DOI: 10.1016/j.foreco.2016.10.022
Scopus记录号: 2-s2.0-84992467231
论文题名: Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks
作者: Reis L.P. ; de Souza A.L. ; Mazzei L. ; dos Reis P.C.M. ; Leite H.G. ; Soares C.P.B. ; Torres C.M.M.E. ; da Silva L.F. ; Ruschel A.R.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2016
卷: 382 起始页码: 161
结束页码: 167
语种: 英语
Scopus关键词: Logging (forestry)
; Neural networks
; Sustainable development
; Cutting intensity
; Economic sustainability
; Environmental aspects
; Forest structure
; Production models
; Selective harvest
; Sustainable economics
; Training and testing
; Forestry
; artificial neural network
; diameter
; economic analysis
; forest management
; growth
; sustainability
; tree
; tropical forest
; Amazonia
; Brazil
; Para [Brazil]
; Tapajos National Forest
英文摘要: The prognosis of forest structure along the cutting cycle, using models of individual trees, is one of the alternatives to manage tropical forests aiming at sustainability. Currently, in forest management practiced in the Amazon Region, growth and production models are not used to predict the future stock of the forest. Thus, the sustainable economic and environmental aspects of this activity remain uncertain. The aim of this present work was to model the growth of individual trees in a forest managed in the Amazon Region, by using artificial neural networks (ANN) to serve as subsidy to the wielder in obtaining future stock after logging, thus reducing uncertainty on forest management sustainability. Selective harvest was carried out in 1979 with an intensity of 72.5 m3 ha−1 in a 64 ha area in the Tapajós National Forest - PA. In 1981, 36 permanent plots (50 m × 50 m) were installed at random and inventoried. There were nine successive measurements in 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010, and 2012. In the modeling of the future diameter, training and testing of ANN were carried out, including different semi-independent competition indexes (DSICI). All ANN, with and without DSICI, presented correlation above 99%, RMSE below 11%, and EF above 0.98. Based on the prognosis of tree growth, we were able to conclude that ANN can be effectively used to assist in the management of tropical forests and, thus, allow for the most suitable cutting intensity and cutting cycle per species, ensuring environmental and economic sustainability of forest management. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64641
Appears in Collections: 影响、适应和脆弱性
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作者单位: Universidade Federal de Viçosa, Campus Universitário de Viçosa, Departamento de Engenharia Florestal, Avenida Peter Henry Rolfs, s/n, CEP 36.570-900, ViçosaMG, Brazil; Embrapa Amazônia Oriental, Travessa Doutor Enéas Pinheiro, CEP 66.095-903, Belém, PA, Brazil
Recommended Citation:
Reis L.P.,de Souza A.L.,Mazzei L.,et al. Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks[J]. Forest Ecology and Management,2016-01-01,382