INTEGRATION OF IPHONE LiDAR WITH QUADCOPTER AND FIXED WING UAV PHOTOGRAMMETRY FOR THE FORESTRY APPLICATIONS

Q2 Social Sciences
Y. Yadav, S. K. P. Kushwaha, M. Mokros, J. Chudá, M. Pondelík
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引用次数: 0

Abstract

Abstract. The recent innovations in remote sensing technologies have given rise to the efficient mapping and monitoring of forests. The developments in the sensor implementation have mainly focused on optimizing the payload of the UAV system and allowed the users to acquire the data simultaneously with a range of active and passive sensors like high-resolution RGB cameras and multispectral cameras LiDAR (Laser Imaging Detection and Ranging). The main objective of this research contribution is to combine the Digital Elevation Model (DEMs) from quadcopter Unmanned Aerial Vehicles (UAVs), Fixed Wing UAV-based cameras, and iPhone datasets for the forest plots. The datasets from two vegetation seasons, namely leaf-off and leaf-on, were used to combine the Digital Elevation Models from different data acquisition platforms. This internship research work aims to create and experiment with new methods, techniques, and technologies for the applications of UAV photogrammetry and iPhone LiDAR in forest napping and inventory management. CHMs are also generated in this work which helps assess the conditions of the forests in the recreational areas, and the possibility of solutions like iPhone LiDAR and UAV photogrammetry would be highly efficient and economical. The leaf-off and leaf-on datasets were processed in Agisoft Metashape Professional software to generate dense point clouds for the forest plots. The point cloud from the leaf-on dataset was rasterized to generate a DSM whereas the leaf-off point cloud generated a DSM of the forest plots after ground filtering with Cloth Simulation Filter (CSF) plugin. The iPhone LiDAR point was also rasterized to a DTM product after pre-processing steps and noise removal. The Canopy Height Models (CHMs) were generated by subtracting UAV and iPhone LiDAR based DTMs from the UAV leaf on DSM. Finally, the accuracy assessment of CHMs from UAB datasets and their integration with iPhone LiDAR has been assessed using the accurate tree heights measured during the forest field visits. The proposed methodology can be used for forest mapping purposes where a moderate accuracy is requested.
IPHONE激光雷达与四轴飞行器和固定翼无人机摄影测量技术在林业应用中的集成
摘要最近遥感技术方面的创新使人们能够对森林进行有效的测绘和监测。传感器实现的发展主要集中在优化无人机系统的有效载荷,并允许用户同时使用一系列主动和被动传感器获取数据,如高分辨率RGB相机和多光谱相机LiDAR(激光成像探测和测距)。这项研究贡献的主要目的是将四轴无人机(uav)的数字高程模型(dem)、基于固定翼无人机的相机和iPhone数据集结合起来,用于森林地块。利用两个植被季节的数据集,即落叶季和落叶季,将来自不同数据采集平台的数字高程模型进行组合。这项实习研究工作旨在为无人机摄影测量和iPhone激光雷达在森林小睡和库存管理中的应用创造和试验新的方法、技术和技术。在这项工作中也产生了chm,有助于评估休闲区森林的状况,并且像iPhone激光雷达和无人机摄影测量这样的解决方案的可能性将是高效和经济的。在Agisoft Metashape Professional软件中对落叶和落叶数据集进行处理,生成森林样地的密集点云。树叶上的点云通过栅格化生成DSM,树叶下的点云通过布料模拟过滤器(Cloth Simulation Filter, CSF)插件进行地面滤波后生成森林样地的DSM。经过预处理步骤和去噪后,iPhone LiDAR点也被栅格化为DTM产品。通过在DSM上减去基于无人机的树冠高度模型和基于iPhone LiDAR的树冠高度模型,生成树冠高度模型。最后,利用在森林实地考察中测量到的精确树高,对UAB数据集的chm及其与iPhone LiDAR的整合进行了精度评估。所建议的方法可用于要求适度精度的森林制图。
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
949
审稿时长
16 weeks
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