Individual Tree Biomass Models Research Articles

Developing national and regional individual tree biomass models and analyzing impact of climatic factors on biomass estimation for poplar plantations in China

National and regional climate-sensitive biomass equations were developed for poplar plantations in China, with mean annual temperature and precipitation significantly affecting predicted biomass. In the context of global climate change, information on forest biomass becomes more and more important, and the impact of climate change on biomass estimation is also receiving increasing attention. Using the dummy variable modeling approach and the error-in-variable simultaneous equations approach, national and regional one- and two-variable individual tree biomass models were developed for poplar plantations in China. The models were based on above- and below-ground biomass data of 450 and 147 destructive sample trees, respectively, collected from poplar plantations in 15 provinces of three regions in China. In addition, combined with climate data of mean annual temperature (T) and mean annual precipitation (P), climate-sensitive individual tree biomass models were established, and the impact of climatic factors on biomass estimation was analyzed. The coefficients of determination of national above- and below-ground biomass models developed in this study were more than 0.90 and 0.82, whereas the mean prediction errors were less than 5% and 10%, respectively. For climate-sensitive biomass models, aboveground biomass was only impacted by T, while belowground biomass was affected by both T and P, and impact of the latter was greater than that of the former. Considering the comprehensive effect of climatic factors to above- and belowground estimation, the total biomass estimates of poplar trees with the same size would reach the maximum in the regions for T = 17 °C and P = 1200 mm, which might provide reference for the scientific management of poplar plantations in China.

Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods

Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset at a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics.

Individual Tree Parameters Estimation for Plantation Forests Based on UAV Oblique Photography

The parameters of individual trees in forests are important for accurate forestry inventory and sustainable management. Unmanned aerial vehicle (UAV) oblique photogrammetry technology plays an important role in forest surveys because of its flexibility, low cost, high spatial data resolution. Few studies have committed to extract individual tree parameters and estimate individual tree biomass using oblique photography data. In this study, images of larch( Larix gmelinii ) and Chinese pine( Pinus tabuliformis ) plantations with different ages were acquired by UAV oblique photogrammetry. Then, three dimension (3D) point clouds were constructed recovered by the structure from motion (SFM) algorithm and than normalized. The watershed segmentation, point cloud segmentation (PCS) and object-oriented multiresolution segmentation (MRS) methods were used to delineate individual trees and extract tree height and crown area. Finally, stepwise regression was used to fitting individual tree biomass models based on the point cloud metrics, crown area obtained from individual tree segmentation and measured biomass. The results indicated that: most suitable segmentation method are determined for different tree species at different ages; optimal prediction models of individual tree biomass can be constructed by combining the point cloud metrics with the tree crown area obtained by segmentation, accuracy of the models are all greater than 0.8. The results show that UAV oblique photography technology can be used to accurately extract the individual tree parameters of larch and Chinese pine plantation forests in northern China and can meet the requirements of large-scale and low-cost forestry inventory.

Deriving Individual-Tree Biomass from Effective Crown Data Generated by Terrestrial Laser Scanning

Biomass reflects the state of forest management and is critical for assessing forest benefits and carbon storage. The effective crown is the region above the lower limit of the forest crown that includes the maximum vertical distribution density of branches and leaves; this component plays an important role in tree growth. Adding the effective crown to biomass equations can enhance the accuracy of the derived biomass. Six sample plots in a larch plantation (ranging in area from 0.06 ha to 0.12 ha and in number of trees from 63 to 96) at the Mengjiagang forest farm in Huanan County, Jiamusi City, Heilongjiang Province, China, were analyzed in this study. Terrestrial laser scanning (TLS) was used to obtain three-dimensional point cloud data on the trees, from which crown parameters at different heights were extracted. These parameters were used to determine the position of the effective crown. Moreover, effective crown parameters were added to biomass equations with tree height as the sole variable to improve the accuracy of the derived individual-tree biomass estimates. The results showed that the minimum crown contact height was very similar to the effective crown height, and an increase in model accuracy was apparent (with R a 2 increasing from 0.846 to 0.910 and root-mean-square error (RMSE) decreasing from 0.372 kg to 0.286 kg). The optimal model for deriving biomass included tree height, crown length from minimum contact height, crown height from minimum contact height, and crown surface area from minimum contact height. The novelty of the article is that it improves the fit of individual-tree biomass models by adding crown-related variables and investigates how the accuracy of biomass estimation can be enhanced by using remote sensing methods without obtaining diameter at breast height.

