Tree-crown biomass estimation in forest species of the Ural and of Kazakhstan (original) (raw)
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Simultaneous estimation as alternative to independent modeling of tree biomass
Annals of forest science, 2015
& Key message In this paper it is shown that a simultaneous adjustment provides more efficient estimates of total tree biomass than with independent modelling for biomass estimates by compartments (canopy, bole and roots). & Context When modeling tree biomass, it is important to consider the additivity property, since the total tree biomass must be equal to the sum of the biomass of the components. & Objective The aim of this study was to assess the simultaneous estimation performance, considering the additivity principle with respect to independent estimate when modeling biomass components and total biomass. & Methods Individual modeling of total biomass and biomass components of leaves, branches, bole without bark, bole bark, and roots was performed on Pinus elliottii Engelm trees derived from forest stands in southern Brazil. Five nonlinear models were tested, and the best performance for estimating the total biomass of each component was selected, characterizing the independent estimation. The models selected for each component were fitted using the nonlinear seemingly unrelated regression method, which characterizes simultaneous estimation. & Results Independent fitting of coefficients for biomass components and total biomass was not satisfactory, as the sum of the biomass component estimates diverged from the total biomass. This was not observed when the simultaneous fitting was used, which takes into account the additivity principle, and resulted in more effective estimators. & Conclusion The simultaneous estimation method must be used in modeling tree biomass.
Canadian Journal of Forest Research-revue Canadienne De Recherche Forestiere, 2008
Allometric biomass models for individual trees are typically specific to site conditions and species. They are often based on a low number of easily measured independent variables, such as diameter in breast height and tree height. A prevalence of small data sets and few study sites limit their application domain. One challenge in the context of the actual climate change discussion is to find more general approaches for reliable biomass estimation. Therefore, nonparametric approaches can be seen as an alternative to commonly used regression models. In this pilot study, we compare a nonparametric instance-based k-nearest neighbour (k-NN) approach to estimate single-tree biomass with predictions from linear mixed-effect regression models and subsidiary linear models using data sets of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) from the National Forest Inventory of Finland. For all trees, the predictor variables diameter at breast height and tree height are known. The data sets were split randomly into a modelling and a test subset for each species. The test subsets were not considered for the estimation of regression coefficients nor as training data for the k-NN imputation. The relative root mean square errors of linear mixed models and k-NN estimations are slightly lower than those of an ordinary least squares regression model. Relative prediction errors of the k-NN approach are 16.4% for spruce and 14.5% for pine. Errors of the linear mixed models are 17.4% for spruce and 15.0% for pine. Our results show that nonparametric methods are suitable in the context of single-tree biomass estimation.
Current Science
Linear regression models were developed for four ecologically and economically important tree species of Meghalaya, India, viz. Betula alnoides, Duabanga grandiflora, Magnolia champaca and Toona ciliata. In the present study a non-destructive approach has been used for measurement of required variables, i.e. diameter at breast height (DBH), basal diameter, tree height, end-diameters and length of frustum. Comparison of various models of relationship on the basis of adj. R 2 values showed that the value for linear function (V = f (d 2 h)) was more than 0.90 for all the four tree species, except lowest diameter class of T. ciliata (10-30 cm diameter class). Hence this linear regression equation was selected for development of diameter class-wise volume equations. Volume of the stem was taken as the dependent variable, while DBH and tree height were used as independent variables, transformed in the form of d 2 h to develop regression equation. Similarly, linear regression equations for each tree species were also developed using linear function [(V = f (d 2 ))], considering tree volume as an dependent variable and DBH as an independent variable, transformed in the form of V = d 2 . The present study is among a few attempts to develop regression models without the felling of trees since 1977 and an initial attempt using advanced measurement equipment in North East (NE) India, under the current regime of ban on tree felling. The regression equations developed in this study can be used for estimation of timber yield and carbon content of the selected tree species found in the Meghalaya forests.
Estimation of above-ground biomass in forest stands from regression on their basal area and height
Forestry Studies, 2016
A generic regression model for above-ground biomass of forest stands was constructed based on published data (R2= 0.88,RSE= 32.8 t/ha). The model was used 1) to verify two allometric regression models of trees from Scandinavia applied to repeated measurements of 275 sample plots from database of Estonian Network of Forest Research (FGN) in Estonia, 2) to analyse impact of between-tree competition on biomass, and 3) compare biomass estimates made with different European biomass models applied on standardized forest structures. The model was verified with biomass measurements from hemiboreal and tropical forests. The analysis of two Scandinavian models showed that older allometric regression models may give biased estimates due to changed growth conditions. More biomass can be stored in forest stands where competition between trees is stronger. The tree biomass calculation methods used in different countries have also substantial influence on the estimates at stand-level. A common dat...
