UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1)

Štroner, Martin; Urban, R.; Křemen, Tomáš; Braun, Jaroslav

doi:10.1080/22797254.2023.2179942

Cited by 13 publications

(10 citation statements)

References 48 publications

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“…The analysis indicates that for the 60 m flight, approximately 39% of the ChPs exhibit calculated distances falling within a range of 1 to 2 cm, with an RMSE value of 2 cm. For the 100 m flight, about 35% of the ChPs have calculated distances ranging from 2 to 3 cm, with an RMSE value of 4 cm, as reported in [27]. The 4 cm error was also obtained in [27], but after rasterization of LiDAR-UAS point cloud averaging the data within a 0.1 m cell, providing significant smoothening.…”

Section: Quality Assessment Of the Lidar-uas Point Cloudsmentioning

confidence: 75%

“…For the 100 m flight, about 35% of the ChPs have calculated distances ranging from 2 to 3 cm, with an RMSE value of 4 cm, as reported in [27]. The 4 cm error was also obtained in [27], but after rasterization of LiDAR-UAS point cloud averaging the data within a 0.1 m cell, providing significant smoothening. These data suggest the degree of agreement between LiDAR-derived elevations and the ChPs at different flight heights.…”

Section: Quality Assessment Of the Lidar-uas Point Cloudsmentioning

confidence: 75%

“…In [27], a DTM was created for three small (around 1.5 ha) different forest sites, differing in terms of the nature of the forest and the terrain. The data acquisition was carried out with a DJI Zenmuse L1 mounted on a DJI Matrice 300 RTK UAS at a 100 m height under leaf-off conditions.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing LiDAR-UAS Derived Digital Terrain Models with Hierarchic Robust and Volume-Based Filtering Approaches for Precision Topographic Mapping

Oniga,

Loghin,

Macovei

et al. 2023

Remote Sensing

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Airborne Laser Scanning (ALS) point cloud classification in ground and non-ground points can be accurately performed using various algorithms, which rely on a range of information, including signal analysis, intensity, amplitude, echo width, and return number, often focusing on the last return. With its high point density and the vast majority of points (approximately 99%) measured with the first return, filtering LiDAR-UAS data proves to be a more challenging task when compared to ALS point clouds. Various algorithms have been proposed in the scientific literature to differentiate ground points from non-ground points. Each of these algorithms has advantages and disadvantages, depending on the specific terrain characteristics. The aim of this research is to obtain an enhanced Digital Terrain Model (DTM) based on LiDAR-UAS data and to qualitatively and quantitatively compare three filtering approaches, i.e., hierarchical robust, volume-based, and cloth simulation, on a complex terrain study area. For this purpose, two flights over a residential area of about 7.2 ha were taken at 60 m and 100 m, with a DJI Matrice 300 RTK UAS, equipped with a Geosun GS-130X LiDAR sensor. The vertical and horizontal accuracy of the LiDAR-UAS point cloud, obtained via PPK trajectory processing, was tested using Check Points (ChPs) and manually extracted features. A combined approach for ground point classification is proposed, using the results from a hierarchic robust filter and applying an 80% slope condition for the volume-based filtering result. The proposed method has the advantage of representing with accuracy man-made structures and sudden slope changes, improving the overall accuracy of the DTMs by 40% with respect to the hierarchical robust filtering algorithm in the case of a 60 m flight height and by 28% in the case of a 100 m flight height when validated against 985 ChPs.

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Section: Quality Assessment Of the Lidar-uas Point Cloudsmentioning

confidence: 75%

Section: Quality Assessment Of the Lidar-uas Point Cloudsmentioning

confidence: 75%

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Enhancing LiDAR-UAS Derived Digital Terrain Models with Hierarchic Robust and Volume-Based Filtering Approaches for Precision Topographic Mapping

Oniga,

Loghin,

Macovei

et al. 2023

Remote Sensing

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“…The UAV-LiDAR data were collected on 30 September 2021, using the Zenmuse L1 system (DJI Co., Shenzhen, Beijing), flown at 100 m above ground level, with a flight speed of 5 m•s −1 . The Zenmuse L1 system has a vertical scanning accuracy of 5 cm and a horizontal scanning accuracy of 10 cm, respectively [44]. In the experimental area, a lateral overlap rate of 50% was employed, and a total of four scans were conducted over the study area to improve the point cloud density.…”

