Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Preprocessing
2.3. Research Method
2.3.1. Classification of Dominant Tree Species Based on the GEE Platform
2.3.2. Estimation of Forest Carbon Storage
2.3.3. Selection of Modeling Factors
2.3.4. Selection of Sample Data
2.3.5. Carbon Density Inversion Model Construction
2.3.6. Accuracy Evaluation
3. Results
3.1. Classification Results for Dominant Species
3.2. Sample Data Filtering
3.3. Model Estimation Results and Accuracy Evaluation
3.4. Forest Carbon Density Inversion Mapping
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Tree Species and Group | BEF | RSR | WD | CF |
---|---|---|---|---|
Chinese fir wood and other firs | 1.634 | 0.246 | 0.307 | 0.520 |
Pinus massoniana | 1.472 | 0.187 | 0.380 | 0.460 |
Other pines | 1.631 | 0.206 | 0.424 | 0.511 |
Poplar | 1.446 | 0.227 | 0.378 | 0.496 |
Hard broadleaf | 1.674 | 0.261 | 0.598 | 0.497 |
Soft broadleaf | 1.586 | 0.289 | 0.443 | 0.485 |
Vegetation Factor | Formula |
---|---|
NDVI | |
RVI | |
EVI | |
DVI | |
GRVI |
Category | UA (Pixels/%) | PA (Pixels/%) | OA (Pixels/%) | Kappa |
---|---|---|---|---|
Forest | 92.86 | 86.67 | 93.79 | 0.9145 |
Water | 96.88 | 96.87 | ||
Building lands | 92.68 | 100 | ||
other lands | 93.33 | 91.80 |
Method | Category | UA (Pixels/%) | PA (Pixels/%) | OA (Pixels/%) | Kappa |
---|---|---|---|---|---|
RF | Chinese fir wood and other firs | 78.57 | 50.00 | 87.30 | 0.7747 |
Pinus massoniana | 70.69 | 85.42 | |||
Other pines | 72.09 | 62.00 | |||
Poplar | 97.62 | 97.62 | |||
Hard broadleaf | 68.08 | 68.09 | |||
Soft broadleaf | 92.84 | 95.92 | |||
SVM | Chinese fir wood and other firs | 55.29 | 61.84 | 66.74 | 0.6006 |
Pinus massoniana | 63.37 | 75.29 | |||
Other pines | 70.33 | 68.09 | |||
Poplar | 92.31 | 100 | |||
Hard broadleaf | 57.53 | 65.63 | |||
Soft broadleaf | 60.38 | 35.56 |
Tree Species and Group | Number (Piece) | Min (m3·hm−2) | Max (m3·hm−2) | Average (m3·hm−2) | Standard Deviation (m3·hm−2) |
---|---|---|---|---|---|
Chinese fir wood and other firs | 1000 | 6.04 | 99.11 | 34.21 | 22.75 |
Pinus massoniana | 1000 | 6.92 | 61.67 | 31.48 | 13.72 |
Other pines | 1000 | 6.00 | 85.11 | 40.40 | 21.86 |
Poplar | 1000 | 12.22 | 93.33 | 47.75 | 15.59 |
Hard broadleaf | 1000 | 8.97 | 70.41 | 37.25 | 17.29 |
Soft broadleaf | 1000 | 3.97 | 87.14 | 32.32 | 19.83 |
Total | 6000 | 3.97 | 99.11 | - | - |
Model | Tree Species and Group | R2 | RMSE (t·hm−2) | MAE (t·hm−2) |
---|---|---|---|---|
MLR | Chinese fir wood and other firs | 0.312 | 6.1289 | 4.9009 |
Pinus massoniana | 0.199 | 3.8054 | 3.1529 | |
Other pines | 0.286 | 7.8201 | 6.4654 | |
Poplar | 0.090 | 4.5832 | 3.9105 | |
Hard broadleaf | 0.407 | 8.3541 | 6.8165 | |
Soft broadleaf | 0.386 | 5.0962 | 4.0865 | |
SVM | Chinese fir wood and other firs | 0.342 | 6.6304 | 5.3309 |
Pinus massoniana | 0.249 | 5.3851 | 4.5164 | |
Other pines | 0.294 | 7.7728 | 6.3876 | |
Poplar | 0.165 | 4.3904 | 3.7138 | |
Hard broadleaf | 0.445 | 8.0824 | 6.5731 | |
Soft broadleaf | 0.431 | 4.8909 | 3.9716 | |
RF | Chinese fir wood and other firs | 0.5346 | 4.2854 | 3.6629 |
Pinus massoniana | 0.5363 | 2.2501 | 1.9841 | |
Other pines | 0.6220 | 4.8738 | 4.1110 | |
Poplar | 0.4054 | 3.1805 | 2.7538 | |
Hard broadleaf | 0.7672 | 4.4380 | 3.7781 | |
Soft broadleaf | 0.6534 | 2.9607 | 2.5848 |
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Pang, Z.; Zhang, G.; Tan, S.; Yang, Z.; Wu, X. Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method. Forests 2022, 13, 2004. https://doi.org/10.3390/f13122004
Pang Z, Zhang G, Tan S, Yang Z, Wu X. Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method. Forests. 2022; 13(12):2004. https://doi.org/10.3390/f13122004
Chicago/Turabian StylePang, Ziheng, Gui Zhang, Sanqing Tan, Zhigao Yang, and Xin Wu. 2022. "Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method" Forests 13, no. 12: 2004. https://doi.org/10.3390/f13122004
APA StylePang, Z., Zhang, G., Tan, S., Yang, Z., & Wu, X. (2022). Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method. Forests, 13(12), 2004. https://doi.org/10.3390/f13122004