基于对象分类方法的松树枯萎病检测

Jeongmook Park, Woodam Sim, Jung-soo Lee
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引用次数: 2

摘要

本研究采用基于对象的分类方法提取松材枯萎病的侵染区域(ob -侵染区域),找出ob -侵染区域的特殊分布特征。选取尺度24,形状0.1,颜色0.9,紧凑度0.5,平滑度0.5作为基于对象的ob感染区域分类的最优加权值。分类的总准确率达到99%,Kappa系数也很高,为0.97。感染区面积约90 ha,占总面积的16%。疫区集中分布在5、6级,占总数的97%。中大型DBH类ob感染区集中分布,占总数的99%。从obo侵染区的地形特征来看,200 m以下发生的危害约占86%,坡度小于10度的危害约占91%。低洼丘陵和起伏边坡多发生灾害。此外,大部分疫区到道路和居民区的可达性小于300 m。总体而言,人工效应大于自然效应对松树枯萎病传播的影响。
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Detection of Trees with Pine Wilt Disease Using Object-based Classification Method
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.
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