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

被引：1

作者：

Bai Y. ^{[1
,2
]}

Pang Y. ^{[1
,2
]}

Xia X. ^{[1
,3
]}

Jia W. ^{[4
]}

机构：

[1] Research Institute of Forest Resource Information Techniques, CAF, Beijing

[2] Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing

[3] School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei

[4] Forestry College, Northeast Forestry University, Harbin

来源：

Linye Kexue/Scientia Silvae Sinicae | 2022年 / 58卷 / 01期

关键词：

3-PG(physiological principles in predicting growth) model; Destructive sampling data; Larix olgensis; Parameterization;

D O I：

10.11707/j.1001-7488.20220111

中图分类号：

学科分类号：

摘要：

【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. © 2022, Editorial Department of Scientia Silvae Sinicae. All right reserved.

引用

页码：98 / 110

页数：12

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