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相关实验视频

Updated: Jan 7, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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在混合森林生态系统中,整合多源数据用于树冠间隙检测和分布建模.

Petar Donev1, Hong Wang2,3, Shuhong Qin4

  • 1College of Earth Sciences and Engineering, Hohai University, Nanjing, China. petardonev@hhu.edu.cn.

Environmental monitoring and assessment
|December 20, 2025
PubMed
概括

分析森林树冠间隙 (CGs) 显示了五年来的季节性变化. 空中LiDAR数据为细分这些关键森林生态系统特征提供了最高的准确性.

关键词:
在CHM中使用CHM.李达尔 (LiDAR) 是一种激光雷达.分区的统计模型.

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科学领域:

  • 林业科学 林业科学
  • 生态生态学 生态生态学
  • 遥感 遥感 遥感 遥感

背景情况:

  • 森林树冠缺口 (CGs) 对森林动态,生物多样性和生态系统弹性至关重要.
  • 了解季节性CG变化对于有效的森林管理至关重要.

研究的目的:

  • 为了分析森林树冠间隙的季节性变化,在五年内.
  • 为了评估CG细分的不同遥感数据源的准确性.
  • 为了建模CG的空间和时间动态.

主要方法:

  • 利用多种来源的数据:无人机 (UAV) RGB图像,卫星多光谱图像,合成孔径雷达 (SAR) 和光检测和测距 (LiDAR).
  • 采用统计模型,包括韦布尔分布和马尔科夫链,用于空间和时间CG分析.
  • 评估了细分精度,空中LiDAR在较小的间隙中达到87%.

主要成果:

  • 空中LiDAR显示出最高的细分精度 (87%),其次是无人机RGB (84%) 和卫星数据 (70%).
  • 差距大小分布在五年内发生了变化,最初的差距较小,较大的差距在以后增加,特别是在春季和秋季.
  • 季节性分析显示了CG扩张和收缩的明显模式.

结论:

  • 结合多源遥感数据和统计建模,为CG细分和分析提供了强大的方法.
  • 这些发现支持对森林生态系统的灵活监测,有助于可持续森林管理实践.
  • 了解季节性CG动态对于预测森林弹性和生物多样性至关重要.