Tracking the impacts of El Niño drought and fire in human-modified Amazonian forests

Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change

Landslides pose a noteworthy threat in urban settlements globally, especially in areas experiencing extreme climate and rapid engineering. However, researches focusing on the long-term uninterrupted land use and land cover change (LULCC) impacted on landslide susceptibility mapping (LSM) in rapid urban expansion areas remains limited, let alone different temporal scenarios adjacency thresholds. This work aims to refine the temporal LSM considering spatiotemporal land use and land cover (LULC) and to provide decision makers with governing factors in landslides control during urbanization in mountainous areas. Herein, annual LULC data and landslide inventory spanning from 1992 to 2022 were utilized to map dynamic landslide susceptibility in Wanzhou District of the Three Gorges Reservoir Area, China. Initially, the landslide-related factors were filtered as input features of random forest (RF) model before diagnosis via multicollinearity test and Pearson Correlation Coefficient (PCC). The advanced patch-generating land use simulation (PLUS) model was then invited to fuel temporal susceptibility prediction powered by LULCC projections. Finally, the performance of various scenarios was evaluated using Receiver Characteristic Curve (ROC) curves and Shapley Additive exPlanation (SHAP) technique, with discussions on LULCC temporal adjacency thresholds and mutual feedback mechanism between territorial exploitation and landslide occurrences. The results indicate that the precision of LSM is positively correlated with the time horizon, acted by incorporating the latest LULC and LULCC achieving an area under the curve (AUC) of 0.920. The transition of land from forest to cropland and impervious areas should be avoided to minimize the increase in landslide susceptibility. Moreover, a one-year adjacency threshold of LULCC is recommended for optimal model accuracy in future LSM. This dynamic LSM framework can serve as a reference for decision makers in future landslide susceptibility mitigation and land resources utilization in rapid urban expansion areas worldwide.

A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km2), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

Dominant role of the non-forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery

The extreme dry and hot 2015/16 El Niño episode caused large losses in tropical live aboveground carbon (AGC) stocks. Followed by climatic conditions conducive to high vegetation productivity since 2016, tropical AGC are expected to recover from large losses during the El Niño episode; however, the recovery rate and its spatial distribution remain unknown. Here, we used low-frequency microwave satellite data to track AGC changes, and showed that tropical AGC stocks returned to pre-El Niño levels by the end of 2020, resulting in an AGC sink of0.18 0.14 0.26 $$ {0.18}_{0.14}^{0.26} $$ Pg C year-1 during 2014-2020. This sink was dominated by strong AGC increases (0.61 0.49 0.84 $$ {0.61}_{0.49}^{0.84} $$ Pg C year-1) in non-forest woody vegetation during 2016-2020, compensating the forest AGC losses attributed to the El Niño event, forest loss, and degradation. Our findings highlight that non-forest woody vegetation is an increasingly important contributor to interannual to decadal variability in the global carbon cycle.

Impacts of fire and prospects for recovery in a tropical peat forest ecosystem

Uncontrolled fires place considerable burdens on forest ecosystems, compromising our ability to meet conservation and restoration goals. A poor understanding of the impacts of fire on ecosystems and their biodiversity exacerbates this challenge, particularly in tropical regions where few studies have applied consistent analytical techniques to examine a broad range of ecological impacts over multiyear time frames. We compiled 16 y of data on ecosystem properties (17 variables) and biodiversity (21 variables) from a tropical peatland in Indonesia to assess fire impacts and infer the potential for recovery. Burned forest experienced altered structural and microclimatic conditions, resulting in a proliferation of nonforest vegetation and erosion of forest ecosystem properties and biodiversity. Compared to unburned forest, habitat structure, tree density, and canopy cover deteriorated by 58 to 98%, while declines in species diversity and abundance were most pronounced for trees, damselflies, and butterflies, particularly for forest specialist species. Tracking ecosystem property and biodiversity datasets over time revealed most to be sensitive to recurrent high-intensity fires within the wider landscape. These megafires immediately compromised water quality and tree reproductive phenology, crashing commercially valuable fish populations within 3 mo and driving a gradual decline in threatened vertebrates over 9 mo. Burned forest remained structurally compromised long after a burn event, but vegetation showed some signs of recovery over a 12-y period. Our findings demonstrate that, if left uncontrolled, fire may be a pervasive threat to the ecological functioning of tropical forests, underscoring the importance of fire prevention and long-term restoration efforts, as exemplified in Indonesia.

