scholarly journals Comparison of Tree Biomass Modeling Approaches for Larch (Larix olgensis Henry) Trees in Northeast China

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 202 ◽  
Author(s):  
Lihu Dong ◽  
Yue Zhang ◽  
Zhuo Zhang ◽  
Longfei Xie ◽  
Fengri Li
Keyword(s):  
Random Effects ◽  
Aboveground Biomass ◽  
Northeast China ◽  
Model Systems ◽  
Larix Olgensis ◽  
Tree Biomass ◽  
Modeling Approaches ◽  
Biomass Models ◽  
Larix Olgensis Henry ◽  
Biomass Modeling

Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. Although various biomass models have been developed thus far, most of them lack a detailed investigation of the additivity properties of biomass components and inherent correlations among the components and aboveground biomass. This study compared the nonadditive and additive biomass models for larch (Larix olgensis Henry) trees in Northeast China. For the nonadditive models, the base model (BM) and mixed effects model (MEM) separately fit the aboveground and component biomass, and they ignore the inherent correlation between the aboveground and component biomass of the same tree sample. For the additive models, two aggregated model systems with one (AMS1) and no constraints (AMS2) and two disaggregated model systems without (DMS1) and with an aboveground biomass model (DMS2) were fitted simultaneously by weighted nonlinear seemingly unrelated regression (NSUR) and applied to ensure additivity properties. Following this, the six biomass modeling approaches were compared to improve the prediction accuracy of these models. The results showed that the MEM with random effects had better model fitting and performance than the BM, AMS1, AMS2, DMS1, and DMS2; however, when no subsample was available to calculate random effects, AMS1, AMS2, DMS1, and DMS2 could be recommended. There was no single biomass modeling approach to predict biomass that was best for all aboveground and component biomass except for MEM. The overall ranking of models based on the fit and validation statistics obeyed the following order: MEM > DMS1 > AMS2 > AMS1> DMS2 > BM. This article emphasized more on the methodologies and it was expected that the methods could be applied by other researchers to develop similar systems of the biomass models for other species, and to verify the differences between the aggregated and disaggregated model systems. Overall, all biomass models in this study have the benefit of being able to predict aboveground and component biomass for larch trees and to be used to predict biomass of larch plantations in Northeast China.

scholarly journals A Bayesian Approach to Estimating Seemingly Unrelated Regression for Tree Biomass Model Systems

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1302
Author(s):  
Longfei Xie ◽  
Fengri Li ◽  
Lianjun Zhang ◽  
Faris Rafi Almay Widagdo ◽  
Lihu Dong
Keyword(s):  
Bayesian Approach ◽  
Bayesian Methods ◽  
Model Fitting ◽  
Seemingly Unrelated Regression ◽  
Model Systems ◽  
Informative Priors ◽  
Tree Biomass ◽  
Tree Diameter ◽  
Biomass Models ◽  
Unrelated Regression

Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SUR) was used to fit the simultaneous equations of four tree components (i.e., stem, branch, foliage, and root). The model parameters were estimated by feasible generalized least squares (FGLS) and Bayesian methods using either non-informative priors or informative priors. The results showed that adding tree height to the model systems improved the model fitting and performance for the stem, branch, and foliage biomass models, but much less for the root biomass models. The Bayesian methods on the SUR models produced narrower 95% prediction intervals than did the classical FGLS method, indicating higher computing efficiency and more stable model predictions, especially for small sample sizes. Furthermore, the Bayesian methods with informative priors performed better (smaller values of deviance information criterion (DIC)) than those with the non-informative priors. Therefore, our results demonstrated the advantages of applying the Bayesian methods on the SUR biomass models, not only obtaining better model fitting and predictions, but also offering the assessment and evaluation of the uncertainties for constructing and updating tree biomass models.

Construction of compatible and additive individual-tree biomass models for Pinus tabulaeformis in China

2017 ◽  
Vol 47 (4) ◽  
pp. 467-475 ◽  
Author(s):  
WeiSheng Zeng ◽  
LianJin Zhang ◽  
XinYun Chen ◽  
ZhiChu Cheng ◽  
KeXi Ma ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Belowground Biomass ◽  
Pinus Tabulaeformis ◽  
Stem Volume ◽  
Mean Annual Precipitation ◽  
Mean Annual Temperature ◽  
Tree Biomass ◽  
Individual Tree ◽  
Chinese Pine ◽  
Biomass Models

Current biomass models for Chinese pine (Pinus tabulaeformis Carr.) fail to accurately estimate biomass in large geographic regions because they were usually based on limited sample trees on local sites, incompatible with stem volume, and not additive among components and total biomass. This study was based on mensuration data of individual-tree biomass from large samples of Chinese pine. The purpose was to construct compatible and additive biomass models using the nonlinear error-in-variable simultaneous equations and dummy variable modeling approach. This approach could ensure compatibility of an aboveground biomass model with a biomass conversion factor (BCF) and a stem volume model and compatibility of a belowground biomass model with a root-to-shoot ratio (RSR) model. Also, stem, branch, and foliage biomass models were additive to the aboveground biomass model. Results showed that mean prediction errors (MPEs) of the developed one- and two-variable aboveground biomass models were less than 4% and MPEs of the three-component (stem, branch, and foliage) and belowground biomass models were less than 10%. Furthermore, the effects of main climate variables on above- and below-ground biomass were analyzed. Aboveground biomass was related to mean annual temperature (MAT), while belowground biomass had no significant relationship with either MAT or mean annual precipitation (MAP). The developed models provide a good basis for estimating biomass of Chinese pine forests.

