使用基于UAV的SfM和LiDAR点云生成流DTM方法的开发
Jaejun Gou1, Hyeokjin Lee1, Jinseok Park1
1Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Seoul National University, Seoul, 08826, South Korea.
Scientific reports
|January 13, 2026
概括
准确的数字地形模型 (DTM) 对河流研究至关重要. 这项研究整合了UAV-LiDAR和SfM数据,发现简单的形态过器 (SMRF) 最适合在流域中生成DTM.
科学领域:
- 地理空间科学 地理空间科学
- 水文学 水文学
- 生态生态学 生态生态学
背景情况:
- 准确的数字地形模型 (DTM) 对于河流环境中的水文和生态建模至关重要.
- 在溪流地区生成精确的DTM,由于水体和密集的河岸植被,这带来了独特的挑战.
- 现有的方法需要改进,以有效地整合各种点云数据,以提高DTM准确性.
研究的目的:
- 提出和评估一种方法,以利用基于无人机的SfM和LiDAR数据在流域中生成准确的DTM.
- 为了比较三种不同的地面过算法 (CSF,PTIN,SMRF) 在河流环境中去除非地面点的性能.
- 评估生成的DTM的准确性,用于水文和生态建模应用.
主要方法:
- 通过基于无人机的LiDAR (Zenmuse L1) 和SfM (Phantom 4多谱) 获得的点云的集成.
- 使用规范差异水指数 (NDWI) 将水域和非水域分类.
- 应用和比较三种地面过器:布模拟过器 (CSF),渐进式 TIN (PTIN) 和简单形态过器 (SMRF).
主要成果:
- 简单形态过器 (SMRF) 显示出卓越的性能,平均绝对误差 (MAE) 为0.160m,根平均平方误差 (RMSE) 为0.214m.
- SMRF有效地去除了植被,并保留了沿海地形特征.
- SMRF显示出低估水域海拔的趋势,表明需要对淹没的地形进行进一步的精炼.
结论:
- 拟议的方法,集成UAV-LiDAR,SfM和高级过,显著提高DTM在流环境中的准确性.
- 在这项研究中,SMRF被确定为DTM生成的最有效的过器,平衡了植被清除和地形保护.
- 改进的DTM为小河流系统中中尺度水文和生态建模提供了实质性的好处.
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