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Abstract Forest biomass is an essential resource in relation to the green transition and its assessment is key for the sustainable management of forest resources. Here, we present a forest biomass dataset for Europe based on the best available inventory and satellite data, with a higher level of harmonisation and spatial resolution than other existing data. This database provides statistics and maps of the forest area, biomass stock and their share available for wood supply in the year 2020, and statistics on gross and net volume increment in 2010–2020, for 38 European countries. The statistics of most countries are available at a sub-national scale and are derived from National Forest Inventory data, harmonised using common reference definitions and estimation methodology, and updated to a common year using a modelling approach. For those counties without harmonised statistics, data were derived from the State of Europe’s Forest 2020 Report at the national scale. The maps are coherent with the statistics and depict the spatial distribution of the forest variables at 100 m resolution.

Predicting diameter at breast height (DBH) of trees from stump information may be necessary to reconstruct silvicultural practices, to assess harvested timber and wood, or to estimate forest products’ losses caused by illegal cuttings or natural disasters (disturbances). A model to predict DBH of felled trees was developed by the first Italian National Forest Inventory in 1985 (IFNI85). The model distinguished between the two broad groups of conifers and broadleaves and used stump diameter as the sole quantitative variable. Using an original dataset containing data from about 1200 trees of sixteen species recorded throughout Italy, we assessed the performance of that model. To improve the prediction of the DBH of removed trees over large areas and for multiple species, we developed new models using the same dataset. Performance of the new models was tested through indices computed on cross-validated data obtained through the leave-one-out method. A new model that performs better than the old one was finally selected. Compared to the old NFI model, the selected model improved DBH prediction for fourteen species up to 31.28%. This study proved that species specification and stump height are variables needed to improve the models’ performance and suggested that data collection should be continued to get enhanced models, accurate for different ecological and stand conditions.

National forest inventories represent a fundamental source of data and knowledge for forestry and environmental policy and allow for the production of national and regional level statistics on forests. The value of these statistics confirms the need for a sampling design that adequately delivers representation by reducing sampling error, but also for a data quality process that limits the non-sampling errors. The article summarizes the quality control procedures of the three sampling phases adopted in the Italian national forest inventory, carried out between 2003 and 2006. The development of an integrated system of actions and controls which are able to limit subjective interpretations, in order to guarantee harmonized information all over the country, was a considerable effort within the overall project. Critical points to be considered were the consistent number of measures and evaluations undertaken during the three inventory phases, the high variability of observed attributes, the consistent number of surveyors involved, and costs of quality control, especially those related to fieldwork. At the end, examples on the overall quality of the classification performed on land cover and vegetation are discussed, as well as the impact of classification errors on the total forest area estimates. [ABSTRACT FROM AUTHOR]