Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features
"> Figure 1
<p>The location of field plots, and airborne laser scanning (ALS) flight lines in the Mengjiagang Forest Farm, Heilongjiang Province.</p> "> Figure 2
<p>Examples of ALS point clouds for plots with different structural complexities. (<b>a</b>) A complex plot A1 that was 30 × 20 m<sup>2</sup> in size. (<b>b</b>) A simple plot B4 that was 30 × 30 m<sup>2</sup> in size.</p> "> Figure 3
<p>Workflow of the proposed method.</p> "> Figure 4
<p>The diagram of three established profiles in a region growing segmentation segment. (<b>a</b>) 3D view of the segment, where the vertical profile with a 1 m width was established based on the rotation axis that went through the max height point. (<b>b</b>) Top view of the segmentation segment, where the green profile represented the three profiles selected based on the aforesaid rules.</p> "> Figure 5
<p>Blue asterisk represents the upper canopy point cloud of the profile selected using 0.2 m width uniformly-spaced lags. The lags are shown as the green rectangle to obtain the 3 m upper canopy points in each profile. (<b>a</b>), (<b>b</b>), and (<b>c</b>) represent the first, second, and third selected profiles, respectively, using the point number rules.</p> "> Figure 6
<p>Gaussian fitting of three selected profiles, with the number of functions increasing from one to three. The figures show the upper canopy points as a blue asterisk, the combined model as a red line, and the model components using the single function as green imaginary lines. The magenta imaginary lines are the 99% confidence interval of the Gaussian fitting model.</p> "> Figure 6 Cont.
<p>Gaussian fitting of three selected profiles, with the number of functions increasing from one to three. The figures show the upper canopy points as a blue asterisk, the combined model as a red line, and the model components using the single function as green imaginary lines. The magenta imaginary lines are the 99% confidence interval of the Gaussian fitting model.</p> "> Figure 7
<p>The residual of one to three Gaussian fitting functions using 0.2 m width lags. The bar chart shows the difference between the Z-value of points in the 99% confidence interval of three Gaussian fitting functions and the fitting model.</p> "> Figure 7 Cont.
<p>The residual of one to three Gaussian fitting functions using 0.2 m width lags. The bar chart shows the difference between the Z-value of points in the 99% confidence interval of three Gaussian fitting functions and the fitting model.</p> "> Figure 8
<p>The result of the k-means clustering based on the tree number defined in <a href="#sec3dot3dot3-remotesensing-12-01078" class="html-sec">Section 3.3.3</a>. The asterisks represent the original tree points belonging to the segment; the different colored asterisks represent the k-means segmentation results. The circles represent the scaled-down canopy points colored using the k-means segmentation result.</p> "> Figure 9
<p>Tree segments of plot A1 (<b>a</b>,<b>c</b>) and B4 (<b>b</b>,<b>d</b>) generated using the region growing algorithm and morphology segmentation. In (a) and (b), a blue cross represents a measured single-wood position; a green boundary corresponds to a convex hull edge of a single tree derived from the region growing algorithm’s result; and a red boundary corresponds to tree segments that needed further refinement using morphology segmentation, where morphology segmentation successfully detected more than two trees in the region growing segments. (c) and (d) show the segmented point cloud, where the color of points indicates individual tree segments.</p> "> Figure 9 Cont.
<p>Tree segments of plot A1 (<b>a</b>,<b>c</b>) and B4 (<b>b</b>,<b>d</b>) generated using the region growing algorithm and morphology segmentation. In (a) and (b), a blue cross represents a measured single-wood position; a green boundary corresponds to a convex hull edge of a single tree derived from the region growing algorithm’s result; and a red boundary corresponds to tree segments that needed further refinement using morphology segmentation, where morphology segmentation successfully detected more than two trees in the region growing segments. (c) and (d) show the segmented point cloud, where the color of points indicates individual tree segments.</p> "> Figure 10
