Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. 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. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km²) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.

Different types of grasslands cover much of the Earth’s surface. At a closer look, these grasslands present unique and specific floristic and biophysical characteristics. Following an ecosystem approach, we propose a new regionalization of the grassland landscapes that correspond to two thirds of the surface of the state of Rio Grande do Sul. The biophysical factors that control energy and water availability for living organisms are the basis of our analysis. The dominant macroclimate is humid subtropical, but at a meso-scale (1:250,000), additional constraints such as relief and soils modulate this availability. With the support of Geographic Information Systems it was possible to delimit ten grassland ecological systems based on the flora and its relationship to the respective biophysical characteristics as derived from elevation, slope, and soil data. Our analysis adds the currently available floristic data on grasslands to previously established vegetation maps of the same geopolitical territory. Therefore, we extend the hitherto limited understanding of the complexity of the grassland systems. The results add new knowledge to the regionalization of the grassland vegetation of southern Brazil. The proposed delimitation provides a framework for various purposes, such as ecological and biodiversity studies, conservation and mitigation strategies, and sustainable land use. Diferentes tipos de campos cobrem uma grande parte da superfície da Terra. Sob um olhar atento, estes campos apresentam características florísticas e biofísicas únicas e específicas. Seguindo uma abordagem ecossistêmica, propomos uma nova regionalização das paisagens campestres que correspondem a dois terços da superfície do estado do Rio Grande do Sul, no sul do Brasil. Os fatores biofísicos que controlam a energia e a disponibilidade de água para os organismos vivos são a base de nossa análise. O macroclima dominante é subtropical úmido, mas em mesoescala, (1:250.000), condicionantes adicionais como relevo e solos modulam essa disponibilidade. Reclassificamos os dados de altitude, declividade e solos para relacionar os fatores biofísicos com a distribuição dos ecossistemas campestres. Com apoio de Sistemas de Informação Geográfica (SIG) foi possível delimitar dez sistemas ecológicos campestres com base na descrição florística e sua relação com as respectivas características biofísicas. Nossa análise acrescenta os dados florísticos atualmente disponíveis sobre os campos aos mapas de vegetação previamente estabelecidos do mesmo território geopolítico. Portanto, amplificamos a compreensão da complexidade até agora limitada dos sistemas campestres. Os resultados acrescentam novos conhecimentos à regionalização da vegetação dos campos do sul do Brasil. A delimitação proposta fornece uma estrutura para vários propósitos, como estudos de biodiversidade e ecologia, estratégias de conservação e mitigação, e usos sustentáveis do solo.

O objetivo deste trabalho foi avaliar a redução do vigor vegetativo da cobertura vegetal do Pampa do Brasil e do Uruguai, por meio da identificação de tendências negativas em séries temporais de imagens. Utilizaram-se séries temporais de imagens de NDVI/EVI do sensor Modis, de 2000 a 2011; imagens de índices de umidade do solo do "climate forecast system reanalysis"; e dados de precipitação pluvial de estações meteorológicas. O estudo quantificou tendências lineares e não lineares nas séries de NDVI e EVI, em áreas de campos. Na tendência monotônica de Mann-Kendall, a 5% de probabilidade, 81,9% da área total estudada foi significativa com o NDVI, e 74,8%, com o EVI; no entanto, o EVI apresentou contraste superior na estimativa dos parâmetros. Os resultados mostraram maior sinal negativo a oeste, com valores médios de R²>0,15, r<-0,3 e τ <-0,15 na tendência dos índices de vegetação, e tendência decrescente para NDVI, EVI e precipitação pluvial, com menores valores médios de umidade do solo. A tendência negativa dos índices de vegetação, relacionada à combinação da ocorrência de deficit hídrico em solos rasos com o sobrepastoreio, indica alterações no padrão de cobertura vegetal do Pampa, com redução do vigor vegetativo.