Forest tree species diversity plays a critical role in maintaining ecosystem resilience and function. However, large-scale assessments remain challenging due to the limitations of field-based and supervised remote sensing methods, which require costly training data and species-level labeling. In this study, we propose an unsupervised approach to estimating tree species diversity based solely on satellite imagery (Sentinel-2 or Landsat-8) acquired during the 2019 growing season. The method integrates vegetation indices (GNDVI, EVI, NDMI), Self-Organizing Maps, and spectral clustering to derive the Evenness Index without the need for species classification. Validation against field data from over 10,000 hexagonal grid cells (10 square kilometers each) across Poland shows strong agreement, with Pearson's r = 0.87 (Sentinel-2, $R^{2}$ = 0.75) and r = 0.81 (Landsat-8, $R^{2}$ = 0.66). Because the approach does not require ground-based training data, it can be directly integrated into operational forest monitoring frameworks, including national forest inventory programs. This scalable, label-free method enables the repeatable monitoring of tree species diversity at national and continental scales.

Forest mortality (FM) is influenced by several independent factors, including forest fragmentation (FF) at different spatial scales and multi-scales, site conditions, and stand characteristics. The aim of this study was to investigate the relationship and effect of FF at various spatial scales on the probability of Scots pine FM. The presented study also analyzed the relationship of the multi-scale fragmentation index effect on forest dieback. The relationship between multiple stressors emphasizes the distinct role of FF in influencing pine FM probability. Data on forest cover, deadwood volume of Scots pine forest, and environmental variables were obtained from the Forest Information System for Europe, the Polish National Forest Inventory, and existing databases, respectively. A generalized additive model approach was used to develop models. The results showed that, at small (50–600 m), large (800–3000 m), and multi spatial scales, the FF effect on Scots pine FM probabilities was statistically significant. There is a partial effect of multi-scale fragmentation on the probability of Scots pine FM, given a holistic view of the fragmentation effect that captures both small and large-scale effects. The study concludes that to calculate FF for a particular area, analyzing different scales and capturing multi-scale level fragmentation indices is crucial to studying the cumulative effect of fragmentation on the probability of Scots pine FM.