Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Collection of Field Data
2.3. Acquisition of UAV Imagery
2.4. Data Analysis
2.4.1. UAV Image Processing
2.4.2. Generation of the CHMs
2.4.3. Extraction of Metrics from the Generated CHM and Orthomosaics
Sources | Metrics Extracted * | Description/Formula | References |
---|---|---|---|
CHM structural metrics | CHM mean | Mean value of the pixel per plot | [34] |
CHM max | Maximum value of the pixel per plot | [34] | |
CHM cv | Covariance of the pixel per plot | [52] | |
P99, P95, P90, P75, P50, P25 | Percentile height | [14] | |
CD99, CD90, CD75, CD25 | Proportion of the pixels above percentile height threshold to total pixels in % | [14,29] | |
CHM textural metrics | Roughness (Roug) | A degree of irregularity of the surface | [53] |
Angular Second Moment (ASM) | A measure of local homogeneity | [43,54] | |
Contrast (Cont) | A measure analyses the image contrast | [54] | |
Entropy (Ent) and Difference entropy (d_Ent) | A measure of randomness of the pixels within the plot | [26] | |
Variance (Var) and difference variance (d_Var) | A measure of gray tone variance of the pixels within the plot | [26] | |
Correlation (Corr) | A measure of linear dependency of the pixels within the plot | [54] | |
Sum of Average (s_Avg) | A measure of distribution of sum values | [55] | |
RGB spectral metrics | Normalized R, G, B | R, G, B | [56] |
Mean Brightness (m_Bright) | (R + G + B)/3 | [56] | |
Green to Red ratio | G/R | [56] | |
Blue to Green ratio | B/G | [57] | |
Blue to Red ratio | B/R | [58] | |
Normalized Red (nR) | R/(R + G + B) | [59] | |
Normalized Green (nG) | G/(R + G + B) | [56] | |
Normalized Blue (nB) | B/(R + G + B) | [59] | |
Normalized G-R VI (nGRVI) | (G − R)/(G + R) | [56] | |
Normalized R-B VI (nRBVI) | (R − B)/(R + B) | [60] | |
Normalized G-B VI (nGBVI) | (G − B)/(G + B) | [61] | |
Visible-band difference vegetation index (VDVI) | (2G − R − B)/2G + R + B) | [62] | |
Excess Green Index (EGI) | 2G − R − B | [63] | |
Modified green blue vegetation index (MGBI) | (GG − RR)/(GG + RR) | [64] | |
Visible atmospherically resistant index (VARI) | (G − R)/(G + R − B) | [65] |
2.4.4. Accuracy Assessment and Validation
3. Results
3.1. DSMs, DTMs, CHMsm and Orthomosaics
3.2. Variable Selection
3.3. Accuracy of Forest Structural Parameters
3.3.1. Dominant Tree Height
3.3.2. Tree Diameter at Breast Height
3.3.3. Basal Area
3.3.4. Volume
3.3.5. Carbon Stock
3.3.6. Stem Density
3.3.7. Broadleaf Ratio
4. Discussion
4.1. Variable Selection
4.1.1. Structural Variables
4.1.2. Textual Variables
4.1.3. Spectral Variables
4.2. Accuracy of Forest Structural Parameters
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Field Parameters/ Response Variables | Unit | Com16 | Com65-66 | ||||
---|---|---|---|---|---|---|---|
Mean | sd * | Range | Mean | sd * | Range | ||
Hd | m tree−1 | 26.02 | 2.40 | 18.70–28.41 | 24.05 | 2.93 | 18.48–29.29 |
DBH | cm tree−1 | 27.00 | 2.60 | 22.40–31.30 | 22.44 | 3.27 | 13.42–29.95 |
BA | m2 ha−1 | 28.50 | 4.10 | 34.00–17.80 | 16.33 | 5.50 | 3.93–28.76 |
V | m3 ha−1 | 387.00 | 60.20 | 278.90–500.20 | 253.93 | 63.46 | 100.43–361.76 |
CST | MgC ha−1 | 125.20 | 20.00 | 88.00–167.10 | 72.15 | 22.74 | 27.37–178.94 |
