Miloš Kučera’s research while affiliated with The Forest Management Institute and other places

Consistent knowledge about the increment in European forests gained amplified importance in European policies and decision processes related to forest-based bioeconomy, carbon sequestration, sustainable forest management and environmental changes. Until now, large-area increment information from European countries was lacking international comparability. In this study we present a harmonisation framework in accordance with the principles and the approach established for the harmonisation of National Forest Inventories (NFIs) in Europe. 11 European NFIs, representing a broad range of increment measurement and estimation methods, developed unified reference definitions and methods that were subsequently implemented to provide harmonised increment estimates by NUTS regions (Nomenclature of territorial units for statistics of the European Union), main forest types and tree species groups, and to rate the impact of harmonisation measures. The main emphasis was on gross annual increment (GAI), however, also annual natural losses (ANL) and net annual increment (NAI) were estimated. The data from the latest available NFI cycles were processed. The participating countries represent a forest area of about 130 million ha, and 82% of the European Unions' (EU) forest area, respectively. The increments were estimated in terms of volume (m 3 year − 1 , m 3 ha − 1 year − 1) and above-ground biomass (t year − 1 , t ha − 1 year − 1). The harmonised GAI volume estimates deviate in a range of +12.3% to − 26.5% from the estimates according to the national definitions and estimation methods. Within the study area, the harmonised estimates show a considerable range over the NUTS regions for GAI, from 0.6 to 12.3 m 3 ha − 1 year − 1 , and 0.8-6.4 t ha − 1 year − 1 , of volume and above-ground biomass, respectively. The largest increment estimates are found in Central Europe and gradually decrease towards the North, South, West and East. In most countries coniferous forests show larger increment estimates per hectare than broadleaved forests while mixed forests are at an intermediate level. However, in some instances, the differences were small or mixed forests revealed the largest increment Forest Ecology and Management 562 (2024) 121913 2 estimates. The most important tree species groups in the study area are Pinus spp. and Picea spp., contributing 29% and 26% of the estimated total GAI volume, respectively. The shares of the prevalent broadleaved species are smaller with contributions of 9%, 7% and 6% by Quercus spp., Fagus sylvatica and Betula spp. The results underline the importance of harmonisation in international forest statistics. Looking ahead, harmonised large-area increment estimation is pivotal for accurate monitoring and evidence-based policy decisions in the changing context of future forest ecosystems dynamics, management strategies and wood availability.

Wood resources have been essential for human welfare throughout history. Also nowadays, the volume of growing stock (GS) is considered one of the most important forest attributes monitored by National Forest Inventories (NFIs) to inform policy decisions and forest management planning. The origins of forest inventories closely relate to times of early wood shortage in Europe causing the need to explore and plan the utilisation of GS in the catchment areas of mines, saltworks and settlements. Over time, forest surveys became more detailed and their scope turned to larger areas, although they were still conceived as stand-wise inventories. In the 1920s, the first sample-based NFIs were introduced in the northern European countries. Since the earliest beginnings, GS monitoring approaches have considerably evolved. Current NFI methods differ due to country-specific conditions, inventory traditions, and information needs. Consequently, GS estimates were lacking international comparability and were therefore subject to recent harmonisation efforts to meet the increasing demand for consistent forest resource information at European level. As primary large-area monitoring programmes in most European countries, NFIs assess a multitude of variables, describing various aspects of sustainable forest management, including for example wood supply, carbon sequestration, and biodiversity. Many of these contemporary subject matters involve considerations about GS and its changes, at different geographic levels and time frames from past to future developments according to scenario simulations. Due to its historical, continued and currently increasing importance, we provide an up-to-date review focussing on large-area GS monitoring where we i) describe the origins and historical development of European NFIs, ii) address the terminology and present GS definitions of NFIs, iii) summarise the current methods of 23 European NFIs including sampling methods, tree measurements, volume models, estimators, uncertainty components, and the use of air- and space-borne data sources, iv) present the recent progress in NFI harmonisation in Europe, and v) provide an outlook under changing climate and forest-based bioeconomy objectives.

