Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam

Huy, Bao; Kralicek, Karin; Poudel, Krishna P.; Phuong, Vu Tan; Khoa, Phung Van; Hung, Nguyen Dinh; Temesgen, Hailemariam

doi:10.1016/j.foreco.2016.10.021

Cited by 47 publications

(42 citation statements)

References 12 publications

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“…Geralmente, o DAP das árvores é usado como a principal variável para a estimativa do estoque em biomassa e carbono de florestas via meios não-destrutivos e equações alométricas (HUY et al, 2016;MATSSON et al, 2016).…”

Section: Resultsunclassified

MODELAGEM GEOESTATÍSTICA DA BIOMASSA DO FUSTE DE Araucaria angustifolia EM REMANESCENTE DE FLORESTA OMBRÓFILA MISTA

Lima¹,

Pelissari²,

Filho³

2017

Agrarian

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RESUMOO objetivo deste trabalho foi modelar, mapear e relacionar os padrões e as dinâmicas espaciais do estoque de biomassa do fuste de Araucária angustifolia em um remanescente de Floresta Ombrófila Mista na região Sul do Brasil. A base de dados para a estimativa da biomassa de fuste contou com o diâmetro à altura do peito de trinta indivíduos de Araucaria angustifólia. Realizou-se análise de regressão para dez modelos onde se selecionou o que apresentou menor erro padrão da estimativa, maior coeficiente de determinação ajustado e homogeneidade na distribuição dos resíduos. A biomassa do fuste foi estimada em 400 parcelas de 25 m x 25 m e submetidas à análise estatística descritiva e ao teste de normalidade de Kolmogorov-Smirnov ao nível de 5% de significância. As médias apresentaram valores crescentes ao longo do inventário, o que indicou que a floresta não atingiu o seu ponto clímax. As unidades amostrais apresentaram valores semelhantes na distância de alcance, sendo que foram observados valores menores de alcance nos primeiros anos do inventário e maiores nos anos seguintes. O efeito pepita apresentou valores expressivos e o patamar foi alcançado em um mesmo intervalo de valores em todos os anos. Nos mapas temáticos, observou-se que o estoque de biomassa do fuste de Araucaria angustifólia do remanescente aumentou, uma vez que os estoques presentes em classes maiores passaram a apresentar maior quantidade de biomassa. Observou-se também que locais com maior estoque de biomassa do fuste não foram expressivos nos mapas temáticos gerados pela krigagem ordinária pontual. PALAVRAS-CHAVE: distribuição espacial, floresta nacional, mudanças climáticas.

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Section: Resultsunclassified

MODELAGEM GEOESTATÍSTICA DA BIOMASSA DO FUSTE DE Araucaria angustifolia EM REMANESCENTE DE FLORESTA OMBRÓFILA MISTA

Lima¹,

Pelissari²,

Filho³

2017

Agrarian

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“…Weighted non-linear fit was used to account for heteroscedasticity in residuals [21,33] and to compare and select the best predictor(s) for each biomass component of bamboo and AGB independently [18,34,35]. Models were fitted using 'nlme' packages in statistical software R [36].…”

Section: Weighted Nonlinear Models Fit By Maximum Likelihoodmentioning

confidence: 99%

“…Models were fitted using 'nlme' packages in statistical software R [36]. The form of the allometric equation, after Huy et al (2016aHuy et al ( , b, 2019 [18,19,21], was:…”

Section: Weighted Nonlinear Models Fit By Maximum Likelihoodmentioning

confidence: 99%

“…where Y i is the Bcu, Bbr, Ble, or AGB in kg for the i th bamboo culm; α and β are the parameters of the model; and X i is the covariate D (cm), H (m), or D 2 H for the i th sampled culm; and ε i is the random error associated with the i th sampled culm. The variance function was determined as follow [18,19,21]:…”

