Reduction of sampling intensity in forest inventories to estimate the total height of eucalyptus trees

Dantas, Daniel; Pinto, Luiz Otávio Rodrigues; Terra, Marcela de Castro Nunes Santos; Calegário, Natalino; Oliveira, Márcio Leles Romarco de

doi:10.4067/s0717-92002020000300353

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies showed similar results, training ANN to estimate the height of Eucalyptus trees. To reduce sampling intensity, ANN trained with quantitative and qualitative variables and a clone in their input layers showed the best performance, meeting the objective of the study [89]. Furthermore, research has used ANN to predict the commercial volume of Eucalyptus clones by assessing region, farm (for each region) and clone group, increasing the accuracy of the predictions in the study [90].…”

Section: Discussionmentioning

confidence: 81%

Machine Learning: Crown Diameter Predictive Modeling for Open-Grown Trees in the Cerrado Biome, Brazil

Bueno

Costa

Finger

et al. 2022

Forests

View full text Add to dashboard Cite

The Brazilian Cerrado biome is a hotspot due to its ecological importance and high diversity of fauna and flora. We aimed to develop statistical models to predict the crown diameter of open-growing trees using several forest attributes. Potential crown diameter trends in the measured trees were determined by quantile regression. Crown diameter models were developed by regression analyses, artificial neural networks, support vector machine, and random forest techniques. We evaluated 200 trees characterized into 60 species belonging to 30 botanical families. Our equation for potential crown diameter predicts the derived basal area, number of trees, and the necessary growth space of crown diameter at breast height. Artificial neural networks (with the following validation statistics: R2 = 0.90, RMSE = 1.21, MAE = 0.93, and MAPE = 16.25) predicted crown diameter more accurately than the other evaluated techniques. Modeling crown diameter via machine learning represents an important step toward the assessment of crown dynamics by species and can support the decision making of silvicultural practices and other related activities in several rural properties within the Cerrado biome.

show abstract

Section: Discussionmentioning

confidence: 81%

Machine Learning: Crown Diameter Predictive Modeling for Open-Grown Trees in the Cerrado Biome, Brazil

Bueno

Costa

Finger

et al. 2022

Forests

View full text Add to dashboard Cite

show abstract

“…Os procedimentos de mensuração florestal são elementos fundamentais e merecem atenção, por serem a base para a determinação do volume de madeira em uma determinada área (Gregoire et al, 2016). A informação do volume em um povoamento florestal é imprescindível para o manejo de florestas, levando em conta que grande parte dos processos produtivos em uma empresa é dependente dos dados de produtividade que são obtidos por meio do inventário (Dantas et al, 2020a;Terra et al, 2022). O volume para árvore individual serve como ponto de partida para decisões relacionadas a tratos silviculturais, colheita e transporte da produção volumétrica da madeira.…”

Section: Introductionunclassified

Cost, time and accuracy: an optimization of the pre-harvest forest inventory in a eucalyptus stand

Pinto,

Dantas,

Terra

et al. 2024

Pesq. flor. bras.

Self Cite

View full text Add to dashboard Cite

O inventário florestal constitui uma prática fundamental para quantificar e qualificar o potencial de uma floresta. Intensidade de amostragem, tamanho e forma das parcelas são pontos relevantes no planejamento dessa atividade, pois afetam diretamente os custos e tempo de coleta de dados, além de influenciar na sua precisão. Este estudo teve como objetivo avaliar, em termos de precisão, custos e tempo, a utilização de diferentes tamanhos, formas e arranjos de parcelas amostrais na realização do inventário florestal pré-corte de Eucalyptus spp. A pesquisa foi realizada em uma fazenda no norte de Minas Gerais, Brasil, onde foram realizados o censo florestal e a cubagem rigorosa. Foram considerados 16 cenários: dois processos de amostragem (amostragem sistemática e amostragem casual simples), quatro tamanhos de parcela (200, 300, 400 e 500m²) e duas formas (circular e retangular). A intensidade amostral foi fixada em 5 parcelas para todos os cenários. Realizou-se 10.000 simulações pelo método de Monte Carlo, verificando a probabilidade de os valores estarem próximos Ã média. A análise combinada entre erro, custos e tempo de medição apontou que, para um plantio de eucalipto aos 8 anos de idade, a amostragem casual, com parcelas circulares de 300m² representa o cenário amostral ótimo.

show abstract

“…These units are layered and connected to each other by weights that store experimental knowledge and weight the inputs of each unit. Thus, the acquired knowledge becomes available for use (Braga et al 2007, Dantas et al 2020a.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for carbon stock prediction in a tropical forest in Southeastern Brazil

et al. 2021

Self Cite

View full text Add to dashboard Cite

The increasing awareness of global climate change has drawn attention to the role of forests as mitigators of this process as they act as carbon sinks to the atmosphere. Understanding the process of carbon storage in forests and its drivers, as well as presenting consistent models for their estimation, is a current demand. In this sense, the aim of this study was to evaluate the performance of machine learning techniques: support vector machines (SVM) and to propose a new nonlinear model extracted from the training of an artificial neural network (ANN) in the modeling of above ground carbon stock in a secondary semideciduous forest. SVM and ANN construction and training process considered independent variables selected by stepwise: minimum DBH (diameter of breast height -1.3 m), maximum DBH, mean DBH, total height and number of trees, all by plot. SVM and the model extracted from ANN were applied to the data set intended for validation. Both techniques presented satisfactory performance in modeling carbon stock by plot, with homogeneous distribution and low dispersion of residues and predicted values close to those observed. Analysis criteria indicated superior performance of the model extracted from the artificial neural network, which presented a mean relative error of 6.94 %, while the support vector machine presented 13.52 %, combined with lower bias values and higher correlation between predictions and observations.

show abstract

Reduction of sampling intensity in forest inventories to estimate the total height of eucalyptus trees

Abstract: Reduction of sampling intensity in forest inventories to estimate the total height of eucalyptus treesReducción de la intensidad de muestreo en inventarios forestales para estimar la altura total de eucaliptos

Cited by 6 publications

References 10 publications

Machine Learning: Crown Diameter Predictive Modeling for Open-Grown Trees in the Cerrado Biome, Brazil

Machine Learning: Crown Diameter Predictive Modeling for Open-Grown Trees in the Cerrado Biome, Brazil

Cost, time and accuracy: an optimization of the pre-harvest forest inventory in a eucalyptus stand

Machine learning for carbon stock prediction in a tropical forest in Southeastern Brazil

Contact Info

Product

Resources

About