Generalized biomass estimation equations for Betulapapyrifera Marsh.

1981 ◽  
Vol 11 (4) ◽  
pp. 837-840 ◽  
Author(s):  
Mark D. C. Schmitt ◽  
D. F. Grigal
Keyword(s):  
New Hampshire ◽  
Aboveground Biomass ◽  
Biomass Estimation ◽  
Mean Square ◽  
Data Set ◽  
Site Specific ◽  
Specific Data ◽  
Independent Variables ◽  
Breast Height ◽  
Estimation Equations

Aboveground biomass estimation equations were developed and compared for several components of Betulapapyrifera Marsh, trees using diameter at breast height (dbh) alone or dbh and height as independent variables. The data upon which the equations are based were collected by a number of different investigators working in Minnesota, Wisconsin, New Hampshire, and several sites in Maine and New Brunswick. Coefficients of determination ranged from 0.82 to 0.99, with higher values for bole than for crown components. The root mean-square deviation of the observations from the model was in the range 1 – 10 kg for any component. The largest trees in the data set (ca. 30 cm dbh) had total aboveground biomass of about 540 kg. In the absence of site-specific data, these equations provide acceptable estimates of biomass for B. papyrifera.

scholarly journals Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 914
Author(s):  
Adeel Ahmad ◽  
Hammad Gilani ◽  
Sajid Rashid Ahmad
Keyword(s):  
High Resolution ◽  
Literature Review ◽  
Satellite Imagery ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Independent Variables ◽  
Non Linear ◽  
Multiple Variables ◽  
Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

scholarly journals Uncertainty assessment in aboveground biomass estimation at the regional scale using a new method considering both sampling error and model error

2017 ◽  
Vol 47 (8) ◽  
pp. 1095-1103 ◽  
Author(s):  
Yu Fu ◽  
Yuancai Lei ◽  
Weisheng Zeng ◽  
Ruijun Hao ◽  
Guilian Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Aboveground Biomass ◽  
Regional Scale ◽  
Model Fitting ◽  
Model Error ◽  
New Method ◽  
Biomass Estimation ◽  
Calibration Data ◽  
Data Set ◽  
Sources Of Error

Uncertainty associated with multiple sources of error exists in biomass estimation over large areas. This uncertainty affects the accuracy of the resultant biomass estimates. A new method that introduces Taylor series principles into a Monte Carlo simulation procedure was proposed and developed for estimating regional-scale aboveground biomass, along with quantifying the corresponding uncertainty arising from both sampling and model predictions. Additionally, the effect of sample size on estimates during model fitting was studied based on the new method to determine whether the effect of the size of the calibration data set can be neglected when the number of simulations is sufficiently large. The results revealed that the proposed method not only produces more reliable estimates of both biomass and uncertainty but also effectively and separately quantifies the uncertainties associated with different sources of error. The new method also reduced the effect of model uncertainty on final estimates. The uncertainty that was associated with model error increased significantly with decreasing sample sizes during model fitting, and the error was not reduced by increasing the number of Monte Carlo simulations.

scholarly journals Assessment of Aboveground Biomass and Temperature in the Tropical Rainforest of Malaysia

2019 ◽  
Vol 8 (12) ◽  
pp. 1888-1891
Keyword(s):  
Aboveground Biomass ◽  
Sustainable Management ◽  
Tropical Rainforest ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Simple Linear Regression ◽  
Breast Height ◽  
Forest Condition ◽  
Future Value ◽  
The Relationship

The relation between tropical rainforest to the climate variability is very important. This research aims to determine the relationship between aboveground biomass which prefer tree in the tropical rainforest and surrounding temperature. Diameter at breast height (DBH) of ten tree species and surrounding temperature collected data were taken to measure the correlation between the two variables by using statistical test. Furthermore, forest biomass estimation is also important towards the assessment of the productivity, structure and forest condition. The analysis in this research shows that simple linear regression model can be used to predict the future value of DBH for each species. The findings may help the reduction of greenhouse gas emissions with proper conservation and sustainable management.

scholarly journals A novel prediction algorithm for multivariate data sets

2021 ◽  
Vol 4 (2) ◽  
pp. 225-240
Author(s):  
Pinki Sagar ◽  
◽  
Prinima Gupta ◽  
Rohit Tanwar ◽  
◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Data ◽  
Prediction Algorithm ◽  
Series Data ◽  
Data Sets ◽  
Mean Square ◽  
Data Set ◽  
Independent Variables

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.

