植物群落无人机 (UAV) 图像:优化可见光植被指数以提取多种物种覆盖范围
Meng Wang1, Zhuoran Zhang1, Rui Gao1
1Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Plants (Basel, Switzerland)
|June 13, 2025
概括
这项研究提出了一种使用四个植被指数 (VIs) 和机器学习的通用方法,以无人机图像准确监测各种植物群落. 这种方法增强了生态应用的植被绘制.
科学领域:
- 生态遥感器 生态遥感器
- 植物群落监测 植物群落监测
- 地理空间分析是什么?
背景情况:
- 无人驾驶飞行器 (UAV) 可见光遥感为植物社区监测提供了潜力.
- 目前的方法缺乏标准化的植被指数 (VI) 优化,以在多种生态系统中提取多种覆盖范围.
研究的目的:
- 开发一种使用低成本无人机可见光成像来提取多种植物社区覆盖面的通用方法.
- 建立动态的,跨物种兼容的植被分类门,而无需多谱数据.
主要方法:
- 整合了四个植被指数:绿色过剩指数 (EXG),可见带差异植被指数 (VDVI),红绿比例指数 (RGRI) 和红绿蓝植被指数 (RGBVI).
- 频谱分离分析与支持矢量机器 (SVM) 机器学习相结合的应用.
- 开发动态值来对作物,树木和灌木进行分类.
主要成果:
- 所有测试的VI都表现出强大的植被/非植被歧视 (Kappa > 0.84),VDVI表现出卓越的性能.
- 不同植被类型的整体分类准确度超过92.68%.
- 农作物覆盖面提取实现了0.63的最小细分误差,优于其他植被类型.
结论:
- 开发的通用方法有效地将无人机可见光图像与植物社区分析联系起来.
- 这种方法可以实现高分辨率的植被监测,支持生物多样性评估和生态系统服务量化.
- 这些发现促进了无人机在生态恢复和精准农业中的应用.
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