Improving the accuracy of tree biomass estimations for three coniferous tree species in Northeast China

Trees ◽  
2021 ◽  
Author(s):  
Longfei Xie ◽  
Liyong Fu ◽  
Faris Rafi Almay Widagdo ◽  
Lihu Dong ◽  
Fengri Li
Keyword(s):  
Tree Species ◽  
Northeast China ◽  
Coniferous Tree ◽  
Tree Biomass ◽  
Coniferous Tree Species

scholarly journals Fir (Abies spp.) stand biomass additive model for Eurasia sensitive to winter temperature and annual precipitation

2019 ◽  
Vol 65 (3-4) ◽  
pp. 166-179 ◽  
Author(s):  
Vladimir A. Usoltsev ◽  
Katarína Merganičová ◽  
Bohdan Konôpka ◽  
Anna A. Osmirko ◽  
Ivan S. Tsepordey ◽  
...  
Keyword(s):  
Climate Change ◽  
Biomass Allocation ◽  
Aboveground Biomass ◽  
Additive Model ◽  
Tree Biomass ◽  
Common Pattern ◽  
Temperature And Precipitation ◽  
Shoot Ratio ◽  
Biomass Components ◽  
Increasing Temperature

Abstract Climate change, especially modified courses of temperature and precipitation, has a significant impact on forest functioning and productivity. Moreover, some alterations in tree biomass allocation (e.g. root to shoot ratio, foliage to wood parts) might be expected in these changing ecological conditions. Therefore, we attempted to model fir stand biomass (t ha−1) along the trans-Eurasian hydrothermal gradients using the data from 272 forest stands. The model outputs suggested that all biomass components, except for the crown mass, change in a common pattern, but in different ratios. Specifically, in the range of mean January temperature and precipitation of −30°C to +10°C and 300 to 900 mm, fir stand biomass increases with both increasing temperature and precipitation. Under an assumed increase of January temperature by 1°C, biomass of roots and of all components of the aboveground biomass of fir stands increased (under the assumption that the precipitation level did not change). Similarly, an assumed increase in precipitation by 100 mm resulted in the increased biomass of roots and of all aboveground components. We conclude that fir seems to be a perspective taxon from the point of its productive properties in the ongoing process of climate change.

Development and evaluation of aboveground small tree biomass models for naturally regenerated and planted species in eastern Maine, U.S.A.

2014 ◽  
Vol 68 ◽  
pp. 215-227 ◽  
Author(s):  
Andrew S. Nelson ◽  
Aaron R. Weiskittel ◽  
Robert G. Wagner ◽  
Michael R. Saunders
Keyword(s):  
Tree Biomass ◽  
Small Tree ◽  
Biomass Models

scholarly journals Spatial and Seasonal Variations of Standardized Photosynthetic Parameters under Different Environmental Conditions for Young Planted Larix olgensis Henry Trees

Forests ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 522 ◽  
Author(s):  
Qiang Liu ◽  
Fengri Li
Keyword(s):  
Environmental Conditions ◽  
Seasonal Variations ◽  
Seasonal Pattern ◽  
Repeated Measurements ◽  
Seasonal Patterns ◽  
Photosynthetic Parameters ◽  
Relative Depth ◽  
Larix Olgensis ◽  
Vertical Variations ◽  
Larix Olgensis Henry

Understanding the spatial and seasonal variations in leaf physiology is critical for accurately modeling the carbon uptake, physiological processes and growth of entire canopies and stands. For a 17-year-old Larix olgensis Henry plantation, vertical whorl-by-whorl sampling and analyses of seasonally repeated measurements of major photosynthetic parameters were conducted, and the correlations between photosynthetic parameters and environmental conditions, leaf morphological traits and spatial position within the crown were analyzed. According to the correlations, the photosynthetic parameters were standardized based on the environmental conditions to avoid the influence of the changing environment on the patterns of spatial and seasonal variations of photosynthetic parameters. The results showed that the standardized light-saturated net photosynthetic rate (SPmax), standardized dark respiration (SRd) and standardized stomatal conductance under saturated light (Sgs-sat) were all negatively related to the relative depth into the crown (RDINC) throughout the growing season. However, their vertical patterns were different during the development of the phenological phase. In addition, different gradients of environmental conditions also influenced the values and the range of the vertical variation in photosynthesis. High temperature and low humidity usually resulted in smaller values and weaker vertical variations of SPmax and Sgs-sat, but larger values and more obvious vertical variations in SRd. SPmax and Sgs-sat usually exhibited a parabolic seasonal pattern in different vertical positions within the crown; however, SRd generally followed a concave pattern. These seasonal patterns were all weaker with increasing RDINC. Different environments also exhibited a significant influence on the seasonal patterns of photosynthesis. We suggested that standardization is necessary before analyzing spatial and seasonal variations. A single environmental condition could not represent the spatial and seasonal patterns under all gradients of the environment. Spatial and seasonal variations should be simultaneously analyzed because they are related to each other.

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