Aboveground biomass and carbon of the highly diverse Atlantic Forest in Brazil: comparison of alternative individual tree modeling and prediction strategies

The Brazilian Atlantic Forest is a biologically and structurally diverse biome which covers a large area; however, few tree-level biomass models exist for this region. The objective of this study was to evaluate alternative individual tree biomass models for the Atlantic Forest using contrasting prediction approaches, and evaluate their implications for stand-level biomass and carbon. The data comprised 106 destructively sampled trees (dbh; 5.4 – 68.5 cm) from the 16 most abundant species. Different approaches, namely species-specific, functional-trait based, generalized, and previously developed equations, as well as alternative model forms using differing variables including diameter at breast height (dbh), wood specific gravity (wsg), and height to crown base (hcb) were examined. Species-specific models were developed using both ordinary least squares and linear mixed effects with species as a random effect. The various approaches resulted in statistically different estimates of biomass and carbon at both the individual tree- and stand-levels. Compared to a generalized biomass equation for Pan-tropical forests, our results indicate differences of over 46% and 52% for biomass and carbon, respectively. Overall, if no species-specific model is available, we suggest the functional plant trait based on dbh and wsg.

Developing One-variable Individual Tree Biomass Models based on Wood Density for 34 Tree Species in China

Forest ecosystem is the largest carbon bank on land, and biomass models will be essential basis for evaluating carbon sequestration capacity of forests. Based on the general biomass model M=0.3pD7/3 presented by Zeng and Tang, one-variable individual tree aboveground biomass models for all 34 tree species or groups in China were established using the data of wood basic density of all tree species published; and based on the mensuration data of the National Forest Biomass Modeling Program, the aboveground biomass models of 14 tree species or genera were validated. Additionally, compatible belowground biomass models and root-to-shoot ratio models for two species groups, coniferous and broadleaved, were developed and evaluated. The results showed that the averages of absolute relative errors of above- and below-ground biomass estimates from one-variable biomass models developed in this study were less than the error allowances 10% and 15%, respectively. The developed biomass models here could be applied to estimate forest biomass at national level and would be important supplement to the ministerial standards on biomass models which were promulgated and implemented in the recent years.

Developing One-variable Individual Tree Biomass Models Based on Wood Density for 34 Tree Species in China

Forest ecosystem is the largest carbon bank on land, and biomass models will be essential basis for evaluating carbon sequestration capacity of forests. Based on the general biomass model M=0.3pD7/3 presented by Zeng and Tang, one-variable individual tree aboveground biomass models for all 34 tree species or groups in China were established using the data of wood basic density of all tree species published; and based on the mensuration data of the National Forest Biomass Modeling Program, the aboveground biomass models of 14 tree species or genera were validated. Additionally, compatible belowground biomass models and root-to-shoot ratio models for two species groups, coniferous and broadleaved, were developed and evaluated. The results showed that the averages of absolute relative errors of above- and below-ground biomass estimates from one-variable biomass models developed in this study were less than the error allowances 10% and 15%, respectively. The developed biomass models here could be applied to estimate forest biomass at national level and would be important supplement to the ministerial standards on biomass models which were promulgated and implemented in the recent years.

Individual Tree Biomass Models to Estimate Forest Biomass for Large Spatial Regions Developed Using Four Pine Species in China

Individual Tree Biomass Models to Estimate Forest Biomass for Large Spatial Regions Developed Using Four Pine Species in China

Models for Predicting the Biomass of Cunninghamialanceolata Trees and Stands in Southeastern China.

