A Comparison of Three Biomass Estimation Methods: A Case Study of Pinus tabulaeformis Forests in China

2013 ◽  
Vol 726-731 ◽  
pp. 4237-4240
Author(s):  
Fei Li ◽  
Zhong Yu Wang ◽  
Hua Yong Zhang ◽  
Yi Xin Xu ◽  
Lu Han
Keyword(s):  
Linear Model ◽  
Forest Biomass ◽  
Pinus Tabulaeformis ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Hyperbolic Model ◽  
Power Model ◽  
Regression Equations ◽  
Relative Errors

Power model, linear model and hyperbolic model were commonly used to estimate forest biomass via stand volume, however the relative accuracy is unclear forPinus tabulaeformisforests in China. In order to compare the accuracies of these models, data from 130Pinus tabulaeformisforest stands were compiled from published literatures. Data of 100 stands were randomly selected to establish regression equations, the other 30 data were used to compare the accuracies of equations either established in this study or in previous studies. The results show that biomass ofPinus tabulaeformisforests could be well estimated by power model and linear model, while hyperbolic model is likely to result in enormous overestimation or underestimation. The mean relative errors of the power model and linear model established in this study are-0.3% and 1.8% respectively. In comparison with models established by previous studies, these two models have better prediction accuracies.

scholarly journals Removing the Scaling Error Caused by Allometric Modelling in Forest Biomass Estimation at Large Scales

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 602
Author(s):  
Carl Zhou ◽  
Xiaolu Zhou
Keyword(s):  
Large Scale ◽  
Forest Ecosystems ◽  
Forest Biomass ◽  
Field Measurements ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Allometric Relationships ◽  
Data Errors ◽  
Pinus Yunnanensis

To estimate the responses of forest ecosystems, most relationships in biological systems are described by allometric relationships, the parameters of which are determined based on field measurements. The use of existing observed data errors may occur during the scaling of fine-scale relationships to describe ecosystem properties at a larger ecosystem scale. Here, we analyzed the scaling error in the estimation of forest ecosystem biomass based on the measurement of plots (biomass or volume per hectare) using an improved allometric equation with a scaling error compensator. The efficiency of the compensator on reducing the scaling error was tested by simulating the forest stand populations using pseudo-observation. Our experiments indicate that, on average, approximately 94.8% of the scaling error can be reduced, and for a case study, an overestimation of 3.6% can be removed in practice from a large-scale estimation for the biomass of Pinus yunnanensis Franch.

scholarly journals Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Dengsheng Lu ◽  
Qi Chen ◽  
Guangxing Wang ◽  
Emilio Moran ◽  
Mateus Batistella ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Optical Sensors ◽  
Forest Biomass ◽  
Structural Data ◽  
Data Availability ◽  
Estimation Methods ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Availability Constraints ◽  
Data Saturation

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area

2011 ◽  
Vol 115 (12) ◽  
pp. 3599-3614 ◽  
Author(s):  
Erik Næsset ◽  
Terje Gobakken ◽  
Svein Solberg ◽  
Timothy G. Gregoire ◽  
Ross Nelson ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Forest Biomass ◽  
Forest Area ◽  
Biomass Estimation ◽  
Auxiliary Data ◽  
Regional Forest

scholarly journals Consistent forest biomass stock and change estimation across stand, property and landscape levels

2020 ◽  
Author(s):  
Victor Felix Strimbu ◽  
Hans Ole Ørka ◽  
Erik Næsset
Keyword(s):  
Laser Scanning ◽  
Forest Biomass ◽  
Estimation Methods ◽  
Forest Sector ◽  
Estimation Errors ◽  
Cross Sectional ◽  
Field Surveys ◽  
Simulated Sampling ◽  
Different Levels

Stimulating climate change mitigation actions in the forest sector requires methods to quantify the biomass stocks and changes at different geographical levels. Often, differences in data and estimation methods that are available at each level cause inconsistencies in forest parameters estimated at different levels. We propose a method to align model-based and model-assisted estimators to ensure cross-sectional and time series consistency of stock and change estimates of above-ground biomass (AGB). The method adjusts estimates within their confidence intervals using heuristic optimization to minimize the estimation errors. The method is evaluated under simulated sampling in a case study representing a forested area of approximately 50 km2 in southeastern Norway. The area is divided into 93 forest properties encompassing 3324 forest stands. The artificial forest population is generated for two time points using wall-to-wall airborne laser scanning (ALS) data acquired in 2001 and 2016, as well as field surveys conducted within a similar timeframe. The adjusted AGB stock and change estimators at different levels of aggregation are compared to the original unadjusted estimators in terms of bias and RMSE. The results show that the adjusted estimators do not introduce bias, and the increase in RMSE is small for the forest stand-level estimators, and even decreasing for the forest property-level estimators. The method can easily be adapted to complex systems of estimators that need to be consistent.

