Detection of Trees with Pine Wilt Disease Using Object-based Classification Method

Abstract

In this study, regions infected by pine wilt disease were extracted by using object-based classification method (OB-infected region), and the characteristics of special distribution about OB-infected region were figured out. Scale 24, Shape 0.1, Color 0.9, Compactness 0.5, and Smoothness 0.5 was selected as the objected-based, optimal weighted value of OB-infected region classification. The total accuracy of classification was high with 99% and Kappa coefficient was also high with 0.97. The area of OB-infected region was approximately 90 ha, 16% of the total area. The OB-infected region in Age class V and VI was intensively distributed with 97% of the total. Also, The OB-infected region in Middle and Large DBH class was intensively distributed with 99% of the total. In terms of the topographic characteristics of OB-infected region, the damages occurred approximately 86% below the altitude of 200 m, and occurred 91% with a slope less than 10 degree. The damage occurred a lot in low hilly mountain and undulating slope. In addition, the accessibility to road and residential area from OB-infected region was less than 300 m in large part. Overall, it was figured out that artificial effect is stronger than natural effect with regard to the spread of pine wilt disease.

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

“…The application of drone remote sensing technology has dramatically improved the efficiency of forest resource surveys ( Kentsch et al., 2020 ). Traditional monitoring techniques rely on low-level semantic features extracted from remote sensing images, making them susceptible to factors such as noise, lighting, and seasons, which limits their application in complex real-world scenarios ( Park et al., 2016 ). Using drones to aerially photograph areas affected by PWD, the location and degree of diseased trees can be visually observed from the aerial images, and targeted measures can be taken to deal with diseased trees, reducing the workload of manual investigations.…”

Detection of Pine Wilt Disease Using Drone Remote Sensing Imagery and Improved YOLOv8 Algorithm: A Case Study in Weihai, China

“…Satellite remote sensing monitoring has advantages over field surveys in terms of spatial coverage and temporal resolution [6,7]. Some studies have used high-resolution satellite images such as GeoEye-l and IKONOS to identify diseased and healthy pines from pine forest areas using object-oriented classification methods [8][9][10]. Due to the distinct differences in spectral characteristics and color appearance between infected pines and healthy pines, such as the presence of yellow-green and reddish-brown hues, it becomes evident that there are low levels of chlorophyll, water content, and reduced cell activity [11,12].…”

Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection

“…Traditional field survey methods are generally used to control the spread of pests by determining the spatial location of pest occurrence and the degree of damage [1]. However, the emergence of pests is affected by a variety of factors, making it impossible to obtain comprehensive and accurate pest information using traditional methods [2]. Therefore, to overcome these limitations, it is necessary to directly develop a fast and accurate method to detect damage caused by PPC.…”