| 张根壮，周权，孙学文，任利利，温俊宝,2026,应用无人机多光谱遥感对臭椿受害的监测[J].环境昆虫学报,(2):625-636 |
| 应用无人机多光谱遥感对臭椿受害的监测 |
| Application of UAV multispectral remote sensing for monitoring Ailanthus altissima damage |
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| DOI： |
| 中文关键词: 沟眶象 臭椿沟眶象 臭椿 受害等级 无人机 多光谱遥感 |
| 英文关键词:Eucryptorrhynchus scrobiculatus Eucryptorrhynchus brandti Ailanthus altissima damage level UAV multispectral remote sensing |
| 基金项目:国家重点研发计划（2022YFD1400400） |
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| 摘要点击次数: 38 |
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| 中文摘要: |
| 【目的】沟眶象Eucryptorrhynchus scrobiculatus（Motschulsky）和臭椿沟眶象Eucryptorrhynchus brandti（Harold）是专一危害臭椿Ailanthus altissima的钻蛀性害虫，已经对宁夏防护林主要造林树种臭椿造成了严重危害。但二者在林地尺度上对单株臭椿的危害程度尚无高效识别方法，以控制虫害的进一步扩散。【方法】以宁夏青铜峡市曹湾村的臭椿为研究对象，研究基于无人机多光谱图像对沟眶象和臭椿沟眶象危害程度的识别技术。根据枯梢率将臭椿受害分为健康、轻度、中度、重度4个等级，基于无人机多光谱图像，结合机器学习方法构建分类模型，采用了多重-Wilcoxon-秩和检验比较不同受害等级之间同一特征的差异变化。利用方差分析（ANOVA）筛选出对沟眶象和臭椿沟眶象虫害敏感的特征，使用随机森林（RF）、支持向量机（SVM）和K-近邻算法（KNN）三种机器学习模型进行分类比较。【结果】RF、SVM、KNN分类模型的总体精度分别为0.866、0.821、0.795，Kappa系数分别为0.819、0.762、0.722，RF模型的分类效果最佳。【结论】证明了无人机多光谱监测沟眶象和臭椿沟眶象危害程度的可行性。 |
| 英文摘要: |
| 【Aim】Eucryptorrhynchus scrobiculatus (Motschulsky) and Eucryptorrhynchus brandti (Harold) are two serious wood-boring pests specifically inflicting damage on Ailanthus altissima (Mill.) Swingle. They have caused severe damage to A. altissima, a primary afforestation species used in shelterbelts in Ningxia Hui Autonomous Region. However, no efficient method exists to identify the extent of damage to individual A. altissima at the stand scale, which limits effective pest control.【Methods】This study targeted A. altissima in Caowan Village, Qingtongxia City, Ningxia, China, to investigate techniques for identifying pest damage degree caused by E. scrobiculatus and E. brandti using Unmanned Aerial Vehicle (UAV) multispectral imagery. The damage to A. altissima was classified into four levels including Healthy, Lightly, Moderately, and Severely, based on the dead twig rate. UAV multispectral imagery combined with machine learning techniques were used to construct a classification model. To compare differences in the same features across damage grades, the Multiple Wilcoxon Rank Sum Test was applied, while Analysis of Variance (ANOVA) was used to identify pest-sensitive features related to the damage caused by E. scrobiculatus and E. brandti. Furthermore, the classification performance of three machine learning models including Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) was evaluated and compared.【Results】The results showed that the overall accuracies of RF, SVM, and KNN models were 0.866, 0.821, and 0.795, respectively, with corresponding Kappa coefficients of 0.819, 0.762, and 0.722. Among these, the RF model performed the best.【Conclusion】These findings confirmed the feasibility and effectiveness of using UAV multispectral monitoring for detecting and assessing the damage caused by E. scrobiculatus and E. brandti. |
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