Biomass Tables Research Articles

Biomass tables and allometric equations for predicting the fodder and fuelwood production of prominent tree resources in agricultural landscapes of the mid-hills of the North-Western Himalayas

Biomass tables and allometric equations for predicting the fodder and fuelwood production of prominent tree resources in agricultural landscapes of the mid-hills of the North-Western Himalayas

Characterizing Stand and Biomass Tables from Diameter Distribution Models: A Case Study for Mixed-Hardwood Forests in Eastern Tennessee, USA

Abstract Accurately assessing forest structure and productivity is critical to making timely management decisions and monitoring plant communities. This study aims to evaluate the prediction accuracy of site-level stand and biomass tables from the diameter distribution models. The efficacy of the single Weibull function and two finite mixture models was compared for six species groups on three mixed-hardwood sites in eastern Tennessee, USA. To evaluate model performance, two types of stand/biomass tables were generated. The first type was constructed from all species on a given site (site-specific), whereas the second type was built for a single species from all sites (species-specific). Results indicate that both types of stand and biomass tables were consistently well quantified by the two-component mixture model in terms of goodness of fit, parsimony, and robustness. The two-component mixture model better characterized the complex, multimodal diameter distributions than the single Weibull model, which underpredicted the upper portion of the distributions. The three-component model tends to overfit the data, which results in lower prediction accuracy. Among the three models examined, the two-Weibull mixture model is suggested to construct site-level stand/biomass tables, which provides more reliable and accurate predictions to assess forest structure and product class.

TaCAL: un sistema di tariffe del volume legnoso e delle fitomasse delle specie forestali della Calabria

Abstract: In the frame of the ALFORLAB project, a harmonised set of tree volume (and biomass) single entry tables (“tariffs”) was developed for the main 20 forest species of Calabria (Southern Italy), to outline the “coordinates” of volume expression for inventory purposes at both regional and forest planning level. The system, called TaCAL, was based on the double entry volume and biomass functions of the Italian National forest Inventory - INFC 2005 -, and was built studying the height/dbh relationships on 2300 trees measured during the INFC campaign in Calabria, on over 300 sampling plots randomly distributed. For each of the 20 species, or group of similar species, the tariff system is articulated into 9 h/d relationship curves, which are in turn related to corresponding single entry tables of tree volume (stem and branches with dbh above 5 cm), and respective biomass tables. According to INFC 2005 stereometric equations, TaCAL system is suitable for both high forests and coppices. TaCAL provides a uniform volume and biomass estimation system for all main forest species of Calabria as well as, under certain conditions, a unique criterion of expression of the site fertility. TaCAL tariff system has already been integrated into the construction of forecasting models of timber volume and biomass based on angle count samplings, at forest management scale. TaCAL has also provided the basis for ground truth volume estimates to develop forecast models through remote sensing LiDAR data. It must be emphasized that TaCAL represents the first case in Italy in which single tree data by INFC field surveys were used to carry out a comprehensive dendrometric system at a regional level.

Construction of Tree Biomass Tables in Turkey for the Estimating of Biomass Potential for Energy

According to economists and other observers of the energy scene, there is an impending shortage in the civilized world of petroleum and gas. Exhaustion of these nonrenewable fuels prompts consideration of alternative energy sources. One alternative source is forest biomass, which is defined as the quantity constituted by living organisms in the forest ecosystem in terms of mass. The aboveground portions of trees and shrubs transform solar energy in the form of vegetational substances. Because the forest is a renewable natural resource, plantations of trees and natural stands that were previously considered unmerchantable are now being examined as a new source of renewable energy. To help overcome a possible future energy shortage, methods of correctly managing and using forest biomass resources have to be developed now. The need for knowledge of the existing amount of standing timber and its growth in terms of mass, as measured and expressed by consistent methods and standars, is a part of this broad problem. The solution can be approached by producing biomass tables for tree species.

