Alternative models for estimating woody plant biomass

1980 ◽  
Vol 10 (3) ◽  
pp. 367-370 ◽  
Author(s):  
T. R. Crow ◽  
P. R. Laidly
Keyword(s):  
Aboveground Biomass ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Woody Plant ◽  
Plant Biomass ◽  
Nonlinear Models ◽  
Allometric Equation ◽  
Biomass Models ◽  
Homogeneity Of Variance ◽  
Tall Shrub

A number of untransformed regression models were compared to the log-log form of the allometric function for estimating biomass. Models were evaluated using two tree species, Betulapapyrifera Marsh, and Pinusresinosa Ait., and one tall shrub, Ilexverticillata (L.) Gray, with total aboveground biomass as the dependent variable. Using goodness of fit as the criterion, both weighted linear and weighted nonlinear models proved to be acceptable alternatives to the transformed allometric equation. Weighted models retain the advantage of the log-log form, i.e., compatibility with the homogeneity of variance assumption, but avoid the transformational bias.

Bayesian model predicts the aboveground biomass of Caragana microphylla in sandy lands better than OLS regression models

2020 ◽  
Vol 13 (6) ◽  
pp. 732-737
Author(s):  
Yi Tang ◽  
Arshad Ali ◽  
Li-Huan Feng
Keyword(s):  
Bayesian Methods ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Bayesian Method ◽  
Prediction Models ◽  
Bootstrap Method ◽  
Allometric Equation ◽  
Basal Diameter ◽  
Caragana Microphylla ◽  
The Bootstrap Method

Abstract Aims In forest ecosystems, different types of regression models have been frequently used for the estimation of aboveground biomass, where Ordinary Least Squares (OLS) regression models are the most common prediction models. Yet, the relative performance of Bayesian and OLS models in predicting aboveground biomass of shrubs, especially multi-stem shrubs, has relatively been less studied in forests. Methods In this study, we developed the biomass prediction models for Caragana microphylla Lam. which is a widely distributed multi-stems shrub, and contributes to the decrease of wind erosion and the fixation of sand dunes in the Horqin Sand Land, one of the largest sand lands in China. We developed six types of formulations under the framework of the regression models, and then, selected the best model based on specific criteria. Consequently, we estimated the parameters of the best model with OLS and Bayesian methods with training and test data under different sample sizes with the bootstrap method. Lastly, we compared the performance of the OLS and Bayesian models in predicting the aboveground biomass of C. microphylla. Important Findings The performance of the allometric equation (power = 1) was best among six types of equations, even though all of those models were significant. The results showed that mean squared error of test data with non-informative prior Bayesian method and the informative prior Bayesian method was lower than with the OLS method. Among the tested predictors (i.e. plant height and basal diameter), we found that basal diameter was not a significant predictor either in OLS or Bayesian methods, indicating that suitable predictors and well-fitted models should be seriously considered. This study highlights that Bayesian methods, the bootstrap method and the type of allometric equation could help to improve the model accuracy in predicting shrub biomass in sandy lands.

ESTIMATION OF ABOVE GROUND BIOMASS OF THE THREE SITES (GRAZED SITE, PROTECTED SITE AND SEED SOWN SITE) FOR COMPARING THEIR PRODUCTIVITY IN KASHMIR VALLEY

2017 ◽  
Vol 23 (2) ◽  
Author(s):  
AFSHAN ANJUM BABA ◽  
SYED NASEEM UL-ZAFAR GEELANI ◽  
ISHRAT SALEEM ◽  
MOHIT HUSAIN ◽  
PERVEZ AHMAD KHAN ◽  
...  
Keyword(s):  
Protected Areas ◽  
Aboveground Biomass ◽  
Plant Biomass ◽  
Summer Season ◽  
Above Ground Biomass ◽  
Spring Season ◽  
Kashmir Valley ◽  
Ground Biomass ◽  
Maximum Value ◽  
Protected Site

The plant biomass for protected areas was maximum in summer (1221.56 g/m2) and minimum in winter (290.62 g/m2) as against grazed areas having maximum value 590.81 g/m2 in autumn and minimum 183.75 g/m2 in winter. Study revealed that at Protected site (Kanidajan) the above ground biomass ranged was from a minimum (1.11 t ha-1) in the spring season to a maximum (4.58 t ha-1) in the summer season while at Grazed site (Yousmarag), the aboveground biomass varied from a minimum (0.54 t ha-1) in the spring season to a maximum of 1.48 t ha-1 in summer seasonandat Seed sown site (Badipora), the lowest value of aboveground biomass obtained was 4.46 t ha-1 in spring while as the highest (7.98 t ha-1) was obtained in summer.

