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| PÉLLICO NETTO,SYLVIO; SANQUETTA,CARLOS R.; CARON,BRAULIO O.; BEHLING,ALEXANDRE; SIMON,AUGUSTO A.; CORTE,ANA PAULA D.; BAMBERG,ROGÉRIO. |
| ABSTRACTThe objective is to study the dynamics of photosynthetic radiation reaching the soil surface in stands of Acacia mearnsii De Wild and its influence on height growth in stands. This fact gives rise to the formulation of the following hypothesis for this study: "The reduction of the incidence of light inside the stand of black wattle will cause the inflection point in its height growth when this reaches 4 to 5 m in height, i.e. when the stand is between 2 and 3 years of age". The study was conducted in stands in the state of Rio Grande do Sul, Brazil, where diameters at breast height, total height and photosynthetically active radiation available at ground level were measured. The frequency tended to be more intense when the age of the stands... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Burr function; Light intensity; Probabilistic models; Temporal variation. |
| Ano: 2015 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652015000401833 |
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| SCHIKOWSKI,ANA B.; CORTE,ANA P.D.; RUZA,MARIELI S.; SANQUETTA,CARLOS R.; MONTAÑO,RAZER A.N.R.. |
| Abstract Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified.... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Artificial intelligence; Data mining; Random forest; ANN. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703389 |
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| NETTO,SYLVIO P.; PELISSARI,ALLAN L.; CYSNEIROS,VINICIUS C.; BONAZZA,MARCELO; SANQUETTA,CARLOS R.. |
| ABSTRACT The spatial distribution of tropical tree species can affect the consistency of the estimators in commercial forest inventories, therefore, appropriate sampling procedures are required to survey species with different spatial patterns in the Amazon Forest. For this, the present study aims to evaluate the conventional sampling procedures and introduce the adaptive cluster sampling for volumetric inventories of Amazonian tree species, considering the hypotheses that the density, the spatial distribution and the zero-plots affect the consistency of the estimators, and that the adaptive cluster sampling allows to obtain more accurate volumetric estimation. We use data from a census carried out in Jamari National Forest, Brazil, where trees with... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Adaptive cluster sampling; Spatial species distribution; Volume estimation; Zero-plots. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017000401829 |
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