评估无人机和手持激光雷达点云用于辐射转移建模,使用基于voxel的点密度代理
Takumi Fujiwara1, Naoko Miura2, Hiroki Naito2
1Department of Computer Science, National Defense Academy of Japan, Yokosuka 239-8686, Japan.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究评估了基于无人机的光探测和测距 (UAV-LiDAR) 和手持式LiDAR扫描仪 (HLS) 的森林辐射传输模型. 整合两个平台对于全面的3D森林结构绘图至关重要.
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:
- 准确的3D森林结构信息对于改进辐射转移模型 (RTM) 至关重要.
- 不同的LiDAR平台提供互补的数据,但它们的整合需要仔细评估.
研究的目的:
- 评估基于无人机的光探测和测距 (UAV-LiDAR) 和用于森林RTM的手持式LiDAR扫描仪 (HLS) 的潜力.
- 评估数据融合的有效性,以实现森林结构特征的体积完整性.
主要方法:
- 使用基于Voxel的点密度代理 (VPDP) 来分析Larix kaempferi森林中的森林结构.
- 计算覆盖差距比率 (CGR) 来比较UAV-LiDAR和HLS结构捕获.
- 执行了辐射转移模拟和与Sentinel-2 NIR反射率相关的结果.
主要成果:
- 无人机LiDAR有效地捕捉了上方天花板结构 (10-45% CGR),而HLS在底层检测方面表现出色,但显示了高上方天花板CGR (>40%).
- 数据融合将CGR降低到10%以下,这表明了强烈的互补性.
- 无人机LiDAR (r = 0.73-0.75) 与Sentinel-2 NIR反射率的相关性比HLS (r = 0.64-0.69) 更好.
结论:
- 上方的天花板建模对于纳迪尔视觉传感器至关重要.
- 虽然HLS集成并没有改善反射率相关性,但融合对于体积完整性至关重要.
- 50-100厘米的voxel大小平衡了结构细节和辐射稳定性,指导了森林监测LiDAR平台的选择.
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