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Soil loss is one of the main causes of pauperization and alteration of agricultural soil properties. Various empirical models (e.g., USLE) are used to predict soil losses from climate variables which in general have to be derived from spatial interpolation of point measurements. Alternatively, Artificial Neural Networks may be used as a powerful option to obtain site-specific climate data from independent factors. This study aimed to develop an artificial neural network to estimate rainfall erosivity in the Ribeira Valley and Coastal region of the State of São Paulo. In the development of the Artificial Neural Networks the input variables were latitude, longitude, and annual rainfall and a mathematical equation of the activation function for use in the study area as the output variable. It was found among other things that the Artificial Neural Networks can be used in the interpolation of rainfall erosivity values for the Ribeira Valley and Coastal region of the State of São Paulo to a satisfactory degree of precision in the estimation of erosion. The equation performance has been demonstrated by comparison with the mathematical equation of the activation function adjusted to the specific conditions of the study area.

RESUMO O presente trabalho teve como objetivo determinar quais variáveis dimensionais da folha são mais adequadas para utilização na estimativa da área foliar do antúrio (AnthuriumNo Brasil, o antúrio (Anthurium andreanum) tem se destacado como espécie importante para produção de folhas e flores de corte e para cultivo em vaso. Devido ao importante papel das plantas ornamentais na diversificação da agricultura tropical, existe grande interesse em conhecer características do crescimento e desenvolvimento dessa espécie vegetal visando a melhorar seu potencial produtivo.A área foliar é um dos principais parâmetros do crescimento vegetal, pois está relacionada com diversos processos fisiológicos da planta, tais como fotossíntese, respiração e transpiração. Dentre os métodos conhecidos de determinação da área foliar, merece destaque o procedimento que relaciona a área foliar com as dimensões lineares da folha (PEREIRA, 1987), devido à rapidez na obtenção de dados, quando se utiliza amostra de folhas, e por não ser, necessariamente, destrutivo (PEDRO-JÚNIOR et al., 1986).No intuito de obterem-se dados de área foliar confiáveis, faz-se necessário a determinação de qual das dimensões foliares, comprimento (C), largura (L) ou seu produto (CxL), é mais adequada para estimativa da área foliar real da planta. Para isso, são ajustadas funções de regressão baseadas nas dimensões lineares da folha.A escolha da função de regressão que melhor estima a área foliar, geralmente, é baseada no

Cerrado is the second largest biome in Brazil, covering about 2 million km2. This biome has experienced land use and land cover changes at high rates due to agricultural expansion so that more than 50% of its natural vegetation has already been removed. Therefore, it is crucial to provide technology capable of controlling and monitoring the Cerrado vegetation suppression in order to undertake the environmental conservation policies. Within this context, this work aims to develop a new methodology to detect deforestation in Cerrado through the combination of two Deep Learning (DL) architectures, Long Short-Term Memory (LSTM) and U-Net, and using Landsat and Sentinel image time series. In our proposed method, the LSTM evaluates the time series in relation to the time axis to create a deforestation probability map, which is spatially analyzed by the U-Net algorithm alongside the terrain slope to produce final deforestation maps. The method was applied in two different study areas, which better represent the main deforestation patterns present in Cerrado. The resultant deforestation maps based on cost-free Sentinel-2 images achieved high accuracy metrics, peaking at an overall accuracy of 99.81%±0.21 and F1-Score of 0.8795±0.1180. In addition, the proposed method showed strong potential to automate the PRODES project, which provides the official Cerrado yearly deforestation maps based on visual interpretation.