scholarly journals Simultaneous estimation as an alternative to young eucalyptus aboveground biomass modeling in ecophysiological experiments

2021 ◽  
Vol 43 ◽  
pp. e52126
Author(s):  
Thiago Wendling Gonçalves de Oliveira ◽  
Rafael Rubilar ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Alexandre Behling
Keyword(s):  
Least Squares ◽  
Aboveground Biomass ◽  
Forest Biomass ◽  
Seemingly Unrelated Regression ◽  
Biomass Estimation ◽  
Simultaneous Estimation ◽  
Efficiency Gain ◽  
Unrelated Regression ◽  
Biomass Components ◽  
Biomass Modeling

Accurate forest biomass estimates require the selection of appropriate models of individual trees. Thus, two properties are required in tree biomass modeling: (1) additivity of biomass components and (2) estimator efficiency. This study aimed to develop a system of equations to estimate young eucalyptus aboveground biomass and guarantee additivity and estimator efficiency. Aboveground eucalyptus biomass models were calibrated using four methods:  generalized least squares (GLS), weighted least squares (WLS), seemingly unrelated regression (SUR), and weighted seemingly unrelated regression (WSUR). The approaches were compared with regard to performance, additivity, and estimator efficiency. The methods did not differ with regard to the mean biomass estimation; therefore, their performance was similar. The GLS and WLS approaches did not satisfy the additivity principle, as the sum of the biomass components was not equal to total biomass. However, this was not observed with the SUR and WSUR approaches. With regard to estimator efficiency, the WSUR approach resulted in narrow confidence intervals and an efficiency gain of over 20%. The WSUR approach should be used in forest biomass modeling as it resulted in effective estimators while ensuring equation additivity, thus providing an easy and accurate alternative to estimate the initial biomass of eucalyptus stands in ecophysiological models.

Simultaneous estimation of biomass models for 13 tree species: effects of compatible additivity requirements

2017 ◽  
Vol 47 (6) ◽  
pp. 765-776 ◽  
Author(s):  
Thomas Nord-Larsen ◽  
Henrik Meilby ◽  
Jens Peter Skovsgaard
Keyword(s):  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Model Systems ◽  
Simultaneous Estimation ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Biomass Models ◽  
Unrelated Regression ◽  
Biomass Components ◽  
Species Specific

A desirable feature of biomass models distinguishing different tree components is compatible additivity of the component functions. Due to forcing of parameter estimates, such additivity is achieved at an expense of precision of the component functions. This study aimed to analyse the loss of precision incurred by forcing of parameters in tree biomass models due to (i) additivity constraints, (ii) combining global and species-specific parameters, and (iii) estimating component functions simultaneously as a system instead of as individual equations. Based on biomass data from 697 trees including 13 different species, we estimated a set of compatibly additive, nonlinear biomass models using simultaneous estimation and compared these with less restricted model systems. In line with other similar studies, the overall model system explained 88%–99% of the variation in individual biomass components. Compared with the unrestricted model, restricting parameters to obtain compatible additivity resulted in a change in RMSE of –0.6% to 5.4%. When restricting parameter estimates using both species-specific and global parameters, RMSE increased by 1.7%–13.1%. Estimating model parameters using simultaneous estimation (nonlinear iterated seemingly unrelated regression, NSUR) increased model bias compared with ordinary least squares estimation (OLS) for most biomass components. Contrary to expectations, NSUR estimation did not lead to a reduction in the standard error of estimates.

scholarly journals Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 581 ◽  
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
Xingang Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Least Squares ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Digital Images ◽  
Predictive Performance ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Biomass Estimation ◽  
High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

scholarly journals Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1073 ◽  
Author(s):  
Li ◽  
Li ◽  
Li ◽  
Liu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Variable Selection ◽  
Aboveground Biomass ◽  
Forest Type ◽  
Learning Algorithms ◽  
Forest Biomass ◽  
Machine Learning Algorithms ◽  
Biomass Estimation ◽  
Landsat 8

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

scholarly journals Individual Plant Allometric Equations for Estimating Aboveground Biomass and Its Components for a Common Bamboo Species (Bambusa procera A. Chev. and A. Camus) in Tropical Forests

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 316 ◽  
Author(s):  
Bao Huy ◽  
Giang Thanh ◽  
Krishna Poudel ◽  
Hailemariam Temesgen
Keyword(s):  
Tropical Forests ◽  
Aboveground Biomass ◽  
Nonlinear Models ◽  
Generalized Least Squares ◽  
Forest Degradation ◽  
Seemingly Unrelated Regression ◽  
Simultaneous Estimation ◽  
Individual Plant ◽  
Bamboo Species ◽  
Species Specific

Bamboo forests play an important role in achieving the objectives of the United Nations program on Reducing Emission from Deforestation and Forest Degradation. We developed and validated a modeling system that simultaneously estimate aboveground biomass and its components for a common bamboo species (Bambusa procera A. Chev. and A. Camus) in tropical forests. Eighty-three bamboo culms were destructively sampled from seventeen 100 m2 sample plots located in different parts of the Central Highlands in Viet Nam to obtain total plant aboveground biomass (AGB) and its components. We examined the performance of weighted nonlinear models fit by maximum likelihood and weighted nonlinear seemingly unrelated regression fit by generalized least squares for predicting bamboo biomass. The simultaneous estimation of AGB and its components produced higher reliability than the models of components and total developed separately. With a large number of bamboo species, it may not be feasible to develop species- specific biomass models, hence genus-specific allometric models may be considered.

