globalchange  > 影响、适应和脆弱性
DOI: 10.1016/j.foreco.2013.07.014
Scopus记录号: 2-s2.0-84880990429
论文题名:
Tree height prediction approaches for uneven-aged beech forests in northwestern Spain
作者: Castaño-Santamaría J.; Crecente-Campo F.; Fernández-Martínez J.L.; Barrio-Anta M.; Obeso J.R.
刊名: Forest Ecology and Management
ISSN:  0378-1127
出版年: 2013
卷: 307
起始页码: 63
结束页码: 73
语种: 英语
英文关键词: Generalized models ; Height-diameter ; Mixed models ; Neural networks
Scopus关键词: Artificial neural network methods ; Back propagation artificial neural network (BPANN) ; Generalized models ; Height-diameter ; High degree of variability ; Mixed models ; Root mean squared errors ; Traditional approaches ; Backpropagation ; Neural networks ; Forestry ; accuracy assessment ; artificial neural network ; back propagation ; calibration ; comparative study ; deciduous forest ; diameter ; forestry modeling ; height ; numerical model ; performance assessment ; prediction ; Forestry ; Models ; Neural Networks ; Spain
英文摘要: Artificial neural network methods appear to be a reliable alternative to traditional methods of tree height prediction in even-aged stands. However, this has not been demonstrated for uneven-aged forests. Two back-propagation artificial neural networks were constructed, and their performance in estimating the height of pure uneven-aged stands of common beech (Fagus sylvatica L.) in northwestern Spain was compared with that of the models most commonly used to estimate tree height (nonlinear calibrated local and generalized mixed-effects models and generalized fixed-effects models). All approaches produced accurate results, reducing the root mean squared error by more than 22% relative to basic nonlinear regression. Nonetheless, considering practical use of the models, the traditional approaches require measurement of several trees for calculation of stand-specific variables (generalized models) and for model calibration (mixed-effects models). Back-propagation artificial neural networks require less sampling effort because no height measurements are required for their implementation. However, this technique was not the best height predictor, because of the high degree of variability in site quality between stands. In this case, the local mixed-effects models yielded the best results and provided the best balance between the accuracy of the model and sampling effort. © 2013 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66384
Appears in Collections:影响、适应和脆弱性

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作者单位: Grupo de Investigación en Sistemas Forestales Atlánticos (GIS-Forest), Departamento de Biología de Organismos y Sistemas, Universidad de Oviedo, Escuela Politécnica de Mieres, C/Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Spain; Unidad Mixta de Investigación en Biodiversidad, Universidad de Oviedo-CSIC-Principado de Asturias, Edificio de investigación, 5planta, C/Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Spain; Departamento de Ingeniería Agroforestal, Universidad de Santiago de Compostela, Escuela Politécnica Superior, R/Benigno Ledo, Campus Universitario, 27002 Lugo, Spain; Departamento de Matemáticas, Universidad de Oviedo, C/Calvo Sotelo s/n, 33006 Oviedo, Spain

Recommended Citation:
Castaño-Santamaría J.,Crecente-Campo F.,Fernández-Martínez J.L.,et al. Tree height prediction approaches for uneven-aged beech forests in northwestern Spain[J]. Forest Ecology and Management,2013-01-01,307
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