Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Plot and Climate Data
2.2. Random Forests Algorithm
2.3. Climate-Sensitive Stand Biomass Model Development
2.4. Model Validation and Evaluation
3. Results
3.1. The Optimal Model
3.2. Relative Importance of Stand and Climate Factors
3.3. Partial Dependence of Stand Biomass on Stand and Climate Factors
4. Discussion
4.1. Applications of the Random Forests Algorithm for Estimating Stand Biomass
4.2. Relationship between Stand Factors and Stand Biomass
4.3. Relationship between Climate Factors and Stand Biomass
4.4. Uncertainty Analysis on Stand Biomass Estimation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Factors | Variables | Units | Mean | Min. | Max. | S.D. | Description |
---|---|---|---|---|---|---|---|
Stand | AGB | t/ha | 53.98 | 2.75 | 168.07 | 33.49 | Stand aboveground biomass |
TB | t/ha | 72.05 | 3.74 | 226.50 | 44.53 | Stand total biomass | |
H | m | 12.0 | 4.2 | 24.0 | 4.0 | Stand average height | |
Dg | cm | 13.4 | 6.0 | 26.4 | 4.3 | Stand quadratic mean diameter at breast height | |
V | m3/ha | 82.81 | 3.28 | 282.25 | 53.22 | Stand volume | |
Ba | m2/ha | 13.55 | 0.93 | 38.48 | 7.63 | Stand basal area | |
N | trees/ha | 1021 | 263 | 3933 | 595 | Stand density | |
Age | a | 28 | 11 | 60 | 9 | Stand average age | |
Climate | AHM | - | 22.5 | 11.7 | 39.8 | 4.9 | Annual heat-moisture index (MAT + 10)/(MAP/1000)) |
CMD | - | 185 | 35 | 382 | 74 | Hargreaves climate moisture deficit | |
DD_0 | days | 1537 | 425 | 3250 | 513 | Degree-days below 0 °C | |
DD_18 | days | 5332 | 3601 | 7867 | 788 | Degree-days below 18 °C | |
DD18 | days | 191 | 12 | 489 | 108 | Degree-days above 18 °C | |
DD5 | days | 1882 | 1012 | 2707 | 354 | Degree-days above 5 °C | |
EMT | °C | −30.5 | −43.8 | −17.7 | 4.2 | Extreme minimum temperature over a 30-year period | |
EREF | °C | 701 | 510 | 912 | 60 | Extreme maximum temperature over a 30-year period | |
EXT | - | 32.6 | 25.5 | 35.1 | 1.5 | Hargreaves reference evaporation | |
MAP | mm | 625 | 382 | 1050 | 146 | Mean annual precipitation | |
MAT | °C | 3.6 | −4.0 | 9.2 | 2.4 | Mean annual temperature | |
MCMT | °C | −16.0 | −27.0 | −5.6 | 3.9 | Mean coldest month temperature | |
MWMT | °C | 20.4 | 14.8 | 24.0 | 1.9 | Mean warmest month temperature | |
NFFD | days | 171 | 111 | 224 | 20 | The number of frost-free days | |
PAS | mm | 52 | 14 | 133 | 21 | Precipitation as snow between August in previous year and July in current year | |
TD | °C | 36.4 | 25.0 | 45.2 | 3.8 | Temperature difference between MWMT and MCMT, or continentality |
Model | RMSE (t ha−1) | RRMSE |
---|---|---|
AGB = 0.2249V0.9558AHM0.1536TD0.2228 | 5.2501 ± 1.1921 | 9.8075% ± 2.4826% |
AGB = 0.7202BA0.9639H0.4079AHM−0.1341TD0.3287 | 4.9730 ± 0.6162 | 9.2079% ± 0.7672% |
AGB model based on RF with ntree = 900 and mtry = 12 | 3.8008 ± 1.1350 | 7.0671% ± 2.1095% |
TB = 0.2117V0.9471AHM0.1056TD0.3718 | 6.4483 ± 1.5948 | 9.0377% ± 2.5060% |
TB = 0.7295BA0.9420H0.4296AHM−0.1687TD0.4362 | 6.9059 ± 0.9002 | 9.5804% ± 0.8853% |
TB model based on RF with ntree = 300 and mtry = 13 | 5.1963 ± 1.5904 | 7.2418% ± 2.2730% |
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He, X.; Lei, X.; Zeng, W.; Feng, L.; Zhou, C.; Wu, B. Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China. Sustainability 2022, 14, 5580. https://doi.org/10.3390/su14095580
He X, Lei X, Zeng W, Feng L, Zhou C, Wu B. Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China. Sustainability. 2022; 14(9):5580. https://doi.org/10.3390/su14095580
Chicago/Turabian StyleHe, Xiao, Xiangdong Lei, Weisheng Zeng, Linyan Feng, Chaofan Zhou, and Biyun Wu. 2022. "Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China" Sustainability 14, no. 9: 5580. https://doi.org/10.3390/su14095580