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作 者:孙伟韬 SUN Weitao(East China Academy of Inventory and Planning,National Forestry and Grassland Administration,Hangzhou 310000,Zhejiang,China)
机构地区:[1]国家林业和草原局华东调查规划院,浙江杭州310000
出 处:《桉树科技》2023年第4期39-46,共8页Eucalypt Science & Technology
摘 要:通过森林病虫害监测研究能够更好地管理和保护森林资源,减少病害和虫害对生态系统的影响,同时提高森林可持续发展的能力。常规的森林病虫害监测主要通过识别影像尺度特征得出监测结果,忽略了植被水分含量对影像的影响,导致监测结果误差较大。因此,提出基于多光谱遥感影像的森林病虫害监测方法。分步提取森林多光谱遥感影像的核心光谱,计算多光谱遥感影像的光谱响应,进而得出病虫害特征向量,识别出森林病虫害特征。根据森林植被水分含量层级归类生成森林病虫害监测结果。结果表明,所提方法应用后得出的监测结果误差较小,平均仅为0.010125,森林病虫害监测效果较好。利用该方法可以及早发现病虫害的蔓延趋势和程度,以防止病虫害的进一步传播和对森林造成更大的破坏,满足了森林保护的现实需求。Monitoring and studying forest diseases and pests can better protect and manage forest resources,reduce the impact of diseases and pests on ecosystems,and improve the potential for sustainable forest development.Conventional forest pest and disease monitoring mainly uses image scale feature recognition to obtain monitoring results,neglecting the impact of vegetation moisture content on images which can result in significant errors in interpreting results.Therefore,this study of forest pest monitoring based on multi-spectral remote sensing images was proposed.The core spectra of forest multispectral remote sensing images were extracted step by step,the spectral response based on the multispectral remote sensing images was calculated.Next,the characteristic vectors of diseases and pests were obtained,the characteristics of forest diseases and pests were identified and the monitoring results of forest diseases and pests were generated by hierarchical classification according to the water content of forest vegetation.The results showed that the monitoring data obtained by the proposed method have a small error,with an average of only 0.010125,indicating that the monitoring effect is good.By using this method,the degree of pests and disease incidence can be detected early and the trends of their spread monitored.Therefore,multispectral remote sensing images can help prevent further spread of pests and diseases,help to limit the damage to forests and meet the practical needs of forest protection.
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