Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. 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. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr⁻¹), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.

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.

This study applied a deforestation model for the entire State of Rondônia assuming three scenarios of deforestation: business as usual, optimistic and pessimistic. Those scenarios were constructed for the time-period of 2012-2050 using the Dinamica EGO software. Rondônia deforestation dataset was provided by the Agência Ambiental do Estado de Rondônia (Rondônia State Environmental Agency) and was used as input of the deforestation modeling. Based on this study results, we estimated that 32%, 37% and 47% of Rondônia’s native forest could be fully deforested by 2050 assuming the optimistic, business as usual and pessimistic scenarios, respectively. Regardless of the chosen scenario, we expect that deforestation will be spatially concentrated in Northern Rondônia in the next decades. The greatest concern, however, could be the integrity of the protected areas assuming the business as usual and/or pessimistic scenario. In addition, we expect a substantial increase of the forest fragmentation by 2050.

A expansão e as mudanças no uso e cobertura da terra na parte sul da Amazônia ocidental estão vinculadas principalmente à s atividades do extrativismo madeireiro sem regras de manejo e à produção agropecuária, que resultaram na conversão de florestas nativas, especialmente ao longo de cursos d´água. Esse estudo teve como objetivo verificar a distribuição diamétrica de espécies florestais com maior índice de valor de importância da mata ciliar do Rio Acre, AC. O inventário florestal foi realizado em oito municípios cortados pelo Rio Acre, utilizando unidades amostrais com dois estágios (conglomerados) e técnicas de amostragem aleatória estratificada pela faixa marginal do Rio Acre. Foram instaladas 27 parcelas primárias, dentro das quais foram distribuídas quatro parcelas secundárias. Para estudo da classificação diamétrica, foram avaliadas as funções de densidade probabilística de Weibull 2 e 3 parâmetros pelo método da máxima verossimilhança. Pela análise gráfica, verificou-se que 86% das espécies analisadas apresentaram tendência de distribuição com assimetria positiva. A distribuição da função Weibull com dois parâmetros estimou melhor a frequência de espécies por classe de diâmetros. Diante da heterogeneidade das espécies, recomendam-se novas pesquisas para verificar se o comportamento da distribuição segue a mesma tendência.

Spatial modeling is a tool to represent deforestation and predict future scenarios according to different landscape change. Establishing 80% Legal Reserve Area (LR) in the Amazon since 90th, the Brazilian forestry code has made clear the biodiversity conservation profile of the largest tropical forest in the world. However, this mechanism did not prevent the advance of deforestation, which in recent years has increased again. This remote tool aims to monitor the deforestation, simulating its possible future trajectories, and thus generate information that can be used to assist in the management of deforestation reduction. The spatial modeling in the prediction of different deforestation scenarios based on public policies and their changes to the state of Acre (north of Brazil). Using the methodological processes of the Dinamica EGO software, three scenarios were projected up to the year 2050: (1) deforestation “Business as usual”, (2) deforestation with 50% LR and (3) deforestation with 80% LR provided by law. Based on these results it was evident that maintaining and respect 80% LR, it’s possible reduce the CO2 emissions more than 76%, avoiding around 119,534,836 t of CO2 and influences positively on reducing deforestation. Dinamica EGO proved to be an effective to represent the deforestation.