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3D remote sensing of plant community using scanning lidar systems

使用扫描激光雷达系统对植物群落进行 3D 遥感

基本信息

批准号：
13306020
负责人：
OMASA Kenji
金额：
$ 35.53万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (A)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2004
项目状态：
已结题

项目摘要

Forest area was measured 3-dimensionally using helicopter-borne scanning lidar system with high spatial resolution. Accuracy of the lidar system at several fright conditions was examined and flight condition of the system could be optimized. As a result, woody tree height could be estimated accurately with RMSE less than 20 cm. Next, algorithms for automatic detection of tree height and position were examined using the lidar data. A newly designed algorithm (Crown Extraction filtering : CE filtering) was compared with the conventional ones, such as Watershed method and Local maximum filtering(LM filtering). It was shown that tree height was estimated more accurately from Digital Canopy Height Model(DCHM) with tree tops identified by CE filtering after smoothing. As a result, error of identification of tree tops using this method was 4.9 % and the one of tree heights ranged -0.25 to 0.42 m with RMSE of 0.38 m. Moreover, we designed an algorithm to identify outline of each tree and it enabled to map spatial distribution of forest biomass.On the other hand, understory of trees was measured in forest using a portable scanning lidar system and the diameter at breast height(DBH) of each tree was estimated. From the data of DBH, the forest biomass was estimated. This method enabled to estimate forest biomass without disturbing understory and felling trees. In addition, potable lidar data of trees measured from several points on the ground were merged into one 3D image to complement blind regions. Then, a complete 3D model of several trees was generated by a technique of computer graphics. From the 3D model, several parameters such as tree height, stem diameter, maximum canopy diameter and canopy area in the horizontal cross-section, and canopy volume were computed and the errors were evaluated.

利用机载高空间分辨率扫描激光雷达系统对森林面积进行了三维测量。在几个飞行条件下的激光雷达系统的精度进行了检查和系统的飞行条件可以优化。结果表明，该方法能较准确地估测林木高度，RMSE小于20 cm.接下来，使用激光雷达数据检查用于自动检测树的高度和位置的算法。将一种新设计的算法（树冠提取滤波：CE滤波）与传统的分水岭法和局部最大值滤波（LM滤波）进行了比较。结果表明，采用平滑后的CE滤波识别树顶，利用数字冠层高度模型（DCHM）可以更准确地估计树高。结果表明，该方法识别树顶的误差为4.9%，树高的误差范围为-0.25 ~0.42m，RMSE为0.38m。此外，我们还设计了一种算法来识别每棵树的轮廓，从而能够绘制森林生物量的空间分布图。另一方面，我们利用便携式扫描激光雷达系统测量了森林中树木的林下植被，并估算了每棵树的胸径。根据胸径数据，估算了森林生物量。这种方法能够在不干扰林下植被和砍伐树木的情况下估计森林生物量。此外，从地面上的几个点测量的树木的便携式激光雷达数据被合并到一个3D图像中，以补充盲区。然后，通过计算机图形学技术，生成一个完整的三维模型的几棵树。从三维模型中，几个参数，如树高，干直径，最大冠层直径和冠层面积的水平横截面，冠层体积计算和误差进行了评估。