The majority of the existing studies on timber price forecasting are based on ARIMA/SARIMA autoregressive moving average models, while vector autoregressive (VAR) and exponential smoothing (ETS) models have been employed less often. To date, timber prices in primary timber markets have not been forecasted with ANN methodology. This methodology was used only for forecasting lumber futures. Low-labor-intensive and relatively simple solutions that can be used in practice as a tool supporting decisions of timber market participants were sought. The present work sets out to compare RBF and MLP artificial neural networks with the Prophet procedure and with classical models (i.e., ARIMA, ETS, BATS, and TBATS) in terms of their suitability for forecasting timber prices in Poland. The study material consisted of quarterly time series of net nominal prices of roundwood (W0) for the years 2005–2021. MLP was found to be far superior to other models in terms of forecasting price changes and levels. ANN models exhibited a better fit to minimum and maximum values as compared to the classical models, which had a tendency to smooth price trends and produce forecasts biased toward average values. The Prophet procedure led to the lowest quality of projections. Ex-post error-based measures of prediction accuracy revealed a complex picture. The best forecasts for alder wood were obtained using the ETS model (with RMSE and MAE values of approx. 0.38 € m⁻³). ETS also performed well with respect to beech timber, although in this case BATS was just as good in terms of RMSE, while the difference between ETS and neural models amounted to as little as 0.64 € m⁻³. Birch timber prices were most accurately predicted with BATS and TBATS models (MAE 0.86 € m⁻³, RMSE 1.04 € m⁻³). The prices of the most popular roundwood types in Poland, i.e., Scots pine, Norway spruce, and oaks, were best forecasted using ANNs, and especially MLP models. Among the neural models for oak (MAE 4.74 € m⁻³, RMSE 8.09 € m⁻³), pine (MAE 2.21 € m⁻³, RMSE 2.83 € m⁻³), beech (MAE 2.31 € m⁻³, RMSE 2.70 € m⁻³), alder (MAE 1.88 € m⁻³, RMSE 2.40 € m⁻³), and spruce (MAE 2.44 € m⁻³, RMSE 2.58 € m⁻³), the MLP model was the best (the RBF model for birch). Of the seven models used to forecast the prices of six types of wood, the worst results were obtained for oak wood, while the best results were obtained for alder.

Variation of the bark depends on tree age, origin, geographic location, or site conditions like temperature and water availability. Most of these variables are characterized by very high variability but above of all are also affected by climate changes. This requires the construction of improved bark thickness models that take this complexity into account. We propose a new approach based on time series. We used a recurrent neural network (ANN) to build the bark thickness model and compare it with stem taper curves adjusted to predict double bark thickness. The data includes 750 felled trees from common oak and 144 Scots pine—trees representing dominant forest-forming tree species in Europe. The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built recurrent ANN and calculated bark thickness along the stem. We tested different network structures with one- and two-time window delay and three learning algorithms—Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate Gradient. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error as well as graphical analysis of observed and estimated values. The results show that recurrent ANN is a universal approach that offers the most precise estimation of bark thickness at a particular stem height. The ANN recursive model had an advantage in estimating trees that were atypical for height, as well as upper and lower parts on the stem.

This Data Descriptor presents a high-resolution (100 × 100 m) gridded dataset of the Standardized Climatic Water Balance for Poland, spanning 72 years from 1951 to 2023. The dataset was developed as part of a research initiative focused on assessing long-term water availability and drought conditions under changing climate regimes. Input data set includes monthly temperature and precipitation records from over 2,000 meteorological stations, as well as elevation data from the Shuttle Radar Topography Mission. We describe the full methodology, including spatial interpolation using a hybrid regression-spline model, calculation of potential evapotranspiration via the Thornthwaite method, and standardization of the climatic water balance to produce Standardized Climatic Water Balance for Poland values that are comparable across space and time. The resulting dataset comprises monthly raster layers of the Standardized Climatic Water Balance for Poland, along with interpolated layers of air temperature and precipitation. All files are publicly available and suitable for use in environmental monitoring, drought risk assessment, forest ecosystem analysis, and climate adaptation planning at regional and local scales.

The objective of this study was to analyze price variability and the factors influencing the formation of monthly prices of by-products of the wood industry in Poland between October 2017 and January 2025. The analysis considered the impact of economic variables, including energy commodity prices (natural gas and coal) and industrial wood prices, on the pricing of wood industry by-products. The adopted approach enabled the identification of key determinants shaping the prices of these by-products. The effectiveness of two tree-based regression models—Random Forest (RF) and CatBoost (CB)—was compared in the analysis. Although RF offers greater interpretability and lower computational requirements, CB proved more effective in modeling dynamic, time-dependent phenomena. The results indicate that industrial wood prices exerted a weaker influence on by-product prices than natural gas prices, suggesting that the energy sector plays a leading role in shaping biomass prices. Coal prices had only a marginal impact on the biomass market, implying that changes in coal availability and pricing did not directly translate into changes in the prices of wood industry by-products. The growing role of renewable energy sources derived from natural gas and wood biomass is contributing to the emergence of a distinct market, increasingly independent of the traditional coal market. In Poland, due to limited access to alternative energy sources, biomass plays a critical role in the decarbonization of the energy sector.

Categorical raster maps derived from remote sensing classifications are widely used to describe land cover, landforms, and other environmental characteristics. However, these products are usually analysed at the pixel level, which limits the identification of larger spatial structures and coherent landscape units. The J-image Segmentation (JSEG) algorithm provides a promising framework for region delineation, but it was originally developed for natural color imagery and relies on fixed scale assumptions that are poorly suited to thematic geospatial data. To address these limitations, we developed GeoJSEG, a modified version of JSEG designed for remote sensing categorized spatial datasets. The method operates directly on classified raster layers, introduces user-defined scale parameters, and employs Jensen–Shannon Divergence during region merging, enabling segmentation that better reflects the spatial organization of geographic phenomena. GeoJSEG was evaluated using synthetic categorical maps, natural RGB images, orthophoto-derived data, land-cover maps, and geomorphon representations of terrain forms. For the synthetic class map, the segmentation quality measure $\overline{J}$ decreased from 0.088 to 0.012 (better quality), while for orthophoto data, it decreased from 0.059 to 0.025 (better quality). Improvements were also observed for land-cover data and most natural-image datasets. The results demonstrate that GeoJSEG extends JSEG toward scale-aware regionalization of thematic raster data and provides a practical tool for post-classification analysis of remote sensing products.