The quantification of forests available for wood supply (FAWS) is essential for decision-making with regard to the maintenance and enhancement of forest resources and their contribution to the global carbon cycle. The provision of harmonized forest statistics is necessary for the development of forest associated policies and to support decision-making. Based on the National Forest Inventory (NFI) data from 13 European countries, we quantify and compare the areas and aboveground dry biomass (AGB) of FAWS and forest not available for wood supply (FNAWS) according to national and reference definitions by determining the restrictions and associated thresholds considered at country level to classify forests as FAWS or FNAWS. FAWS represent between 75 and 95 % of forest area and AGB for most of the countries in this study. Economic restrictions are the main factor limiting the availability of forests for wood supply, accounting for 67 % of the total FNAWS area and 56 % of the total FNAWS AGB, followed by environmental restrictions. Profitability, slope and accessibility as economic restrictions, and protected areas as environmental restrictions are the factors most frequently considered to distinguish between FAWS and FNAWS. With respect to the area of FNAWS associated with each type of restriction, an overlap among the restrictions of 13.7 % was identified. For most countries, the differences in the FNAWS areas and AGB estimates between national and reference definitions ranged from 0 to 5 %. These results highlight the applicability and reliability of a FAWS reference definition for most of the European countries studied, thereby facilitating a consistent approach to assess forests available for supply for the purpose of international reporting.

Key message Volume predictions of sample trees are basic inputs for essential National Forest Inventory (NFI) estimates. The predicted volumes are rarely comparable among European NFIs because of country-specific dbh-thresholds and differences regarding the inclusion of the tree parts stump, stem top, and branches. Twenty-one European NFIs implemented harmonisation measures to provide consistent stem volume predictions for comparable forest resource estimates. Context The harmonisation of forest information has become increasingly important. International programs and interest groups from the wood industry, energy, and environmental sectors require comparable information. European NFIs as primary source of forest information are well-placed to support policies and decision-making processes with harmonised estimates. Aims The main objectives were to present the implementation of stem volume harmonisation by European NFIs, to obtain comparable growing stocks according to five reference definitions, and to compare the different results. Methods The applied harmonisation approach identifies the deviations between country-level and common reference definitions. The deviations are minimised through country-specific bridging functions. Growing stocks were calculated from the un-harmonised, and harmonised stem volume estimates and comparisons were made. Results The country-level growing stock results differ from the Cost Action E43 reference definition between − 8 and + 32%. Stumps and stem tops together account for 4 to 13% of stem volume, and large branches constitute 3 to 21% of broadleaved growing stock. Up to 6% of stem volume is allocated below the dbh-threshold. Conclusion Comparable volume figures are available for the first time on a large-scale in Europe. The results indicate the importance of harmonisation for international forest statistics. The presented work contributes to the NFI harmonisation process in Europe in several ways regarding comparable NFI reporting and scenario modelling.

Key message The proposed height–diameter model applicable to many tree species in the multi-layered and mixed stands across Czech Republic shows a high accuracy in the height prediction. This model can be useful for estimating forest yield and biomass, and simulation of the vertical stand structures. Abstract We developed a generalized nonlinear mixed-effects height–diameter (H–D) model applicable to Norway spruce (Picea abies (L.) Karst.), European beech (Fagus sylvatica L.) and other conifer and broadleaved tree species using the modelling method that includes dummy variables accounting for species-specific height differences and random component accounting for within- and between-sample plot height differences and randomness in the data. We used two large datasets: the first set (model fitting dataset) originated from permanent research sample plots and second set (model-testing dataset) originated from the Czech national forest inventory (NFI) sample plots. The former dataset comprises 224 sample plots with 29,390 trees and the latter dataset comprises 14,903 sample plots with 382,540 trees, each representing wide variabilities of tree size, ecological zone, growth condition, stand structure and development stage, and management regime across the country. Among the four versatile growth functions evaluated as base functions with diameter at breast height (DBH) included as a single predictor, the Chapman-Richards function showed the most attractive fit statistics. This function was then extended through the integration of other predictor variables, which better describe the stand density (stand basal area), stand development and site quality (dominant height), competition (ratio of DBH to quadratic mean DBH), that would act as modifiers of the original parameters of the function. The mixed-effects H–D model described a large part of the variations in the H–D relationships ($R_{{{\text{adj}}}}^{2}$ = 0.9182; RMSE = 2.7786) without substantial trends in the residuals. Testing this model against model-testing dataset confirmed the model’s high accuracy. The model may be used for estimating forest yield and biomass, and therefore will serve as an important tool for decision making in forestry.

