2022 Volume 104 Issue 2 Pages 99-105
For early detection of pine trees damaged by pine wilt disease and prevention of their oversight in field surveys, we conducted time-series measurements and field surveys in a parcel (about 130 × 40 m) of coastal black pine groves by using a UAV equipped with an optical camera and a UAV equipped with a multispectral camera. By visual interpretation of NDVI and RGB images, five, three, three, and six areas were identified with reduced NDVI or discolored tree canopy on 31 July, 15 August, 28 August, and 18 September 2019, respectively. Of the 26 pine trees that were confirmed during the field surveys by 17 September to be damaged by pine wilt disease, 16 were found by reading aerial images in or near these 17 areas (detection rate: 61.5%). Although there is a limit to detection by reading aerial images, our detection of about 60% of pine trees damaged by pine wilt disease will support the conducting of a detailed field survey of the surrounding areas, thereby enabling us to prevent oversight by cross-checking from the air and ground. The results also suggested that NDVI images may be able to detect damaged trees about 2 weeks earlier than RGB images.