Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest
<p>Study area and point clouds for four forest types: (<b>a</b>) deciduous forest stand; (<b>b</b>) conifer-1 forest stand; (<b>c</b>) mixed forest stand; (<b>d</b>) conifer-2 forest stand. The background picture is a panchromatic aerial photo of Washington Park Arboretum overlaid with corresponding point cloud data (red dots). The 3-D perspective view of point cloud data (green points) are shown next to each plot.</p> ">
<p>Flowchart of forest inventory parameters estimation from TLS. The dash line box represents the TLS-based forest inventory parameters which were compared with field-based manual measurements.</p> ">
<p>Dimensional information for each voxel is shown in (<b>a</b>); the voxel data structure (2 × 2 × 2) visualization is shown in (<b>b</b>); the schematic diagram for the PCS is shown in (<b>c</b>); and, the voxel-based PCS plane for an individual tree PCD is shown in (<b>d</b>).</p> ">
<p>Schematic diagram of occlusion is shown in (<b>a</b>), and 3-D cylinder primitive fitting technique is shown in (<b>b</b>).</p> ">
<p>Thirteen horizontal slice planes for individual tree PCD at a 2 m distance interval with 0.01 steps (Z values represent the height of each slice plane at meters unit).</p> ">
<p>Comparison between measured and modeled stem location and DBH approximations for four forest stands: (<b>a</b>) conifer-2 forest stand; (<b>b</b>) conifer-1 forest stand; (<b>c</b>) deciduous forest stand; and (<b>d</b>) mixed forest stand. Solid circles represent the measured stem location, star symbols represent modeled stem location, solid line circles represent measured DBH approximation, and the dashed line circles represent modeled DBH approximation.</p> ">
<p>Comparison between measured and modeled stem location and DBH approximations for four forest stands: (<b>a</b>) conifer-2 forest stand; (<b>b</b>) conifer-1 forest stand; (<b>c</b>) deciduous forest stand; and (<b>d</b>) mixed forest stand. Solid circles represent the measured stem location, star symbols represent modeled stem location, solid line circles represent measured DBH approximation, and the dashed line circles represent modeled DBH approximation.</p> ">
<p>Comparison between measured and modeled height (in m) using PCS; solid line is a prediction model for TLS- based height (N = 25, RMSE = 0.75 m, p < 0.001).</p> ">
<p>Comparison between measured and modeled DBH (in cm) using the PCS for the four forest stands; the solid line is a linear prediction model for TLS-based DBH (N = 25, RMSE = 9.1739 cm, p < 0.001).</p> ">
<p>Schematic diagram for canopy volume estimation from TLS where: (<b>a</b>) is the raw PCD visualization result with bounding box; (<b>b</b>) is the voxel-based canopy volume visualization results with 10 times amplification of sampling spacing for each voxel size; and, (<b>c</b>) is the voxel-based canopy volume visualization results with the sampling spacing voxel size. In (b) and (c) the wireframe (top) and surface representation forms of voxels are shown as well.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection
2.3. Algorithm Development
2.3.1. Voxel Modeling
2.3.2. Terrain Model for Normalizing PCD
2.4. Point Cloud Slicing (PCS)
2.4.1. DBH, Basal Area, and Tree Heights Estimation
2.4.2. Stem and Canopy Volume Estimation
3. Results
3.1. Horizontal PCS for an Individual Tree
3.2. DBH and Stem Location Estimation
3.3. Basal Area and Tree Height Estimation
3.4. Canopy and Stem Volume Estimation
4. Discussion
5. Conclusions
Acknowledgments
References
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Conifer-1 | Conifer-2 | Deciduous | Mixed | |||||
---|---|---|---|---|---|---|---|---|
Field-TBA | TLS-TBA | Field-TBA | TLS-TBA | Field-TBA | TLS-TBA | Field-TBA | TLS-TBA | |
0.1122 | 0.0951 | 1.7979 | 1.0405 | 0.279 | 0.1379 | 0.6866 | 0.4632 | |
0.2148 | 0.1979 | 2.4522 | 1.7554 | 0.1548 | 0.1445 | 0.3308 | 0.1995 | |
— | 0.2215 | 2.7523 | 1.7064 | 0.6151 | 0.4681 | 0.2624 | 0.219 | |
0.2615 | 0.2481 | 1.9433 | 0.9331 | 0.2715 | 0.1878 | 0.3494 | 0.3536 | |
0.7451 | 0.7375 | 1.7932 | 0.7118 | 0.1069 | 0.0789 | 0.026 | 0.0139 | |
0.6793 | 0.6319 | 1.5725 | 0.7744 | 0.1878 | 0.3257 | |||
0.2725 | 0.2971 | |||||||
0.4441 | 0.4383 | |||||||
SBAA | 2.7295 | 2.8673 | 12.3116 | 6.9217 | 1.4275 | 1.0172 | 1.8431 | 1.575 |
Coefficient | Estimate | Standard Error | t-Value | P-Value | |
---|---|---|---|---|---|
DBH-prediction | Intercept | 13.993 | 4.299 | 3.255 | 0.003 |
Manually measured-based DBH | 0.682 | 0.044 | 15.407 | 0.000 | |
Height-prediction | Intercept | 8.436 | 3.177 | 2.656 | 0.014 |
TLS-based tree height | 0.609 | 0.110 | 5.552 | 0.000 |
BP Value | Degree of Freedom | P-Value | Chi-Square Value (95% level) | |
---|---|---|---|---|
TLS-DBH prediction | 1.725 | 1 | 0.189 | 3.841 |
TLS-height prediction | 0.293 | 1 | 0.585 | 3.841 |
Conifer-1 | Conifer-2 | Deciduous | Mixed | |||||
---|---|---|---|---|---|---|---|---|
Field-STV | TLS-STV | Field-STV | TLS-STV | Field-STV | TLS-STV | Field--STV | TLS-STV | |
0.88 | 0.74 | 22.73 | 13.15 | 4.40 | 2.97 | 2.23 | 1.10 | |
— | 1.69 | 26.08 | 18.67 | 3.22 | 1.94 | 0.78 | 0.72 | |
1.58 | 1.53 | 35.37 | 21.93 | 2.94 | 2.45 | 4.93 | 3.75 | |
2.43 | 2.20 | 24.20 | 11.62 | 3.42 | 3.46 | 2.03 | 1.40 | |
7.65 | 6.37 | 24.61 | 9.77 | 0.16 | 0.09 | 0.59 | 0.44 | |
7.06 | 6.16 | 22.06 | 10.86 | 1.55 | 2.69 | |||
3.04 | 2.95 | |||||||
3.65 | 3.94 | |||||||
FPV t | 21.40 | 25.58+0.0045 | 155.06 | 86.01+0.0038 | 15.69 | 13.60+0.0017 | 10.56 | 7.42+0.0037 |
Share and Cite
Moskal, L.M.; Zheng, G. Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest. Remote Sens. 2012, 4, 1-20. https://doi.org/10.3390/rs4010001
Moskal LM, Zheng G. Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest. Remote Sensing. 2012; 4(1):1-20. https://doi.org/10.3390/rs4010001
Chicago/Turabian StyleMoskal, L. Monika, and Guang Zheng. 2012. "Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest" Remote Sensing 4, no. 1: 1-20. https://doi.org/10.3390/rs4010001