An Advanced Framework for Multi-Scale Forest Structural Parameter Estimations Based on UAS-LiDAR and Sentinel-2 Satellite Imagery in Forest Plantations of Northern China
"> Figure 1
<p>The workflow of this study. The approaches of forest structural parameter estimations in multi-scales (from individual tree level to landscape level) were assessed by using field data, UAS-LiDAR transects and Sentinel-2A imagery.</p> "> Figure 2
<p>Overview of study area and distribution of sample plots, the triangle areas are the UAS LiDAR sampling area (total 19 sample areas), the green dots are Larch plots and the orange dots are Chinese pine plots (<b>A</b>); Digital Aircraft photogrammetry of a sample area (one of 19 sample areas) (<b>B</b>); The digital elevation model (DEM) generated by LiDAR data of a sample area (one of 19 sample areas) (<b>C</b>).</p> "> Figure 3
<p>Individual tree segmentation results by PCS algorithm in typical sample plots. (<b>a1</b>–<b>c1</b>,<b>a2</b>–<b>c2</b>) are the vertical view of individual tree segmentation, the red boundary represents the edge of individual tree crown. (<b>d1</b>–<b>f1</b>,<b>d2</b>–<b>f2</b>) are the point clouds of sample plots. (<b>g1</b>–<b>i1</b>,<b>g2</b>–<b>i2</b>) are the three-dimensional view of individual tree segmentation results, the individual tree is rendered in different colors.</p> "> Figure 4
<p>The tree height comparison scatter plot between typical sample plots of <span class="html-italic">Larch</span> and <span class="html-italic">Chinese pine</span> extracted by the PCS algorithm ((<b>a1</b>–<b>f1</b>) are <span class="html-italic">Larch</span> sample plots and (<b>a2</b>–<b>f2</b>) are <span class="html-italic">Chinese pine</span> sample plots).</p> "> Figure 5
<p>The scatterplots of forest structural parameters predicted by approach 1 and the importance ranking of the variables for forest structural parameters estimation by approach 1 (76 plots). (<b>A</b>,<b>E</b>) are DBH; (<b>B</b>,<b>F</b>) are height; (<b>C</b>,<b>G</b>) are volume; (<b>D</b>,<b>H</b>) are AGB. Note: the detail of approach 1; see <a href="#remotesensing-14-03023-f001" class="html-fig">Figure 1</a>.</p> "> Figure 6
<p>Wall-to-wall map of forest structural parameters distribution in landscape level by approach 1. (<b>A</b>) is DBH; (<b>B</b>) is tree height; (<b>C</b>) is volume; and (<b>D</b>) is AGB. Note: the detail of approach 1; see <a href="#remotesensing-14-03023-f002" class="html-fig">Figure 2</a>.</p> "> Figure 7
<p>The scatterplots of forest structural parameters predicted by approach 2 and the importance ranking of variables for forest structural parameters estimation by approach 2. (<b>A1</b>–<b>H1</b>) are stage 1, and (<b>A2</b>–<b>H2</b>) are stage 2. (<b>A</b>,<b>E</b>) are DBH; (<b>B</b>,<b>F</b>) are height; (<b>C</b>,<b>G</b>) are volume; (<b>D</b>,<b>H</b>) are AGB. Note: the detail of approach 2; see <a href="#remotesensing-14-03023-f001" class="html-fig">Figure 1</a>.</p> "> Figure 8
<p>Wall-to-wall map of forest structural parameters distribution at landscape level by approach 2. (<b>A</b>) is DBH; (<b>B</b>) is tree height; (<b>C</b>) is volume; and (<b>D</b>) is AGB. Note: the detail of approach 2; see <a href="#remotesensing-14-03023-f002" class="html-fig">Figure 2</a>.</p> "> Figure 9
<p>The scatterplots of forest structural parameters predicted by approach 3 and the importance ranking of variables for forest structural parameters estimation by approach 3 (76 plots). (<b>A</b>,<b>E</b>) are DBH; (<b>B</b>,<b>F</b>) are height; (<b>C</b>,<b>G</b>) are volume; (<b>D</b>,<b>H</b>) are AGB. Note: the detail of approach 3; see <a href="#remotesensing-14-03023-f001" class="html-fig">Figure 1</a>.</p> "> Figure 10
