Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Model Development
- Individual tree size: DBH of the individual tree.
- Competition factors: stand density index (SDI), stand origin (ORG), stand canopy density (SCD), and Simpson’s diversity index (SIDI). ORG was divided into 2 categories: plantation and natural [2].
- Bioclimatic factors: Geographical factors, including latitude, LAT (°), longitude, LONG (°), and elevation, ELEV (m). Meteorological factors, including average annual temperature, TEMP (°C) and annual precipitation, PREC (mm).
- Topographical factors: Slope, (SL), aspect, (AZ), and slope position (TPI). There are nine slope directions in the aspect: flat land without slope direction, eight slope directions from north to south and east to west. TPI was categorized into six types based on the topography: ridge, uphill, mid-slope, downhill, valley, and flat ground.
- Soil factors: Soil thickness, ST (cm), and humus thickness, HT (cm).
2.4. Model Validation
3. Results
3.1. ML Algorithms
3.1.1. Model Construction
3.1.2. Model Evaluation
3.2. Disentangling the Factors That Contribute to Tree Growth
3.2.1. Variation Importance Analysis
3.2.2. Partial Dependency Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Statistics | Unit | Betula spp. | Cunninghamia lanceolata (Lamb.) Hook. | ||||||
---|---|---|---|---|---|---|---|---|---|
Min | Median | Mean | Max | Min | Median | Mean | Max | ||
Annual growth rate | % | 0.03 | 1.33 | 2.03 | 45.08 | 0.06 | 3.44 | 4.69 | 49.31 |
DBH | cm | 5.0 | 10.1 | 11.8 | 83.0 | 5.0 | 9.7 | 10.7 | 49.0 |
Latitude | ° | 21.5 | 41.1 | 41.9 | 53.5 | 18.7 | 26.3 | 26.9 | 32.7 |
Longitude | ° | 81.6 | 116.0 | 113.6 | 134.3 | 98.3 | 114.6 | 113.5 | 121.5 |
Elevation | m | 70 | 1340 | 1679 | 4100 | 20 | 580 | 628 | 2160 |
Annual average temperature | °C | −3.9 | 4.6 | 4.6 | 24.6 | 12.6 | 19.0 | 19.1 | 25.6 |
Annual precipitation | mm | 56.7 | 439.3 | 491.8 | 2053.1 | 666.0 | 1488.0 | 1476.0 | 2274.0 |
Soil thickness | cm | 2 | 45 | 45 | 200 | 15 | 80 | 77 | 150 |
Humus thickness | cm | 0 | 4 | 6 | 70 | 0 | 5 | 8 | 60 |
SinSL | - | 0.0 | 0.3 | 0.3 | 0.8 | 0.0 | 0.5 | 0.5 | 0.8 |
(CosAZ + 1)/2 | - | 0.0 | 0.5 | 0.4 | 1.0 | 0.0 | 0.5 | 0.5 | 1.0 |
Stand canopy density | - | 0.2 | 0.7 | 0.7 | 1.0 | 0.2 | 0.7 | 0.7 | 1.0 |
Simpson’s diversity index | - | 0.0 | 0.4 | 0.4 | 0.9 | 0.0 | 0.3 | 0.3 | 0.9 |
Stand density index | - | 23.0 | 414.5 | 417.9 | 1365.7 | 29.0 | 610.1 | 631.8 | 2060.9 |
Variable | Description | Calculation Criteria |
---|---|---|
SL | Sine of slope | SinSL |
AZ | Cosine variation of aspect | (CosAZ + 1)/2 |
SDI | Stand density index | |
SIDI | Simpson diversity index |
Statistical Indices | Equation | Ideal |
---|---|---|
BIAS: | 0 | |
Root mean square error (RMSE): | 0 | |
Mean absolute error (MAE): | 0 | |
Coefficient of correlation (R2): | 1 | |
Willmott’s Index of Agreement (WIA): | 1 | |
Total relative error (TRE): | 0 |
Model | Betula spp. | Cunninghamia lanceolata (Lamb.) Hook. | ||||
---|---|---|---|---|---|---|
RMSE | R2 | MAE | RMSE | R2 | MAE | |
GBRT | 1.865 | 0.345 | 1.196 | 3.626 | 0.327 | 2.594 |
KNN | 1.599 | 0.519 | 0.946 | 3.144 | 0.491 | 2.114 |
RF | 1.537 | 0.560 | 0.937 | 3.059 | 0.526 | 2.108 |
Species | Verify | Annual Growth-Rata (%) | Future DBH (cm) | ||||
---|---|---|---|---|---|---|---|
GBRT | KNN | RF | GBRT | KNN | RF | ||
Betula spp. | BIAS | 0.008 | −0.004 | −0.005 | −0.01 | −0.02 | −0.04 |
RMSE | 1.888 | 1.582 | 1.528 | 0.90 | 0.74 | 0.75 | |
MAE | 1.200 | 0.933 | 0.932 | 0.60 | 0.47 | 0.48 | |
R2 | 0.345 | 0.540 | 0.571 | 0.981 | 0.987 | 0.987 | |
WIA | 0.675 | 0.840 | 0.832 | 0.995 | 0.997 | 0.997 | |
TRE | 0.398 | −0.186 | −0.253 | −0.070 | −0.137 | −0.324 | |
Cunninghamia lanceolata (Lamb.) Hook. | BIAS | −0.049 | 0.001 | −0.028 | −0.04 | −0.03 | −0.08 |
RMSE | 3.612 | 3.127 | 3.048 | 1.53 | 1.31 | 1.30 | |
MAE | 2.597 | 2.097 | 2.091 | 1.18 | 0.95 | 0.96 | |
R2 | 0.319 | 0.489 | 0.515 | 0.900 | 0.926 | 0.928 | |
WIA | 0.655 | 0.815 | 0.798 | 0.974 | 0.981 | 0.982 | |
TRE | −1.049 | 0.013 | −0.607 | −0.312 | −0.222 | −0.588 |
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Zhang, H.; Feng, Z.; Wang, S.; Ji, W. Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms. Sustainability 2022, 14, 8346. https://doi.org/10.3390/su14148346
Zhang H, Feng Z, Wang S, Ji W. Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms. Sustainability. 2022; 14(14):8346. https://doi.org/10.3390/su14148346
Chicago/Turabian StyleZhang, Hanyue, Zhongke Feng, Shan Wang, and Wenxu Ji. 2022. "Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms" Sustainability 14, no. 14: 8346. https://doi.org/10.3390/su14148346