Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

• Main Authors: Carrijo JVN, Miguel EP, Teixeira Do Vale A, Matricardi EAT, Monteiro TC, Rezende AV, Inkotte J
• Format: Article
• Language: English
• Published: Italian Society of Silviculture and Forest Ecology (SISEF) 2020-02-01
• Series: iForest - Biogeosciences and Forestry
• Subjects: artificial neural networks; cerrado; higher heating value; biomass; modelling; forestry
• Online Access: https://iforest.sisef.org/contents/?id=ifor3209-012
• ISSN: 1971-7458; 1971-7458

The use of artificial intelligence to generate information of the savanna’s energy capacity may support sustainable management of those areas. We assessed the efficacy of artificial neural networks (ANNs) combined with satellite data to estimate the energy potential (Pe) for cerradão, a dense savannah-like vegetation type in Brazil. We conducted a forest inventory for measuring dendrometric variables and sampling woody materials and barks in a cerradão area in the state of Tocantins, Brazil. The Pe of cerradão biomass was estimated based on the observed higher calorific power and drier biomass values. Six vegetation indices were retrieved from a RapidEye image and tested for correlation to choose the optimum vegetation index for biomass modeling. The basal area and the Normalized Difference Vegetation Index were used as predictors in the Pe modeling. We estimated an average of 19.234 ± 0.411 GJ ton-1 and 19.878 ± 1.090 GJ ton-1 for higher heating values of the wood species and barks, respectively, and an average Pe of 1022.660 GJ ha-1. The best ANN showed an error of 11.3% by using a structure of two, eight, and one neurons in the input layer, in the hidden layer, and in the output layer, respectively, as well as activation functions of the tangential and sigmoidal types. The validation tests showed no significant difference between the observed and ANN-predicted values. Based on our results, we concluded that Pe can be efficiently predicted by combining ANNs and remotely sensed data, which ultimately is a promising tool for forest sustainable management of the cerrado ecosystems.