Section: Uav-lidar Datamentioning

confidence: 99%

Individual Tree Segmentation Based on Seed Points Detected by an Adaptive Crown Shaped Algorithm Using UAV-LiDAR Data

Yu,

Lei,

2024

Remote Sensing

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Unmanned aerial vehicle–light detection and ranging (UAV-LiDAR) provides a convenient and economical means of forest data acquisition that can penetrate canopy gaps to obtain abundant ground information, offering huge potential in forest inventory. Individual tree segmentation is a prerequisite to obtain individual tree details but is highly dependent on the accuracy of seed point detection. However, most of the existing methods, such as the local maximum (LM) and CHM-based methods, are strongly dependent on the window size, and, for individual tree segmentation, they can result in over-segmentation and under-segmentation, especially in natural forests. In this paper, we propose an adaptive crown shaped algorithm for individual tree segmentation without consideration of the window size. It was implemented in four plots with different forest types and topographies (i.e., planted coniferous forest with flat terrain, coniferous forest with sloping terrain, mixed forest with flat terrain and broadleaf forest with flat terrain). First, the normalized point clouds were rotated and blocked at multiple angles to extract the surface points of the forest. Then, the crown boundaries were delineated by analyzing the crown profiles to extract the treetops as seed points. Finally, a region growing method based on seed points was applied for individual tree segmentation. Our results showed that the recall, precision and F1-score of seed point detection reached 91.6%, 95.9% and 0.94, respectively, and that the accuracy rates for individual tree segmentation for the four plots were 87.7%, 80.6%, 73.2% and 70.5%, respectively. Our proposed method can effectively detect seed points via the adaptive crown shaped algorithm and reduce the impacts of elongated branches by applying distance thresholds between trees, enhancing the accuracy of seed point detection and subsequently improving the precision of individual tree segmentation. In addition, the proposed algorithm demonstrated superior performance in comparison to LM and CHM-based methods for the calculation of seed points, as well as outperforming PCS in individual tree segmentation. The proposed method demonstrates effectiveness and feasibility in dense forests and natural forests, providing an important reference for future research on seed point detection and individual tree segmentation.

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“…An uneven object with varying sizes and shapes can be accurately represented in a digital 3D model with high detail and speed. TLS applies the spatial polar method to find the spatial coordinates of points; its uses are evident in engineering and construction [21,22], industry [23,24], and land mapping [25,26]. Laser scanning and photogrammetric techniques typically use ground control points (GCP) for georeferencing [27].…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Point Clouds Obtained by SfM Photogrammetry and the TLS Method—Study in Quarry Environment

Kovanič,

Peťovský,

Topitzer

et al. 2024

Land

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Thanks to the development of geodetic methods and equipment, there has been a transition from conventional methods to modern technologies, which can efficiently and accurately acquire a large amount of data in a short time without the need for direct contact with the measured object. Combined technologies such as Structure from Motion (SfM), Multi-View Stereo (MVS) photogrammetry using Unmanned Aerial Systems (UAS), and terrestrial laser scanning (TLS) are often used for monitoring geohazards and documenting objects in quarries to obtain detailed and accurate information about their condition and changes. This article deals with the analysis of point clouds obtained with different settings in terms of average absolute point distance, average point density, and time range for surveying and office work. The numerical and graphical results of the research lead to conclusions for scientific and practical applications for activities in the mining industry.

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UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1)

Cited by 13 publications

References 48 publications

Enhancing LiDAR-UAS Derived Digital Terrain Models with Hierarchic Robust and Volume-Based Filtering Approaches for Precision Topographic Mapping

Enhancing LiDAR-UAS Derived Digital Terrain Models with Hierarchic Robust and Volume-Based Filtering Approaches for Precision Topographic Mapping

Individual Tree Segmentation Based on Seed Points Detected by an Adaptive Crown Shaped Algorithm Using UAV-LiDAR Data

Spatial Analysis of Point Clouds Obtained by SfM Photogrammetry and the TLS Method—Study in Quarry Environment

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