Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry
"> Figure 1
<p>Unmanned aerial vehicle (UAV) flight plan for the test site: (<b>a</b>) contour flight and (<b>b</b>) detailed flight along the tree rows.</p> "> Figure 2
<p>Overview of data processing steps and their main parameter settings.</p> "> Figure 3
<p>Maximum tree wall height estimations from UAV (<b>orange</b>) and light detection and ranging (LiDAR) (<b>blue</b>) point clouds for each 0.25 m in apple tree rows A, B, and C for 2018 (<b>top</b>) and 2019 (bottom).</p> "> Figure 4
<p>Scatter plots of estimated tree wall heights for grid cells for 2018 and 2019.</p> "> Figure 5
<p>(<b>a</b>) Tree wall height estimations from UAV (orange) and LiDAR (blue) point clouds for each grid cell in apple tree row C for 2018. (<b>b</b>) Difference in maximum tree wall height estimations between UAV and reference point cloud (<b>c</b>).</p> "> Figure 6
<p>Detailed view of row A (42–50 m) in 2019 as an RGB image (<b>top</b>) and point clouds from UAV (orange) and LiDAR (blue) data superimposed with tree wall height curves from UAV (orange) and LiDAR (blue) model (<b>bottom</b>).</p> "> Figure 7
<p>Detected areas of underestimation (yellow boxes) in UAV point cloud compared to LiDAR point cloud (color indicates distance to the nearest point in reference point cloud; all distances are given in meters) depicted as overview and magnified for a subsection.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Site
2.2. UAV Measurements
2.3. Ground-Based Reference Measurements
2.4. Data Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Year | Row | Point Density (Points/m3) | Median Points per 0.25 m Section | PC Complete-Ness (%) | ME (m) | MAE (m) | R2 |
---|---|---|---|---|---|---|---|
2018 | A | 50,746 | 22,202 | 84.1 | −0.09 | 0.20 | 0.83 |
2018 | B | 45,443 | 16,291 | 76.6 | −0.18 | 0.23 | 0.87 |
2018 | C | 50,067 | 24,065 | 86.6 | −0.05 | 0.18 | 0.81 |
2019 | A | 37,920 | 9038 | 73.1 | −0.22 | 0.23 | 0.90 |
2019 | B | 44,118 | 18,033 | 82.0 | −0.15 | 0.21 | 0.91 |
2019 | C | 40,045 | 17,641 | 86.2 | −0.17 | 0.24 | 0.81 |
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Hobart, M.; Pflanz, M.; Weltzien, C.; Schirrmann, M. Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry. Remote Sens. 2020, 12, 1656. https://doi.org/10.3390/rs12101656
Hobart M, Pflanz M, Weltzien C, Schirrmann M. Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry. Remote Sensing. 2020; 12(10):1656. https://doi.org/10.3390/rs12101656
Chicago/Turabian StyleHobart, Marius, Michael Pflanz, Cornelia Weltzien, and Michael Schirrmann. 2020. "Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry" Remote Sensing 12, no. 10: 1656. https://doi.org/10.3390/rs12101656