Ana Paula Dalla Corte

On the use of data mining for estimating carbon storage in the trees

Forests contribute to climate change mitigation by storing carbon in tree biomass. The amount of carbon stored in this carbon pool is estimated by using either allometric equations or biomass expansion factors. Both of the methods provide estimate of the carbon stock based on the biometric parameters of a model tree. This study calls attention to the potential advantages of the data mining technique known as instance-based classification, which is not used currently for this purpose. The analysis of the data on the carbon storage in 30 trees of Brazilian pine (Araucaria angustifolia) shows that the instance-based classification provides as relevant estimates as the conventional methods do. The coefficient of correlation between the estimated and measured values of carbon storage in tree biomass does not vary significantly with the choice of the method. The use of some other measures of method performance leads to the same result. In contrast to the convention methods the instance-based classification does not presume any specific form of the function relating carbon storage to the biometric parameters of the tree. Since the best form of such function is difficult to find, the instance-based classification could outperform the conventional methods in some cases, or simply get rid of the questions about the choice of the allometric equations.

Dinâmica da estrutura da comunidade de lauráceas no período 1995-2004 em uma floresta de araucária no sul do estado do Paraná, Brasil.

Several species of Lauraceae family are among the most common ones in the Araucaria Forest. They have good silvicultural and economic potential, but unfortunately they are still very poorly studied. This paper analyzes the structural changes of eight species of Lauraceae family during 1995-2004 in a forest located in the center-south region of Parana State, Brazil. The species were investigated through structural and dynamic indicators: importance value (IVI), abundance, basal area, trunk volume, diameter distribution, spatial pattern, recruitment, mortality and increments. The results showed that the structural position of the Lauraceae family was kept almost unaltered in the community during the ten-year period. It was also noticed that the Lauraceae family is increasing in terms of abundance due to the fact that recruitment has been greater than mortality. However, growth rates have been very low, either in diameter and basal area/volume. Although most Lauraceae species show a slow dynamic behavior, some of them have experienced an intense chance process, such as Canela-amarela ( Nectandra grandiflora Nees & Mart. ex Nees) which increased most of its structural and dynamic indicators, becoming the most remarkable species among all those studied. This species showed recruitment greater than mortality, a J-shaped diameter distribution and very wide spatial distribution. These elements have turned this species one of the three most important in the community together with Araucaria angustifolia (Bertol.) Kuntze and Ilex paraguariensis A.St.-Hil. It was concluded that, in ten years, it is already possible to distinguish important ecological processes that may be very useful in formulating Management Plans for similar Araucaria forests.

Simultaneous estimation as alternative to independent modeling of tree biomass

Key messageIn 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).ContextWhen 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.ObjectiveThe 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.MethodsIndividual 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.ResultsIndependent 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.ConclusionThe simultaneous estimation method must be used in modeling tree biomass.

A model based on environmental factors for diameter distribution in black wattle in Brazil.

This article discusses the dynamics of a diameter distribution in stands of black wattle throughout its growth cycle using the Weibull probability density function. Moreover, the parameters of this distribution were related to environmental variables from meteorological data and surface soil horizon with the aim of finding a model for diameter distribution which their coefficients were related to the environmental variables. We found that the diameter distribution of the stand changes only slightly over time and that the estimators of the Weibull function are correlated with various environmental variables, with accumulated rainfall foremost among them. Thus, a model was obtained in which the estimators of the Weibull function are dependent on rainfall. Such a function can have important applications, such as in simulating growth potential in regions where historical growth data is lacking, as well as the behavior of the stand under different environmental conditions. The model can also be used to project growth in diameter, based on the rainfall affecting the forest over a certain time period.

Prognose da Estrutura Diamétrica em Floresta Ombrófila Mista

This study aimed to evaluate the accuracy of diameter projections, using the transition matrix and movement ratio models in different temporal amplitudes and diameter class intervals, for a Mixed Ombrophyilous Forest fragment located in the municipality of Sao Joao do Triunfo, Parana state. The data were collected from a continuous inventory that has been carried out in a 3.5 ha sampling area since 1995. The data collected between 2000 and 2005 were divided into classes of 5 cm and 10 cm and grouped into four temporal sampling amplitudes, with projections conducted for 2004, 2006, 2008 and 2010, respectively. Efficiency of projection values was verified based on the Friedman and Kolmogorov-Smirnov test. The results indicate that the two tested models reliably estimate the number of trees of this forest fragment only for the temporal amplitude of 2 years.