A Comparison of Two Biomass Estimation Methods: A Case Study of Several Pine Forests in China

2013 ◽  
Vol 807-809 ◽  
pp. 806-809
Author(s):  
Fei Li ◽  
Hua Yong Zhang ◽  
Zhong Yu Wang ◽  
Li Zhang
Keyword(s):  
Linear Function ◽  
Power Function ◽  
Goodness Of Fit ◽  
Pinus Tabulaeformis ◽  
Estimation Methods ◽  
Pinus Koraiensis ◽  
Biomass Estimation ◽  
Function Model ◽  
Stand Volume ◽  
Function Models

Power function model and linear function model were commonly used to express the relationships between stand volume and biomass. However, the relative accuracy is still unclear. In order to compare the accuracy of the two types of model, field measurement data of 279 pine forest stands in China were collected from published literatures. Using the data collected, the relationships between stand volume and aboveground biomass (AGB) of Pinus koraiensis forest, Pinus armandii forest, Pinus massoniana forest, Pinus tabulaeformis forest and Pinus forest were established. The mean relative error and mean absolute values of relative errors were employed to test the errors of the established equations. The goodness-of-fit and errors of these two types of model were compared. The results show that the power function models could generally express the relationships better than the linear function models. Also, the errors of the power function models are generally lower than those of the linear function models.

scholarly journals Biomass Estimation Errors Associated with the Use of Published Regression Equations of Paper Birch and Trembling Aspen

2002 ◽  
Vol 19 (3) ◽  
pp. 128-136 ◽  
Author(s):  
J.R. Wang ◽  
A.L. Zhong ◽  
J.P. Kimmins
Keyword(s):  
Mean Squared Error ◽  
Forest Biomass ◽  
Significant Proportion ◽  
Graphical Form ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Trembling Aspen ◽  
Estimation Errors ◽  
Biomass Equations ◽  
Paper Birch

Abstract Since forest biomass contributes a significant proportion of global carbon cycle, obtaining accurate estimate of forest biomass is important. The root mean squared error (RMSE), the percents of the mean observed values were used to compare the precision of local and published biomass equations for paper birch and trembling aspen. With the exception of stemwood biomass equations, the biomass equations for these two species tended to be stand specific. Measured as percent of mean observed values, the values of biomass/tree predicted from the published equations for paper birch varied from 49.9% to 140.2% for foliage and from 155% to 238.7% for live branches; the estimates for trembling aspen ranged from 71.8% to 81.3% for foliage and from 55.3% to 164.5% for live branches. There were large discrepancies between the measured data and the published equations in graphical form as well as biomass estimates, particularly for foliage, live branches, and stembark. Clearly, published regression equations should be checked for their applicability before they are used to estimate the biomass of particular stands.

Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India

2019 ◽  
Vol 191 (9) ◽  
Author(s):  
Prem Chandra Pandey ◽  
Prashant K. Srivastava ◽  
Tilok Chetri ◽  
Bal Krishan Choudhary ◽  
Pavan Kumar
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
Biomass Estimation

Estimates of Carbon Storage in the Aboveground Biomass of Victorias Forests

1992 ◽  
Vol 40 (5) ◽  
pp. 631 ◽  
Author(s):  
PF Grierson ◽  
MA Adams ◽  
PM Attiwill
Keyword(s):  
Carbon Fixation ◽  
Forest Type ◽  
Forest Biomass ◽  
Carbon Accumulation ◽  
Total Carbon ◽  
Biomass Estimation ◽  
Regression Equations ◽  
Carbon Density ◽  
Critical Data ◽  
Mountain Ash