Sdens | stems ha−1 | 363.60 | 50.70 | 300.00–508.00 | 298.67 | 73.27 | 516.00–136.00 |
BLr | ratio | 0.59 | 0.08 | 0.45–0.72 | 0.80 | 0.16 | 0.30–1.00 |
UAV Parameter | Com16 | Com65-66 |
---|---|---|
Flight altitude | 200 m | 150 m |
Front overlap | 80% | 90% |
Side overlap | 80% | 90% |
Flight time | 30 min | 30 min |
Average flight speed | 26 m/s | 13 m/s |
Ground sampling distance | 1.6 cm/pixel | 1.6 cm/pixel |
Image resolutions | 8192 × 5460 (pixels) | 8192 × 5460 (pixels) |
Response Variable * | Selected Predictor Variables ** | ||
---|---|---|---|
MLR | RF | ||
Com16 (n = 17) | Hd | P95 + CD90 + CD99 + CD25 + nR + CHM_max | CD90 + ASM + P75 + P90 + P50 + P95 + s_Avg |
DBH | P25 + CD95 + CD50 + nR + CD25 | CHM_cv + P50 | |
BA | CD99 + P50 + CD50 + CHM_max | P50 + P75 + P25 + CHM_cv + P90 + s_Avg CHM_mean + P95 + P99 | |
V | CHM_mean + CD99 + nR + CD95 | CHM_cv + CHM_mean + s_Avg | |
CST | CHM_mean + CD99 + nR + CD95 | CHM_mean + CHM_cv + s_Avg + P90 + P50 + P25 + P95 + P75 + Cont + CD99 | |
Sden | CD25 + nR + CHM_max + CD95 + CD99 | d_Ent + CHM_max + ASM | |
BLr | nR + CHM_max + CD25 + CHM_cv + CD95 | Corr + nRBVI + s_Avg | |
Com65-66 (n = 43) | Hd | P99 + CHM_max | P90 + P95 + P99 + P75 + CHM_max + s_Avg + P50 + CHM_mean |
DBH | CHM_max + CHM_cv + nG + CD90 | P95 + P99 + CD75 + P90 + CHM_max + CD90 + P50 + P75 | |
BA | CHM_max + nR+ CD75 + CD95 + Cont | nRBVI + B/R + CHM_max + P95 + CD75 + nGBVI + P99 + nB + P90 + nR +P75 | |
V | P25 + CD50 | CD75 + P50 + CD99 + CD50 + P90 + P75 + CHM_mean + CHM_max | |
CST | CHM_max + CD95 + nB + VARI | CHM_max + P99 + CHM_cv + CD50 + Var + d_Var + Cont + P25 + Roug | |
Sden | nR + CD99 + CD75 + CHM_cv | nR + Cont + nGBVI + B_R + CD90 + nRBVI | |
BLr | CHM_max + d_Var + ASM + Roug + CD75 | CHM_max + Corr + B/G + Cont + nB + P50 |
Forest Compartments | Response Variable | MLR | RF | ||
---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||
Com16 (n = 17) | Hd | 0.91 | 0.68 | 0.89 | 0.76 |
DBH | 0.84 | 0.96 | 0.87 | 0.86 | |
BA | 0.98 | 0.63 | 0.92 | 1.16 | |
V | 0.91 | 17.46 | 0.91 | 18.01 | |
CST | 0.93 | 5.33 | 0.91 | 5.90 | |
S_den | 0.64 | 21.36 | 0.86 | 13.28 | |
BLr | 0.82 | 0.04 | 0.86 | 0.03 | |
Com65-66 (n = 43) | Hd | 0.86 | 1.02 | 0.92 | 0.67 |
DBH | 0.46 | 2.23 | 0.89 | 0.99 | |
BA | 0.53 | 3.55 | 0.87 | 1.83 | |
V | 0.37 | 49.39 | 0.88 | 22.02 | |
CST | 0.37 | 19.04 | 0.88 | 8.17 | |
S_den | 0.46 | 54.20 | 0.83 | 30.36 | |
BLr | 0.61 | 0.10 | 0.87 | 0.06 |
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Karthigesu, J.; Owari, T.; Tsuyuki, S.; Hiroshima, T. Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery. Remote Sens. 2024, 16, 1783. https://doi.org/10.3390/rs16101783
Karthigesu J, Owari T, Tsuyuki S, Hiroshima T. Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery. Remote Sensing. 2024; 16(10):1783. https://doi.org/10.3390/rs16101783
Chicago/Turabian StyleKarthigesu, Jeyavanan, Toshiaki Owari, Satoshi Tsuyuki, and Takuya Hiroshima. 2024. "Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery" Remote Sensing 16, no. 10: 1783. https://doi.org/10.3390/rs16101783
APA StyleKarthigesu, J., Owari, T., Tsuyuki, S., & Hiroshima, T. (2024). Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery. Remote Sensing, 16(10), 1783. https://doi.org/10.3390/rs16101783