Testing the performance of a forest ecosystem model (FORECAST) against 29 years of field data in a Pseudotsuga menziesii plantation

2007 ◽  
Vol 37 (10) ◽  
pp. 1808-1820 ◽  
Author(s):  
Juan A. Blanco ◽  
Brad Seely ◽  
Clive Welham ◽  
J. P. (Hamish) Kimmins ◽  
Tanya M. Seebacher
Keyword(s):  
Pseudotsuga Menziesii ◽  
Forest Ecosystem ◽  
Model Performance ◽  
Field Measurements ◽  
Stem Density ◽  
Model Forecast ◽  
Regional Data ◽  
Data Set ◽  
Site Specific ◽  
Specific Data

The ability of the forest ecosystem management model FORECAST to project a 29-year record of stand response to factorial thinning and fertilization treatments in a Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) plantation at Shawnigan Lake (Vancouver Island, British Columbia, Canada) was assessed. Model performance was evaluated firstly using for calibration a regional data set and secondly with site-specific data from control plots. Model output was compared against field measurements of height, diameter, stem density, component biomass (aboveground), and litterfall rates and estimates of nutrient uptake, foliar N efficiency, and understory vegetation biomass. When calibrated with regional data, results from graphical comparisons, three measures of goodness-of-fit, and equivalence testing demonstrated that FORECAST can produce predictions of good to moderate accuracy depending on the variable of interest. Model performance was generally better when compared with field measurements (e.g., top height, diameter at breast height, and stem density) as opposed to outputs derived from allometric and volume equations. Use of site-specific data to calibrate the model always improved performance, although improvements were modest for most variables, with the exception of branch and foliage biomass. The benefits of site-specific calibration, however, should be weighed against the costs of obtaining such data. The intended use of the model will likely determine the level of effort expended in its calibration.

scholarly journals Biomass functions applicable to European beech

2012 ◽  
Vol 51 (No. 4) ◽  
pp. 147-154 ◽  
Author(s):  
E. Cienciala ◽  
M. Černý ◽  
J. Apltauer ◽  
Z. Exnerová
Keyword(s):  
Tree Height ◽  
European Beech ◽  
Biomass Estimation ◽  
Managed Forests ◽  
Independent Variables ◽  
Breast Height ◽  
Allometric Functions ◽  
Branch Biomass ◽  
Independent Variable ◽  
Total Tree

This material describes parameterization of allometric functions applicable to biomass estimation of European beech trees. It is based on field data from destructive measurements of 20 full-grown trees with diameter at breast height (dbh) from 5.7 to 62.1 cm. The parameterization was performed for total tree aboveground biomass (AB; besides stump), stem and branch biomass, respectively. The allometric functions contained two or three parameters and used dbh either as a single independent variable or in combination with tree height (H). These functions explained 97 to 99% of the variability in the measured AB. The most successful equation was that using both dbh and H as independent variables in combination with three fitted parameters. H, as the second independent variable, had rather a small effect on improving the estimation: in the case of AB, H as independent variable improved prediction accuracy by 1–2% whereas in the case of branch biomass by about 5%. The parameterized biomass equations are applicable to tree specimens of European beech grown in typically managed forests.

scholarly journals Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia

2021 ◽  
Vol 22 (9) ◽  
Author(s):  
Rahmanta Setiahadi
Keyword(s):  
Aboveground Biomass ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Expansion Factor ◽  
Individual Tree ◽  
Biomass Expansion Factor ◽  
Community Forests ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Tree Component

