We launched Collection 6 (1985-2020)

Articles Mapbiomas

Here you can find and learn more about the scientific articles prepared by the MapBiomas team, they are related to issues involving the mapping of land use in Brazil.

Cesar et al. - A Large-Scale Deep-Learning Approach for Multi-Temporal Aqua and Salt-Culture Mapping

Aquaculture and saliculture are relevant economic activities in the Brazilian Coastal Zone (BCZ). However, automatic discrimination of such activities from other water-related coverages / uses is not an easy task. In this sense, convolutional neural networks (CNN) have the advantage of predicting the class label of a given pixel, providing as input a local region (patches or named chips) around that pixel. Both the convolutional nature and the semantic segmentation capability provide the U-Net classifier with the ability to access the "context domain" instead of just isolated pixel values. Supported in the context domain, we present the results of the analyzes.


Alencar et al - Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform.

The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform.


Saraiva et al- Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning

In this paper, we propose a method to automatically detect and map center pivot irrigation systems using U-Net, an image segmentation convolutional neural network architecture, applied to a constellation of PlanetScope images from the Cerrado biome of Brazil. Our objective is to provide a fast and accurate alternative to map center pivot irrigation systems with very high spatial and temporal resolution imagery.


Parente et al - Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and Google Earth Engine cloud computing.

This work has mapped, annually and in an unprecedented way, the totality of the Brazilian pastures, from 1985 to 2017. With an overall accuracy of about 90%, the 33 maps produced indicated the pasture area varying from ~118 Mha ±3.41% (1985) to ~178 Mha ±2.53% (2017), with this expansion occurring mostly in the northern region of the country and to a lesser extent in the midwest.


Diniz et al - Brazilian Mangrove Status: Three Decades of SatelliteData Analysis.

This manuscript presents a Google Earth Engine (GEE)-managed pipeline to compute the annual status of Brazilian mangroves from 1985 to 2018, along with a new spectral index, the Modular Mangrove Recognition Index (MMRI), which has been specifically designed to better discriminate mangrove forests from the surrounding vegetation. If compared separately, the periods from 1985 to 1998 and 1999 to 2018 show distinct mangrove area trends.