Analysis of Vegetation Infection Information Using Unmanned Aerial Vehicle with Optical Sensor

• Published in: Sensors and materials 2019-01, Vol.31 (10), p.3319
• Main Authors: Jung, Kap Yong, Park, Joon Kyu
• Format: Article
• Language: English
• Subjects: Big Data; Color imagery; Data management; Forest management; Image acquisition; Image detection; Infrared imagery; Insects; Medical imaging; Normalized difference vegetative index; Optical measuring instruments; Pests; Pine; Trees; Unmanned aerial vehicles; Vegetation index
• ISSN: 0914-4935
• DOI: 10.18494/SAM.2019.2465

The forests (approx. 640000 ha) of Korea comprise coniferous forest (41%), broad-leaved forest (27%), and mixed stand forest (29%). They appear to be vulnerable to fire, diseases, and pests. The pine tree is one of the typical Korean species of trees. It was more than 50% of the whole forest area of the country in the 1960s, but the area of pine tree forests has been reduced to 30% because of recent changes in the forest ecosystem and damage caused by diseases and insect pests. In particular, pine wilt disease is currently spreading over Korea. In this study, an unmanned aerial vehicle (UAV) with an optical sensor was used to detect infected trees and to support big data on forest management. Red, green, and blue (RGB) images and near-infrared (NIR) images were acquired using UAV. The infected trees were detected using the RGB images, and the normalized difference vegetation index (NDVI) values were calculated using NIR images. The NDVIs of infected trees were lower than those of non-infected ones, and infected trees that were not detected as infected ones in the RGB images also have lower NDVIs than the neighboring trees that were detected as being infected. Through further research, if a distinct feature of the NDVI of infected trees is discovered, it will be helpful for the early detection of infected trees. Hence, this research is expected to be applied to the detection of infected trees and to support big data on forest management.