UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline
UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline
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DOI：
10.3390/rs10060912
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发表时间：
2018-06-01
期刊：
影响因子：
5
通讯作者：
Koch, Barbara
中科院分区：
文献类型：
--
作者：
Frey, Julian;Kovach, Kyle;Koch, Barbara
利用无人机进行森林结构分析目前越来越受欢迎。鉴于平台成本的降低，以及可用于分析数据输出的算法数量，应用程序的数量迅速增长。森林结构不仅与林业的经济价值有关，而且与生物多样性和脆弱性问题有关。LiDAR仍然是森林结构评估最有前途的技术，但适用于无人机应用的小型LiDAR传感器价格昂贵，并且仅限于少数制造商。从二维图像序列中估计3D结构被称为“运动结构”（SfM），通过摄影测量重建类似于从LiDAR传感器绘制的点云来克服这种限制。这些技术在高度结构化的地形中的结果强烈依赖于在图像采集过程中采用的方法，因此结构指数可能容易受到飞行活动中的错误规范的影响。在本文中，我们概述了图像重叠和地面采样距离如何影响图像重建的完整性在2D和3D。较高的图像重叠和较粗的GSD对重建质量具有明显的积极影响。因此，GSD中的更高精度要求必须通过更高的图像重叠来补偿。图像重叠度> 95%且分辨率> 5 cm时可获得最佳结果。研究发现，最重要的环境因素是风和地形海拔，这可能是植被密度的指标。
Structural analysis of forests by UAV is currently growing in popularity. Given the reduction in platform costs, and the number of algorithms available to analyze data output, the number of applications has grown rapidly. Forest structures are not only linked to economic value in forestry, but also to biodiversity and vulnerability issues. LiDAR remains the most promising technique for forest structural assessment, but small LiDAR sensors suitable for UAV applications are expensive and are limited to a few manufactures. The estimation of 3D-structures from two-dimensional image sequences called Structure from motion' (SfM) overcomes this limitation by photogrammetrically reconstructing point clouds similar to those rendered from LiDAR sensors. The result of these techniques in highly structured terrain strongly depends on the methods employed during image acquisition, therefore structural indices might be vulnerable to misspecifications in flight campaigns. In this paper, we outline how image overlap and ground sampling distances affect image reconstruction completeness in 2D and 3D. Higher image overlaps and coarser GSDs have a clearly positive influence on reconstruction quality. Therefore, higher accuracy requirements in the GSD must be compensated by a higher image overlap. The best results are achieved with an image overlap of > 95% and a resolution of > 5 cm. The most important environmental factors have been found to be wind and terrain elevation, which could be an indicator of vegetation density.