树木的三维(3D)建模在各个领域有许多应用,比如森林和城市规划,森林健康监测,和碳封存,仅举几例。无人机(UAV)摄影测量最近作为一种低成本,快速,城市和森林树木三维建模的精确方法取代了昂贵的传统方法,如地块测量和测量。有许多商业和开源软件程序可用,每个无人机数据的处理方式都不同,以生成森林3D建模和摄影测量产品,包括点云,数字表面模型(DSM)顶篷高度模型(CHM),和森林地区的正射影像。这项研究的目的是比较三个广泛使用的商业软件包,即,AgiSoftPhotoscan(Metashape)V1.7.3,PIX4DMapper(Pix4D)V4.4.12和DJITerraV3.7.6,用于从点云密度和重建质量三个角度处理森林区域的无人机数据,计算时间,DSM评估高度精度(z)和DSM上树木检测的能力。三个数据集,无人机在同一天在三个不同的飞行高度捕获,在这项研究中使用。第一,第二,第三个数据集是在60米的海拔高度收集的,100米,120米,分别在塔利的一个森林地区,纽约虽然第一个和第三个数据集是水平采集的,第二个数据集位于偏离最低点20度,以调查倾斜图像的影响.结果表明,Pix4D和AgiSoft产生的点云密度是DJITerra的2.5倍。然而,使用迭代最近点方法(ICP)进行的重建质量评估表明,DJITerra在点云中的间隙较少,并且在生成树木点云方面比AgiSoft和Pix4D表现更好,电力线和电线杆,尽管产生的点数较少。换句话说,关键点检测的性能和改进的匹配算法是生成改进的最终产品的关键因素。计算时间比较表明,AgiSoft和DJITerra的处理时间大约是Pix4D的一半。此外,DSM高程剖面表明,三个软件之间的估计高度变化范围为0.5m至2.5m。DJITerra的估计高度通常大于AgiSoft和Pix4D。此外,DJITerra在树木的高度轮廓建模方面优于AgiSoft和Pix4D,建筑物,电线和电线杆,其次是AgiSoft和Pix4D。最后,在树木检测能力方面,由于点云中的间隙较少,DJITerra在生成全面的DSM方面优于AgiSoft和Pix4D。因此,它是树检测应用的首选。本文的结果可以通过理解所采用的软件的准确性来帮助3D模型用户对生成的3D模型的可靠性有信心。
Three-dimensional (3D) modeling of trees has many applications in various areas, such as
forest and urban planning,
forest health monitoring, and carbon sequestration, to name a few. Unmanned Aerial Vehicle (UAV) photogrammetry has recently emerged as a low cost, rapid, and accurate method for 3D modeling of urban and
forest trees replacing the costly traditional methods such as plot measurements and surveying. There are numerous commercial and open-source software programs available, each processing UAV data differently to generate forest 3D modeling and photogrammetric products, including point clouds, Digital Surface Models (DSMs), Canopy Height Models (CHMs), and orthophotos in
forest areas. The objective of this study is to compare the three widely-used commercial software packages, namely, AgiSoft Photoscan (Metashape) V 1.7.3, PIX4DMapper (Pix4D) V 4.4.12, and DJI Terra V 3.7.6 for processing UAV data over
forest areas from three perspectives: point cloud density and reconstruction quality, computational time, DSM assessment for height accuracy (z) and ability of tree detection on DSM. Three datasets, captured by UAVs on the same day at three different flight altitudes, were used in this study. The first, second, and third datasets were collected at altitudes of 60 m, 100 m, and 120 m, respectively over a forested area in Tully, New York. While the first and third datasets were taken horizontally, the second dataset was taken 20 degrees off-nadir to investigate the impact of oblique images. Results show that Pix4D and AgiSoft generate 2.5 times denser point clouds than DJI Terra. However, reconstruction quality evaluation using the Iterative Closest Point method (ICP) shows DJI Terra has fewer gaps in the point cloud and performed better than AgiSoft and Pix4D in generating a point cloud of trees, power lines and poles despite producing a fewer number of points. In other words, the outperformance in key points detection and an improved matching algorithm are key factors in generating improved final products. The computational time comparison demonstrates that the processing time for AgiSoft and DJI Terra is roughly half that of Pix4D. Furthermore, DSM elevation profiles demonstrate that the estimated height variations between the three software range from 0.5 m to 2.5 m. DJI Terra\'s estimated heights are generally greater than those of AgiSoft and Pix4D. Furthermore, DJI Terra outperforms AgiSoft and Pix4D for modeling the height contour of trees, buildings, and power lines and poles, followed by AgiSoft and Pix4D. Finally, in terms of the ability of tree detection, DJI Terra outperforms AgiSoft and Pix4D in generating a comprehensive DSM as a result of fewer gaps in the point cloud. Consequently, it stands out as the preferred choice for tree detection applications. The results of this paper can help 3D model users to have confidence in the reliability of the generated 3D models by comprehending the accuracy of the employed software.