• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.280-288
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• 2021

Understanding the occurrence characteristics of pine wilt disease (PWD) is essential for determining a suitable strategy to minimize the damage caused by PWD. Thus, in this study, we characterized various environmental conditions, including meteorological factors, geographical factors, and artificial factors influencing the occurrence of PWD. The occurrence data of PWD from May 2016 to April 2018 and spatial data of various environmental factors, including natural and anthropogenic factors, were collected. We evaluated the relative contribution of the environmental variables on the number of dead pine trees by PWD. In this study, among the 17 natural and anthropogenic factors, the factors affecting the occurrence of dead trees by PWD were verified. The results showed that altitude and temperature from May to August, among natural factors, and distance to building and forest road among anthropogenic factors were the most influential factors on the occurrence of PWD.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.317-325
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• 2021

Pine wilt disease first appeared in Busan in 1998; it is a serious disease that causes enormous damage to pine trees. The Korean government enacted a special law on the control of pine wilt disease in 2005, which controls and prohibits the movement of pine trees in affected areas. However, existing forecasting and control methods have physical and economic challenges in reducing pine wilt disease that occurs simultaneously and radically in mountainous terrain. In this study, the authors present the use of a deep learning object recognition and prediction method based on visual materials using an unmanned aerial vehicle (UAV) to effectively detect trees suspected of being infected with pine wilt disease. In order to observe pine wilt disease, an orthomosaic was produced using image data acquired through aerial shots. As a result, 198 damaged trees were identified, while 84 damaged trees were identified in field surveys that excluded areas with inaccessible steep slopes and cliffs. Analysis using image segmentation (SegNet) and image detection (YOLOv2) obtained a performance value of 0.57 and 0.77, respectively.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.665-675
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• 2014

In this paper spectral characteristics and spectral patterns of pine wilt disease at different development stage were analyzed in Geoje-do where the disease has already spread. Ground-based hyperspectral imaging containing hundreds of wavelength band is feasible with continuous screening and monitoring of disease symptoms during pathogenesis. The research is based on an hyperspectral imaging of trees from infection phase to witherer phase using a ground based hyperspectral camera within the area of pine wilt disease outbreaks in Geojedo for the analysis of pine wilt disease. Hyperspectral imaging through hundreds of wavelength band is feasible with a ground based hyperspectral camera. In this research, we carried out wavelength band change analysis on trees from infection phase to witherer phase using ground based hyperspectral camera and comparative analysis with major vegetation indices such as Normalized Difference Vegetation Index (NDVI), Red Edge Normalized Difference Vegetation Index (reNDVI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index 2 (ARI2). As a result, NDVI and reNDVI were analyzed to be effective for infection tree detection. The 688 nm section, in which withered trees and healthy trees reflected the most distinctions, was applied to reNDVI to judge the applicability of the section. According to the analysis result, the vegetation index applied including 688 nm showed the biggest change range by infection progress.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.165-171
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• 2011

This study was conducted to investigate the effect on pine wilt disease by health-thinning, thinning and sapling tending in red pine forest (Pinus densiflora). As a part of developing forestry control methods for pine wilt disease control. In case of putting in pine sawyer (Monochamus alternatus) with pine wood nematode (Bursaphelenchus xylophilus), the specimen trees in health-thinning, thinning and control treatment were withered more than 50%, although there were not statistically significant differences in treatments. In treatment site, thinning slashes had influenced on the spread of pine wilt disease (experiment 1). In thinning stand of sapling pine, site with thinning slashes had highest mortality (> 90%). There were approximately 10% mortality in the site of carried thinning slashes from case and the site of non-thinning with released M. alternatus (experiment 2). The larvae had not appeared in young tree stump with health-thinning and thinning at April, the current emergence year of M. alternatus, but there were larvae in sapling tree stump with thinning at May, the current emergence year of M. alternatus. In case of stands with infected young and sapling tree by pine wilt disease, there is no effects of on pine wilt disease control by health-thinning, thinning, saplings tending at April and May, the current emergence year of M. alternatus, and leaved thinning slashes had influenced on the spread of pine wilt disease as habitation of M. alternatus.

• Journal of Korean Society of Forest Science
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• v.102 no.2
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• pp.300-304
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• 2013

Pine wilt disease caused by pine wood nematode, Bursaphelenchus xylophilus (Steiner et Buhrer) Nickle, has become the most serious threat to pine trees in Korea since 1988. Pine wood nematode is transferred to healthy trees by Monochamus alternatus (Coleoptera: Cerambycidae) during its maturation feeding and female oviposition. A typical control method against insect vectors in Korea is fumigation of the dead trees by using metam-sodium SL (25%). However, this method is not environment friendly because of the forest contamination by chemical application and destroying landscape by plastic cover. Portable Bark Remover (PBR) was invented to reduce these environmental problems. The vectors oviposit under the bark of the newly dead trees only. Debarking infested trees prevents the vectors from laying eggs and eventually, they can not complete their life cycle. The PBR is a modified debarking device that is attached on the top of the electrical chain saw, which allows ease and rapid debarking of the infested trees. The new method by PBR is expected to be more economic and effective than other conventional methods such as "crushing", "burning" and "fumigation".

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.28-34
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• 2010

The spatial distribution characteristics of damaged trees by the pine wilt disease appear scattered spots spreading from single dead trees. That is the reason why it is difficult to early detect damage and to prevent from extensive damage. Thus, it is very important to forecast and analyze the damage occurrences, to establish strategies for prevention, and to supervise them. However, conventional survey which observes around roads or residential areas by naked eyes was impossible to investigate completely, missing target areas and dangerous areas. Therefore, aerial forecasting techniques on the damaged area were developed using GIS, GPS, and helicopters for an accurate observation of systematic and scientific approach in this study. Moreover, advantages of the techniques application were confirmed to survey 972 dead tree samples at 349 position-coordinates in 32 cities (about $28,810km^2$), 2005. This study is expected to apply widely to find dead trees and the causes, particularly by pine wilt disease.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.359-373
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• 2019

Bursaphelenchus xylophilus(Pine wilt disease) is a serious threat to the pine forest in Korea. However, dead wood observation by Pine wilt disease is based on field survey. Therefore, it is difficult to observe large-scale forests due to physical and economic problems. In this paper, high resolution images were obtained using the unmanned aerial vehicle (UAV) in the area where the pine wilt disease recurred. The damaged tree due to pine wilt disease was detected using Artificial Neural Network (ANN), Support Vector Machine (SVM) supervision classification technique. Also, the accuracy of supervised classification results was calculated. After conducting supervised classification on accessible forests, the reliability of the accuracy was verified by comparing the results of field surveys.

• 수목보호
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• s.5
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• pp.57-64
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• 1999

• Landscaping Tree
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• s.88
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• pp.29-37
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• 2005

• 수목보호
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• s.4
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• pp.16-31
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• 1998