Measurement of forest biomass is a time-, money-, and labor-consuming activity.Developing methodological approaches for quantifying biomass in natural ecosystems has motivated countless investigations in several vegetation types worldwide. In this study, we developed a model to estimate the above-ground biomass (AGB) in a study site spatially located in Western Pará state, Brazilian Amazon. We applied artificial neural networks and remotely sensed data for adjusting our model based on forest inventory data. We tested four vegetation indices retrieved from a Landsat-5 thematic mapper scene and assessed their correlations with the field measured AGB. We trained 286 artificial neural networks using the vegetation stratus (two phytophysiognomies: dense lowland ombrophilous forest and dense submontane ombrophilous forest) and the normalized difference vegetation index (NDVI) and aerosol-free vegetation index as predictors variables of the AGB. We selected the artificial neural network model showing higher correlation coefficient and concentration of residuals in the center classes, which indicates higher predictive capabilities of biomass estimation. The generated model architecture (4-13-1) was composed by 4 predictor neurons in the input layer, 13 neurons in the hidden layer, activated by logistic functions, and a single neuron (the AGB in ton • ha −1 ) in the output layer, activated by an identity function. We observed that the NDVI and aerosol free vegetation index showed the best performance to estimate the AGB in the study area. Using artificial neural network and a Landsat-5 image combined, we were able to accurately predict (estimation error of ∼20%) AGB in tropical forest. This is a promising methodological approach that can be applied to assess ecosystem services related to carbon stock in tropical regions.

The goal of this study was to assess the temporal dynamics of an accumulation reservoir in an accessible and accurate way. The study was conducted on the Serra da Mesa Dam (GO) using orbital images. To estimate the flat area of the dam surface, Landsat TM and OLI images for the period 1998 to 2018 were used. The images were processed using the Google Earth Engine platform (GEE) in order to obtain the dam surface area (km²) and relate it to the flow, altimetric height and volume of the reservoir over the years. The dam showed constant variation of water since its inception, with a decreasing trend. The highest values of the reservoir measurement metrics were observed in the years coincident with the largest areas of the dam, and inversely proportional to the years of the appearance of new dams upstream. More than 90% of the altimetric height variation of water could be explained by the flat area of the dam. The processing platform using the GEE is effective to provide extensive temporal analysis using a large volume of data in a short time, with accurate and robust results.