Articles Mapbiomas

Learn more about the scientific articles prepared by the MapBiomas team related to land cover and land use mapping in Brazil.

Alencar et al. Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning

The paper presents a new strategy using machine learning to map monthly burned areas from 1985 to 2020 using Landsat image mosaics and minimum NBR values. This new dataset contributes to the understanding of the long-term spatial and temporal dynamics of fire regimes that are fundamental to design appropriate public policies to reduce and control fires in Brazil.

Cayo et al. Mapping Three Decades of Changes in the Tropical Andean Glaciers Using Landsat Data Processed in the Earth Engine.

This paper presents the mapping and retreat dynamics of tropical Andean glaciers (TAGs) from the use of Landsat time series images from 1985 to 2020, with digital processing and classification of the satellite images on the Google Earth Engine platform.

Santos et al - Assessing the Wall-to-Wall Spatial and Qualitative Dynamics of the Brazilian Pasturelands 2010–2018, Based on the Analysis of the Landsat Data Archive

In this study, the spatio-temporal dynamics of pasture quality in Brazil between 2010 and 2018 were mapped and evaluated, considering three degradation classes: Absent (D0), Intermediate (D1) and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, despite the accentuated spatial dynamics.

Coelho-Junior et al - Unmasking the impunity of illegal deforestation in the Brazilian Amazon: a call for enforcement and accountability

This article provides a perspective on the dynamics of deforestation alerts, validated and refined by MapBiomas Alert (, in the Brazilian Amazon and the actions of federal and state public enforcement agencies highlighting the urgency to reduce and combat deforestation.

Nunes et al - Unmasking secondary vegetation dynamics in the Brazilian Amazon

This article uses MapBiomas annual land cover time series data to generate the first estimates of the extent of VS, age and net carbon absorption in the Brazilian Amazon between 1985 and 2017.

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.

Arruda et al - An alternative approach for mapping burn scars using Landsat imagery, Google Earth Engine, and Deep Learning in the Brazilian Savanna

In this study, we developed an alternative approach for mapping burned areas in the Cerrado biome in Brazil, using Landsat imagery and Deep Learning algorithm, implemented on the Google Earth Engine and on Google Cloud Storage platform.

Rosa et al. - Hidden destruction of older forests threatens Brazil’s Atlantic Forest and challenges restoration programs.

Understanding the dynamics of native forest loss and gain is critical for biodiversity conservation and ecosystem services, especially in regions experiencing intense forest transformations. We quantified native forest cover dynamics on an annual basis from 1990 to 2017 in Brazil’s Atlantic Forest.

Souza et al. - Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine.

In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine.

Parente et al. - Monitoring the brazilian pasturelands: A new mapping approach based on the landsat 8 spectral and temporal domains.

In this study, we utilized the entire set of Landsat 8 images available for Brazil in 2015, from which dozens of seasonal metrics were derived, to produce, through objective criteria and automated classification strategies, a new pasture map for the country.

Fendrich et al. - Disclosing contrasting scenarios for future land cover in Brazil: Results from a high-resolution spatiotemporal model.

In this work, we construct a model to evaluate the possible consequences of policy actions on land cover dynamics in the near-future at a high-resolution scale.

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 - Next Generation Mapping: Combining Deep  Learning, Cloud Computing, and Big Remote Sensing Data.

This study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region.

Souza, Jr. et al - Long-Term Annual Surface Water Change in the Brazilian Amazon Biome: Potential Links with Deforestation, Infrastructure Development and Climate Change.

In this study, we present a long-term spatiotemporal analysis of surface water annual change and address potential connections with deforestation, infrastructure expansion and climate change in this region.

Rosa, Marcos R - Comparação e análise de diferentes metodologias de mapeamento da cobertura florestal da Mata Atlântica.

The differences found in the products point to distinct and complementary uses, with the Atlas base most suitable for identifying deforestation and analyzing biodiversity conservation and MapBiomas for analysis of fragmentation, restoration and water protection. The option for the product that will be used varies according to the objective of the proposed work.

Costa et al - Novas tecnologias e sensoriamento remoto: aplicação de uma oficina didática para a disseminação das  potencialidades dos produtos e ferramentas do MapBiomas.

This work aimed to report the experiences of the application of a didactic workshop that dealt with the potential of the products and tools of the Mapbiomas Project in a class of the Graduate Program in Modeling in Earth and Environmental Sciences, from the State University of Feira de Santana.

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.

Leandro Parente and Laerte Ferreira - Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based  on the Automated Classification of MODIS Images from 2000  to 2016.

This study mapped, through objective criteria and automatic classification methods (Random Forest) applied to MODIS (Moderate Resolution Imaging Spectroradiometer) images, the totality of the Brazilian pastures between 2000 and 2016.

Mas et al - Analysis of High Temporal Resolution Land Use/Land Cover Trajectories.

In this study, methods, originally developed to assess life course trajectories, are explored in order to evaluate land change through the analysis of sequences of land use/cover. Annual land cover maps which describe land use/land cover change for the 1985–2017 period for a large region in Northeast Brazil were analyzed.

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.