Highly accurate and extensive datasets are needed for the practical implementation of precision forestry as a method of forest ecosystem management. Proper processing of huge datasets involves the necessity of the appropriate selection of methods for their analysis and optimization. In this paper, we propose a concept for and implementation of a data preprocessing algorithm, and a method for the empirical verification of selected individual tree detection (ITD) algorithms, based on Airborne Laser Scanning (ALS) data. In our study, we used ALS data and very extensive dendrometric field measurements (including over 21,000 trees on 522 circular sample plots) in the economic and protective coniferous stands of north-eastern Poland. Our algorithm deals well with the overestimation problems of tree top detection. Furthermore, we analyzed segmentation parameters for the two currently dominant ITD methods: Watershed (WS) and Local Maximum Filter with Growing Region (LMF+GR). We optimized them with respect to minimizing the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Additionally, our results show the crucial importance of the quality of empirical data for the correct evaluation of the accuracy of ITD algorithms.

In this study, we analyzed the change in tree height of 2594 Scots pine (Pinus sylvestris L.) trees with respect to the distribution among different forest sites: HCfs—hydrogenic coniferous forest site; MCfs—mineral coniferous forest site; MMfs—mineral mixed forest site. We obtained tree height information from three independent airborne laser scanning (ALS) point clouds acquired in north-eastern Poland over a 5-year interval in 2007, 2012, and 2017 using verified tree crown segments. We performed a comparative analysis of digital terrain models (DTMs) calculated from analyzed point clouds, indicating that the highest elevation differences were observed in the case of data from 2007. The analyses showed that tree growth varies significantly depending on the forest site productivity and the stage of tree development, which was described as initial tree height instead of age—commonly used in such studies. In conclusion, it is possible to indicate the significant information potential of using multitemporal ALS data to track individual tree height changes. These field data, combined with meteorological data, can be successfully used to predict changes in the abundance of stands depending on the forest site productivity. We have built Scots pine growth models for each forest site, which indicates that it is possible to predict the change in the tree stand height.

The accurate detection of individual tree crowns and estimation of tree density is essential for effective forest management, biodiversity assessment, and ecological monitoring. The precision of tree crown detection algorithms plays a critical role in providing reliable data for these applications, where even slight inaccuracies can lead to significant deviations in tree population estimates and ecological indicators. Various algorithmic parameters, such as pixel size and crown segmentation thresholds, can substantially impact tree crown detection accuracy. This study aims to explore the influence of tree stand features and parameters on the effectiveness of the individual tree crown detection method based on a watershed algorithm, leading to identifying optimal configurations that enhance the reliability of forest inventories and support sustainable management practices. Our analysis of the algorithm results shows that the features of the tree stand, such as tree height variance and tree crown size variance, significantly impact the algorithm’s output in precisely estimating tree count. Consequently, adjusting the pixel size of a canopy height model in the context of tree stand features is necessary to minimize error. Additionally, our findings show that there is a need to carefully assess the criterion of membership of a detected tree crown in a circular sample plot, which we based on the point cloud.

In the context of intensifying climate change, dendroclimatic research provides insight into tree responses to environmental variability. This study assessed relationships between temperature, precipitation, and radial growth of four major forest species in Central Europe: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst), pedunculate oak (Quercus robur L.), and black alder (Alnus glutinosa (L.) Gaertn.). Analyses were conducted at three independent sites to account for local variability and enhance robustness. We tested three hypotheses: (1) replication improves reliability in dendrochronological studies, (2) multi-species analysis strengthens interpretation of climate–growth relationships, and (3) black alder exhibits distinct precipitation sensitivity. Results showed species- and site-specific responses. The first hypothesis was supported, as replication enhanced the robustness of climate–growth signals in Scots pine, Norway spruce, and pedunculate oak showed broadly consistent responses, while black alder maintained its distinct pattern. Scots pine responded strongly to summer rainfall (June–August, including July of the previous year), Norway spruce to summer rainfall (June–July of the current and previous year), and pedunculate oak to summer rainfall (June–July, with additional effects in August). Black alder exhibited positive correlations with winter precipitation (December–January of the previous year) and negative with summer rainfall (May, June, August; September–November of the previous year), suggesting moisture-related stress. Temperature sensitivity occurred in winter and early spring (December–April, especially February–March) for most species, except black alder, which also responded positively to summer temperatures (May, July–September). These findings highlight the importance of species traits and site conditions in dendroclimatic studies and support replicated multi-species approaches to guide adaptive forest management under climate change.