The pool of carbon in the world's forests is of similar magnitude to that in the atmosphere, yet little attention has been given to improving measures of carbon in terrestrial biomass. Much of the critical data for forest biomass on which models of global carbon cycling rely is, in fact, based on the accurate sampling of less than 100 ha of forest. Uncertainties in biomass estimation at the local and regional level may be responsible for much of the current speculation as to unidentified sinks for carbon. We have used a forest inventory (i.e. records of forest volume obtained for harvesting purposes) approach to quantify the biomass of forests in Victoria, Australia. Forests were analysed by type, age and region. Regression equations were developed for the accumulation of biomass with age across all productivity classes for each forest type. The mean carbon density for above-ground components of Victorian native forests is 157 tonnes ha-1 (t ha-1), although forest types range in mean carbon density from 250 to 18 t ha-1. Pinus radiata D. Don plantations in Victoria have a mean carbon density of 91 t ha-1 in the above-ground components. Total carbon stored in above-ground biomass is estimated to be 1.2 X 109 t. Rates of carbon fixation vary with forest age, species and site. Mountain ash (Eucalyptus regnans F. Muell.) forests fix around 9 t of carbon ha-1 annually during the first few years of growth, decreasing to 6 t ha-1 by age 10. Rates of carbon accumulation by other forests are generally less than this and, at the lower end of the range, box-ironbark forests, mallee and woodlands accumulate between 0.5 and 2 t ha-1 year-1. P. radiata plantations in Victoria will accumulate around 7 t carbon ha-1 year-1.

scholarly journals INVESTIGATING THE USABILITY OF UAV OBTAINED MULTISPECTRAL IMAGERY IN TREE SPECIES SEGMENTATION

2019 ◽  
Vol XLII-2/W18 ◽  
pp. 159-165
Author(s):  
M. Pap ◽  
S. Kiraly ◽  
S. Moljak
Keyword(s):  
Tree Species ◽  
Low Cost ◽  
Forest Biomass ◽  
Bioenergy Crops ◽  
Biomass Estimation ◽  
Multispectral Imagery ◽  
Short Rotation ◽  
Segmentation Algorithms ◽  
Breeding Efficiency

Abstract. A widely used form of renewable energy are bioenergy crops. One form of it is the energy forestry that includes short rotation coppice plantations in which fast growing species of tree or woody shrub are grown (e.g. poplar, willow). The accurate prediction of forest biomass and volume can be used for the evaluation of plant breeding efficiency as well. The automatic tracking of plant development by traditional methods is quite difficult and labor intensive. Since energy forestries often contain different trees for estimating their volume it is essential to find segments containing the same tree species in the image.We investigated the applicability of a low cost UAV and an intermediate cost UAV in the field of agricultural image segmentation that is the first stage of biomass estimation (Gatziolis et al., 2015, Gaulton et al., 2015).This paper is a case study that shows the results of several segmentation algorithms applied on imagery obtained by a low cost UAV with low-cost camera, and imagery gathered by a UAV and camera set that are of higher quality and price. In the case study, we have observed two small forestry areas that contained six different tree species and their hybrids. Our results show that more expensive, better-equipped drone shots do not necessary provide significantly better segmentation.

Biorefinery implementation for recovery debottlenecking at existing pulp mills- Part II: Techno-economic evaluation

TAPPI Journal ◽  
2012 ◽  
Vol 11 (8) ◽  
pp. 17-24 ◽  
Author(s):  
HAKIM GHEZZAZ ◽  
LUC PELLETIER ◽  
PAUL R. STUART
Keyword(s):  
Cost Estimation ◽  
Process Model ◽  
Black Liquor ◽  
Pulp Mill ◽  
Value Added ◽  
Economic Assessment ◽  
Extraction Process ◽  
Estimation Methods ◽  
Precipitation Process

The evaluation and process risk assessment of (a) lignin precipitation from black liquor, and (b) the near-neutral hemicellulose pre-extraction for recovery boiler debottlenecking in an existing pulp mill is presented in Part I of this paper, which was published in the July 2012 issue of TAPPI Journal. In Part II, the economic assessment of the two biorefinery process options is presented and interpreted. A mill process model was developed using WinGEMS software and used for calculating the mass and energy balances. Investment costs, operating costs, and profitability of the two biorefinery options have been calculated using standard cost estimation methods. The results show that the two biorefinery options are profitable for the case study mill and effective at process debottlenecking. The after-tax internal rate of return (IRR) of the lignin precipitation process option was estimated to be 95%, while that of the hemicellulose pre-extraction process option was 28%. Sensitivity analysis showed that the after tax-IRR of the lignin precipitation process remains higher than that of the hemicellulose pre-extraction process option, for all changes in the selected sensitivity parameters. If we consider the after-tax IRR, as well as capital cost, as selection criteria, the results show that for the case study mill, the lignin precipitation process is more promising than the near-neutral hemicellulose pre-extraction process. However, the comparison between the two biorefinery options should include long-term evaluation criteria. The potential of high value-added products that could be produced from lignin in the case of the lignin precipitation process, or from ethanol and acetic acid in the case of the hemicellulose pre-extraction process, should also be considered in the selection of the most promising process option.

Sign in / Sign up

Export Citation Format

Share Document