High Resolution 3D Model of Heritage Landscapes Using UAS LiDAR: The Tajos de Alhama de Granada, Spain
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. LiDAR System for UAS
2.3. Photogrammetric System for UAS
2.4. Methods
2.4.1. Mission Planning
- the study area has an area of 61 hectares (Figure 11), so it is necessary to plan the flights by dividing the area into several parts. Considering the duration of the flights in relation to the range of the battery, it is decided there is four points from which to carry out the flights and the Tajos is divided into five areas. In Figure 12, the 5 flights can be identified by the 5 pairs of circles that indicate the calibration prior to data collection for each of them.
- To capture field data from each of the strategic points, two consecutive flights will be executed: one with the DJI Matrice 300 drone, to which the Velodyne Puck Hi-Res LiDAR will be attached (Figure 13 and Figure 14), and another with the DJI Mavic 3 pro drone that will take the photos that will be used to color the points that will be obtained with the LiDAR (Figure 15).
2.4.2. Georeferencing and Processing
2.4.3. Point Cloud Product
3. Results
4. Discussion
- the ability to reconstruct fine elements that interfered with this heritage landscape, such as the high-tension electric cables that crossed the Tajos Natural Monument at various points.
- The high density of points that the LiDAR system was capable of offering, with great precision in terms of the coordinates generated in each point detected on the ground, compared to photogrammetric methods.
- The processing of the data obtained with the LiDAR required less time than any program to obtain the point cloud from the captured photographs, although we recognize this advantage did not apply in our case, since we had to do two flights, the LiDAR and the photogrammetric, to be able to color the LiDAR point cloud.
- the size of the drone needed to transport the LiDAR system was larger and heavier than drones with high-resolution cameras on the market.
- Working with LiDAR requires more sophisticated components and sensors, which adds complexity to the management of all field operations.
- These two disadvantages mentioned increased the cost of using a LiDAR system compared to a photogrammetric survey.
- The points obtained with LiDAR did not have the color that corresponded to them in reality as they did in photogrammetry.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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LiDAR | Features |
---|---|
Real Time Kinematic (RTK) module | GNSS capabilities: (GPS: L1C/A; L2C/L2P; BDS: B1I B2I; GLO: G1 G2; GAL: E1 E5b; QZSS: L1 L2) Fixed RTK uncertainty: Horizontal: 1 cm + 1 ppm; vertical: 1.5 cm + 1 ppm |
Sensor LiDAR | Velodyne Puck Hi-Res. 16 beams, 2 returns and 3 to 5 cm positional uncertainty |
FOV Vertical | −10° to +10° Resolution 1.33° |
FOV Horizontal | 360° Resolution 0.1° |
Wave length | 903 nm |
IMU | Honeywell high-resolution 0.08/0.03 degrees uncertainty |
Hasselblad Camera Boarding the Mavic 3 pro RTK | Features |
---|---|
Real Time Kinematic (RTK) module | GNSS capabilities: (GPS: L1C/A; L2C/L2P; BDS: B1I B2I; GLO: G1 G2; GAL: E1 E5b; QZSS: L1 L2) Fixed RTK uncertainty: Horizontal: 1 cm + 1 ppm; vertical: 1.5 cm + 1 ppm |
Sensor size | CMOS 4/3 |
Pixel size | 3.2 µm |
Image size | 5280 × 3956 |
Focal | 24 mm equivalent |
Focus | 1 m a ∞ |
ISO | 100–6400 |
Shutter speed | 8-1/8000 s |
Image formats | JPEG |
DNG (RAW) |
GNSS | LiDAR | |||||
---|---|---|---|---|---|---|
X (m) | Y (m) | Z (m) | Z (m) | ZGNSS-ZLid | ABS (ZGNSS-ZLid) | |
PC01 | 412,289.911 | 4,095,554.729 | 886.029 | 885.996 | 0.033 | 0.033 |
PC02 | 412,327.580 | 4,095,537.463 | 888.438 | 888.475 | −0.037 | 0.037 |
PC03 | 412,199.919 | 4,095,657.578 | 882.833 | 882.745 | 0.088 | 0.088 |
PC04 | 412,154.845 | 4,095,639.854 | 884.071 | 884.052 | 0.019 | 0.019 |
PC05 | 412,657.949 | 4,095,809.973 | 850.159 | 850.189 | −0.03 | −0.03 |
PC06 | 412,673.244 | 4,095,752.229 | 850.802 | 850.844 | −0.042 | 0.042 |
PC07 | 412,289.298 | 4,094,665.816 | 946.680 | 946.691 | −0.011 | −0.011 |
PC08 | 412,287.500 | 4,094,679.555 | 946.538 | 946.538 | 0 | 0 |
Mean Error | 0.0025 | 0.0325 |
Parameter | Initial Values | Optimized Values | Difference |
---|---|---|---|
Focal (pixel) | 3713.29 | 3714.07 | 0.78 |
Cx (pixel) | 2647.02 | 2641.059 | −5.961 |
Cy (pixel) | 1969.28 | 1970.352 | 1.072 |
K1 | −0.11257524 | −0.110056581 | 0.002518659 |
K2 | 0.01487443 | 0.0083427 | −0.00653173 |
K3 | −0.02706411 | −0.022595434 | 0.004468676 |
P1 | −0.00008572 | −0.000008925 | 0.000076795 |
P2 | 0.0000001 | −0.000296146 | −0.000296246 |
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Vílchez-Lara, M.d.C.; Molinero-Sánchez, J.G.; Rodríguez-Moreno, C.; Gómez-Blanco, A.J.; Reinoso-Gordo, J.F. High Resolution 3D Model of Heritage Landscapes Using UAS LiDAR: The Tajos de Alhama de Granada, Spain. Land 2024, 13, 75. https://doi.org/10.3390/land13010075
Vílchez-Lara MdC, Molinero-Sánchez JG, Rodríguez-Moreno C, Gómez-Blanco AJ, Reinoso-Gordo JF. High Resolution 3D Model of Heritage Landscapes Using UAS LiDAR: The Tajos de Alhama de Granada, Spain. Land. 2024; 13(1):75. https://doi.org/10.3390/land13010075
Chicago/Turabian StyleVílchez-Lara, María del Carmen, Jorge Gabriel Molinero-Sánchez, Concepción Rodríguez-Moreno, Antonio Jesús Gómez-Blanco, and Juan Francisco Reinoso-Gordo. 2024. "High Resolution 3D Model of Heritage Landscapes Using UAS LiDAR: The Tajos de Alhama de Granada, Spain" Land 13, no. 1: 75. https://doi.org/10.3390/land13010075
APA StyleVílchez-Lara, M. d. C., Molinero-Sánchez, J. G., Rodríguez-Moreno, C., Gómez-Blanco, A. J., & Reinoso-Gordo, J. F. (2024). High Resolution 3D Model of Heritage Landscapes Using UAS LiDAR: The Tajos de Alhama de Granada, Spain. Land, 13(1), 75. https://doi.org/10.3390/land13010075