长白落叶松解析木数据参数化3-PG模型

doi:10.11707/j.1001-7488.20220111

Abstract

Abstract:

Objective: In order to verify the usability of destructive sampling data in 3-PG(physiological principles in predicting growth) model parameterization, this study predicted the growth of Larix olgensis using the 3-PG model calibrated by destructive sampling data, which can help to expand the data source of 3-PG model parameterization and provide a reference for model data selection. Method: Taking L. olgensis plantation in Mengjiagang forest farm as the study object, the destructive sampling data collected in 2019 was calculated to simulate the continuous observation data. The biomass of foliage, stem, and root, and stand volume were calculated based on the relevant biomass formulas. According to the sensitivity analysis results, the 3-PG model was parameterized by direct calculation, reference, and iterative fitting. Then the modeling results were verified by continuous observation data and fixed plot data, and the model output was regressed with observed data. Result: The results showed that the fitting accuracy was very high, and the model output data could well reflect the growth of the stand (n=138). The determination coefficient (R²) values of DBH, the ratio of foliage to stem biomass, stem biomass, total biomass and volume were all above 0.95 (P < 0.01). In contrast, the root biomass had a relatively lower fitting accuracy (R²=0.88). The model's predicting accuracy was verified by continuous observation data and fixed plot data. High correlations were observed between the model output and continuous observation data (n=140) and fixed plot data (n=87). The R² of all values ranged from 0.81 to 0.97 (P < 0.01). According to the sensitivity analysis results, foliage∶stem partitioning ratio at DBH=20 cm (pFS20) and Maximum fraction of NPP to roots (pRx) showed high sensitivity to DBH, root biomass, stem biomass, and volume. Conclusion: 3-PG model calibrated by destructive sampling data had a relatively high fitting accuracy and prediction accuracy. The results of this study would expand the data source of 3-PG model parameterization and provide a new basis for the 3-PG model to simulate the growth of L. olgensis plantation.

Key words: 3-PG(physiological principles in predicting growth) model, destructive sampling data, parameterization, Larix olgensis

CLC Number:

Yu Bai,Yong Pang,Xiaoyun Xia,Weiwei Jia. 3-PG Model Parameterization Using Destructive Sampling Data of Larix olgensis[J]. Scientia Silvae Sinicae, 2022, 58(1): 98-110.

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References 0

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样地号 Plot ID	林班号 Compartment ID	小班号 Sub-compartmentID	树龄 Age/a	面积 Area/hm²	立地指数 Site index	株数 Stem number/hm^-2	平均胸径 Mean DBH/cm	平均树高 Meanheight/m	蓄积量 Volume/(m³·hm^-2)
LYS720	64	11	52	0.06	21.48	642	24.82	26.67	337.62
LYS03	68	13	37	0.06	23.06	1 017	19.81	23.92	370.47
LYS04	81	2	36	0.06	26.22	1 100	18.59	22.35	324.16
LYS13	70	5	22	0.06	21.94	1 167	13.21	15.65	191.64
LYS14	70	5	35	0.06	21.85	1 467	13.82	16.02	198.77
LYS15	70	5	21	0.06	22.32	1 167	14.37	16.20	153.40

