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Updated: Jan 11, 2026

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收集和处理基于无人机的远程传感数据,用于森林恢复监测

Dmytro Movchan¹, Charumitha Selvaraj¹, Zhouxin Xi¹

¹Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre.

Journal of visualized experiments : JoVE

|November 10, 2025

概括

这项研究引入了一种使用带有LiDAR和多谱传感器的无人机,结合深度学习的新方法,用于精确的3D树木绘图. 这项技术有助于比传统方法更有效地监测回收土地上的森林恢复.

科学领域:

林业林业林业林业林业
遥感遥感遥感遥感
地理空间分析是什么

背景情况:

传统的植被监测是劳动密集型和空间有限的.
像LiDAR和多光谱 (MS) 图像等遥感 (RS) 技术提供了广泛的监控.
远程驾驶飞机系统 (RPAS) 提高了数据收集灵活性和分辨率.

研究的目的:

开发和介绍一个采集,处理和合并基于RPAS的LiDAR和MS数据的协议.
通过使用深度学习 (DL) 插件实现自动化个体3D树划分.
支持在回收石油和天然气井地点有效监测森林回收.

主要方法:

使用的RPAS配备LiDAR和MS传感器用于数据采集.
开发并应用了一种专有深度学习模型用于树段.
集成数据处理,对齐和合并,用于3D树划分.

主要成果:

DL模型成功地将树木边界分为不同的森林生态系统,传感器和数据分辨率.
该协议为基于RPAS的LiDAR和MS数据集成提供了一个实用的工作流.
证明了监测森林恢复方法的有效性.

结论:

基于RPAS的LiDAR和MS数据,用DL处理,为植被监测提供了一个可扩展和高效的解决方案.
这种方法显著改进了传统的基于现场的评估方法.
开发的工具和协议适用于各种森林景观和回收监测工作.

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