Using existing equations to estimate the biomass of a single tree or a forest stand still involves large uncertainties. In this study, we developed individual-tree biomass models for Chinese Fir (Cunninghamia lanceolata.) stands in Fujian Province, southeast China, by using 74 previously established models that have been most commonly used to estimate tree biomass. We selected the best fit models and modified them. The results showed that the published model ln(B(Biomass)) = a + b * ln(D) + c * (ln(H))2 + d * (ln(H))3 + e * ln(WD) had the best fit for estimating the tree biomass of Chinese Fir stands. Furthermore, we observed that variables D(diameter at breast height), H (height), and WD(wood density)were significantly correlated with the total tree biomass estimation model. As a result, a natural logarithm structure gave the best estimates for the tree biomass structure. Finally, when a multi-step improvement on tree biomass model was performed, the tree biomass model with Tree volume(TV), WD and biomass wood density conversion factor (BECF),achieved the highest simulation accuracy, expressed as ln(TB) = −0.0703 + 0.9780 * ln(TV) + 0.0213 * ln(WD) + 1.0166 * ln(BECF). Therefore, when TV, WD and BECF were combined with tree biomass volume coefficient bi for Chinese Fir, the stand biomass (SB)model included both volume(SV) and coefficient bi variables of the stand as follows: bi = Exp(−0.0703+0.9780*ln(TV)+0.0213 * ln(WD)+1.0166*ln(BECF)). The stand biomass model is SB = SV/TV * bi.

Individual Tree Biomass Models to Estimate Forest Biomass for Large Spatial Regions Developed Using Four Pine Species in China

Individual Tree Biomass Models to Estimate Forest Biomass for Large Spatial Regions Developed Using Four Pine Species in China

Development of monitoring and assessment of forest biomass and carbon storage in China

Addressing climate change has become a common issue around the world in the 21st century and equally an important mission in Chinese forestry. Understanding the development of monitoring and assessment of forest biomass and carbon storage in China is important for promoting the evaluation of forest carbon sequestration capacity of China. The author conducts a systematic analysis of domestic publications addressing “monitoring and assessment of forest biomass and carbon storage” in order to understand the development trends, describes the brief history through three stages, and gives the situation of new development. Towards the end of the 20th century, a large number of papers on biomass and productivity of the major forest types in China had been published, covering the exploration and efforts of more than 20 years, while investigations into assessment of forest carbon storage had barely begun. Based on the data of the 7th and 8th National Forest Inventories, forest biomass and carbon storage of the entire country were assessed using individual tree biomass models and carbon conversion factors of major tree species, both previously published and newly developed. Accompanying the implementation of the 8th National Forest Inventory, a program of individual tree biomass modeling for major tree species in China was carried out simultaneously. By means of thematic research on classification of modeling populations, as well as procedures for collecting samples and methodology for biomass modeling, two technical regulations on sample collection and model construction were published as ministerial standards for application. Requests for approval of individual tree biomass models and carbon accounting parameters of major tree species have been issued for approval as ministerial standards. With the improvement of biomass models and carbon accounting parameters, thematic assessment of forest biomass and carbon storage will be gradually changed into a general monitoring of forest biomass and carbon storage, in order to realize their dynamic monitoring in national forest inventories. Strengthening the analysis and assessment of spatial distribution patterns of forest biomass and carbon storage through application of remote sensing techniques and geostatistical approaches will also be one of the major directions of development in the near future.

Generic linear mixed-effects individual-tree biomass models for Pinus massoniana in southern China

Quantification of forest biomass is important for practical forestry and for scientific purposes. It is fundamental to develop generic individual-tree biomass models suitable for large-scale forest biomass estimation. However, compatibility of forest biomass estimates at different scales may become a problem. We developed generic individual-tree biomass models using a mixed-effects modeling approach based on aboveground biomass data of Masson pine (Pinus massoniana Lamb.) from nine provinces in southern China. Mixed-effects modeling could provide an effective approach to solving the compatibility of forest biomass estimates at different scales. A simple allometric function requiring diameter at breast height was used as a base model to construct generic individual-tree mixed-effects biomass models. Two factors of tree origin (natural and planted forests) and geographic region (nine provinces or three subregions) were included as random effect factors in the models. The results showed that the mixed-effects model not only provided more accurate estimates, but also possessed good universality compared with the population average model. We, therefore, recommend the mixed-effects model 17 to estimate national and regional-scale biomass for Masson pine in southern China. The mixed-effects modeling approach is versatile and can also be applied to construct generic individual-tree models for other tree species and variables.

