Choosing the Dependent Variable in SAR Backscatter – Forest Biomass Models

2021 ◽  
Author(s):  
M.J. Ducey ◽  
X. Huang ◽  
B. Ziniti ◽  
N. Torbick
Keyword(s):  
Forest Biomass ◽  
Biomass Models

scholarly journals Using linear mixed model and dummy variable  model approaches to construct compatible single-tree biomass equations at different scales – A case study for Masson pine in Southern China

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 101-115 ◽  
Author(s):  
L.Y. Fu ◽  
W.S. Zeng ◽  
S.Z. Tang ◽  
R.P. Sharma ◽  
H.K. Li
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Southern China ◽  
Forest Biomass ◽  
Tree Biomass ◽  
Variable Model ◽  
Masson Pine ◽  
Dummy Variable ◽  
Single Tree ◽  
Biomass Models

The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine (Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and dummy variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the dummy variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models.  

scholarly journals Generic additive allometric models and biomass allocation for two natural oak species in northeastern China

2020 ◽  
Author(s):  
Shengwang Meng ◽  
Fan Yang ◽  
Haibin Wang ◽  
Wei Wang ◽  
Sheng Hu ◽  
...  
Keyword(s):  
Biomass Allocation ◽  
Large Scale ◽  
Forest Biomass ◽  
Tree Height ◽  
Northeastern China ◽  
Carbon Accounting ◽  
Biomass Estimation ◽  
Stem Biomass ◽  
Biomass Models ◽  
Mongolian Oak

Abstract Background: Accurate quantification of forest biomass through allometric equations is crucial for global carbon accounting and climate change mitigation. Current models for oak species could not accurately estimate biomass in northeastern China, since they were usually established limited to Mongolian oak (Quercus mongolica) on local sites, and specifically, no biomass models were available for Liaodong oak (Quercus wutaishanica). The goal of this study was, therefore, to develop generic biomass models for both oak species on large scale and evaluate biomass allocation patterns within tree components. Results: The stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branch increases with increasing D, H, CW, CL, while a reverse trend was found for bark and foliage. The root-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve fitting results of branch and foliage biomass models. Conclusion: We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

scholarly journals The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1428
Author(s):  
Erick O. Osewe ◽  
Ioan Dutcă
Keyword(s):  
Nonlinear Models ◽  
Forest Biomass ◽  
Tree Height ◽  
European Beech ◽  
Biomass Estimation ◽  
Parameter Estimates ◽  
Variable Model ◽  
Separate Variable ◽  
Biomass Models ◽  
Fagus Sylvatica L

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q < 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty.

scholarly journals A Comparison of Statistical Methods for Estimating Forest Biomass from Light Detection and Ranging Data

2008 ◽  
Vol 23 (4) ◽  
pp. 223-231 ◽  
Author(s):  
Yuzhen Li ◽  
Hans-Erik Andersen ◽  
Robert McGaughey
Keyword(s):  
Regression Models ◽  
Forest Biomass ◽  
Washington State ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
South Central ◽  
Biomass Models ◽  
Individual Site ◽  
Biological Interpretation ◽  
Study Sites

Abstract Strong regression relationships between light detection and ranging (LIDAR) metrics and indices of forest structure have been reported in the literature. However, most papers focus on empirical results and do not consider LIDAR metric selection and biological interpretation explicitly. In this study, three different variable selection methods (stepwise regression, principle component analysis [PCA], and Bayesian modeling averaging [BMA]) were compared using LIDAR data from three study sites: Capitol Forest in western Washington State, Mission Creek in central Washington State, and Kenai Peninsula in south central Alaska. Separate aboveground biomass regression models were developed for each site as well as common models using three study sites simultaneously. Final biomass models have R2 values ranging from 0.67 to 0.88 for three study sites. PCA indicates that three LIDAR metrics (mean height, coefficient variation of height, and canopy LIDAR point density) explain the majority of variation contained within a larger set of metrics. Within each study area, forest biomass models using these three predictor variables had similar R2 values as the stepwise and BMA regression models. Individual site models using these three variables are recommended because these models are straightforward in terms of model form and biological interpretation and are easily adopted for application.

Estimation of Forest Biomass Energy Resources Based on Bottom-up Approach in China

2015 ◽  
Vol 8 (1) ◽  
pp. 272-275
Author(s):  
Lan Zhang ◽  
Dan Yu ◽  
Caihong Zhang ◽  
Weidong Zhang
Keyword(s):  
Forest Biomass ◽  
Estimation Method ◽  
Energy Resource ◽  
Biomass Energy ◽  
Energy Resources ◽  
Theoretical Ground ◽  
Estimation Model ◽  
Bottom Up ◽  
Rational Development ◽  
Calculation Results

Currently, the forest biomass energy development is at an initial stage and the estimation method for the forest biomass energy resource reserve is to be unified and refined although there is a great value and potential in the development and utilization of forest biomass energy in China. Based on the existing studies, the present paper analyzes the origins and types of forest biomass energy resources in the perspective of sustainable forestry management, constructs the estimation model using a bottom-up approach, and estimates the total existing forest biomass energy resource reserve in China based on the data of the 7th Forest Resource Survey. The estimation method and the calculation results provide the important theoretical ground for promoting the rational development of forest biomass energy in China.

scholarly journals Generic Additive Allometric Models and Biomass Allocation for Two Natural Oak Species in Northeastern China

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Shengwang Meng ◽  
Fan Yang ◽  
Sheng Hu ◽  
Haibin Wang ◽  
Huimin Wang
Keyword(s):  
Biomass Allocation ◽  
Tree Height ◽  
Northeastern China ◽  
Seemingly Unrelated Regression ◽  
Biomass Estimation ◽  
Model Parameters ◽  
Stem Biomass ◽  
Crown Width ◽  
Biomass Models ◽  
Mongolian Oak

Current models for oak species could not accurately estimate biomass in northeastern China, since they are usually restricted to Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) on local sites, and specifically, no biomass models are available for Liaodong oak (Quercuswutaishanica Mayr). The goal of this study was, therefore, to develop generic biomass models for both oak species on a large scale and evaluate the biomass allocation patterns within tree components. A total of 159 sample trees consisting of 120 Mongolian oak and 39 Liaodong oak were harvested and measured for wood (inside bark), bark, branch and foliage biomass. To account for the belowground biomass, 53 root systems were excavated following the aboveground harvest. The share of biomass allocated to different components was assessed by calculating the ratios. An aboveground additive system of biomass models and belowground equations were fitted based on predictors considering diameter (D), tree height (H), crown width (CW) and crown length (CL). Model parameters were estimated by jointly fitting the total and the components’ equations using the weighted nonlinear seemingly unrelated regression method. A leave-one-out cross-validation procedure was used to evaluate the predictive ability. The results revealed that stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branches increases with increasing D, H, CW and CL, while a reverse trend was found for bark and foliage. The root-to-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve the fitting results of the branch and foliage biomass models. We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

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