Dominant Height Model for Site Classification of<i>Eucalyptus grandis</i>Incorporating Climatic Variables

Scolforo, José Roberto Soares; Maestri, Roberto; Filho, Antônio Carlos Ferraz; Mello, José Márcio de; Oliveira, Antônio Donizette de; Assis, Adriana Leandra de

doi:10.1155/2013/139236

Cited by 21 publications

(15 citation statements)

References 15 publications

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“…For example, Scolforo et al (2013) used exploratory data analysis to select climatic variables and use it as input on Chapman and Richards model so that it increased its precision in 33%. Like the ANN, this kind of modified models can be potentially used to simulate site capacity with no planting history or in case of climatic changes.…”

Section: Discussionmentioning

confidence: 99%

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Cosenza

Soares

Alcântara³

et al. 2017

CERNE

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Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history.

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Section: Discussionmentioning

confidence: 99%

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Cosenza

Soares

Alcântara³

et al. 2017

CERNE

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“…Another advantage of using dominant height as a predictor variable is its response to climatic conditions and the possibility to explain this variation using regression models (e.g. Ferraz Filho et al, 2011;Scolforo et al, 2013). Other variables related to the plot's metrics, such as diameter, were explained using age (Mehtatälo, 2005), and could be considered interchangeable.…”

Section: Figurementioning

confidence: 99%

HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZIL

Filho

Mola‐Yudego

Ribeiro

et al. 2018

CERNE

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HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZILFERRAZ FILHO, A. C.; MOLA-YUDEGO, B.; RIBEIRO, A.; SCOLFORO, J. R. S.; LOOS, R. A.; SCOLFORO, H. F. Height-diameter models for Eucalyptus sp. plantations in Brazil. CERNE, v. 24, n. 1, p. 9-17, 2018. HIGHLIGHTSHeight-diameter models are provided for eucalyptus plantations in Brazil.A wide variety of stand conditions (age and tree densities) were covered in the database.Mixed modeling was used due to the hierarchical structure of the data (trees in plots).The models use diameter, age, and dominant height as predictor variables. ABSTRACTThe use of height-diameter models have important applications in the management of eucalyptus plantations. The aim of this paper is to provide adequate height models based on the diameter and stand variables that could be easily used and applied to plantation's management. The data was based on tests located in Espírito Santo and Bahia. A total of 312 plots, including 35 different treatments (different initial planting densities, number and severity of thinning operations, fertilization regimes, among others), and four different clones. This resulted in 2,407 conditions for 85,608 tree measurements. The constructed equations were based on mixed-effect models, in order to accommodate possible autocorrelations, to address variations between sites and to obtain locally calibrated estimates. Among the different model structures and combination of variables tested and presented, the best model showed a coefficient of determination of 0.953 and 0.988, for the fixed and fixed+random parts, respectively. The model included a random factor for measurements-within-tree and tree-within-plot. The bias of the fixed part was 0.208 cm (0.0659%). We believe the models in this study can have broad applications in management and inventory methods applied to plantations. HEIGHT-DIAMETER MODELS FOR EUCALYPTUS SP. PLANTATIONS IN BRAZIL 10 CERNEFERRAZ FILHO et al.

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“…Due to scale effects [77], because our study sites were between 8 and 32 km from each other, climate and topographical attributes were not stronger determinants [19,[78][79][80][81][82] than soils for forest productivity. Additionally, the low number of study sites is a limitation of this work.…”

Section: Effects Of Climate and Topography On Forest Productivitymentioning

confidence: 99%

Forest Site Classification in the Southern Andean Region of Ecuador: A Case Study of Pine Plantations to Collect a Base of Soil Attributes

Quichimbo

Jiménez

Veintimilla

et al. 2017

Forests

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Forest site classification adapted to the respective site conditions is one prerequisite for sustainable silviculture. This work aims to initiate the forest site classification for pine plantations in the southern Andean region of Ecuador. Forest productivity, estimated by the dominant height of 20-year-old trees (DH 20 ), was related to data from climate, topography, and soil using 23 plots installed in pine plantations in the province of Loja. Forest site productivity was classified as: low (class C: 13.4 m), middle (class B: 16.6 m), and high (Class A: 22.3 m). Strong determinants to differentiate the forest site classes were: the short to medium term available Ca and K stocks (organic layer + mineral soil standardized to a depth of 60 cm), soil acidity, the C:N ratio, clay and sand content, forest floor thickness, altitude, and slope. The lowest forest productivity (Class C) is mainly associated with the lowest short to medium term available K and Ca stocks. Whereas, in site classes with the highest forest productivity, pines could benefit from a more active microbial community releasing N and P, since the soil pH was about 1 unit less acidic. This is supported by the lowest forest floor thickness and the narrowest C:N ratio.

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Dominant Height Model for Site Classification ofEucalyptus grandisIncorporating Climatic Variables

Cited by 21 publications

References 15 publications

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

HEIGHT-DIAMETER MODELS FOR Eucalyptus sp. PLANTATIONS IN BRAZIL

Forest Site Classification in the Southern Andean Region of Ecuador: A Case Study of Pine Plantations to Collect a Base of Soil Attributes

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