In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

Monitoring tree diseases in forests is crucial for managing pathogens, particularly as climate change and globalization lead to the emergence and spread of tree diseases. Object detection algorithms for monitoring tree diseases through remote sensing rely on bounding boxes to represent trees. However, this approach may not be the most efficient. Our study proposed a solution to this challenge by applying object detection to unmanned aerial vehicle (UAV)-based imagery, using point labels that were converted into equally sized square bounding boxes. This allowed for effective and extensive monitoring of black pine (Pinus nigra L.) trees with vitality-related damages. To achieve this, we used the “You Only Look Once’’ version 5 (YOLOv5) deep learning algorithm for object detection, alongside a 16 by 16 intersection over union (IOU) and confidence threshold grid search, and five-fold cross-validation. Our dataset used for training and evaluating the YOLOv5 models consisted of 179 images, containing a total of 2374 labeled trees. Our experiments revealed that, for achieving the best results, the constant bounding box size should cover at least the center half of the tree canopy. Moreover, we found that YOLOv5s was the optimal model architecture. Our final model achieved competitive results for detecting damaged black pines, with a 95% confidence interval of the $F_1$ score of 67–77%. These results can possibly be improved by incorporating more data, which is less effort-intensive due to the use of point labels. Additionally, there is potential for advancements in the method of converting points to bounding boxes by utilizing more sophisticated algorithms, providing an opportunity for further research. Overall, this study presents an efficient method for monitoring forest health at the single tree level, using point labels on UAV-based imagery with a deep learning object detection algorithm.

Scots pine (Pinus sylvestris), traditionally considered a drought-tolerant species, is increasingly threatened by climatic stress and the opportunistic pathogen Diplodia sapinea in Central Europe. Using linear mixed models, this study investigated the basal area increment (BAI) of 164 trees in Northern Bavaria using tree-ring analysis and the detection of D. sapinea in shoots to investigate whether pathogen detection is associated with radial growth and climate sensitivity. Climate–growth analyses (based on chronologies spanning 82–127 years) identified the summer climatic water balance (CWB, June–August), previous-year spring CWB (March–May), and current-year spring temperatures (February, March) as the main growth drivers. Model results demonstrate a strong size-dependency during the 2003–2023 study period: while small trees in which D. sapinea was detected in shoots (basal area (BA) ≤ 800 cm²) appear to exhibit higher climatic sensitivity than trees without detectable D. sapinea, larger pines (BA ≥ 1500 cm² ≙ DBH ≥ 43.7 cm) exhibited trends pointing toward a near-complete climatic decoupling. These larger trees exhibited a significant growth deficit of −3.69 cm²/year during cold spring conditions (−2 °C) compared to healthy trees. In the largest size class (BA ≥ 2200 cm²), this productivity loss reached its peak at −10.83 cm²/year under high lagged spring water availability (+95 mm CWB in the previous year’s spring). While healthy trees reached an annual BAI of 17 cm²/year under these favorable conditions, infected trees were limited to 10 cm²/year, representing a reduction of approximately 41% and confirming the profound disruption of climatic growth stimuli in mature trees. Our findings indicate that mature pine stands are more vulnerable to productivity losses than predicted by purely abiotic models, although fixed effects explain a limited portion of overall growth variation (marginal R² = 20.81%, compared to a conditional R² = 71.99% including random effects). Consequently, despite high individual-tree and site-level variability, these size-dependent associations with D. sapinea shoot colonization must be increasingly integrated into forest resilience assessments as crucial population-level trends.