Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil

• Published in: The Science of the total environment Vol. 645; pp. 655 - 661
• Main Authors: Rocha, Samuel José Silva Soares da, Torres, Carlos Moreira Miquelino Eleto, Jacovine, Laércio Antônio Gonçalves, Leite, Helio Garcia, Gelcer, Eduardo Monteiro, Neves, Karina Milagres, Schettini, Bruno Leão Said, Villanova, Paulo Henrique, Silva, Liniker Fernandes da, Reis, Leonardo Pequeno, Zanuncio, José Cola
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15-12-2018
• Subjects: Artificial intelligence; Brazil; Climate Change; Ecosystem; Environmental Monitoring; Forests; Neural Networks (Computer); Prognosis; Trees; Tropical forests; Brazil; Artificial intelligence; Prognosis; Tropical forests
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2018.07.123

Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition index and climatic and categorical variables, to predict tree survival and mortality in Semideciduous Seasonal Forests in the Atlantic Forest biome. Numerical and categorical trees variables, in permanent plots, were used. The Agricultural Reference Index for Drought (ARID) and the distance-dependent competition index were the variables used. The overall efficiency of classification by ANNs was higher than 92% and 93% in the training and test, respectively. The accuracy for classification and number of surviving trees was above 99% in the test and in training for all ANNs. The classification accuracy of the number of dead trees was low. The mortality accuracy rate (10.96% for training and 13.76% for the test) was higher with the ANN 4, which considers the climatic variable and the competition index. The individual tree-level model integrates dendrometric and meteorological variables, representing a new step for modeling tree survival in the Atlantic Forest biome.
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•The tree survival and mortality in rainforest were estimated with artificial intelligence;•The accuracy rate of the surviving trees classification was above 99%;•Artificial Neural Networks with inclusion of meteorological variables improve modeling of mortality in the Atlantic Forest.