不同林分起源的相容性生物量模型构建

Biomass equations for individual-trees have appeared frequently in the ecological and forestry literature over the last 60 years as biomass estimation is a prerequisite for studies on forest productivity,nutrient cycling and for calculating carbon sequestration,storage and other structural and functional attributes of forest ecosystems. Over the same period of time,the methods of developing biomass equations for total tree and component biomass have evolved from single equation least squares to multivariate adjustment in proportion and simultaneous equations,both linear and nonlinear. The single equation approach relates total tree biomass and its components such as stem,wood,bark,branches,and foliage to predictor variables such as diameter at breast height,height and sometime also crown width using log transformed data through least squares regression. The equation for each component is estimated separately without taking into account( 1) the inherent correlation among the biomass components measured on the same sample trees and( 2) the logical constraint between the sum of predicted biomass for tree components and the prediction for the total tree. As a result,biomass equations developed through this approach fall short of statistical efficiency in parameter estimation and lack compatibility among the component equations( Parresol 1999). The lack of compatibility means inconsistency in logic in the sense that the predicted values from summing the biomass equations of tree components do not equal to the predicted value from theequation for the total tree biomass. Development of compatible individual-tree biomass models were well reported in the literature,while how to construct these biomass models for trees from different stand origin has not been investigated so far. In this paper,generalized models on total above-ground biomass and its four components( stem wood,stem bark,branch, and foliage) for trees from different stand origin were established using the methods of adjustment in proportion and nonlinear simultaneous equations. Totally 150 Masson pine( Pinus masson iana) trees were sampled for biomass investigation in southern China. For the two approaches mentioned above,i. e. adjustment in proportion and nonlinear simultaneous equations,controlling jointly from level to level by ratio functions and controlling directly under total biomass by proportion functions were employed. Covariate variables of one-,two- and three-variable biomass models were obtained from five stand variable candidates of diameter at breast height,tree total height,diameter at ground level,height to crown base and crown width. Weighted least square regression was used to remove the heteroscedasticity of biomass models. The results showed that both methods of adjustment in proportion and nonlinear simultaneous equations could efficiently ensure that the total biomass is equal to the summary of its components with high prediction accuracy. However,the prediction accuracy of nonlinear simultaneous equations was generally much higher than that of adjustment in proportion. The approach of controlling directly by proportion functions was slightly better than the one controlling jointly by ratio functions. The function of each component biomass itself as weighted function could remove heteroscedasticity effectively. The biomass models for each component with three variables( diameter at breast height,height and crown width) had the highest prediction accuracy,following by the two variables( diameter at breast height and height),and the single variable( diameter at breast height) model. The discrepancies among the models were very small,however. For balancing the model prediction accuracy and survey cost in constructing biomass model for trees from different stand origin,we suggest adapting the nonlinear simultaneous equations of controlling directly under total biomass with diameter at breast height and height as covariables.

Forest Biomass Estimation Based on Remote Sensing Method for North Daxingan Mountains

Using the Landsat 5 TM images in 2002 as source data，the paper constructed individual tree biomass models of seven principal species based on the data from field surveying and fixed Plots in Tahe and Amur forest Region in Daxiangan Mountains. The remote sensing biomass model between TM images and data from forest fixed Plots was developed by the methods of multiple linear regression and BP neutral net. The result showed that R in multiple linear regression model was 0.764 and the model passed the F test, D-W test and multi-collinearity test. In the independent sample estimation，The neutral net model with the precision of 91.25% was significantly higher than multiple linear regression model with the precision of 81.02%. Although the“black-box”neutral net model could not give the concrete analytical equation, this kind of model with high precision might be applied to estimate the forest biomass in large level forest biomass.