Extreme droughts have been recurrent in the Amazon over the past decades, causing socio-economic and environmental impacts. Here, we investigate the vulnerability of Amazonian forests, both undisturbed and human-modified, to repeated droughts. We defined vulnerability as a measure of (i) exposure, which is the degree to which these ecosystems were exposed to droughts, and (ii) its sensitivity, measured as the degree to which the drought has affected remote sensing-derived forest greenness. The exposure was calculated by assessing the meteorological drought, using the standardized precipitation index (SPI) and the maximum cumulative water deficit (MCWD), which is related to vegetation water stress, from 1981 to 2016. The sensitivity was assessed based on the enhanced vegetation index anomalies (AEVI), derived from the newly available Moderate Resolution Imaging Spectroradiometer (MODIS)/Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) product, from 2003 to 2016, which is indicative of forest's photosynthetic capacity. We estimated that 46% of the Brazilian Amazon biome was under severe to extreme drought in 2015/2016 as measured by the SPI, compared with 16% and 8% for the 2009/2010 and 2004/2005 droughts, respectively. The most recent drought (2015/2016) affected the largest area since the drought of 1981. Droughts tend to increase the variance of the photosynthetic capacity of Amazonian forests as based on the minimum and maximum AEVI analysis. However, the area showing a reduction in photosynthetic capacity prevails in the signal, reaching more than 400 000 km 2 of forests, four orders of magnitude larger than areas with AEVI enhancement. Moreover, the intensity of the negative AEVI steadily increased from 2005 to 2016. These results indicate that during the analysed period drought impacts were being exacerbated through time. Forests in the twenty-first century are becoming more vulnerable to droughts, with larger areas intensively and negatively responding to water shortage in the region. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Tropical secondary forests sequester carbon up to 20 times faster than old-growth forests. This rate does not capture spatial regrowth patterns due to environmental and disturbance drivers. Here we quantify the influence of such drivers on the rate and spatial patterns of regrowth in the Brazilian Amazon using satellite data. Carbon sequestration rates of young secondary forests (<20 years) in the west are ~60% higher (3.0 ± 1.0 Mg C ha−1 yr−1) compared to those in the east (1.3 ± 0.3 Mg C ha−1 yr−1). Disturbances reduce regrowth rates by 8–55%. The 2017 secondary forest carbon stock, of 294 Tg C, could be 8% higher by avoiding fires and repeated deforestation. Maintaining the 2017 secondary forest area has the potential to accumulate ~19.0 Tg C yr−1 until 2030, contributing ~5.5% to Brazil’s 2030 net emissions reduction target. Implementing legal mechanisms to protect and expand secondary forests whilst supporting old-growth conservation is, therefore, key to realising their potential as a nature-based climate solution.

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.

We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20–35% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.

Extreme droughts in Amazonia cause anomalous increase in fire occurrence, disrupting the stability of environmental, social, and economic systems. Thus, understanding how droughts affect fire patterns in this region is essential for anticipating and planning actions for remediation of possible impacts. Focused on the Brazilian Amazon biome, we investigated fire responses to the 2010 and 2015/2016 Amazonian droughts using remote sensing data. Our results revealed that the 2015/2016 drought surpassed the 2010 drought in intensity and extent. During the 2010 drought, we found a maximum area of 846,800 km 2 (24% of the Brazilian Amazon biome) with significant (p ≤ 0.05) rainfall decrease in the first trimester, while during the 2015/2016 the maximum area reached 1,702,800 km 2 (47% of the Brazilian Amazon biome) in the last trimester of 2015. On the other hand, the 2010 drought had a maximum area of 840,400 km 2 (23% of the Brazilian Amazon biome) with significant (p ≤ 0.05) land surface temperature increase in the first trimester, while during the 2015/2016 drought the maximum area was 2,188,800 km 2 (61% of the Brazilian Amazon biome) in the last trimester of 2015. Unlike the 2010 drought, during the 2015/2016 drought, significant positive anomalies of active fire and CO 2 emissions occurred mainly during the wet season, between October 2015 and March 2016. During the 2010 drought, positive active fire anomalies resulted from the simultaneous increase of burned forest, non-forest vegetation and productive lands. During the 2015/2016 drought, however, this increase was dominated by burned forests. The two analyzed droughts emitted together 0.47 Pg CO 2 , with 0.23 Pg CO 2 in 2010, 0.15 Pg CO 2 in 2015 and 0.09 Pg CO 2 in 2016, which represented, respectively, 209%, 136%, 82% of annual Brazil's national target for reducing carbon emissions from deforestation by 2017 (approximately 0.11 Pg CO 2 year −1 from 2006 to 2017). Finally, we anticipate that the increase of fires during the droughts showed here may intensify and can become more frequent in Amazonia due to changes in climatic variability if no regulations on fire use are implemented.