Souza et al. Landsat sub-pixel land cover dynamics in the Brazilian Amazon
This study proposes a novel approach to characterize and measure land cover dynamics in the Amazon biome. First, we defined 10 fundamental land cover classes: forest, flooded forest, shrubland, natural grassland, pastureland, cropland, outcrop, bare and impervious, wetland, and water. Second, we mapped the land cover based on the compositional abundance of Landsat sub-pixel information that makes up these land cover classes: green vegetation (GV), non-photosynthetic vegetation, soil, and shade. Third, we processed all Landsat scenes with <50% cloud cover. Then, we applied a step-wise random forest machine learning algorithm and empirical decision rules to classify intra-annual and annual land cover classes between 1985 and 2022. Finally, we estimated the yearly land cover changes in forested and non-forested ecosystems and characterized the major change drivers.
Baeza et al. Two decades of land cover mapping in the Río de la Plata grassland region: The MapBiomas Pampa initiative
This work describes and analyzes the land cover changes in the entire Río de la Plata Grasslands (RPG) region for the first two decades of the 21st century, especially those related to grassland loss. In 20 years, RPG region lost, at least, 2.4 million ha of grassland (9% of the remaining grassland area in 2001). Most of these losses are concentrated in Brazil and Uruguay and are associated with new agricultural or forestry areas that increased by 5% and 100%, respectively.
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 designing 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.
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 (http://alerta.mapbiomas.org/), in the Brazilian Amazon and the actions of federal and state public enforcement agencies highlighting the urgency to reduce and combat deforestation.
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 spatial-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.
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 the 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 the Landsat archive using Google Earth Engine.