<p>The diagram of region growing algorithm segments.</p> "> Figure 11
<p>Examples of refinement results using morphology segmentation: (<b>a</b>) the segment contained two canopies with a large tree height difference, (<b>b</b>) the segment contained two canopies with relatively equal heights, (<b>c</b>) the refinement segment contained one over-segmentation crown and one complete crown, and (<b>d</b>) the unchanged segment contained one over-segmentation crown and one complete crown.</p> "> Figure 12
<p>A tree segment with more than three crowns contained a multiple-tree centralized distribution. (<b>a</b>) Points in the segment. (<b>b</b>–<b>d</b>) Gaussian fitting of the profile selected from the segment that contained three canopies, where the number of functions increased from one to three. Blue points represent the upper canopy points, with the combined model represented with a red line. The magenta lines are the 99% confidence interval of three Gaussian function fitting models.</p> ">
Abstract
:1. Introduction
2. Study Site and Datasets
2.1. Study Area
2.2. ALS Data
2.3. Ground Survey Data
3. Methods
3.1. Region Growing
3.2. Segments Filter
3.3. Tree Number Definition Using the Profile Morphology
3.3.1. Profile Establishment
3.3.2. Profile Selection
3.3.3. Tree Number Definition in Each Segment
3.4. k-Means Segmentation
3.5. Accuracy Evaluation
4. Results and Discussion
4.1. Accuracy of the ITC Segmentation
4.2. Discussion
4.2.1. Region Growing Segments
4.2.2. Morphology Segments
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Plot ID | Mean DBH (cm) | Mean Height (m) | Min Height (m) | Max Height (m) | Crown Width East–West (m) | Crown Width North–South (m) | Stem Density (stems/ha) |
---|---|---|---|---|---|---|---|
A1 | 25.76 | 24.23 | 14.8 | 28.8 | 5.26 | 4.86 | 1216 |
A2 | 31.48 | 28.93 | 23.1 | 33.1 | 5.01 | 5.00 | 1050 |
A3 | 23.85 | 22.82 | 19.3 | 28.1 | 4.89 | 5.02 | 1367 |
A4 | 21.67 | 22.19 | 17.3 | 26.2 | 4.21 | 3.36 | 1850 |
B1 | 19.48 | 21.11 | 14.8 | 25.9 | 2.98 | 3.06 | 1617 |
B2 | 17.65 | 20.58 | 15.1 | 26.4 | 2.65 | 2.78 | 1783 |
B3 | 16.69 | 19.66 | 14.1 | 24.3 | 2.88 | 3.03 | 1817 |
B4 | 15.56 | 19.92 | 13.1 | 27.2 | 2.77 | 2.80 | 2117 |
Autocorrelation Coefficient | Profile 1 | Profile 2 | Profile 3 |
---|---|---|---|
Gaussian function 1–2 | 0.6433 | 0. 9396 | 0. 9687 |
Gaussian function 2–3 | 0.9986 | 0.9505 | 0. 9362 |
Plot Information | Region Growing Method | Morphology Segmentation Method | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Plot ID | Measured Tree | Density (num/ha) | Segment Trees | TP | FN | FP | c | r | p | F | Segment trees | TP | FN | FP | c | r | p | F |
A1 | 63 | 1216 | 53 | 52 | 10 | 1 | 82.54% | 0.84 | 0.98 | 0.90 | 60 | 58 | 3 | 2 | 92.06% | 0.95 | 0.97 | 0.96 |
A2 | 73 | 1050 | 68 | 67 | 5 | 1 | 91.78% | 0.93 | 0.99 | 0.96 | 72 | 70 | 3 | 2 | 95.89% | 0.96 | 0.97 | 0.97 |
A3 | 82 | 1367 | 67 | 66 | 13 | 1 | 80.49% | 0.84 | 0.99 | 0.90 | 79 | 74 | 7 | 4 | 90.24% | 0.91 | 0.95 | 0.93 |
A4 | 111 | 1850 | 78 | 75 | 30 | 2 | 67.57% | 0.71 | 0.97 | 0.82 | 97 | 93 | 8 | 4 | 83.78% | 0.92 | 0.96 | 0.94 |
B1 | 97 | 1617 | 77 | 74 | 20 | 2 | 76.29% | 0.79 | 0.97 | 0.87 | 92 | 82 | 13 | 8 | 84.54% | 0.86 | 0.91 | 0.89 |
B2 | 107 | 1783 | 86 | 79 | 22 | 3 | 73.83% | 0.78 | 0.96 | 0.86 | 96 | 89 | 12 | 6 | 83.18% | 0.88 | 0.94 | 0.91 |
B3 | 109 | 1817 | 8 | 73 | 28 | 3 | 65.14% | 0.72 | 0.96 | 0.82 | 98 | 91 | 13 | 4 | 83.49% | 0.88 | 0.96 | 0.91 |
B4 | 128 | 2117 | 82 | 80 | 32 | 1 | 62.50% | 0.71 | 0.99 | 0.83 | 114 | 106 | 19 | 6 | 82.81% | 0.85 | 0.95 | 0.89 |
Total | 770 | / | 580 | 566 | 160 | 8 | 73.50% | 0.78 | 0.99 | 0.87 | 707 | 663 | 78 | 36 | 86.10% | 0.89 | 0.95 | 0.92 |
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Ma, Z.; Pang, Y.; Wang, D.; Liang, X.; Chen, B.; Lu, H.; Weinacker, H.; Koch, B. Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features. Remote Sens. 2020, 12, 1078. https://doi.org/10.3390/rs12071078
Ma Z, Pang Y, Wang D, Liang X, Chen B, Lu H, Weinacker H, Koch B. Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features. Remote Sensing. 2020; 12(7):1078. https://doi.org/10.3390/rs12071078
Chicago/Turabian StyleMa, Zhenyu, Yong Pang, Di Wang, Xiaojun Liang, Bowei Chen, Hao Lu, Holger Weinacker, and Barbara Koch. 2020. "Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features" Remote Sensing 12, no. 7: 1078. https://doi.org/10.3390/rs12071078