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 provisions in the European and Polish law oblige to increase the use of energy obtained from renewable energy sources. In order to meet these demands, it is necessary to take into account the singlefamily housing sector that has a significant energy potential. Among the unconventional heat sources that have gained the highest popularity, it is worthy to note installations using solar energy for instance to prepare DHW (domestic hot water) or be used in buildings’ heating. It is also worth mentioning that this type of technical solutions can be successfully used as well in new as modernized old residential buildings. Our paper discusses the problems associated with planning of solar installations with flat and vacuum solar collectors mounted on the roofs of the single-family buildings. During the analysis we used GIS&T technology, multi-criteria decision support as well as detailed spatial data obtained by modern remote sensing techniques including airborne laser scanning. The proposed methodology and tools were used on the example of the selected part of a single-family housing estate located in Bialystok. The research focused on issues of the multi-criteria assessment of the assembly of use of roofs for solar collectors, including data on a solar potential, exposure, slope, surface and roofs’ shape. Additionally, we took into account roof zones on being in a shadow caused by roof elements, roof infrastructure and high vegetation. Based on the simulation results we analyzed possibility of plate and tube solar collectors’ application that would provide warm water for 1-4 habitants of houses. The results of our research showed high utility of the proposed methods in the field of spatial and economic assessment of the use of solar collectors in the single-family housing sector.

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.