<p>Wall-to-wall map of UAS-LiDAR metrics distribution predicted by Sentinel-2 vegetation indices ((<b>A</b>) is h25; (<b>B</b>) is h50; (<b>C</b>) is h75; and (<b>D</b>) is h95. Note: the detail of UAS-LiDAR metrics; see <a href="#remotesensing-14-03023-t002" class="html-table">Table 2</a>) and up-scaling forest structural parameters distribution by approach 3 in landscape level ((<b>E</b>) is DBH; (<b>F</b>) is tree height; (<b>G</b>) is volume; and (<b>H</b>) is AGB. Note: the detail of approach 3; see <a href="#remotesensing-14-03023-f002" class="html-fig">Figure 2</a>).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Data
2.3. UAS-LiDAR Data Acquisition and Pre-Processing
2.4. The Metrics Derived from UAS-LiDAR Data
2.5. Sentinel-2 Data Acquisition and Pre-Processing
2.6. Vegetation Indices Derived from Sentinel-2 Data
2.7. Individual Tree Crown Segmentation and Tree Height Extracted
2.8. Estimation of Forest Structural Parameters by Random Forest
2.9. Forest Structural Parameters Upscaling Estimation Approaches
2.10. Model Accuracy Evaluation
3. Results
4. Discussion
4.1. Estimation of Forest Structural Parameters by UAS-LiDAR
4.2. Estimation of Forest Structural Parameters by UAS-LiDAR Combined with Multi-Spectral Imageries
4.3. Sample Amplification Using UAS-LiDAR Data
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Tree Species | Tree Height (m) | DBH (cm) | Volume (m3·ha−1) | AGB (Mg·ha−1) |
---|---|---|---|---|
Larch | 5.9–20.3 | 6.2–29.2 | 62.5–375.0 | 28.8–157.1 |
Chinese pine | 7.8–19.6 | 9.8–31.2 | 108.6–372.4 | 77.0–169.8 |
Parameters | Value |
---|---|
Weight (kg) | 15.5 |
Flight height (m) | 60 |
Maximum flight speed (m/s) | 10 |
Cruising radius (km) | 2 |
LiDAR type | Rigel VUX-1 |
Scanning angle (°) | ±60° |
Point density (pts/m3) | ≥25 |
Wavelength (nm) | 1055 |
Scanning frequency (Hz) | 10–200 |
Measurement range (m) | 3–920 |
The resolution of scanning angle (°) | 0.001 |
Metrics | Description |
---|---|
h25, h50, h75, h95 | The percentiles of the canopy height distributions by first echo (25th, 50th, 75th, and 95th) |
hmean | The mean height of all points after normalization |
hcv | The coefficient of variation of height of all points after normalized (the ratio of the standard deviation to the mean) |
hskewness, hkurtosis | The skewness and kurtosis of the heights of all points by first echo |
d1, d3, d5, d7, d9 | The proportion of points above the quantiles (10th, 30th, 50th, 70th, and 90th) to total number of points |
Vegetation Indices | Formula | Reference |
---|---|---|
Normalized Difference Vegetation Index (NDVI) | (B7 − B4)/(B7 + B4) | [47] |
MERIS Terrestrial Chlorophyll Index (MTCI) | (B6 − B5)/(B5 − B4) | [48] |
Water Index (WI) | B8a/B9 | [49] |
Corrected Transformed Vegetation Index (CTVI) | [50] | |
Enhanced Vegetation Index Red Edge 1(B5) (EVIRE1) | 2.5 × (B5 − B4)/(1 + B5 + 6 × B4 − 7.5 × B2) | [51] |
Enhanced Vegetation Index Red Edge 2(B6) (EVIRE2) | 2.5 × (B6 − B4)/(1 + B6 + 6 × B4 − 7.5 × B2) | [52] |
Nir infrared Enhanced Vegetation Index (EVINI) | 2.5 × (B8 − B4)/(1 + B8 + 6 × B4 − 7.5 × B2) | [53] |
Modified Simple Ratio Nir infrared (MSRNIR) | [54] | |
Modified Simple Ratio Red Edge 3(B7) (MSRRE3) | [55] | |
Modified Simple Ratio Red Edge 4 (B8a) (MSRRE4) | [56] | |
Nonlinear Index Red Edge 1(B5) (NLIRE1) | (B52 − B4)/(B52 + B4) | [57] |
Nonlinear Index Red Edge 2(B6) (NLIRE2) | (B62 − B4)/(B62 + B4) | [58] |
