Pine wood nematode (Bursaphelenchus xylophilus) is an invasive pathogen in South Korea, where it has caused pine wilt disease (PWD) with extremely high mortality of native pine species (Pinus densiflora, Pinus thunbergii, and Pinus koraiensis). Since the disease spreads by its vectors, native pine sawyer beetles (Monochamus alternatus and Monochamus saltuarius), the cost of monitoring the expansion has been rapidly increasing. Furthermore, it is even more costly to eliminate new and isolated infections since unremoved infected trees act as new sources of infection through the preferred oviposition of the beetles on such trees. The methodology of combining unmanned aerial vehicle (UAV) and object detection based on deep learning provides the opportunity to solve such problems, as UAV with RGB camera can provide high spatial resolution aerial image and digital surface model (DSM), which can be used for object detection with excellent results. In this study, we evaluated the performance of this method to detect dead pine trees in PWD-damaged areas. In particular, to ensure low omission error of monitoring, YOLOv3 was employed for object detection as the model design is focused on minimizing the omission error. We also modified the model so that the positions and crown diameter could be estimated. Four detection models were trained using four different combinations between aerial images (R, G, B) and DSM from UAV. Among them, the model from RGB showed the highest performance (recall: 0.9909, precision: 0.8438) and was selected as the optimal model. Our results suggest that our method can contribute to low-cost and effective monitoring of the dead pine trees while maintaining low omission error, which is critical for PWD management.

Thermal imaging drones have transformed wildlife monitoring by facilitating the efficient and noninvasive monitoring of animal populations across large areas. In this study, an optimized flight path design was developed for monitoring wildlife on Guleopdo Island, South Korea using the DJI Mavic 3T drone equipped with a thermal camera. We employed a strata-based sampling technique to reclassify topographical and land cover information, creating an optimal survey plan. Using sampling strata, key waypoints were derived, on the basis of which nine flight paths were designed to cover ~50% of the study area. The results demonstrated that an optimized flight path improved the accuracy of detecting Formosan sika deer (Cervus nippon taiouanus). Population estimates indicated at least 128 Formosan sika deer, with higher detection efficiency observed during cloudy weather. Customizing flight paths based on the habitat characteristics proved crucial for efficient monitoring. This study highlights the potential of thermal imaging drones for accurately estimating wildlife populations and supporting conservation efforts.

Increased drought frequency due to climate change is intensifying tree mortality, a critical issue in forest ecosystem management, especially in vulnerable subalpine ecosystems. Korean fir (Abies koreana E.H. Wilson), an endemic species of South Korea that grows in subalpine areas, is threatened by climate change-induced drought. However, our understanding of drought’s impact on tree mortality, particularly its seasonal and legacy effects, remains limited. To better understand drought-related mortality of Korean fir, we conducted annual mortality surveys, starting in 2012, at 10 fixed transects in Jirisan National Park, identified seasonal and legacy effects using redundancy analysis, and modeled Korean fir mortality, incorporating biotic and abiotic factors, using random forests. We found that early growing season drought had the greatest impact on Korean fir mortality, with legacy effects extending up to three years, while late growing season drought affected mortality only in the previous year. The mortality model achieved high predictive accuracy (94%) and revealed significant site- and size-dependent mortality patterns. These findings provide critical insights into the complex interactions between biotic and abiotic factors affecting tree mortality and offer valuable guidance for conservation strategies aimed at preserving climate-sensitive species in the face of ongoing climate change.