Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests
"> Figure 1
<p>The shape and spatial distribution of field sampling plots within the coverage of lidar data. The background true color image was from GoogleEarth<sup>TM</sup>. (<b>a</b>) the shape and size of field sampling plots; (<b>b</b>) the spatial distribution of field sampling plots, the white polygon was the area covered by Unmanned Aerial Vehicle (UAV) stereo imagery.</p> "> Figure 2
<p>Generation of forest aboveground biomass (AGB) reference using Lidar data. (<b>a</b>) Model development (32 plots); (<b>b</b>) model validation (31 plots); (<b>c</b>) forest AGB map with a resolution of 15 m from lidar data.</p> "> Figure 3
<p>Ortho-rectified mosaic images and digital surface model (DSM) of UAV stereo imagery acquired in leaf-on and leaf-off conditions (pixel size 8.6 cm). (<b>a</b>) the mosaic image of leaf-on with the flying trajectory showed as yellow lines; (<b>b</b>) the mosaic image of leaf-off with ground control points (GCPs) used in the stereoscopic processing showed as yellow dots; (<b>c</b>) the digital surface model of stereo imagery acquired on leaf-on season, the dash and solid lines indicates positions of two profiles used in the following sections; (<b>d</b>) the digital surface model of stereo imagery acquired on leaf-off season;.</p> "> Figure 4
<p>Elevation of ground surfaces extracted from the point cloud of UAV stereo imagery of leaf-off filtered by the method of iterative median filter (IMF). (<b>a</b>) The number and percentage of point removed at each iteration; (<b>b</b>) the results of each iteration along a vertical profile; (<b>c</b>) the results of each iteration over a 30 m × 30 m plot; (<b>d</b>) profiles along the white dash line in <a href="#remotesensing-11-00889-f003" class="html-fig">Figure 3</a>c with an approximate length of 1150 m; (<b>e</b>) the profiles along the yellow solid line in <a href="#remotesensing-11-00889-f003" class="html-fig">Figure 3</a>c with an approximate length of 600 m.</p> "> Figure 5
<p>Forest spatial structures extracted by the synergy of point cloud of leaf-on stereo imagery and the filtered point cloud of leaf-off stereo imagery. (<b>a</b>) leaf-on UAV mean canopy height model (MCHM), i.e., MCHM extracted from point cloud of leaf-on stereo imagery subtracted by the digital terrain model (DTM) produced by IMF; (<b>b</b>) lidar MCHM, the MCHM from lidar data; (<b>c</b>) profiles of leaf-on UAV MCHM and lidar MCHM along the same line as <a href="#remotesensing-11-00889-f004" class="html-fig">Figure 4</a>d; (<b>d</b>) profiles of leaf-on UAV MCHM and lidar MCHM along the same line as <a href="#remotesensing-11-00889-f004" class="html-fig">Figure 4</a>e.</p> "> Figure 6
<p>The sampling plots of forest AGB used for model developments. (<b>a</b>) the spatial distributions of sampling plots, type A: yellow dots, type B: blue dots, type C: yellow stars, type D: blue stars; the background image was the same as <a href="#remotesensing-11-00889-f006" class="html-fig">Figure 6</a>a; (<b>b</b>) the relative positions of sampling plots with different sizes.</p> "> Figure 7
<p>The MCHM of 45 m × 45 m field sampling plots. (<b>a</b>–<b>d</b>) were the MCHM of UAV stereo imagery with a resolution of 0.5 m corresponding to the sampling plots of A1, B1, C1, and D1. (<b>e</b>–<b>h</b>) were the vertical distribution of points corresponding to (<b>a</b>–<b>d</b>).</p> "> Figure 8
<p>Comparisons of forest AGB estimated from lidar and UAV stereo data using models developed at different plot sizes (24 plots for each plot size, i.e. 4 types ×6 plots/types). RMSE, root mean square error.</p> "> Figure 9
<p>Validation of forest AGB maps taking those from lidar as references (574 points). Points (<b>a</b>–<b>c</b>) are the results using forest AGB maps from the predictions model developed on sampling plots of size 15 m × 15 m, 30 m × 30 m and 45 m × 45 m, respectively.</p> ">
Abstract
:1. Introduction
2. Test Sites and Data Preparation
2.1. Test Sites
2.2. Inventory of Ground Reference
2.3. Forest AGB Maps from Lidar Data
2.4. Collections of UAV Stereo Imagery
2.5. Stereoscopic Processing
3. Methods
3.1. Extraction of Canopy Height Model
3.2. Mapping of Forest AGB Using MCHM of UAV Stereo Imagery
4. Results
4.1. UAV Stereo Imagery
4.2. Extraction of Forest Canopy Heights
4.3. Model Development and Validation of Forest AGB Maps of UAV
5. Discussions
5.1. Extraction of Ground Surface
5.2. The applicable Areas of UAV Stereo Imagery
5.3. Uncertainties
6. Conclusion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Species | Allometric Equations * |
---|---|
white birch | |
larch |
GCP# | X Error | Y Error | Z Error | GCP# | X Error | Y Error | Z Error |
---|---|---|---|---|---|---|---|
leaf-off p1 | 0.05 | 0.24 | −0.98 | leaf-on p1 | −0.13 | −0.23 | −1.11 |
leaf-off p2 | 0.26 | −0.90 | 0.90 | leaf-on p2 | 0.20 | −0.35 | 0.96 |
leaf-off p3 | 0.08 | −0.18 | 0.34 | leaf-on p3 | −0.34 | −0.13 | 0.48 |
leaf-off p4 | 0.80 | 0.44 | −0.08 | leaf-on p4 | 0.67 | 0.22 | 0.37 |
leaf-off p5 | −0.01 | 0.38 | −0.92 | leaf-on p5 | −0.13 | 0.60 | −0.54 |
leaf-off p6 | 0.01 | 0.29 | 1.0 | leaf-on p6 | −0.02 | −0.13 | 0.72 |
leaf-off p7 | −1.18 | −0.28 | −0.27 | leaf-on p7 | −0.26 | 0.01 | −0.87 |
Standard deviation | 0.59 | 0.48 | 0.80 | Standard deviation | 0.34 | 0.32 | 0.83 |
Plot Size | a | b | R2 | RMSE (Mg/ha) | Relative RMSE |
---|---|---|---|---|---|
15 m | 5.569 | 1.020 | 0.94 | 11.4 | 21.5% |
30 m | 4.637 | 1.074 | 0.94 | 9.6 | 18.1% |
45 m | 4.831 | 1.056 | 0.95 | 8.2 | 15.5% |
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Ni, W.; Dong, J.; Sun, G.; Zhang, Z.; Pang, Y.; Tian, X.; Li, Z.; Chen, E. Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests. Remote Sens. 2019, 11, 889. https://doi.org/10.3390/rs11070889
Ni W, Dong J, Sun G, Zhang Z, Pang Y, Tian X, Li Z, Chen E. Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests. Remote Sensing. 2019; 11(7):889. https://doi.org/10.3390/rs11070889
Chicago/Turabian StyleNi, Wenjian, Jiachen Dong, Guoqing Sun, Zhiyu Zhang, Yong Pang, Xin Tian, Zengyuan Li, and Erxue Chen. 2019. "Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests" Remote Sensing 11, no. 7: 889. https://doi.org/10.3390/rs11070889