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在农业景观中改进无人机检测Solenopsis invicta的特征选择框架

Chun-Han Shih^1,2, Cheng-En Song², Su-Fen Wang¹

¹Department of Geography, National Changhua University of Education, Changhua 500, Taiwan.

|August 28, 2025

概括

无人机可以使用多光谱图像检测红色进口火. 这项技术为入侵物种监测和综合防治策略提供了成本效益高的方法.

科学领域:

生态学
遥感技术
侵袭性物种管理

背景情况:

红色进口火 (RIFA; *Solenopsis invicta*) 在台湾对生态,农业和公共健康构成重大威胁.
有效的监测对于管理入侵物种种群和减轻其影响至关重要.

研究的目的:

评估使用无人驾驶飞行器 (UAV) 来检测RIFA山丘的可行性.
确定用于RIFA山丘识别的关键光谱和纹理特征.

主要方法:

通过使用DJI Phantom 4无人机获取蓝色,绿色,红色,红边和近红外频段的数据.
植被指数 (NDVI,SAVI,PPR) 和纹理特征是从图像中得出的.
为了确定特征显著性,采用了统计分析,包括ANOVA F-score和随机森林递归特征消除.

主要成果:

植被指数 (NDVI,SAVI,PPR) 和光谱特征 (NIR) 被确定为检测与RIFA山丘相关的生态特征最重要的预测指标.
纹理特征显示中等重要性,表明在非线性模型中捕捉空间模式的潜力.
这项研究证明了基于无人机的多光谱数据对RIFA土检测的有用性.

结论:

无人机提供了一个廉价且高精度的平台,用于获取RIFA监测所必需的多谱数据.
这些发现支持开发综合RIFA害虫防治的有效大规模检测协议.
这种方法对各种入侵物种的监测具有更广泛的意义.

关键词:

无人机多谱图像功能选择图像分类检测入侵物种红色进口火 (RIFA)

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