4D monitoring of mountain areas using the UAV-PPK workflow

D. Dinkov
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引用次数: 0

Abstract

The mapping and three-dimensional modeling of mountain areas and the study of land cover change dynamics are current tasks in preserving and maintaining protected natural parks and forests. In this context, recent developments in digital photogrammetry using the SfM-MVS method to process captured imagery and the development of unmanned aerial systems (UAS) allow for reducing the costs, time, and the use of human resources and obtaining and repeatable 3D topographic data for moun-tainous regions. We will call this acquired 3D high-resolution topographic data (HRTD) 4D data in the context of an additional temporal component. The main objective of this study is to evaluate the applicability of PPK (Post-Processing Kinematic) direct georeferencing of images captured by UAVs and processed through the SfM-MVS method to obtain HRTDs for 4D land cover analysis. We analyze a 3D HRTD with an acquisition interval of two years for a mountain test area in Plana Mountain near Sofia. The test area has a diverse vegetation cover, including coniferous forest, grassland, hay meadows, shrubs, and single deciduous trees. We conducted multiple surveys of the test area with a budget PPK-UAV configuration (DJI Phantom 4 Pro with a single-frequency PPK-GNSS kit installed) from March 2020 to October 2022. Two autumn surveys from September 2020 and October 2022 were se-lected, which possess the most-good performance on numerical data accuracy. We performed 3D data analysis on 1) Assessment of the accuracy of PPK-SfM-MVS photogrammetry generated topographic data (3D clouds and DSM); 2) Investigation of the errors in the individual specific surfaces (for the individual isolated sections) using the M3C2 tool for comparing and evaluating dense point clouds; 3) Determining land cover changes in the demarcated areas using a surface of differences (DoD). Accuracy analysis showed that the PPK solution provides comparable accuracy (about RMSE3D = 0.067 m for the 2020 data, georeferencing (PPK+1GCP) and RMSE3D about 0.13 m for the 2022 data, georeferencing (PPK only)) like the GCP method. The multi-temporal topographic reconstructions based on UAV- PPK-SfM allowed us to quantify and qualitatively determine the land cover changes that occurred. The UAV-PPK-SfM workflow in the context of 4D land surface monitoring and the results suggested that even low-cost UAV-PPK systems can provide data suitable for measuring geomorphic change at the scale of the acquired data. The multi-temporal topographic reconstructions based on UAV- PPK-SfM allowed us to quantify and qualitatively determine the land cover changes that occurred. The UAV-PPK-SfM workflow in the context of 4D land surface monitoring and the results suggested that even low-cost UAV-PPK systems can provide data suitable for measuring geomorphic change at the scale of the acquired data.
使用无人机- ppk工作流程对山区进行4D监测
山区测绘和三维建模以及土地覆被动态变化研究是保护和维护自然公园和森林的当前任务。在这种情况下,数字摄影测量的最新发展使用SfM-MVS方法来处理捕获的图像和无人机系统(UAS)的发展,可以降低成本、时间和人力资源的使用,并获得山区可重复的3D地形数据。我们将这种获得的3D高分辨率地形数据(HRTD)称为4D数据,其中包含额外的时间成分。本研究的主要目的是评估PPK (Post-Processing Kinematic)对无人机捕获的图像进行直接地理参考,并通过SfM-MVS方法进行处理,获得用于四维土地覆盖分析的HRTDs的适用性。我们分析了索非亚附近Plana山山区试验区的三维HRTD,采集间隔为两年。试验区植被覆盖多样,包括针叶林、草地、干草草甸、灌木和单一落叶乔木。从2020年3月到2022年10月,我们使用预算PPK-UAV配置(安装了单频PPK-GNSS套件的DJI Phantom 4 Pro)对测试区域进行了多次调查。选择2020年9月和2022年10月的两次秋季调查,在数值数据精度上表现最好。我们进行了三维数据分析:1)评估PPK-SfM-MVS摄影测量生成的地形数据(3D云和DSM)的精度;2)利用M3C2工具对密集点云进行对比评价,对单个特定面(单个孤立断面)的误差进行调查;3)利用差异面(surface of difference, DoD)确定划定区域的土地覆盖变化。精度分析表明,PPK方案提供了与GCP方法相当的精度(2020年数据的RMSE3D = 0.067 m,地理参考(PPK+1GCP), 2022年数据的RMSE3D约为0.13 m,地理参考(仅PPK))。基于无人机- PPK-SfM的多时相地形重建使我们能够定量和定性地确定发生的土地覆盖变化。无人机- ppk - sfm在四维地表监测背景下的工作流程和结果表明,即使是低成本的无人机- ppk系统也可以在获得的数据尺度上提供适合测量地貌变化的数据。基于无人机- PPK-SfM的多时相地形重建使我们能够定量和定性地确定发生的土地覆盖变化。无人机- ppk - sfm在四维地表监测背景下的工作流程和结果表明,即使是低成本的无人机- ppk系统也可以在获得的数据尺度上提供适合测量地貌变化的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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