This study analyzes the spatiotemporal variability of rainfall and temperature (minimum, maximum and average) trends at 47 stations throughout the Brazilian Legal Amazon for the period 1973–2013. Annual, wet season and dry season trends were quantified by Sen's slope for each station and the entire region. The Mann–Kendall test was used to determine the statistical significance of the trends. For the whole region, minimum, maximum and average annual temperatures showed increasing trend of approximately 0.04 °C per year. The rainfall showed an insignificant trend for most stations for annual and seasonal series. Nevertheless, some stations showed significant increasing trends in the annual and wet season rainfalls while a few stations showed decreasing trends for the dry season rainfall. A positive trend of the annual range between wet and dry season rainfall was found in some stations, caused mainly by an increasing trend in wet season rainfall.

Amazonia is home to more than half of the world’s remaining tropical forests, playing a key role as reservoirs of carbon and biodiversity. However, whether at a slower or faster pace, continued deforestation causes forest fragmentation in this region. Thus, understanding the relationship between forest fragmentation and fire incidence and intensity in this region is critical. Here, we use MODIS Active Fire Product (MCD14ML, Collection 6) as a proxy of forest fire incidence and intensity (measured as Fire Radiative Power—FRP), and the Brazilian official Land-use and Land-cover Map to understand the relationship among deforestation, fragmentation, and forest fire on a deforestation frontier in the Brazilian Amazonia. Our results showed that forest fire incidence and intensity vary with levels of habitat loss and forest fragmentation. About 95% of active fires and the most intense ones (FRP > 500 megawatts) were found in the first kilometre from the edges in forest areas. Changes made in 2012 in the Brazilian main law regulating the conservation of forests within private properties reduced the obligation to recover illegally deforested areas, thus allowing for the maintenance of fragmented areas in the Brazilian Amazonia. Our results reinforce the need to guarantee low levels of fragmentation in the Brazilian Amazonia in order to avoid the degradation of its forests by fire and the related carbon emissions.

Tropical forests play an important role as a reservoir of carbon and biodiversity, specifically forests in the Brazilian Amazon. However, the last decades have been marked by important changes in the Amazon, particularly those associated with climatic extremes. Quantifying the variability of rainfall patterns, hence, is essential for understanding changes and impacts of climate upon this ecosystem. The aim of this study was to analyse spatiotemporal trends in rainfall along the Brazilian Legal Amazon between 1998 and 2015. For this purpose, rainfall data derived from the Tropical Rainfall Measuring Mission satellite (TRMM) and nonparametric statistical methods, such as Mann–Kendall and Sen’s Slope, were used. Through this approach, some patterns were identified. No evidence of significant rainfall trends (p ≤ 0.05) for annual or monthly (except for September, which showed a significant negative trend) averages was found. However, significant monthly negative rainfall anomalies were found in 1998, 2005, 2010, and 2015, and positive in 1999, 2000, 2004, 2009, and 2013. The annual pixel-by-pixel analysis showed that 92.3% of the Brazilian Amazon had no rainfall trend during the period analysed, 4.2% had significant negative trends (p ≤ 0.05), and another 3.5% had significant positive trends (p ≤ 0.05). Despite no clear temporal rainfall trends for most of the Amazon had negative trends for September, corresponding to the peak of dry season in the majority of the region, and negative rainfall anomalies found in 22% of the years analysed, which indicate that water-dependent ecological processes may be negatively affected. Moreover, these processes may be under increased risk of disruption resulting from other drought-related events, such as wildfires, which are expect to be intensified by rainfall reduction during the Amazonian dry season.

Tropical forests provide essential environmental services to human well-being. In the world, Brazil has the largest continuous area of these forests. However, in the state of Maranhão, in the eastern Amazon, only 24% of the original forest cover remains. We integrated and analyzed active fires, burned area, land use and land cover, rainfall, and surface temperature datasets to understand forest fragmentation and forest fire dynamics from a remote sensing approach. We found that forest cover in the Maranhão Amazon region had a net reduction of 31,302 km² between 1985 and 2017, with 63% of losses occurring in forest core areas. Forest edges extent was reduced by 38%, while the size of isolated forest patches increased by 239%. Forest fires impacted, on average, around 1031 ± 695 km² year⁻¹ of forest edges between 2003 and 2017, the equivalent of 60% of the total burned forest in this period. Our results demonstrated that forest fragmentation is an important factor controlling temporal and spatial variability of forest fires in the eastern Amazon region. Thus, both directly and indirectly, forest fragmentation can compromise biodiversity and carbon stocks in this Amazon region.

Continuous mapping of vegetation height is critical for many forestry applications, such as planning vegetation management in power transmission line right-of-way. Satellite images from different sensors, including SAR (Synthetic Aperture Radar) from Sentinel 1 (S1) and multispectral from Sentinel 2 (S2), can be used for producing high-resolution vegetation height maps at a broad scale. The main objective of this study is to assess the potential of S1 and S2 satellite data, both in a single and a multisensor approach, for modeling canopy height in a transmission line right-of-way located in the Atlantic Forest of Paraná, Brazil. For integrating S1 and S2 data, we used three machine learning algorithms (LR: Linear Regression, CART: Classification and Regression Trees, and RF: Random Forest) and airborne LiDAR (Light Detection and Ranging) measurements as the reference height. The best models were obtained using the RF algorithm and 20 m resolution features from only S2 data (cross-validated RMSE of 4.92 m and R² of 0.58) or multisensor data (cross-validated RMSE of 4.86 m and R² of 0.60). Although the multisensor model presented the best performance, it was not statistically different from the single-S2 model. Thus, the use of only S2 to estimate canopy height has practical advantages, as it reduces the need to process SAR images and the uncertainties due to S1 noise or differences between the acquisition dates of S2 and S1.