원문정보
초록
영어
Pine wilt disease (PWD) has recently caused substantial pine tree losses in South Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of South Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using several methods, i.e., a pixel-based and object-based image analysis (OBIA). Furthermore, compared the accuracy of two types of global positioning system (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the object-based classification was more accurate than the pixel-based classification at classifying trees with PWD. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected, while the GPS device 2 is uncorrected device. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 meters, while that of the GPS device 2 data was 0.14 meters. In conclusion, object-based classification is superior to the pixel-based classification, even both method can distinguish between healthy trees and those with PWD based on LC data. On the other hand, there were some differences between the hand-held and drone GPS datasets from both areas.