Remote sensing techniques and data are becoming increasingly popular in forest management, e.g. for change detection and health condition analysis. Tree species recognition is a fundamental issue in taking forest inventories, especially in carbon budget modelling. Hyperspectral imagery provides an accurate classification results for large areas based on a relatively small amount of training data. A hyperspectral image of a forest stand in north-eastern Poland taken using an AISA (Airborne Imaging Spectrometer for Application) Eagle camera was transformed to extract the most valuable spectral differences and was classified into seven tree types (birch, European beech, oak, hornbeam, European larch, Scots pine, and Norway spruce) using nine classification algorithms. The highest overall accuracy and kappa coefficient were 90.3% and 0.9 respectively using three minimum noise fraction bands and maximum likelihood classifier. Hyperspectral imaging of forests can be used to classify major forest tree species with a good degree of accuracy. It is time-efficient and user-friendly; however, the data and software required means that this approach is still expensive at present.

Spruce stands in the mountains of Central Europe are particularly valuable, not only because of their natural and scenic values but also because of their role in the protection of watersheds and soil. Over the past decades, these stands were frequently exposed to massive deforestation caused by various biotic, abiotic and anthropogenic factors. The current health condition of spruce stands in the Eastern Sudetes shows that the next phase of deterioration of these stands has begun. Bearing in mind past experiences and the current situation in these mountain areas, it is particularly important to understand the processes and factors that may short-term (2012–2016) spruce health analyses based on the normalized difference red-edge index and RapidEye satellite imagery, which is being delivered annually. Aided by remote sensing data and Boosted Regression Trees, it was possible to determine the topographic and tree stand features having the greatest impact on the vitality of spruce in all analyzed areas during 2012–2016. As the results show, the highest impact on the value of the normalized difference red-edge index indicator comes from a height above sea level, age of stands, terrain slopes, and exposure. In various areas, these factors may affect the vitality of spruce to varying degrees but not always in the same way. Our models accurately explained 74–81% of the randomly selected input data (predicted 72–80% of the excluded data) for site A, 50–58% for site B (48–57% for tested data) and 54–70% for site C (52–69% for excluded data).

This study examines the applicability of the 3-30-300 rule in five medium-sized Polish municipalities. The rule states that residents should be able to see at least three trees from their homes, neighborhoods should have at least 30% tree canopy coverage, and public green spaces should be within 300 m. The method proposed in this study shows that the tree visibility component of the 3-30-300 concept is the most fluctuating index, and it strongly depends on the settings of the algorithm parameter, as well as on the placement of artificially generated observers. This demonstrates the complexity of the issue and the need to further specify the nuances of the 3-30-300 rule. The work shows that all variables of the 3-30-300 rule can be calculated based on publicly available data, such as point clouds, which are increasingly being made available for free for research and implementation purposes. The study concludes that the proposed solution is effective in assessing the availability of green spaces and emphasizes the need for qualitative improvements in the management of urban green spaces. While the 3-30-300 rule can serve as the foundation for future urban planning, complementary strategies are needed to ensure long-term sustainability and better access to green spaces.

(1) Background: Like many other countries, Poland is obliged to report forest area to the Climate Convention (UNFCCC Kyoto Protocol) and the Food and Agriculture Organization of the United Nations (FAO/UN). Differences between national and international forest definitions lead to differences between actual and reported forest area. Remote sensing is a useful tool for estimating forest area for reporting purposes. One of the most important parts of the estimation is the choice of a basal area to calculate the percentage of vegetation cover. (2) Methods: Height, crown projection area, and minimum complex area were used to classify the area with forest vegetation. Percentage canopy cover was determined using three different methods based on segmentation polygons, triangular grid and canopy height model pixels. The accuracy of the above methods was verified by manual vectorization performed on a selected set of test plots in the Milicz study area according to the international definitions. The differences were examined using three statistical metrics. (3) Conclusions: This paper compares for the first time methods for determining the area for which canopy cover is calculated (using data from (ALS) and discusses the differences between them in the context of accuracy (the correspondence between the results and the reference data) and the complexity of the process (time and effort required to perform the analysis). This is important in the context of reporting, estimating carbon stocks and biodiversity to mitigate the effects of climate change. Method 2 proved to be the most accurate method, Method 1 was found to be the worst option. Accuracy was better in the case of the Kyoto Protocol definition.

Within the last few decades, ALS-enhanced two-phase forest inventory has emerged as viable alternative to standard inventory designs. As a relatively new and compound method, there still remains significant potential for its optimisation. One key aspect concerns the design of the second-phase sampling. Apart from well-known designs such as random, systematic, or stratified sampling—which often involve some degree of uncertainty regarding their realisations—there are relatively less common, structurally guided sampling designs (SGS), which can facilitate the unambiguous allocation of balanced and well-optimised samples. Unlike traditional stratification, the SGS design does not rely on fixed divisions, which may induce additional errors due to pre-defined and potentially non-representative strata. Instead of geographical (spatial) sample deployment, the SGS uses the multidimensional space of covariates, e.g., ALS metrics, to optimise sample allocation. SGS can be powered by different engines. While some algorithms for SGS, such as the cube method or local pivotal method, have been briefly tested in recent studies, no thorough attention has yet been paid to data clustering algorithms. Therefore, this study compares the performance of several popular data clustering algorithms for structurally guided sampling to train the model for growing stock volume estimation in a two-phase ALS-enhanced forest inventory design. The results showed that hierarchical clustering was competitive with other methods but outperformed them in terms of the highest stability of estimates, even at lower sampling intensity levels. The use of data clustering methods can ensure unambiguous yet more optimal sample distribution, minimising sampling variation or estimation error caused by the randomness of other sampling methods or the inflexibility of pre-defined strata.

Two change detection techniques (NDVI differencing and post-classification analysis) were compared, in order to detect canopy cover changes in forests on the area of twelve forest districts in the Sudety and West Beskidy Mountains in Poland, using 2012 and 2013 Black-Bridge satellite images. Although the classification accuracy of the respective images was high (about 95%), the accuracy of the difference in bi-temporal images was much worse because of the short time between the dates of images and the imperfection of the algorithm calculating the unclear boundary between the forest and no-forest areas. NDVI differencing method and thresholding brought much better overall results, although roads, clouds and fogs caused much problem performing pseudo-changes.