdovi) smo predstavili štiri variante prostorskega informacijskega sloja FNAWS (gozdovi nerazpoložljivi za oskrbo z lesom). Po najstrožji varianti (najostrejša merila) smo med FNAWS uvrstil 6,8 % vseh gozdov, po najmilejši (najbolj ohlapna merila) pa kar 17,6 % vseh gozdov. Na podlagi izkušenj, pridobljenih pri izdelavi karte FNAWS, smo oblikovali dvostopenjski protokol (kabinet, teren) za uvrstitev trajnih vzorčnih ploskev Nacionalne gozdne inventure (NGI) med FAWS/FNAWS. Pri primerjavi obstoječe uvrstitve ploskev Monitoringa gozdov in gozdnih ekosistemov (MGGE) leta 2018 in NGI 2020 na terenu z narejenim slojem FNAWS smo ugotovili dobro ujemanje uvrstitve ploskev med negospodarjene (90 %). We analyzed the selected criteria (slope and altitude of forest stands, increment, distance to forest roads) that can affect the availability of forests for wood supply in each ecological regions. With the selected criteria (slope, altitude, increment, forest reserves, first protective regime area of the Triglav National Park, and protection forests) we presented four variants of the spatial information layer FNAWS (forests not available for wood supply). According to the strictest variant (the strictest criteria), we classified 6.8 % of all forests in FNAWS, and 17.6 % of all forests according to the mildest one (the loosest criteria). Based on the experiences acquired at the making of the FNAWS map, we designed a two-level protocol (office, field) for classifying the National Forest Inventory (NFI) sampling plots in FAWS/FNAWS. Comparing the existing classification of the Forest and Forest Ecosystems Monitoring (MGGE) plots in 2018 and NGI in the field with the FNAWS layer done, we found a good match of the classification of plots in the non-managed ones (90 %).

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.