Spatial Diffusion Characteristics of Pine Wilt Disease at the Forest Stand Scale and Prediction of Individual Tree Mortality Risk.
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| Title: | Spatial Diffusion Characteristics of Pine Wilt Disease at the Forest Stand Scale and Prediction of Individual Tree Mortality Risk. |
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| Authors: | Jiang, Xuefei1,2 (AUTHOR), Liu, Ting1,2 (AUTHOR), Bao, Guangdao1,2,3 (AUTHOR) bao-gd@outlook.com, Zhai, Chang3,4 (AUTHOR), Ren, Zhibin1,4 (AUTHOR), Ding, Mingming1,2 (AUTHOR), Xu, Xingshuai1,3 (AUTHOR), Xu, Sa1,3,4 (AUTHOR) |
| Source: | Remote Sensing. Dec2025, Vol. 17 Issue 24, p3930. 28p. |
| Subjects: | Tree mortality, Random forest algorithms, Spatial analysis (Statistics), Conifer wilt, Aerial surveillance, Introduced species, Environmental security |
| Geographic Terms: | China, Liaoning Sheng (China) |
| Abstract: | Highlights: What are the main findings? Based on a three-year time-series UAV monitoring dataset, the study reconstructed the stand-scale diffusion process of pine wilt disease (PWD). The results revealed a dominant short-range spread (50% of events within 17.2 m) and a clear seasonal variation in mortality latency, which was shortest in spring and summer. A tree-level mortality risk prediction framework was developed using multi-source remote sensing features, with the random forest model achieving the best performance (AUC = 0.96) and accurately identifying 98.6% of high-risk trees. What is the implication of the main finding? The results highlight that localized diffusion dominates PWD transmission, suggesting a 28 m sanitation radius and enhanced surveillance within 141 m for effective control. The proposed risk prediction framework provides a practical basis for dynamic early warning and precision management of pine wilt disease at the individual-tree scale. Pine wilt disease (PWD) is one of the fastest-spreading invasive forest pathogens worldwide, causing rapid mortality of infected trees and posing a severe threat to global forest ecosystem security and carbon sink capacity. However, the spatial dynamics and diffusion characteristics of PWD at the stand scale remain poorly understood. In this study, we selected a typical epidemic area in Qingyuan County, Liaoning Province, China, as the study site. By integrating 23 phases of unmanned aerial vehicle (UAV) multispectral imagery, airborne LiDAR data, and field survey observations, we reconstructed the spatiotemporal diffusion process of PWD from 2023 to 2025 and developed a stand-scale, tree-level mortality risk prediction model. Our results show that 50% of transmission events occurred within 17.2 m, and the spatial autocorrelation range was approximately 28 m. The peak of the lethal latency period occurred 17 days after infection, with 40% of mortality events occurring within 11–22 days and 50% of infected trees dying within 40 days. The latency period was significantly shorter in spring and summer than in winter ( p < 0.01 ). Among tree-level mortality risk prediction approaches, the random forest model performed best, improving overall accuracy by more than 15% compared with other methods and correctly identifying 98.6% of high-risk individuals. The distance to the nearest infected or dead tree was identified as the dominant predictor, followed by tree height and vegetation parameters reflecting host physiological status. This study reveals the spatial diffusion characteristics of PWD at the stand scale and proposes a tree-level risk prediction framework, providing a theoretical foundation and technical support for dynamic monitoring, early warning, and precision management of PWD. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Based on a three-year time-series UAV monitoring dataset, the study reconstructed the stand-scale diffusion process of pine wilt disease (PWD). The results revealed a dominant short-range spread (50% of events within 17.2 m) and a clear seasonal variation in mortality latency, which was shortest in spring and summer. A tree-level mortality risk prediction framework was developed using multi-source remote sensing features, with the random forest model achieving the best performance (AUC = 0.96) and accurately identifying 98.6% of high-risk trees. What is the implication of the main finding? The results highlight that localized diffusion dominates PWD transmission, suggesting a 28 m sanitation radius and enhanced surveillance within 141 m for effective control. The proposed risk prediction framework provides a practical basis for dynamic early warning and precision management of pine wilt disease at the individual-tree scale. Pine wilt disease (PWD) is one of the fastest-spreading invasive forest pathogens worldwide, causing rapid mortality of infected trees and posing a severe threat to global forest ecosystem security and carbon sink capacity. However, the spatial dynamics and diffusion characteristics of PWD at the stand scale remain poorly understood. In this study, we selected a typical epidemic area in Qingyuan County, Liaoning Province, China, as the study site. By integrating 23 phases of unmanned aerial vehicle (UAV) multispectral imagery, airborne LiDAR data, and field survey observations, we reconstructed the spatiotemporal diffusion process of PWD from 2023 to 2025 and developed a stand-scale, tree-level mortality risk prediction model. Our results show that 50% of transmission events occurred within 17.2 m, and the spatial autocorrelation range was approximately 28 m. The peak of the lethal latency period occurred 17 days after infection, with 40% of mortality events occurring within 11–22 days and 50% of infected trees dying within 40 days. The latency period was significantly shorter in spring and summer than in winter ( p < 0.01 ). Among tree-level mortality risk prediction approaches, the random forest model performed best, improving overall accuracy by more than 15% compared with other methods and correctly identifying 98.6% of high-risk individuals. The distance to the nearest infected or dead tree was identified as the dominant predictor, followed by tree height and vegetation parameters reflecting host physiological status. This study reveals the spatial diffusion characteristics of PWD at the stand scale and proposes a tree-level risk prediction framework, providing a theoretical foundation and technical support for dynamic monitoring, early warning, and precision management of PWD. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17243930 |
