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Site classification for Eucalyptus sp. in a tropical region of Brazil.

Ataídes M FilhoSylvio P NettoSebastião A MachadoAna P Dalla CorteAlexandre Behling
Published in: Anais da Academia Brasileira de Ciencias (2023)
The aim was to determine the productive capacity of a forest site by applying different methods of fitting, combined with geostatistical techniques, to stands of Eucalyptus sp. in a tropical region of Brazil. Data were collected from 845 plots of a continuous forest inventory over four years. The classification of local production capacity was performed using growth curves obtained by the guide curve (GC) method, algebraic difference approach (ADA) and generalized algebraic difference approach (GADA) methods and ordinary kriging through the spherical, exponential, and Gaussian models to determine the spatial dependence of the variables site, geographical boundaries of site index classes, and their respective areas for each hectare in an annual production unit (APU). The modified Chapman-Richards model, fitted by the generalized difference approach method (GADA), provided the best statistical results, an improvement of 12.23% and 39.80% on the ADA and GC methods, respectively. The exponential model selected to express the spatial distribution of dominant height showed a high degree of spatial dependence.
Keyphrases
  • climate change
  • machine learning
  • deep learning
  • artificial intelligence
  • high resolution
  • mass spectrometry
  • psychometric properties