A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland

Spatial measures of forest biomass are important to implement sustainable forest management, monitor global change, and model forest productivity. Several methods for estimating forest biomass by remote sensing have been developed, but their comparative advantages have not been evaluated for large areas in Canada. This study compares four methods to map forest biomass on an extended pilot region (20,000 km 2) located in western Newfoundland. The methods include: (i) Direct Radiometric Relationships (DRR), (ii) k-Nearest Neighbors ( k-NN), (iii) Land Cover Classification (LCC), and (iv) Biomass from Cluster Labeling Using Structure and Type (BioCLUST). The results of each method were evaluated using an independent set of ground survey plots and compared with a baseline biomass map generated from biomass tables applied to forest inventory stand maps. Considering the root mean square error (RMSE) assessed with the inventory plots, the DRR, k-NN, and BioCLUST methods provided similar results, with average RMSE values of 59, 59, and 58 t/ha, respectively. Bias values were lowest for the k-NN method followed by DRR, BioCLUST, and LCC (6, −8, 17, and 42 t/ha, respectively). Assessed with the baseline map, the BioCLUST method produced the lowest RMSE (41 t/ha) and bias (−4 t/ha) followed by the DRR and k-NN methods, with RMSE values of 47 and 54 t/ha and bias values of 9 and 23 t/ha, respectively. The method using biomass tables applied on the classified TM image (LCC) provided the greatest RMSE and bias, but may be suitable for applications that do not require a high level of precision. The BioCLUST and LCC methods provided practical advantages for the type of data sets available. Overall, the choice of a method rests on both the availability of data sets and the level of precision of the results required.

Biomass estimation of Bambusa tulda grown at Eastern Terai, Nepal

With a view to prepare biomass tables of Bambusa tulda grown at Belbari, Morang district of Eastern Nepal, a total of 153 culms was selected from 59 clumps. Measurements of diameter at 15 cm of the base (D15), vertical height of the culm, green weight of the culm, branches and foliage were taken in the field. The samples were oven dried in laboratory at Kathmandu. To estimate the biomass, a regression model was developed on the basis of oven dry and green weight. The model used was W = a + b × (D2 L). Based on the oven dry weight, the R2 values were obtained for culm, branch and foliage components, which were 92, 81 and 83%, respectively. Similarly, R2 values for culm and foliage components on the basis of green weight were 92 and 82%, respectively. The R2 values obtained for branch and foliage components were slightly lower as compared to the culm. This equation could be useful in estimating bamboo biomass of managed natural stands or plantations in similar site conditions.

Wood biomass estimation in dry miombo woodland in Zimbabwe

This paper is concerned with the wood yield of two of the most common indigenous tree species of Zimbabwean miombo woodland, Brachystegia spiciformis and Julbernardia globiflora. Simple wood yield models were calculated for each of the two species in regenerating dry miombo woodland in central Zimbabwe, in an area which receives a mean annual rainfall of 766 mm. The data were analysed at two levels: initially each stem was treated as an individual, and then stems were combined in coppice stools. The two species were analysed separately at first. Total dry wood biomass was measured in kilograms and stem volume was estimated using stem diameter ( d) at 30 cm above ground, stem length ( L) (as opposed to tree height) or a combination of both. All data were transformed using the log 10 transformation. The best yield model, accounting for 0.98 of the variation for both species, proved to be one using Σ(d 2 L) as the independent variable. A second model, using the diameter of the largest stem in the coppice stool, also gave good r 2 values and in view of the time needed to measure all the stems in each coppice stool, this model was considered to be most useful in developing biomass tables. The functions developed for each species separately were not significantly different and therefore the data were combined to produce one model for both species.

Linear regression models for biomass table construction, using cluster samples

Biomass tables are often constructed from cluster samples by means of ordinary least squares regression estimation procedures. These procedures assume that sample observations are uncorrelated, which ignores the intracluster correlation of cluster samples and results in underestimates of the model error. We tested alternative estimation procedures by simulation under a variety of cluster sampling methods, to determine combinations of sampling and estimation procedures that yield accurate parameter estimates and reliable estimates of error. Modified, generalized, and jack-knife least squares procedures gave accurate parameter and error estimates when sample trees were selected with equal probability. Regression models that did not include height as a predictor variable yielded biased parameter estimates when sample trees were selected with probability proportional to tree size. Models that included height did not yield biased estimates. There was no discernible gain in precision associated with sampling with probability proportional to size. Random coefficient regressions generally gave biased point estimates with poor precision, regardless of sampling method.

On the error of estimates from successive forest inventories with SPR

The approach used by Cunia to combine the error from sample plots with the error from volume or biomass tables when Continuous Forest Inventory (CFI) estimates of current values and growth are calculated is extended to the CFI systems using Sampling with Partial Replacement (SPR). The formulae are derived for the case of SPR on two measurement occasions when (i) volume or biomass tables are constructed from linear regressions for which an estimate of the covariance matrix of the regression coefficients is known, and (ii) the sample plots or points are selected by random sampling independently of the given volume or biomass regression functions.

A method of forcing additivity of biomass tables when using nonlinear models

A procedure is presented for estimating the coefficients of allometric models for predicting tree component biomass. The resulting equations force the sum of the component estimates to be equal to the estimate of total biomass. An illustration of the procedure is given using published biomass data and the relationship of this procedure to previously published procedures is discussed.