iForest - Biogeosciences and Forestry, 2016
Biogeosciences and Forestry Biogeosciences and Forestry Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models Pablito M López-Serrano (1) , Carlos A López-Sánchez (2) , Ramón A Díaz-Varela (3) , José J Corral-Rivas (2) , Raúl Solís-Moreno (4) , Benedicto Vargas-Larreta (5) , Juan G Álvarez-González (6) The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area. The objective of the present study was to estimate the biomass of mixed and uneven-aged forests in the Sierra Madre Occidental by using Landsat-5 TM spectral data and forest inventory data. We used the ATCOR3 ® atmospheric and topographic correction module to convert remotely sensed imagery digital signals to surface reflectance values. The usual approach of modeling stand variables by using multiple linear regression was compared with a hybrid model developed in two steps: in the first step a regression tree was used to obtain an initial classification of homogeneous biomass groups, and multiple linear regression models were then fitted to each node of the pruned regression tree. Cross-validation of the hybrid model explained 72.96% of the observed stand biomass variation, with a reduction in the RMSE of 25.47% with respect to the estimates yielded by the linear model fitted to the complete database. The most important variables for the binary classification process in the regression tree were the albedo, the corrected readings of the shortwave infrared band of the satellite (2.08-2.35 µm) and the topographic moisture index. We used the model output to construct a map for estimating biomass in the study area, which yielded values of between 51 and 235 Mg ha-1. The use of regression trees in combination with stepwise regression of corrected satellite imagery proved a reliable method for estimating forest biomass.
Forest Ecology and Management, 2006
Allometric relations for tree growth modelling have been subject to research for decades, partly as empirical models, and partly as process models such as the pipe model, hydraulic architecture, mechanical approaches or the fractal-like nature of plant architecture. Unlike empirical studies, process models aim at explaining the scaling within tree architecture as a function of biological, physical or mechanical factors and at modelling their effect on functionality and growth of different parts of an individual tree. The goal of the underlying study is to link theoretical explanation to empirical approaches of tree biomass estimation by the example of Norway spruce (Picea abies [L.] Karst.). Decisively, this article tries to take allometry out of the purely curve-fitting exercise common in literature and derives implications for the use of allometric biomass functions.
Ecological Indicators, 2021
The mathematical models used applying the Nonlinear Seemingly Unrelated Regressions (NSUR) or Weighted Nonlinear Seemingly Unrelated Regressions (WNSUR) methodologies can contribute to generate acceptable and reliable estimates of total aboveground biomass and its constituent parts, which are needed to implement forest management strategies to maintain desirable and sustainable carbon stocks. The aim of this study was: 1) to fit the sample data with independent nonlinear regression models and present the results obtained from the respective statistical estimates for total biomass aboveground and the constituent parts of trees in native forest trees. 2) To fit the sample data with regression models simultaneously, that is, whose models are composed of appropriate combinations of their coefficients, in order to obtain additivity of the estimates and present better results for the total aboveground biomass and the constituent parts of the trees. 3) To apply weighting procedures to the variances of the fitted models. 4) To evaluate the error due to the regression function on forest biomass estimation. The data came from eight sites located in the states of Parana and Rio Grande do Sul, Brazil, and information was collected on diameter at 1.30 m aboveground (DBH), total height, biomasses of the trunk components (branches and leaves) and total aboveground biomass. Non-linear functions were independently and simultaneously fitted, using DBH and total height as independent variables in the regression models. Independent fitting of equations was performed using generalized nonlinear least squares (ENGLS) and simultaneous fitting of equations was obtained by means of NSUR. Weighting, by applying a variance structure in the two procedures, was done to solve the issue of heteroscedasticity. Numerically, the equations fitted simultaneously performed better and were more efficient than the independently fitted models, which resulted in biological inconsistency, that is, non-additivity of the biomass of constituent parts of the trees and the total biomass. Simultaneous fitting generated superior statistical and biological properties to obtain tree estimates of the of constituent parts of the trees and total aboveground biomass in native forests of southern Brazil. The smaller error due to the regression function used in the forest biomass inventory was obtained by simultaneous fitting. With these results, the procedure using simultaneous and weighted fitting of equations (WNSUR) is recommended to fit biomass equations for native forests in southern Brazil.
Semi-empirical models for assessing biological productivity of Northern Eurasian forests
Ecological Modelling, 2007
System of growth and yield models Phytomass Net primary production Boreal forests Northern Eurasia a b s t r a c t The Richards-Chapman growth function was used as an analytical basis for a unified system of semi-empirical models describing the dynamics of the main biometric characteristics of major types of Northern Eurasian forests. The growth function was applied at the stand level using yield tables. The models received satisfactorily describe the diversity of growth patterns for different species and geographical regions of Northern Eurasia. A special type of model of biological productivity (MBP) has been developed combining the above growth models and multi-dimensional regression equations of phytomass (living biomass). The latter have been parametrized for dominant tree species, site indexes and ecological regions based on 3507 sample plots collected in Northern Eurasia's forests. The MBP account for age dynamics of forest ecosystems and simulate dynamics of seven components of phytomass (stem wood over bark, bark, crown wood, foliage, understory, green forest floor, and roots) as well as net primary production. The model system could be used in different ecological applications, in forest inventory and forest management as a semi-empirical reference information on the growth and productivity of Northern Eurasia's forests. (A. Shvidenko).