Open AccessArtificial neural networks and remote sensing for volumetric prediction in a Eucalyptus sp. plantation
Author(s) -
Alessandro Araujo Amaral de Almeida,
Monica Fabiana Bento Moreira Thiersch,
Lucas Kröhling Bernardi,
Franciane Andrade de Pádua,
Argemiro José Moreno Arteaga,
Cláudio Roberto Thiersch
Publication year - 2021
Publication title -
research, society and development
Language(s) - English
Resource type - Journals
ISSN - 2525-3409
DOI - 10.33448/rsd-v10i12.20466
Subject(s) - artificial neural network , volume (thermodynamics) , residual , satellite , computer science , eucalyptus , satellite imagery , remote sensing , forest inventory , software , artificial intelligence , forestry , forest management , geography , engineering , algorithm , ecology , physics , quantum mechanics , biology , aerospace engineering , programming language
Forest inventory is an important tool for estimating the production of forest stands and normally employs traditional methods for volume estimation. However, as a result of technological advancements, artificial neural networks and remote sensing have assumed a prominent role in the forestry sector since satellite images have different components that correlate with the dendrometric variables and can be used as auxiliary variables. The objective of this work was to evaluate the performance of artificial neural networks regarding the estimation of volume in a Eucalyptus sp. plantation with the use of satellite images. Pre-cut inventory data were used with ages varying between 5.3 and 6.3 years. The variables used were volume, age, 4 bands of the satellite image with a 10 m spatial resolution from Sentinell-2 satellite, ratio between the bands, NDVI, and genetic material. All processing was performed using the free software R. The evaluation criteria for the neural network were percentage of residual standard error and graphical analysis of the residues. The best neural network configuration for volume estimation presented a residual standard error of 10.63% and 12.00% for training and validation, respectively. The methodology proposed in this work proved to be efficient in estimating the volume of the stand.