通过UAS从运动中获得的结构来描述 ponderosa松树林中的异质森林结构
Laura Hanna1, Wade T Tinkham2, Mike A Battaglia3
1Department of Forest and Rangeland Stewardship, Colorado State University, 1472 Campus Delivery, Fort Collins, CO, 80523, USA.
Environmental monitoring and assessment
|May 9, 2024
概括
无人机空中系统 (UAS) 遥感精确地绘制森林结构以进行恢复. 这项技术提供了关于树木,树和开口的详细数据,为干树林的管理提供了信息.
科学领域:
- 林业和远程传感 林业和远程传感
- 生态恢复 生态恢复
- 地理空间分析是什么
背景情况:
- 干树林的恢复旨在恢复历史复杂性和生态功能.
- 传统的森林库存方法缺乏空间分辨率,无法进行详细的结构分析.
- 无人机系统 (UAS) 从运动结构 (SfM) 提供了高分辨率的空间显式森林数据的潜力.
研究的目的:
- 为了评估UAS-SfM的准确性,以估计恢复的Ponderosa松树林中的树木,结块和支架结构属性.
- 为了比较UAS衍生的指标与传统的森林调查数据在各种林业处方.
主要方法:
- 使用UAS-SfM遥感来收集高分辨率图像.
- 估计了树木检测,高度,胸高直径 (DBH),树冠覆盖和密度.
- 从UAS获得的指标与地面森林调查数据进行了比较.
主要成果:
- 高树的检测成功 (F-分数为0.64-0.89),特别是对于高达5.0米的树木.
- 树木高度 (0.34米) 和DBH (-0.04厘米) 的平均误差是最小的.
- 无人驾驶系统准确地描述了森林结构,在某些情况下略高估计了树群密度和树冠面积.
结论:
- UAS-SfM是一个可靠的工具,用于生成波登多萨松生态系统的空间显式森林库存数据.
- 该技术通过在多个尺度上详细描述森林结构,有效地告知管理目标.
- 基于UAS的评估支持恢复干树林的目标.
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