Assessing the extent and magnitude of wildlife impact on forest regeneration (e.g., % browsed seedlings or reduction in regeneration density) remains a central challenge. This study explores the potential of unmanned aircraft systems (UAS) to quantify wildlife impact through the integration of drone-based thermal surveys and vegetation assessments. Specifically, it evaluates whether UAS-derived wildlife density estimates can be linked to browsing intensity and regeneration structure, thereby enabling an indirect assessment of silviculturally relevant forest dynamics. By combining remotely sensed wildlife data with field-based vegetation inventories, the study aims to identify measurable relationships between structural forest characteristics and browsing effects. This approach contributes to the development of spatially efficient, objective, and reproducible monitoring methods at the forest–wildlife interface. Ultimately, the study provides a novel framework for integrating modern remote sensing technologies into wildlife–ecological monitoring and for improving adaptive, evidence-based management in forest ecosystems increasingly affected by high ungulate densities and climate-related stressors. Two silviculturally contrasting study areas were selected: a broadleaf-dominated mixed forest in Hesse, where high ungulate densities were expected, and a pine-dominated site in Brandenburg, anticipated to experience lower browsing pressure. Thermal surveys were conducted using a DJI Matrice 30T drone equipped with a high-resolution infrared camera to detect and geolocate wildlife. In parallel, browsing impact was assessed using a modified circular transect method (“Neuzeller method”). Regeneration was recorded by tree species, height class, and browsing intensity. Statistical analyses and GIS-based spatial visualizations were used to examine the relationship between estimated ungulate densities and browsing levels. Results revealed clear differences in wildlife abundance and browsing intensity between the two sites. In the Heppenheim forest, roe deer densities exceeded 40 individuals per 100 ha, correlating with high browsing pressure—particularly on ecologically and silviculturally valuable species such as sycamore maple and sessile oak. In contrast, the Rochauer Heide exhibited lower densities and a comparatively moderate browsing impact, although certain tree species still showed signs of selective pressure. This study demonstrates that drone-based wildlife monitoring offers an innovative, non-invasive means to indirectly evaluate forest structural conditions in regeneration layers. The findings highlight the relevance of UAV-supported methods for evidence-based wildlife management and the adaptive planning of silvicultural measures. The method enhances transparency and spatial resolution in forest–wildlife management and supports evidence-based decision-making in times of ecological and climatic change.

In the areas of road safety, fruit growing and forestry, there is an increased need for assessing the vitality of standing trees. Various dendro-acoustic emissions (DAEs) potentially allow conclusions to be drawn about the vitality of the trees and various diseases and pest infestations. All types of DAE events produce structure-borne sound inside the tree and can be automatically measured and evaluated by a piezoelectric sensor node attached to the tree trunk. The events are characterized by short, broadband cracking signals. Depending on the type of event, these differ in their number, strength and acoustic center frequency and can be separated from various types of background noise such as traffic, wind, rain, etc. In the frame of a German collaborative research project (TreeMon), an autonomous sensor prototype for long- and short-term monitoring using LoRaWAN technology was developed and successfully tested in the field. We present selected results of various test and field measurements at different trees, including several hardwood species like beech, birch and fruit trees. We compare temporal DAE profiles of single trees with simultaneously recorded weather, dendrometer and sap flow data and demonstrate first interesting correlations between them.