Nonlinear Index Near infrared (NLINIR) | (B82 − B4)/(B82 + B4) | [59] |
Nonlinear Index Red Edge 4(B8a) (NLIRE4) | (B8a2 − B4)/(B8a2 + B4) | [60] |
Sample Plots | The Number of Individual Tree | Accurate Segmentation | Over Segmentation | Omission Segmentation | r | p | f |
---|---|---|---|---|---|---|---|
P1 | 30 | 29 | 2 | 1 | 0.97 | 0.94 | 0.95 |
P2 | 88 | 79 | 6 | 9 | 0.90 | 0.93 | 0.91 |
P3 | 90 | 80 | 6 | 10 | 0.89 | 0.93 | 0.91 |
P4 | 95 | 78 | 9 | 17 | 0.82 | 0.90 | 0.86 |
P5 | 130 | 102 | 12 | 28 | 0.78 | 0.89 | 0.84 |
P6 | 135 | 104 | 13 | 31 | 0.77 | 0.89 | 0.83 |
P7 | 140 | 107 | 17 | 33 | 0.76 | 0.86 | 0.81 |
P8 | 158 | 125 | 23 | 33 | 0.79 | 0.84 | 0.82 |
P9 | 178 | 133 | 34 | 45 | 0.74 | 0.80 | 0.77 |
P10 | 20 | 19 | 0 | 1 | 0.95 | 1.00 | 0.97 |
P11 | 29 | 27 | 2 | 2 | 0.93 | 0.93 | 0.93 |
P12 | 86 | 80 | 5 | 6 | 0.93 | 0.94 | 0.94 |
P13 | 94 | 84 | 8 | 10 | 0.89 | 0.91 | 0.90 |
P14 | 97 | 85 | 8 | 12 | 0.88 | 0.91 | 0.89 |
P15 | 108 | 93 | 11 | 15 | 0.86 | 0.89 | 0.88 |
P16 | 109 | 94 | 8 | 15 | 0.83 | 0.90 | 0.86 |
P17 | 140 | 125 | 14 | 15 | 0.82 | 0.88 | 0.85 |
P18 | 146 | 129 | 15 | 17 | 0.84 | 0.88 | 0.86 |
P19 | 151 | 131 | 14 | 20 | 0.81 | 0.88 | 0.84 |
Plot Number | Accuracy | Tree Height (m) | DBH (cm) | Volume (m3·ha−1) | AGB (Mg·ha−1) |
---|---|---|---|---|---|
100 | R2 | 0.76 | 0.73 | 0.72 | 0.71 |
rRMSE | 12.5% | 15.6% | 17.2% | 17.9% | |
200 | R2 | 0.75 | 0.74 | 0.72 | 0.71 |
rRMSE | 12.3% | 15.1% | 16.6% | 18.0% | |
300 | R2 | 0.77 | 0.76 | 0.73 | 0.73 |
rRMSE | 12.7% | 14.8% | 16.5% | 16.2% | |
400 | R2 | 0.78 | 0.77 | 0.74 | 0.72 |
rRMSE | 11.9% | 14.1% | 15.8% | 16.4% | |
500 | R2 | 0.80 | 0.77 | 0.75 | 0.74 |
rRMSE | 11.5% | 13.6% | 15.1% | 15.2% | |
600 | R2 | 0.80 | 0.78 | 0.75 | 0.76 |
rRMSE | 11.3% | 12.7% | 15.2% | 14.9% | |
700 | R2 | 0.81 | 0.78 | 0.75 | 0.77 |
rRMSE | 11.2% | 13.1% | 15.1% | 15.1% | |
800 | R2 | 0.81 | 0.79 | 0.76 | 0.76 |
rRMSE | 11.0% | 12.5% | 15.2% | 15.2% |
Approaches | Accuracy Indicator | Tree Height (m) | DBH (cm) | Volume (m3·ha−1) | AGB (Mg·ha−1) |
---|---|---|---|---|---|
Approach 1 | R2 | 0.64 | 0.61 | 0.60 | 0.62 |
rRMSE | 24.1% | 22.9% | 27.3% | 25.9% | |
Approach 2 | R2 | 0.78 | 0.75 | 0.73 | 0.73 |
rRMSE | 14.5% | 16.1% | 18.8% | 19.6% | |
Approach 3 | R2 | 0.77 | 0.73 | 0.72 | 0.70 |
rRMSE | 17.2% | 18.5% | 17.1% | 20.2% |
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Wu, X.; Shen, X.; Zhang, Z.; Cao, F.; She, G.; Cao, L. An Advanced Framework for Multi-Scale Forest Structural Parameter Estimations Based on UAS-LiDAR and Sentinel-2 Satellite Imagery in Forest Plantations of Northern China. Remote Sens. 2022, 14, 3023. https://doi.org/10.3390/rs14133023
Wu X, Shen X, Zhang Z, Cao F, She G, Cao L. An Advanced Framework for Multi-Scale Forest Structural Parameter Estimations Based on UAS-LiDAR and Sentinel-2 Satellite Imagery in Forest Plantations of Northern China. Remote Sensing. 2022; 14(13):3023. https://doi.org/10.3390/rs14133023
Chicago/Turabian StyleWu, Xiangqian, Xin Shen, Zhengnan Zhang, Fuliang Cao, Guanghui She, and Lin Cao. 2022. "An Advanced Framework for Multi-Scale Forest Structural Parameter Estimations Based on UAS-LiDAR and Sentinel-2 Satellite Imagery in Forest Plantations of Northern China" Remote Sensing 14, no. 13: 3023. https://doi.org/10.3390/rs14133023
APA StyleWu, X., Shen, X., Zhang, Z., Cao, F., She, G., & Cao, L. (2022). An Advanced Framework for Multi-Scale Forest Structural Parameter Estimations Based on UAS-LiDAR and Sentinel-2 Satellite Imagery in Forest Plantations of Northern China. Remote Sensing, 14(13), 3023. https://doi.org/10.3390/rs14133023