Section: Weighted Nonlinear Models Fit By Maximum Likelihoodmentioning

confidence: 99%

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Individual Plant Allometric Equations for Estimating Aboveground Biomass and Its Components for a Common Bamboo Species (Bambusa procera A. Chev. and A. Camus) in Tropical Forests

Huy

Thanh²,

Poudel

et al. 2019

Forests

Self Cite

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Bamboo forests play an important role in achieving the objectives of the United Nations program on Reducing Emission from Deforestation and Forest Degradation. We developed and validated a modeling system that simultaneously estimate aboveground biomass and its components for a common bamboo species (Bambusa procera A. Chev. and A. Camus) in tropical forests. Eighty-three bamboo culms were destructively sampled from seventeen 100 m2 sample plots located in different parts of the Central Highlands in Viet Nam to obtain total plant aboveground biomass (AGB) and its components. We examined the performance of weighted nonlinear models fit by maximum likelihood and weighted nonlinear seemingly unrelated regression fit by generalized least squares for predicting bamboo biomass. The simultaneous estimation of AGB and its components produced higher reliability than the models of components and total developed separately. With a large number of bamboo species, it may not be feasible to develop species- specific biomass models, hence genus-specific allometric models may be considered.

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“…In recent years, several studies have examined natural forest biomass in Vietnam. These were carried out at national and regional scales, mainly based on inventory measurement, and exploited the correlation between AGB and stand structure parameters to improve the allometric equation [16][17][18][19][20]. Remote sensing is also commonly used to estimate biomass carbon stocks and disturbance assessment.…”

Section: Introductionmentioning

confidence: 99%

Combination of SAR Polarimetric Parameters for Estimating Tropical Forest Aboveground Biomass

Tuong¹,

Tani²,

Wang³

et al. 2020

Pol. J. Environ. Stud.

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There is a demand for better information on forest biomass in tropical regions for use in carbon accounting. This needs robust above-ground biomass (AGB) estimation in different forest types. Our study sought to improve biomass estimation by selecting the best regression models based on observations of the contribution of radar signals to AGB in five forest types in Vietnam. Data from PALSAR and PALSAR-2, which covered the forest area, were used to extract 16 polarimetric radar (PolSAR) parameters in 2007 and 2016. This study was designed as a comparative experiment of four regression models: linear, polynomial, support vector machine (SVR) and random forest. First, the contribution of PolSAR data to AGB estimation was evaluated using two approaches: the sample data from all forest types, and the five individual forest types (rich, medium, poor, restoration and bamboo forest). Second, we examined the improvement of AGB prediction by selecting the important variables and assessing the best models for different forest types. The results showed an improvement in the value of R-squared and RMSE using the five individual forest types compared to the combined forest types. In particular, using a multivariate model, RMSE values were enhanced by 9-18% for the rich forest, and by 80-85% for the remaining forest types in all models. SVM provided the best performance for medium and poor forest (RMSE of 8.27 tons ha -1 and 12.38 tons ha -1 , respectively), random forest for bamboo (RMSE of 23.18 tons ha -1 ), and the polynomial regression for the restoration forest (RMSE *of 10.11 tons ha -1 ). Further research is required to derive a more robust AGB estimation model for the rich forest.

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Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Viet Nam

Cited by 47 publications

References 12 publications

MODELAGEM GEOESTATÍSTICA DA BIOMASSA DO FUSTE DE Araucaria angustifolia EM REMANESCENTE DE FLORESTA OMBRÓFILA MISTA

MODELAGEM GEOESTATÍSTICA DA BIOMASSA DO FUSTE DE Araucaria angustifolia EM REMANESCENTE DE FLORESTA OMBRÓFILA MISTA

Individual Plant Allometric Equations for Estimating Aboveground Biomass and Its Components for a Common Bamboo Species (Bambusa procera A. Chev. and A. Camus) in Tropical Forests

Combination of SAR Polarimetric Parameters for Estimating Tropical Forest Aboveground Biomass

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