Development of a Tree Growth Difference Equation and Its Application in Forecasting the Biomass Carbon Stocks of Chinese Forests in 2050

The prediction of biological processes, which involve growth and plant development, is possible via the adjustment of mathematical models. In forest areas, these models assist in management practices, silviculture, harvesting, and soil fertility. Diameter, basal area, and height are predictors of volume and biomass estimates in forest stands. This study utilized different non-linear models for estimating biomass and nutrient values in the aerial biomass and roots of an unmanaged eucalypt stand in Cerrado dystrophic soil. It was hypothesized that the models would estimate the nutrients of the aboveground biomass and roots after meeting the selection and validation criteria. By statistical analysis of the parameters and subsequent validation, the Schumacher–Hall model was presented to be the best fit for biomass and nutrients. This result confirmed the ability of different variables, including diameter, basal area, and height, to be predicted. Estimating the nutrient values in the aboveground biomass and roots allowed a better understanding of the quality of the vegetal residues that remained in the soil. For dystrophic soils, which occur in the Cerrado, these estimates become even more relevant.

Section: Introductionmentioning

confidence: 99%

Modeling Biomass and Nutrients in a Eucalyptus Stand in the Cerrado

Valadão

Carneiro

Ribeiro

et al. 2020

“…China conducts a large-scale forest resource survey every five years to provide good data for statistical forest resources. Using these inventory data, forest biomass can be estimated at provincial or national scales [7]. However, with the continuous change in the forest resource structure of China, there have been some problems with these inventory data in regional biomass estimation [8].To obtain the total volume or biomass of forest, the volume or biomass of one tree is calculated, and then the volumes or biomasses of all the trees in the sample plot are added together.…”

mentioning

confidence: 99%

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Zhu

Zhong-ke

et al. 2020

Self Cite

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

“…Another disadvantage of the existing models is that a large system of mathematical equations is used to model a specific tree or stand variable, and the most appropriate relationship is selected from the list using simple statistical indices. It is worth noting a few works, such as [32][33][34][35][36][37][38]. In [32], the description of tree size variables is made by applying statistical models to the diameter distributions.…”

Section: Stand Density Predictions and Forecastsmentioning

confidence: 99%

Analysis of Longitudinal Forest Data on Individual-Tree and Whole-Stand Attributes Using a Stochastic Differential Equation Model

Rupšys

Petrauskas

2022

This paper focuses on individual-tree and whole-stand growth models for uneven-aged and mixed-species stands in Lithuania. All the growth models were derived using a single trivariate diffusion process defined by a mixed-effect parameters trivariate stochastic differential equation describing the tree diameter, potentially available area, and height. The mixed-effect parameters of the newly developed trivariate transition probability density function were estimated using an approximate maximum likelihood procedure. Using the relationship between the multivariate probability density and univariate marginal (conditional) densities, the growth equations were derived to predict or forecast the individual-tree and whole-stand variables, such as diameter, potentially available area, height, basal area, and stand density. All the results are illustrated using an observed dataset from 53 permanent experimental plots remeasured from 1 to 7 times. The computed statistical measures showed high predictive and forecast accuracy compared with validation data that were not used to find parameter estimates. All the results were implemented in the Maple computer algebra system.