Height-to-diameter at breast height (DBH) ratio (HDR) is an important tree and stand stability measure. Several factors such as stand dynamics, natural and anthropogenic disturbances, and silvicultural tending significantly affect HDR, and, therefore, in-depth investigation of HDR is essential for better understanding of ecological processes in a forest. A nonlinear mixed-effects HDR model applicable to several tree species was developed using the Czech national forest inventory data comprising 13,875 sample plots and 348,980 trees. The predictive performance of this model was evaluated using the independent dataset which was originated from 25,146 trees on 220 research sample plots. Among various tree- and stand-level variables describing tree size, site quality, stand development stage, stand density, inter-tree spacing, and competition evaluated, dominant height (HDOM), dominant diameter (DDOM), relative spacing index (RS), and DBH-to-quadratic mean DBH ratio (dq) were identified as the most important predictors of HDR variations. A random component describing sample plot-specific HDR variations was included through mixed-effects modelling, and dummy variables describing species-specific HDR variations and canopy layer-specific HDR variations were also included into the HDR model through dummy variable modelling. The mixed-effects HDR model explained 79% of HDR variations without any significant trends in the residuals. Simulation results showed that HDR for each canopy layer increased with increasing site quality and stand development stage (increased HDOM) and increasing competition (increased RS, decreased DDOM and dq). Testing the HDR model on the independent data revealed that more than 85% of HDR variations were described for each individual species (Norway spruce, Scots pine, European larch, and European beech) and group of species (fir species, oak species, birch and alder species) without significant trends in the prediction errors. The HDR can be predicted with a higher accuracy using the calibrated mixed-effects HDR model from measurements of its predictors that can be obtained from routine forest inventories. To improve the prediction accuracy, a model needs to be calibrated with the random effects estimated using one to four randomly selected trees of a particular species or group of species depending on the availability of their numbers per sample plot. The HDR model can be applied for stand stability assessment and stand density regulation. The HDR information is also useful for designing a stand density management diagram. Brief implications of the HDR model for designing silviculture strategies and forest management planning are presented in the article.

We present the state and the development of forests on non-forest land in the area of the Czech Republic (CZ) and Slovakia (SK). The forests have a different origin, and are currently outside the interest of forest management, nor the whole forestry related legislation is applicable to them. The national forest inventory (NFI) was performed in CZ in the years 2001–2004 and 2011–2014, while in SK in the years 2005–2006 and 2015–2016. The NFI sampling was applied to all forests, i.e. to those growing on both forest and non-forest land. According to the NFI data, the current proportion of forests on non-forest land was not negligible, since in CZ it reached almost 10%, and in SK even more, 13%. While in CZ they were more evenly spatially distributed, in SK they occurred mainly in the central and eastern parts. Broadleaved tree species accounted for approximately two thirds of their growing stock. Their tree species composition was more diverse than the one on forest land. Carbon stock in tree (aboveground and belowground) biomass of forests on non-forest land was 28.5 ±1.6 million tons in CZ and 20.3 ±2.9 million tons in SK, which represented 7.7 ±0.4 % and 7.7 ±1.1% of the total tree biomass in CZ and SK, respectively. Hence, it is important to take the forests on non-forest land into account, to see their current state positively, to include them to other forests and to try to maximise the use of their functions by society.