Prognose da Estrutura Diamétrica em Floresta Ombrófila Mista / Prognosis of Diameter Structure in a Mixed Ombrophyilous Forest

This study aimed to evaluate the accuracy of diameter projections, using the transition matrix and movement ratio models in different temporal amplitudes and diameter class intervals, for a Mixed Ombrophyilous Forest fragment located in the municipality of Sao Joao do Triunfo, Parana state. The data were collected from a continuous inventory that has been carried out in a 3.5 ha sampling area since 1995. The data collected between 2000 and 2005 were divided into classes of 5 cm and 10 cm and grouped into four temporal sampling amplitudes, with projections conducted for 2004, 2006, 2008 and 2010, respectively. Efficiency of projection values was verified based on the Friedman and Kolmogorov-Smirnov test. The results indicate that the two tested models reliably estimate the number of trees of this forest fragment only for the temporal amplitude of 2 years.

Estimativa de carbono individual para Araucaria angustifolia

Forests are important carbon sinks that contribute to climate change mitigation. Quantifying the carbon stock is critical for measuring such mitigation potential. Araucaria angustifolia (Bert.) O. Ktze. Araucariaceae is a key forest species in southern Brazil, due to its ecological and economic importance. This study aimed at comparing two procedures for estimating individual carbon stock ( C ) of A. angustifolia , in pure stands established in the southern Parana State, Brazil. Individual carbon stocks were determined for thirty trees, as well as correlations between C , dendometric variables, Biomass Expansion Factor ( BEF ) and Root-to-Shoot Ratio ( R ). Regression equations of C , concerning Diameter Breast Height ( DBH ) and total height ( H ), were adjusted and compared to trunk volume ( V ) estimates combined with BEF and R . DBH and H showed high correlation to C , what did not happen to tree age, BEF and R . The regression equations of C , concerning DBH and H , adjusted well to the data set, providing reliable estimates. The same happened to the volume equations combined with expansion factors, which also provided statistically acceptable estimates. No difference was observed between the two procedures tested, being both reliable for estimating the individual carbon stock.

Volume estimation of Cryptomeria japonica logs in southern Brazil using artificial intelligence models

This study aimed to test taper functions and artificial intelligence (AI) models in order to estimate merchantable volumes of Japanese cedar (Cryptomeria japonica) trees in a homogenous plantation in southern Brazil. A total of 30 individuals were rigorously scaled and their total volumes were calculated, including those of the following log assortments: veneer, sawn, pulp and energy. Three AI models, i.e. two variants of k-nearest neighbours (KNN) instance-based classification (one and three nearest neighbours) and an artificial neural network (ANN) approach, were compared with three traditional taper models: fifth-order polynomial, fractional powers and the Garay model. The estimated volumes were compared with the actual volumes by means of the standard error (Syx), bias, precision and accuracy. Total volume estimates proved to be unbiased (maximum bias 5.42%), precise (maximum precision 9.28%) and accurate (maximum accuracy 10.79%) with all of the investigated models. The tested models tended to give lower bias, better precision and accuracy in the middle portion of the stems, but worse estimates at the base and tip (maximum bias −12.41%). In general, the KNN models improved merchantable volume estimation, particularly KNN1, which is a straightforward and simple method. We conclude that AI techniques have appeal for application in forest inventories and that KNN is a particularly interesting alternative for tree volume estimation.

GEOSTATISTICAL MODELING OF TIMBER VOLUME SPATIAL VARIABILITY FOR Tectona grandis L. F. PRECISION FORESTRY

Considerando a hipotese de que os volumes de madeira apresentam dependencia espacial, cujo conhecimento contribui para o manejo de precisao, o objetivo deste trabalho foi estimar a variabilidade espacial do volume de sortimentos de madeira e identificar seus padroes espaciais em povoamentos de Tectona grandis. Utilizou-se um conjunto de dados de 1.038 arvores para ajustar funcoes de afilamento e estimar os volumes para fuste total, serraria e lenha em 273 parcelas alocadas em povoamentos de T. grandis ao oitavo ano de idade, o qual representa o segundo desbaste que possibilita volumes comerciais. Modelos de semivariogramas foram aplicados para ajustar a dependencia espacial e a krigagem pontual foi utilizada para compor mapas de volume. A modelagem geoestatistica permitiu estimar a variabilidade espacial de T. grandis e desenvolver mapas de volume de madeira. Assim, tratamentos silviculturais, como desbaste e poda, bem como planejamento de intervencoes espaciais, podem ser recomendados para produtos de madeira almejados.

Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data

Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.