机载激光雷达和航空数码影像单木树高提取

用激光雷达(LiDAR)数据和航空数码影像相结合进行单木水平树高反演.对研究区的LiDAR点云数据进行滤波和分类,根据地形特点、地表植被状况以及其他地类的分布,采用Tin Filter滤波算法提取地面回波点和植被回波点.用面向对象的方法对高空间分辨率(25 cm)的航空数码影像进行单株木检测.通过多尺度、树冠模式的分割创建影像对象和类层次,用最邻近距离和成员函数法进行影像对象的分类,并基于分类结果进行再分割.对分割后的树冠多边形进行边缘优化,以准确识别单株木.将植被回波点和影像分割后得到的树冠多边形进行叠加,计算多边形内的LiDAR数据最大高程差值,与实测树高进行相关分析,建立单木树高估测回归方程,平均估测精度为74.89%.

Extraction of Individual Tree Height Using a Combination of Aerial Digital Camera Imagery and LiDAR

LiDAR (light detection and ranging) is an active remote sensing technique, LiDAR instruments measure the roundtrip time for a pulse of laser energy to travel between the sensor and a target, provide a distance or range from the instrument to the object. In this research individual tree height was measured by combining IiDAR and aerial digital camera imagery. According to the terrain, vegetation and other things of the surface, the IiDAR point clouds were filtered and classified. The Tin Filter algorithm was used for the extraction of ground points and vegetation points. Object-oriented methods were used to identify the single tree in digital camera imagery. Multi-scale and canopy mode were applied in the segmentation, and the nearest neighbors and member function methods were the main classification algorithm, and the edges of canopy polygons were optimized for improving the precision of single tree identification. The classified vegetation points were overlapped with the canopy polygons, and the max height in the polygon was calculated for the correlation with ground measured tree heights. The regression equation was established, and the mean evaluation precision was 74.89 % .