基于无人驾驶飞行器红边谱特征的干扰检测
Liga Bai1, Xiaojun Huang1,2,3, Ganbat Dashzebeg4
1College of Geographical Science, Inner Mongolia Normal University, Hohhot, China.
Frontiers in plant science
|September 17, 2025
概括
无人驾驶飞行器 (UAV) 遥感有效地检测了蒙古森林中的埃拉尼斯·雅各布斯尼虫害. 红边光谱指数和机器学习模型准确地绘制了害虫分布图,有助于森林管理.
科学领域:
- 林业 林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 在全球和经济上,北极针叶树林至关重要.
- 蒙古的森林面临着埃拉尼斯·雅各布索尼害虫的森林砍伐.
- 及时获取害虫数据对于有效的森林管理至关重要.
研究的目的:
- 通过无人机遥感,提供快速准确的害虫发生数据.
- 为了检测Erannis jacobsoni引起的植被干扰.
- 分析害虫的空间分布以进行控制.
主要方法:
- 使用无人机 (UAV) 拍摄害虫影响地区的图像.
- 计算的常规和红边光谱指数.
- 应用机器学习算法 (例如,反向传播神经网络) 和特征选择 (SFS,SPA).
主要成果:
- 红边特征在害虫检测方面比传统特征更有效.
- 机器学习模型的准确度超过82%,其中BPNN表现最好.
- 害虫分布显示出从疫情中心向外扩散.
结论:
- 无人机遥感为检测植被干扰提供了独特的优势.
- 机器学习模型能够准确地识别和绘制Erannis jacobsoni感染的地图.
- 了解空间模式有助于制定有针对性的害虫控制策略.
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