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.

Aboveground Biomass of Reaumuria soongorica Shrub Effect on Soil Moisture and Nutrient Spatial Distribution in Arid and Semi-Arid Region

2013 ◽  
Vol 726-731 ◽  
pp. 3803-3806
Author(s):  
Bing Ru Liu ◽  
Jun Long Yang
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Moisture Content ◽  
Aboveground Biomass ◽  
Soil Moisture Content ◽  
Plant Biomass ◽  
Soil Layer ◽  
Soil Nutrient ◽  
Desert Plant ◽  
Important Species

In order to revel aboveground biomass of R. soongorica shrub effect on soil moisture and nutrients spatial distribution, and explore mechanism of the changes of soil moisture and nutrients, soil moisture content, pH, soil organic carbon (SOC) and total nitrogen (TN) at three soil layers (0-10cm,10-20cm, and 20-40cm) along five plant biomass gradients of R. soongorica were investigated. The results showed that soil moisture content increased with depth under the same plant biomass, and increased with plant biomass. Soil nutrient properties were evidently influenced with plant biomass, while decreased with depth. SOC and TN were highest in the top soil layer (0-10 cm), but TN of 10-20cm layer has no significant differences (P < 0.05). Moreover, soil nutrient contents were accumulated very slowly. These suggests that the requirement to soil organic matter is not so high and could be adapted well to the desert and barren soil, and the desert plant R. soongorica could be acted as an important species to restore vegetation and ameliorate the eco-environment.

scholarly journals Flexible Bayesian Nonlinear Model Configuration

2021 ◽  
Vol 72 ◽  
pp. 901-942
Author(s):  
Aliaksandr Hubin ◽  
Geir Storvik ◽  
Florian Frommlet
Keyword(s):  
Regression Models ◽  
Nonlinear Models ◽  
Model Averaging ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Parametric Models ◽  
Monte Carlo Algorithm ◽  
Wide Range ◽  
Nonlinear Features ◽  
Interpretable Models

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through  flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a  flexible approach for the construction and selection of highly  flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional  flexibility on the possible types of features to be considered. This  flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modi ed mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.  

Estimation of plant biomass based on the allometric equation

1981 ◽  
Vol 11 (4) ◽  
pp. 833-834 ◽  
Author(s):  
David O. Yandle ◽  
Harry V. Wiant Jr.
Keyword(s):  
Linear Regression ◽  
Plant Biomass ◽  
Allometric Equation ◽  
Arithmetic Mean ◽  
Measurement Scale ◽  
Simple Linear Regression ◽  
The Mean ◽  
The One ◽  
Biased Estimates

Estimation of the parameters in the allometric equation by fitting a simple linear regression to the logarithmically transformed variables results in biased estimates of the arithmetic mean. This bias expressed as a percent of the mean approaches the limit −(1 − e−σ2/2) (100) as n increases. An adjusted estimator developed by Finney rather than the one given by Baskerville should be used when s2 is large and n is small. A change of measurement scale of the x or y variables presents no difficulty, but problems arise if variables are transformed to logarithms other than base e.

scholarly journals Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Pablito M. López-Serrano ◽  
José Luis Cárdenas Domínguez ◽  
José Javier Corral-Rivas ◽  
Enrique Jiménez ◽  
Carlos A. López-Sánchez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aboveground Biomass ◽  
Goodness Of Fit ◽  
Accurate Estimation ◽  
Support Vector ◽  
Landsat 8 ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Physical Variables ◽  
Selection Of

An accurate estimation of forests’ aboveground biomass (AGB) is required because of its relevance to the carbon cycle, and because of its economic and ecological importance. The selection of appropriate variables from satellite information and physical variables is important for precise AGB prediction mapping. Because of the complex relationships for AGB prediction, non-parametric machine-learning techniques represent potentially useful techniques for AGB estimation, but their use and comparison in forest remote-sensing applications is still relatively limited. The objective of the present study was to evaluate the performance of automatic learning techniques, support vector regression (SVR) and random forest (RF), to predict the observed AGB (from 318 permanent sampling plots) from the Landsat 8 Landsat 8 Operational Land Imager (OLI) sensor, spectral indexes, texture indexes and physical variables the Sierra Madre Occidental in Mexico. The result showed that the best SVR model explained 80% of the total variance (root mean square error (RMSE) = 8.20 Mg ha−1). The variables that best predicted AGB, in order of importance, were the bands that belong to the region of red and near and middle infrared, and the average temperature. The results show that the SVR technique has a good potential for the estimation of the AGB and that the selection of the model hyperparameters has important implications for optimizing the goodness of fit.

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