Viscum album L. is a plant of great importance due to its influence on the host trees and, by extension, entire ecosystems. The species is also significant to humans—on the one hand, because of its use in medicine, and on the other, because of the growing threat it poses to the stability of conifer stands. Therefore, it is important to recognize the future range of three mistletoe subspecies (Viscum album subsp. album, V. album subsp. austriacum, and V. album subsp. abietis). Modelling of the potential range of these subspecies was performed using MAXENT software. Locations were collected from literature and databases. A total number of 3335 stands were used. Bioclimatic data for the current conditions and three future scenarios (SSP 1.26, SSP 3.70, SSP 5.85) were downloaded from the CHELSA database. The results confirmed that the temperature is the key variable on the potential range of the analysed subspecies. V. album subsp. abietis is withdrawing from its range according to all scenarios. In the case of V. album subsp. austriacum, a slight range shift is visible. Only the V. album subsp. album will expand non-directionally. The reason is most likely a very large number of host species and greater genetic variability compared to the subspecies found on conifers.

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.

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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.