Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure
"> Figure 1
<p>©GoogleEarth extract of the Fontanafredda site located at 44.6°N, 7.98°E. Yellow pins correspond to the elementary sampling unit (ESU) locations. The different flight locations are delimited by the orange polygons (<a href="#remotesensing-09-00111-t001" class="html-table">Table 1</a>).</p> "> Figure 2
<p>Row characteristics measured over each ESU.</p> "> Figure 3
<p>Vineyard characteristics estimation: algorithm overview. GCP stands for Ground Control Points. DPC is the dense point cloud.</p> "> Figure 4
<p>Point density for the dense point cloud generated by <sup>®</sup>Agisoft PhotoScan for each Elementary Sampling Unit.</p> "> Figure 5
<p>Fitting the reference plane for the grid cell for ESU number 2 (<b>a</b>) and 19 (<b>b</b>). The circles correspond to the 10% lowest altitude points of the point cloud. The fitted reference plane is represented by the colored plane. The color indicates the local height of each sub-grid cell.</p> "> Figure 6
<p>(<b>a</b>) Percentage of empty pixels in each ESU as a function of the pixel size: 2 cm (green) or 5 cm (red). (<b>b</b>) Distribution of the number of DPC points per pixel for the 20 ESUs and the 5 cm pixel size. The median is represented by the red horizontal bar. The box represents the 25%–75% percentiles. The whiskers include 99.3% of the data.</p> "> Figure 7
<p>Point density image per 5 cm pixel size (<b>a</b>) and height of the highest point within each pixel (<b>b</b>) for ESU#2 and ESU#16 (<b>c</b>,<b>d</b>). White pixels correspond to empty cells.</p> "> Figure 8
<p>(<b>a</b>) Height cumulative frequency (%). Colored dots correspond to the measured minimum and maximum average height of the plants over each ESU. (<b>b</b>) Box and whisker diagram per ESU of the distribution of the percentage of vegetated pixels detected in the binary image when considering a height threshold varying between 50 cm and 170 cm. The bottom and the top of the box represent the first and third quartile while the red dash is the second quartile (median). A total of 99.3% of the data is included between the whiskers.</p> "> Figure 9
<p>(<b>a</b>) Empirical cumulated distribution function of pixel height computed from the gridded height image for ESU20. Dashed lines indicate the height value <math display="inline"> <semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>R</mi> <mi>o</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics> </math> for <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">F</mi> <mrow> <mi>Height</mi> </mrow> </msub> <mo>=</mo> <mn>0.68</mn> </mrow> </semantics> </math> (<b>b</b>) Root mean square error (RMSE) between row height ground measurements and height computed from the cumulative distribution function at <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">F</mi> <mrow> <mi>Height</mi> </mrow> </msub> </mrow> </semantics> </math>. The minimum is indicated by the vertical line (<math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">F</mi> <mrow> <mi>Height</mi> </mrow> </msub> <mtext> </mtext> <mo>=</mo> <mtext> </mtext> <mn>0.68</mn> </mrow> </semantics> </math> ). (<b>c</b>) Comparison between row height estimates from UAV and ground measurements for the different ESUs.</p> "> Figure 10
<p>(<b>a</b>) Hough transform of the binary image for ESU#6. The four rows seen in the original central image (<b>b</b>) correspond to the four sets of converging curves in the Hough transform. The Hough peak value shown in magenta (converging point of the curves) determines the row orientation. (<b>c</b>) The rotated image with vertical rows.</p> "> Figure 11
<p>(<b>a</b>) Example of profile of vegetation pixels obtained over ESU#17. The peak summits (<math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> </semantics> </math>), e.g., row centers, are identified by the dashed lines. (<b>b</b>) Validation of the row spacing estimates against ground truth.</p> "> Figure 12
<p>(<b>a</b>) Illustration of row width determination for ESU#17, <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> </semantics> </math> is the location of the peak summit and <math display="inline"> <semantics> <mrow> <mi>CP</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> the associated cumulated profile value. (<b>b</b>) Root mean square error as a function of the <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>Width</mi> </mrow> </msub> </mrow> </semantics> </math> value for row width determination. (<b>c</b>) Comparison between the row width estimates with ground measurements.</p> "> Figure 12 Cont.
<p>(<b>a</b>) Illustration of row width determination for ESU#17, <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> </semantics> </math> is the location of the peak summit and <math display="inline"> <semantics> <mrow> <mi>CP</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> the associated cumulated profile value. (<b>b</b>) Root mean square error as a function of the <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>Width</mi> </mrow> </msub> </mrow> </semantics> </math> value for row width determination. (<b>c</b>) Comparison between the row width estimates with ground measurements.</p> "> Figure 13
<p>(<b>a</b>) Vegetation cover fraction for each ESU: ground measurements (green points), UAV estimates based on row width and row spacing (method M1, grey points), UAV estimates computed from the ratio of green pixels to the number of valid pixels (M2, yellow line). The percentage of missing row segments is shown in violet. Grey bars indicate the percentage of invalid pixels in the ESU. (<b>b</b>) Binary images for ESUs 9 and 18 (green: vegetation, brown: soil, white invalid).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Site
2.2. Ground Measurements of the Row Characteristics
2.3. Unmanned Aerial Vehicle (UAV) Measurements
2.4. Characterizing the Row Macro-Structure: Algorithm Overview
3. Fine-Tuning the Pre-Processing of Images (Step A)
3.1. Point Cloud Derived from Overlapping RGB Images
3.2. Deriving the Crop Height Model (CHM)
3.3. Dense Point Cloud Rasterization
3.4. Binary Image Generation: Separating the Row from the Background
4. Estimation of Vineyard Row Characteristics (Step B)
4.1. Row Height
4.2. Row Orientation
4.3. Row Spacing
4.4. Row Width
4.5. Cover Fraction and Percentage of Missing Row Segments
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Flight Number | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Sampled ESUs | 1–4 | 5–9 | 10–11 | 12–20 |
Surface of the flown area (ha) | 2.5 | 4.4 | 3.5 | 9.3 |
Flying Height 1 (m) | 89 | 89 | 202 | 71 |
GSD 2 (cm) | 2.8 | 2.8 | 6.4 | 2.2 |
Images (Nb/ha) | 81 | 36 | 15 | 33 |
Tie points 3 (Nb/m2) | 2.5 | 2.8 | 6.7 | 2.7 |
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Weiss, M.; Baret, F. Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure. Remote Sens. 2017, 9, 111. https://doi.org/10.3390/rs9020111
Weiss M, Baret F. Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure. Remote Sensing. 2017; 9(2):111. https://doi.org/10.3390/rs9020111
Chicago/Turabian StyleWeiss, Marie, and Frédéric Baret. 2017. "Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure" Remote Sensing 9, no. 2: 111. https://doi.org/10.3390/rs9020111