Harmonizing biomass tables by generalized least squares

To construct biomass tables for various tree components that are consistent with each other, one may use linear regression techniques with dummy variables. When the biomass of these components is measured on the same sample trees, one should also use the generalized rather than ordinary least squares method. A procedure is shown which allows the estimation of the covariance matrix of the sample biomass values and circumvents the problem of storing and inverting large covariance matrices. Applied to 20 sets of sample tree data, the generalized least squares regressions generated estimates which, on the average were slightly higher (about 1%) than the sample data. The confidence and prediction bands about the regression function were wider, sometimes considerably wider than those estimated by the ordinary weighted least squares.

Forcing additivity of biomass tables: use of the generalized least squares method

The generalized least squares procedure is applied to sample tree data for which additive biomass tables are required. This procedure is proposed as an alternative to the ordinary weighted least squares in order to account for the fact that several biomass components are measured on the same sample trees. The biomass tables generated by the generalized and the ordinary least squares are very similar, the confidence intervals are sometimes wider, sometimes narrower, but the prediction intervals are always narrower for the generalized least squares method.

Forcing additivity of biomass tables: some empirical results

Three methods for insuring the additivity of biomass tables are described and their application to a sample of trees illustrated. The advantages and drawbacks of each method are identified and the results obtained compared with each other. All three methods yield biomass tables that are not significantly different from each other (at least for the sample data used), but their estimated precision and conditions of applicability are different. At least one of the methods can be applied to the cases where the weights to use in the least squares regression procedure may differ from one to the next tree component, when sample data of some component are missing from some sample trees and when nonlinear seem better than linear regression functions.

On the error of tree biomass tables constructed by a two-phase sampling design

It is common with forest biomass inventories to use one-way tree biomass tables (by diameter) constructed from (i) a two-way biomass table (by diameter and height) previously constructed for application to a large forest region, and (ii) a diameter–height relationship established from a sample of trees selected from the forest area being currently inventoried. A procedure is described to estimate the error of the one-way biomass table so constructed when the two-way biomass table and the diameter–height relationship are estimated by linear regression statistical techniques. The use of this procedure is then illustrated by applying it to a sample of 56 trees measured for biomass, diameter, and height and a sample of 224 trees measured for diameter and height alone.

Effect of cluster sampling in biomass tables construction: linear regression models

To construct tree biomass tables it is customary to select the sample trees by cluster sampling and then to apply the classical least squares regression techniques under the assumption of simple random sampling. A modified linear regression procedure is proposed for which the assumption of simple random sampling is no longer required. The procedure can be used when (i) the regression of a biomass component on tree characteristics other than biomass can be approximated reasonably well by a linear function and (ii) the number of sample clusters is sufficiently large. Applied to two large cluster samples of trees, where the cluster size is approximately equal to five trees, and compared with the classical linear regression approach, the modified procedure results in biomass tables which arc essentially the same. The confidence intervals, however, are quite different. The classical least squares regression method results in intervals which, on the average, are about 60% as large as those calculated by the modified procedure.

Effect of cluster sampling in biomass tables construction: ratio estimators models

To minimize field costs, it is ordinarily efficient to select sample trees by cluster sampling; that is, to select several trees from the same geographical location. But when the sample trees are used to construct biomass tables, it is also customary to use regression techniques based on the simple random sampling assumption. This procedure gives rise to errors in the statistical analysis that are largely ignored.A method is shown to evaluate the validity of the regression analysis when the undefined relationship between a dependent variable y and an independent variable x can be sufficiently well approximated by a line of the form y = βx. This method is then applied to two samples of trees selected in clusters of approximately five trees. The regression of nine variables y (various biomass components) on six variables x (functions of tree diameter and height) are then considered. It is shown that analyzing data selected by cluster sampling as data selected by simple random sampling results in relatively large errors in the interval estimates of β. More specifically, the confidence intervals based on the random sampling assumption are ordinarily found to be between 60 and 80% of those based on the cluster sampling assumption. Within the regression model used, however, the point estmates of β remained unchanged.

Use of dummy variables to harmonize tree biomass tables

When constructing biomass tables for various tree components, it is often necessary to force their regression functions to be similar in shape and consistent in spacing. A procedure using dummy variables is presented for the harmonization of the regression functions of the biomass of the tree bole up to various top diameters. The procedure allows one to calculate several regressions simultaneously to test their similarity, control their spacing, and compare statistically various regression models.