Since forests can play an efficient role in the mitigation of greenhouse gas emissions, objective information about the actual carbon stock is very important. Therefore, the presented paper analysed the carbon stock in the living merchantable trees (with diameter at breast height above 7 cm) of the Czech forests with regard to groups of tree species and tree compartments (wood under bark with diameter above 7 cm, wood under bark with diameter below 7 cm, bark, green twigs, foliage, stump and roots). We examined its regional distribution and relationship to the number of inhabitants and the gross domestic product. The data used for the analysis originated from 13,929 forest plots of the first Czech National Forest Inventory performed between 2001 and 2004. The total tree carbon stock was obtained as a sum of the carbon stock in the individual tree compartments estimated from the biomass amount in the compartments multiplied by the relative carbon content. Wood biomass amount was calculated by multiplying a particular part of tree volume with species-specific green wood density. The total amount of carbon stored in forest trees in the Czech Republic was over 327 mill. t, which is about 113 t of carbon per ha of forests. The highest carbon amount (160 mill. t, i.e. 49.0% of the total amount) was fixed in spruce. The minimum carbon amount fixed in the forest cover (14.35 mill. t) was calculated for Ústecký kraj (region), while the maximum carbon amount (51.51 mill. t) was found in Jihočeský kraj.

Individual tree-based growth models precisely describe the growth of individual trees irrespective of stand complexity. These models are more useful than the stand-based growth models for effective management of forests. We developed an individual tree diameter growth model for Norway spruce (Picea abies /Linnaeus/ H. Karsten) using permanent research plot data collected from Krkonoše National Park in the Czech Republic. The model was tested against a part of the Czech National Forest Inventory (NFI) data that originated from the western region of the country. Among various models derived by a generalized algebraic difference approach (GADA), the GADA model derived from the Chapman-Richards function best suited to our data. Tree-specific parameters unique to each growth series, which describe tree-specific growth conditions, were estimated simultaneously with global parameters common to all growth series using the iterative nested regressions. The model described most of the variations in diameter growth for model calibration data (R²_adj = 0.9901, RMSE = 0.5962), leaving no significant trends in the residuals. A test against NFI data also confirms that the model is precise enough for predictions of diameter growth for ranges of site quality, tree size, age, and growth condition. The model also possesses biologically desirable properties because it produces the curves with growth rates and asymptotes that increase with increasing site quality. The GADA model is path-invariant and therefore applicable for both forward and backward predictions, meaning that the model can precisely predict diameter growth at any past ages of the trees.

Since forests can play an efficient role in the mitigation of greenhouse gas emissions, objective information about the actual carbon stock is very important. Therefore, the presented paper analysed the carbon stock in the living merchantable trees (with diameter at breast height above 7 cm) of the Czech forests with regard to groups of tree species and tree compartments (wood under bark with diameter above 7 cm, wood under bark with diameter below 7 cm, bark, green twigs, foliage, stump and roots). We examined its regional distribution and relationship to the number of inhabitants and the gross domestic product. The data used for the analysis originated from 13,929 forest plots of the first Czech National Forest Inventory performed between 2001 and 2004. The total tree carbon stock was obtained as a sum of the carbon stock in the individual tree compartments estimated from the biomass amount in the compartments multiplied by the relative carbon content. Wood biomass amount was calculated by multiplying a particular part of tree volume with species-specific green wood density. The total amount of carbon stored in forest trees in the Czech Republic was over 327 mill. t, which is about 113 t of carbon per ha of forests. The highest carbon amount (160 mill. t, i.e. 49.0% of the total amount) was fixed in spruce. The minimum carbon amount fixed in the forest cover (14.35 mill. t) was calculated for Ústecký kraj (region), while the maximum carbon amount (51.51 mill. t) was found in Jihočeský kraj.