Abstract. Setiahadi R. 2021. Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia. Biodiversitas 22: 3899-3909. The use of allometric equation and biomass expansion factor can facilitate more efficient tree biomass estimation. This study evaluates the accuracy of the allometric equation and expansion factor for quantifying the individual tree aboveground biomass in community forest tree species. Destructive sampling n on 120 trees from four different species: Falcataria moluccana, Melia azedarach, Swietenia macrophylla, and Tectona grandis. For each tree sample, aboveground biomass measured at every tree component, i.e., stem, branches, and leaves. The allometric equation developed using regression analysis with several predictor variables, such as diameter at breast height (D), squared diameter at breast height combined with tree height (D2H), and D and H separately. On another side, the biomass expansion factor was calculated based on the total aboveground biomass and stem biomass ratio. The results found the highest mean aboveground biomass for all species are M. azedarach (326.36±88.40 kg tree-1), S. macrophylla (244.47±98.73 kg tree-1), T. grandis (173.31±80.97 kg tree-1), and F. moluccana (56.56±23.10 kg tree-1). The most significant average biomass expansion factor observed in M. azedarach (1.78±0.03), adhered by T. grandis (1.66±0.09), S. macrophylla (1.61±0.04), and F. moluccana (1.59±0.06). The equation ln? = lna + b x ln (D) was best for estimating aboveground biomass in each tree component and a total of four species with an accuracy of more than 90%.

Individual tree biomass estimation equations for plantation-grown white spruce in northern Minnesota

1985 ◽  
Vol 15 (4) ◽  
pp. 738-739 ◽  
Author(s):  
R. B. Harding ◽  
D. F. Grigal
Keyword(s):  
Stand Structure ◽  
White Spruce ◽  
Biomass Estimation ◽  
Prediction Equations ◽  
Tree Biomass ◽  
Eastern Canada ◽  
Individual Tree ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Estimation Equations

Prediction equations for biomass of white spruce (Piceaglauca (Moench) Voss) were developed for 115 sample trees using the allometric models Y = ADB and Y = ADBHC, where Y is mass, D is diameter at breast height, and H is total height. The addition of height to the model reduced the Sy•x for all estimates except that for biomass of branches and needles. Comparison of results to other estimation equations developed in eastern Canada showed that biomass estimates were variable. Variations in stand structure and age between natural and plantation-grown trees are possible reasons for these differences.

scholarly journals The Variation Driven by Differences between Species and between Sites in Allometric Biomass Models

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 976 ◽  
Author(s):  
Dutcă
Keyword(s):  
Aboveground Biomass ◽  
Tree Height ◽  
Site Specific ◽  
Allometric Models ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Biomass Models ◽  
Anova Model ◽  
Species Specific ◽  
Variance Explained

Background and Objectives: It is commonly assumed that allometric biomass models are species-specific and site-specific. However, the magnitude of species and site dependency in these models is not well-known. This study aims to investigate the variation in allometric models (i.e., aboveground biomass predicted by diameter at breast height and tree height) that has originated from the differences between tree species and between sites, thereby contributing to a better understanding of species and site-specificity issue in these models. Materials and Methods: The study is based on two large biomass datasets of 4921 and 5199 trees, from Eurasia and Canada. Using a nested ANOVA model on relative aboveground biomass residuals (with species and site as random effects), the proportion of variance explained by species or site was assessed by means of Variance Partition Coefficient (VPC). Results: The proportion of variance explained by species (VPCspecies = 42.56%, SE = 6.10% for Dataset 1 and VPCspecies = 47.54%, SE = 6.07% for Dataset 2) was larger than that explained by site (VPCsite = 20.08%, SE = 3.35% for Dataset 1 and VPCsite = 8.27%, SE = 1.38% for Dataset 2). The proportion of variance explained by site decreased by 24%–44% and the proportion of variance explained by species changed only slightly, when height is included in the allometric biomass models (i.e., models based on diameter at breast height alone, compared to models based on diameter at breast height and tree height). Conclusions: Allometric biomass models were more species-specific than they were site-specific. Therefore, the species (i.e., differences between species) seems to be a more important driver of variability in allometric models compared to site (i.e., differences between sites). Including height in allometric biomass models helped reduce the dependency of these models, on sites only.

Application of private site-specific data to regional evaluation of earthquake and faulting potential in Southern California

1982 ◽  
Author(s):  
D.L. Lamar ◽  
J. L. Smith ◽  
J. W. La Violette ◽  
K. Custis ◽  
P.J. Scrivner
Keyword(s):  
Southern California ◽  
Site Specific ◽  
Specific Data
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