A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling
"> Figure 1
<p>(<b>a</b>) LiDAR (Light Detection and Ranging) sensor and GNSS (Global Navigation Satellite System) receiver mounted on an all-terrain vehicle; and (<b>b</b>) diagram of LiDAR sensor 2D scanning and displacement along the alleys.</p> "> Figure 2
<p>(<b>a</b>) <b><span class="html-italic">dx</span></b> and <b><span class="html-italic">dy</span></b> deviations based on the angle <b><span class="html-italic">α</span></b> and <b><span class="html-italic">dxy</span></b>; and (<b>b</b>) <b><span class="html-italic">dxy</span></b> based on distance d and angle <b><span class="html-italic">β</span></b>.</p> "> Figure 3
<p>(<b>a</b>) Original point cloud from one tree row; and (<b>b</b>) point cloud after the filtering process.</p> "> Figure 4
<p>(<b>a</b>) Top view of a non-classified point cloud from orange trees; (<b>b</b>) clustering classification into groups each representing an individual tree; and (<b>c</b>) classification of points in transversal sections along the row.</p> "> Figure 5
<p>Point clouds from objects scanned with a mobile terrestrial laser scanner mounted on an all-terrain vehicle: (<b>a</b>) cylinder; (<b>b</b>) body of cone; (<b>c</b>) square; (<b>d</b>) triangle; and (<b>e</b>) circle.</p> "> Figure 6
<p>3D georeferenced point cloud derived from the developed mobile terrestrial laser scanning system used in a 25 ha commercial orange grove.</p> "> Figure 7
<p>Convex-hull and alpha-shape algorithms to model orange trees according to two approaches: clusters (individual trees) and transversal sections of the row (0.26 m long). The top picture is an image of the point cloud.</p> "> Figure 8
<p>Detail of the convex-hull and alpha-shape models over a single 0.26 m long transversal section of a tree row.</p> "> Figure 9
<p>Top view of the convex-hull modeling over transversal sections of an orange tree row.</p> "> Figure 10
<p>3D canopy structure of a single tree modeled according to different algorithms. The top picture is an image of the point cloud.</p> "> Figure 11
<p>Shapefiles produced after either segmenting the row into transversal sections or into individual trees.</p> "> Figure 12
<p>Canopy volume and height maps generated according to the two data processing methods: Method 1, classifying the point cloud into individual trees (cluster) and subsequently applying the alpha-shape algorithm (α = 0.75); and Method 2, dividing the rows into 0.26 m long sections and further applying the convex-hull algorithm.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of Data Acquisition and Processing
2.1.1. The Equipment and Data Acquisition
2.1.2. Data Processing
2.2. Demonstrating and Evaluating the Proposed Method
2.2.1. Validation of the Point Cloud Accuracy—Laboratory Testing
2.2.2. Data Acquisition in a Commercial Orange Grove
2.2.3. Evaluating Point Cloud Classification and 3D Modeling Options
2.2.4. Mapping of Canopy Volume and Height of a Commercial Orange Grove
3. Results and Discussion
3.1. Validation of the Point Cloud Accuracy—Laboratory Testing
3.2. Data Acquisition in a Commercial Grove
3.3. Modeling of 3D Objects from the Point Cloud
3.4. Mapping of Canopy Volume and Height in a Commercial Orange Grove
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Objects | a | b | c | ||||||
---|---|---|---|---|---|---|---|---|---|
(i) | (ii) | (iii) | (i) | (ii) | (iii) | (i) | (ii) | (iii) | |
(cm) | |||||||||
Square | 98.60 | 98.29 | 100.00 | 94.23 | 100.25 | 100.00 | - | - | - |
Triangle | 98.22 | 98.33 | 100.00 | 84.64 | 88.48 | 87.00 | - | - | - |
Circle | 101.17 | 99.96 | 100.00 | - | - | - | - | - | - |
Cylinder I | 79.58 | 79.78 | 80.00 | 29.58 | 30.13 | 30.00 | - | - | - |
Cylinder II | 80.92 | 83.21 | 83.00 | 19.74 | 20.28 | 20.00 | - | - | - |
Body of cone | 62.65 | 63.31 | 64.00 | 44.95 | 45.05 | 45.00 | 31.15 | 30.90 | 31.00 |
Algorithm | Number of Sections Per Tree | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 3 | 5 | 7 | 10 | ||||||
Mean Canopy Volume of 25 Individual Trees (m3) | ||||||||||
Cube-fit | 22.65 | a | - | - | - | - | ||||
Cylinder-fit | 11.90 | c | - | - | - | - | ||||
Convex-hull | 16.07 | b,a | 14.71 | a,ab | 13.97 | a,ab | 13.46 | a,ab | 12.86 | a,b |
α-shape (α = 0.75) | 14.31 | c,a | 11.57 | b,b | 10.02 | b,bc | 8.66 | b,cd | 7.34 | b,d |
α-shape (α = 0.50) | 12.16 | c,a | 9.77 | b,b | 8.19 | b,bc | 6.98 | c,cd | 5.72 | c,d |
α-shape (α = 0.25) | 6.19 | d,a | 5.32 | c,b | 4.47 | c,bc | 4.15 | d,c | 3.34 | d,d |
Canopy Variable | Method * | Mean | Minimum | Maximum | Coef. of Variation |
---|---|---|---|---|---|
m3 | |||||
Volume | 1 | 11.94 | 7.64 | 18.57 | 0.09 |
2 | 12.13 | 8.05 | 17.30 | 0.09 | |
m | |||||
Height | 1 | 2.85 | 2.47 | 3.39 | 0.03 |
2 | 2.87 | 2.44 | 3.43 | 0.04 |
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Colaço, A.F.; Trevisan, R.G.; Molin, J.P.; Rosell-Polo, J.R.; Escolà, A. A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling. Remote Sens. 2017, 9, 763. https://doi.org/10.3390/rs9080763
Colaço AF, Trevisan RG, Molin JP, Rosell-Polo JR, Escolà A. A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling. Remote Sensing. 2017; 9(8):763. https://doi.org/10.3390/rs9080763
Chicago/Turabian StyleColaço, André F., Rodrigo G. Trevisan, José P. Molin, Joan R. Rosell-Polo, and Alexandre Escolà. 2017. "A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling" Remote Sensing 9, no. 8: 763. https://doi.org/10.3390/rs9080763