Estimation of Eucalyptus urophylla S.T. Blake stands using artificial neural networks and vegetation índices (Research, Society and Development, 2023)
In Brazil, planted forest areas cover 54.4% and carrying out forest inventories is essential for forest management. Traditional inventory collection methods are laborious and expensive. An alternative has been to combine data from remote sensors and artificial neural networks (ANN) as a comparative approach. The objective of this study is to evaluate the agreement between biomass estimates made by artificial neural networks using vegetation indices and the traditional forest inventory method. An inventory from the year 2011 was used, carried out in the municipality of Rio Verde, Goiás, in a stand of Eucalyptus urophylla S. T. Blake, belonging to the Cooperativa Agroindustrial dos Produtores Rurais do Sudoeste Goiano (COMIGO), 23 random sample plots of 400 m² were collected the diameter, height, volume, and other parameters. RapidEye satellite images were acquired and vegetation indices such as NDVI, MSAVI, NDRE and EVI. Artificial neural networks were trained and validated in the NeuroForest Star program to estimate the volume of trees per plot. The results indicated that the estimates were comparable to traditional forest inventory approaches. Furthermore, correlations ranged from -0.84 to 0.99, with a standard error of the relative estimate (syx %) ranging from 0.20% to 16.59%. The different artificial neural network architectures presented variations in volume estimates, and some were not stable in the validation stage. However, the architectures of the NDRE and MSAVI indices stood out for their stability for volume estimates per plot, demonstrating the effectiveness of ANN as an efficient and accessible method.