A new model of tropical tree diameter growth rate and its application to identify fast-growing native tree species

Scolforo, Henrique Ferraço; Scolforo, José Roberto Soares; Thiersch, Cláudio Roberto; Thiersch, Monica Fabiana Bento Moreira; McTague, J. P.; Burkhart, Harold E.; Filho, Antônio Carlos Ferraz; Mello, José Márcio de; Roise, Joseph

doi:10.1016/j.foreco.2017.06.048

Cited by 16 publications

(13 citation statements)

References 21 publications

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“…In the evaluation of the data, the statistical indices are as follows: bias (BIAS), RMSE, MAE, R 2 , Willmott's index of agreement (WIA), and total relative error (TRE) [37,38]. BIAS reflects the error between the output of the model on the sample and the true value, MAE explains the model stability, R 2 describes the quality of the model, and TRE and RMSE reflect the model in a straightforward manner [39,40]. Besides, WIA is intended to be a descriptive measure, which provides a measure of whether the external predictive of the model is statistically accurate, and it is both a relative and bounded measure that can be widely applied in order to make cross-comparisons between models [41,42].…”

Section: Model Validationmentioning

confidence: 99%

Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms

et al. 2022

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Forests are indispensable materials and spiritual foundations for promoting ecosystem circulation and human survival. Exploring the environmental impact mechanism on individual-tree growth is of great significance. In this study, the effects of biogeoclimate, competition, and topography on the growth of Betula spp. and Cunninghamia lanceolata (Lamb.) Hook., two tree species with high importance value in China, were explored by gradient boosting regression tree (GBRT), k-nearest neighbor (KNN), and random forest (RF) machine learning (ML) algorithms. The results showed that the accuracy of RF was better than KNN, which was better than GBRT. All ML algorithms performed well for future diameter at breast height (DBH) predictions; the Willmott’s indexes of agreement (WIA) of each ML algorithm in predicting the future DBH were all higher than 0.97, and the R2 was higher than 0.98 and 0.90, respectively. The individual tree annual growth rate is mainly affected by the single-tree size, and the external environment can promote or inhibit tree growth. Climate and stand structure variables were relatively more important for tree growth than the topographic factors. Lower temperature and precipitation, higher stand density, and canopy closure were more unfavorable for their growth. In afforestation, the following factors should be considered in order: geographic location, meteorological climate, stand structure, and topography.

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Section: Model Validationmentioning

confidence: 99%

Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms

et al. 2022

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“…Esta espécie tem despertado interesses científicos devido as suas potencialidades bioquímicas e econômicas embora ainda não seja plantada para fins comerciais (Scalon;. Também são destacadas como potencialidades da espécie sua utilização em programas de restauração e produção (Scolforo et al, 2017).…”

Section: Materials E Métodos áRea De Estudounclassified

Efeito de variáveis de copa em funções de afilamento

et al. 2020

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Este trabalho teve como objetivo a avaliação de variáveis de copa e índices morfométricos na predição do afilamento. O estudo adotivo teve o uso de Xylopia brasiliensis , localizado na Reserva Florestal da UFLA, em um fragmento florestal de 5,8 hectares. Uma cubagem não destrutiva foi realizada em 77 áreas da escola em sete classes de diâmetro. Os modelos testados foram Polinômio do 5º grau, Polinômio de potências internas e fracionárias e Kozak, clássicos. Utiliza-se o procedimento em etapas para gerar um novo modelo com uma inclusão de variáveis de copa, denominado de Hradetzky modificado.Os resultados foram de que o modelo Hradetzky modificado foi superior com R²aj = 83,88%, Syx = 1,96 cm, Syx (%) = 54,75 e AIC = -2,176,38, frente a esquerda. O gráfico foi grafado, o mesmo se apresentou mais equilibrado. Conclui que a instalação de variáveis de backup, em funções de trabalho não tem ganho de precisão e acuracidade das informações.

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“…The following goodness-of-fit statistics were used in the data evaluation: Residual (R), bias (BIAS), relative bias (BIAS%), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RMSE%), empirical coefficient of correlation (R 2 emp ), and Theil's inequality coefficient (U 2 ) [12,45]. BIAS was the difference between a population mean of the measurements or test results and an accepted reference or true value, MAE could explain the stability of model fitting, R 2 emp could judge the model, and RMSE, BIAS%, RMSE%, and U 2 could reflect the precision of the model directly and clearly [46,47]. Their formulas are as follows:…”

Section: Model Evaluation and Validationmentioning

confidence: 99%

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

Zhang

Zhu

Chen

et al. 2019

Forests

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Global climate change has raised concerns about the relationship between ecosystems and forests, which is a core component of the carbon cycle and a critical factor in understanding and mitigating the effects of climate change. Forest models and sufficient information for predictions are important for ensuring efficient afforestation activities and sustainable forest development. Based on the theory of difference equations and the general rules of tree growth, this study established a difference equation for the relationship between the ratio of tree diameter at breast height (DBH) to the tree height and age of age of China’s main arbor species. A comparison with equations that represent the traditional tree growth models, i.e., Logistic and Richards equations, showed that the difference equations exhibited higher precision for both fitting and verification data. Moreover, the biomass carbon stocks (BCS) of Chinese forests from 2013 to 2050 were predicted by combining the 8th Chinese Ministry of Forestry and partial continuous forest inventory (CFI) data sets. The results showed that the BCS of Chinese forests would increase from 7342 to 11,030 terra grams of carbon (Tg C) in 2013–2050, with an annual biomass C (carbon) sink of 99.68 Tg C year−1, and they indicated that the Chinese land-surface forest vegetation has an important carbon sequestration capability.

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A new model of tropical tree diameter growth rate and its application to identify fast-growing native tree species

Cited by 16 publications

References 21 publications

Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms

Disentangling the Factors That Contribute to the Growth of Betula spp. and Cunninghami lanceolata in China Based on Machine Learning Algorithms

Efeito de variáveis de copa em funções de afilamento

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

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