干旱区生态环境知识资源中心(Arid): Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison Between Regression and Artificial Neural Networks

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DOI	10.1080/10549811.2020.1754241
	Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison Between Regression and Artificial Neural Networks
	de Lima, Robson B.; Caraciolo Ferreira, Rinaldo L.; Aleixo da Silva, Jose A.; Alves Junior, Francisco T.; de Oliveira, Cinthia P.
通讯作者	de Lima, RB
来源期刊	JOURNAL OF SUSTAINABLE FORESTRY
ISSN	1054-9811
EISSN	1540-756X
出版年	2020-05
英文摘要	The dry tropical forests of the Brazilian semi-arid region are a key component in the sustainable production of coal and firewood for power generation, although their estimates of volume and wood stock depend almost exclusively on equations adjusted from other semi-arid regions or form factor for data of managed species. Therefore, a systematic evaluation of new methodologies such as artificial neural networks and regression models is justifiable for the locale, since it aims to select a tool that reports reliable predictions of volume and that is low cost in the forest management of the region. Our main results show that less reliable estimates of trunk and branch volumes are obtained by simple input models and perceptron networks. The Schumacher-Hall linearized equation provides reliable estimates of volume, although the Multilayer-Perceptron neural networks indicate estimates, which are no less biased. Our results suggest that using volumetric equations to predict trunk and tree branch volume in the Brazilian semi-arid region is still more statistically advantageous, although the use of ANNs is not ruled out. This shows that there are obviously complex relationships between dependent and independent biological factors and that volumetric models are able to better explain such relationships.
英文关键词	Caatinga domain regression analysis artificial neural networks forest management
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000573698400001
WOS关键词	PREDICTING ABOVEGROUND BIOMASS ; ALLOMETRIC EQUATIONS ; INDIVIDUAL TREES ; STAND VOLUME ; STEM VOLUME ; LIDAR ; MODEL ; AREA ; VEGETATION ; DIAMETER
WOS类目	Forestry
WOS研究方向	Forestry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328286
作者单位	[de Lima, Robson B.; Alves Junior, Francisco T.; de Oliveira, Cinthia P.] Univ Estado Amapa, Lab Manejo Florestal, Rua Presidente Vargas 450, BR-68901262 Centro, Macapa, Brazil; [Caraciolo Ferreira, Rinaldo L.; Aleixo da Silva, Jose A.; de Oliveira, Cinthia P.] Univ Fed Rural Pernambuco, Lab Manejo Florestas Nat Jose Serafim Feitosa Fer, Recife, PE, Brazil
推荐引用方式 GB/T 7714	de Lima, Robson B.,Caraciolo Ferreira, Rinaldo L.,Aleixo da Silva, Jose A.,et al. Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison Between Regression and Artificial Neural Networks[J],2020.
APA	de Lima, Robson B.,Caraciolo Ferreira, Rinaldo L.,Aleixo da Silva, Jose A.,Alves Junior, Francisco T.,&de Oliveira, Cinthia P..(2020).Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison Between Regression and Artificial Neural Networks.JOURNAL OF SUSTAINABLE FORESTRY.
MLA	de Lima, Robson B.,et al."Estimating Tree Volume of Dry Tropical Forest in the Brazilian Semi-Arid Region: A Comparison Between Regression and Artificial Neural Networks".JOURNAL OF SUSTAINABLE FORESTRY (2020).