类型Type	参数Parameters	参数值Value	来源Source
异速生长关系与分配 Allometric relationships and partitioning	胸径2 cm时叶与干生物量分配比 Foliage∶stem partitioning ratio at DBH = 2 cm	0.8	拟合 Fitting
	胸径20 cm时叶与干生物量分配比Foliage∶stem partitioning ratio at DBH = 20 cm	0.6	拟合 Fitting
	干生物量与胸径关系中常数 Constant in the stem biomass v. diam. relationship	0.032 52	董利虎等(2019)
	干生物量与胸径关系中指数 Power in the stem biomass v. diam. relationship	2.742 6	董利虎等(2019)
	初级生产力分配给根最大值 Maximum fraction of NPP to roots	0.5	拟合 Fitting
	初级生产力分配给根最小值 Minimum fraction of NPP to roots	0.15	拟合 Fitting
凋落物与根更新 Litterfall and root turnover	最大凋落物速率 Maximum litterfall rate	0.06	拟合 Fitting
凋落物与根更新 Litterfall and root turnover	根月平均凋落速率 Mean monthly root turnover rate	0.005	拟合 Fitting
生长调节 Growth modifier	生长最低温度 Minimum temperature for growth	-2	许晨露等(2012)
	生长最适温度 Optimum temperature for growth	17	孙志虎(2005)
	生长最高温度 Maximum temperature for growth	38	许晨露等(2012)
	植物生长最大林龄 Maximum stand age used in age modifier	100	拟合 Fitting
树干死亡与自疏 Stem mortality and self-thinning	最大林龄的死亡率 Mortality rate for large tree age	0.6	计算 Calculation
	在1 000株·hm^-2每株树最大树干质量 Max. stem mass per tree @ 1 000 trees·hm^-2	240	计算 Calculation
	林分郁闭年龄 Age at full canopy cover	15	拟合 Fitting
冠层电导 Conductance	冠层量子效率 Canopy quantum efficiency	0.05	拟合 Fitting

数据类型 Data type	数据来源 Data source	数据获取时间 Data acquisition time	数据特点 Data characteristics
解析木数据 Destructive sampling data	6块解析木样地 Six destructive sampling plots	2019年 Observed in 2019	模拟连年观测数据，用于标定模型 Used to simulate continuous observation data and calibrate model
密度林数据 Continuous observation data	5块密度林样地 5 continuous observation plots	1975—2020年 Observed from 1975 to 2020	覆盖林龄较广，检验大时间跨度预测精度 Covered a wide range of stand age, used to verify the prediction accuracy of large time span
固定样地数据 Fixed plot data	24块固定样地 24 fixed plots	2012、2014、2016和2020年 Observed in 2012，2014，2016 and 2020	覆盖地理范围广，检验大区域范围预测精度 Covered a wide spatial range, used to verify the prediction accuracy of large area

统计量 Statistic value	R²	斜率 Slope	截距 Intercept	ME	MAE	RMSE	MRE(%)
胸径DBH/cm	0.96	0.98	0.19	-0.12	0.57	0.27	-0.81
叶干生物量比Foliage∶stem bionass ratio	0.91	1.20	-0.01	0.00	0.01	0.05	-7.08
干生物量Stem biomass/(t·hm^-2)	0.93	1.10	1.29	10.63	11.78	2.51	11.01
根生物量Root biomass/(t·hm^-2)	0.83	0.75	10.36	3.38	4.21	1.41	12.29
总生物量Total biomass/(t·hm^-2)	0.93	0.90	15.48	1.49	9.43	0.94	1.06
蓄积量Volume/(m³·hm^-2)	0.93	1.12	-38.27	-11.48	19.20	2.61	-5.15

统计量 Statistic value	R²	斜率break/>Slope	截距break/>Intercept	ME	MAE	RMSE	MRE(%)
胸径DBH/cm	0.97	0.97	0.16	-0.29	0.43	0.53	-1.93
叶干生物量比Foliage∶stem bionass ratio	0.81	0.98	-0.02	-0.02	0.02	0.14	-36.56
干生物量Stem biomass/(t·hm^-2)	0.95	1.22	-7.73	13.91	14.28	3.64	14.00
根生物量Root biomass/(t·hm^-2)	0.85	0.71	8.43	0.38	3.28	0.60	1.36
总生物量Total biomass/(t·hm^-2)	0.94	0.98	0.71	-2.30	8.92	1.48	-1.57
蓄积量Volume/(m³·hm^-2)	0.94	1.20	-63.79	-15.67	23.99	3.86	-6.47

3-PG Model Parameterization Using Destructive Sampling Data of Larix olgensis

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