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 F1 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.

<p>The observation and reporting of flora and fauna with the help of citizen scientists has a long tradition. However, citizen science projects have also a high potential for the reporting and mapping of landforms, as well as for observing landscape dynamics. While remote sensing has opened up new mapping and monitoring possibilities at high spatial and temporal resolutions, there is still a growing demand for gathering (spatial) data directly in the field (reporting on actual events, landform characteristics, and landscape changes, provision of reference data and photos). This becomes even more relevant since climate change effects (e.g. glacier retreat, shift of precipitation regime, melting of permafrost) will likely result in more significant morphological changes with an impact on the landscape.</p><p>In the project citizenMorph (Observation and Reporting of Landscape Dynamics by Citizens; http://citizenmorph.sbg.ac.at) we developed a pilot web-based interactive application that allows and supports citizens to map and contribute field data (spatial data, in-situ information, geotagged photos) on landforms. Such features are, for example, mass movements (e.g. rockfall, landslide, debris flow), glacial features (e.g. rock glacier, moraine, drumlin), volcanic features (e.g. lava flow, lahar, mudpot), or coastal features (e.g. cliff, coastal erosion, skerry). To design and implement a system that fully matches experts’ and citizens’ requirements, that ensures that citizens benefit from participating in citizenMorph, and that provides extensive, high-quality data, citizen representatives (mainly high school students, students, and seniors) actively and directly took part in the development process. These users are considered as particularly critical, sensitive to usability and accessibility issues, and demanding when it comes to using information and communication technology (ICT). In line with the concept of participatory design, citizen representatives were involved in all steps of the development process: specification of requirements, design, implementation, and testing of the system. The generation of a pilot was done using Survey123 for ArcGIS, a survey to collect data in the field, i.e. type and location of the landform, overview image and image series of the landform, and the content management system WordPress to create a website to inform, guide and support the participants. Throughout the survey (https://arcg.is/15WPKv0) and the website, different kinds of information (e.g. project information, guidelines for data collection and reporting, data protection information) are given to the participants. The final citizenMorph system was tested and discussed on several events with citizen representatives in Austria, Germany, and Iceland. Feedback from the tests was gathered using techniques such as observation, focus groups, and interviews/questionnaires. This allowed us to evaluate and improve the system as a whole.</p><p>The collected data, particularly the image series, are used for 3D reconstruction of the surface using Structure from Motion (SfM) and dense image matching (DIM) methods. Moreover, the collected data can be helpful for enriching and validating remote sensing based mapping results and increasing their detail and information content. Having a comprehensive database, holding field data and remote sensing data together, is of importance for any subsequent analysis and for broadening our knowledge about geomorphological landscape dynamics and the prevalence of landforms.</p>