Climate-based Allometric Biomass Equations for Five Major Canadian Timber Species

2021 ◽  
Author(s):  
Kun Xu ◽  
Jinghe Jiang ◽  
Fangliang He
Keyword(s):  
Aboveground Biomass ◽  
Forest Biomass ◽  
Allometric Equations ◽  
Accurate Estimation ◽  
Biomass Estimation ◽  
Change Scenario ◽  
Mean Annual Temperature ◽  
Timber Species ◽  
Trembling Aspen ◽  
Allometric Models

Accurate estimation of forest biomass is essential to quantify the role forests play at balancing terrestrial carbon. Allometric equations based on tree size have been used for this purpose worldwide. There is little quantitative understanding on how environmental variation may affect tree allometries. Even less known is how to incorporate environmental factors into such equations to improve estimation. Here we tested the effects of climate on tree allometric equations and proposed to model forest biomass by explicitly incorporating climatic factors. Among the five major Canadian timber species tested, the incorporation of climate was not found to improve the allometric models. For trembling aspen and tamarack, the residuals of their conventional allometric models were found strongly related to frost-free period and mean annual temperature, respectively. The predictions of the two best climate-based models were significantly improved, which indicate that trembling aspen and tamarack store more aboveground biomass when growing in warmer than in colder regions. We showed that, under the RCP4.5 modest climate change scenario, there would be a 10% underestimation of aboveground biomass for these two species if the conventional non-climate models would still be in use in 2030. This study suggests the necessity to proactively develop climate-based allometric equations for more accurate and reliable forest biomass estimation.

scholarly journals Macroeconomic identification of the priced APT factors on the Johannesburg Stock Exchange

1996 ◽  
Vol 27 (4) ◽  
pp. 104-112 ◽  
Author(s):  
Paul Van Rensburg
Keyword(s):  
Interest Rates ◽  
Risk Premium ◽  
Significant Risk ◽  
Seemingly Unrelated Regression ◽  
Simultaneous Estimation ◽  
Inflation Expectations ◽  
Arbitrage Pricing Theory ◽  
Macroeconomic Variables ◽  
Unrelated Regression ◽  
Non Linear

Employing prespecified macroeconomic variables as potential priced factors, the Arbitrage Pricing Theory (APT) may be modelled as a non-linear seemingly unrelated regression with across equation restrictions. This portrayal allows for the simultaneous estimation of factor sensitivities and the risk premium associated with each factor. The following macroeconomic variables were tested as potential factors: unexpected movements in (rand) gold returns. (dollar) returns on the Dow-Jones Industrial Index, the term structure of interest rates and inflation expectations together with the 'residual market factor' of Burmeister Wall. Using iterated non-linear seemingly unrelated regression (ITNLSUR) estimation techniques, it was found that all of the above variables except for gold price risk are priced, that is, are associated with statistically significant risk premia.

scholarly journals Additive and Non-additive Biomass Equations for Black Wattle

2019 ◽  
Vol 26 (4) ◽  
Author(s):  
Alexandre Behling ◽  
Sylvio Péllico Netto ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Augusto Arlindo Simon ◽  
...  
Keyword(s):  
Effective Property ◽  
Seemingly Unrelated Regression ◽  
Total Biomass ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model ◽  
Biomass Equations ◽  
Biomass Components ◽  
Black Wattle ◽  
Undesirable Property ◽  
Second Procedure

ABSTRACT The objectives of this work were to propose additive equations for biomass components (stem and crown) and total biomass for black wattle (Acacia mearnsii De Wild.) and show the inconsistency of independently adjusted biomass equations. Two procedures were used to fit nonlinear equations of biomass: i) independent and ii) systems of equations. The second procedure, defined by the application of the seemingly unrelated regression model, has better biological and statistical properties to estimate allometric equations of biomass components and total biomass when compared with the independent estimation. An effective property of this procedure is the additivity, i.e., the estimates of component biomass are compatible with those of total biomass. Independent fitted adjusted equations do not consider the dependence between the biomass components, thus, besides the estimates being non-additive, which is an undesirable property, they will result in estimates with larger variance.

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