Critical transitions in the Amazon forest system

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1-3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

Reconfiguration of Amazon's connectivity in the climate system

With the recent increase in deforestation, forest fires, and regional temperatures, the concerns around the rapid and complete collapse of the Amazon rainforest ecosystem have heightened. The thresholds of deforestation and the temperature increase required for such a catastrophic event are still uncertain. However, our analysis presented here shows that signatures of changing Amazon are already apparent in historical climate data sets. Here, we extend the methods of climate network analysis and apply them to study the temporal evolution of the connectivity between the Amazon rainforest and the global climate system. We observe that the Amazon rainforest is losing short-range connectivity and gaining more long-range connections, indicating shifts in regional-scale processes. Using embeddings inspired by manifold learning, we show that the Amazon connectivity patterns have undergone a fundamental shift in the 21st century. By investigating edge-based network metrics on similar regions to the Amazon, we see the changing properties of the Amazon are noticeable in comparison. Furthermore, we simulate diffusion and random walks on these networks and observe a faster spread of perturbations from the Amazon in recent decades. Our methodology innovations can act as a template for examining the spatiotemporal patterns of regional climate change and its impact on global climate using the toolbox of climate network analysis.

Drivers of mangrove vulnerability and resilience to tropical cyclones in the North Atlantic Basin

The North Atlantic Basin (NAB) has seen an increase in the frequency and intensity of tropical cyclones since the 1980s, with record-breaking seasons in 2017 and 2020. However, little is known about how coastal ecosystems, particularly mangroves in the Gulf of Mexico and the Caribbean, respond to these new "climate normals" at regional and subregional scales. Wind speed, rainfall, pre-cyclone forest height, and hydro-geomorphology are known to influence mangrove damage and recovery following cyclones in the NAB. However, previous studies have focused on local-scale responses and individual cyclonic events. Here, we analyze 25 years (1996-2020) of mangrove vulnerability (damage after a cyclone) and 24 years (1996-2019) of short-term resilience (recovery after damage) for the NAB and subregions, using multi-annual, remote sensing-derived databases. We used machine learning to characterize the influence of 22 potential variables on mangrove responses, including human development and long-term climate trends. Our results document variability in the rates and drivers of mangrove vulnerability and resilience, highlighting hotspots of cyclone impacts, mangrove damage, and loss of resilience. Cyclone characteristics mainly drove vulnerability at the regional level. In contrast, resilience was driven by site-specific conditions, including long-term climate trends, pre-cyclone forest structure, soil organic carbon stock, and coastal development (i.e., proximity to human infrastructure). Coastal development is associated with both vulnerability and resilience at the subregional level. Further, we highlight that loss of resilience occurs mostly in areas experiencing long-term drought across the NAB. The impacts of increasing cyclone activity on mangroves and their coastal protection service must be framed in the context of compound climate change effects and continued coastal development. Our work offers descriptive and spatial information to support the restoration and adaptive management of NAB mangroves, which need adequate health, structure, and density to protect coasts and serve as Nature-based Solutions against climate change and extreme weather events.

Anthropogenic land-use legacies underpin climate change-related risks to forest ecosystems

Forest ecosystems with long-lasting human imprints can emerge worldwide as outcomes of land-use cessation. However, the interaction of these anthropogenic legacies with climate change impacts on forests is not well understood. Here, we set out how anthropogenic land-use legacies that persist in forest properties, following alterations in forest distribution, structure, and composition, can interact with climate change stressors. We propose a risk-based framework to identify anthropogenic legacies of land uses in forest ecosystems and quantify the impact of their interaction with climate-related stress on forest responses. Considering anthropogenic land-use legacies alongside environmental drivers of forest ecosystem dynamics will improve our predictive capacity of climate-related risks to forests and our ability to promote ecosystem resilience to climate change.

Accounting for trait variability and coordination in predictions of drought-induced range shifts in woody plants

Functional traits offer a promising avenue to improve predictions of species range shifts under climate change, which will entail warmer and often drier conditions. Although the conceptual foundation linking traits with plant performance and range shifts appears solid, the predictive ability of individual traits remains generally low. In this review, we address this apparent paradox, emphasizing examples of woody plants and traits associated with drought responses at the species' rear edge. Low predictive ability reflects the fact not only that range dynamics tend to be complex and multifactorial, as well as uncertainty in the identification of relevant traits and limited data availability, but also that trait effects are scale- and context-dependent. The latter results from the complex interactions among traits (e.g. compensatory effects) and between them and the environment (e.g. exposure), which ultimately determine persistence and colonization capacity. To confront this complexity, a more balanced coverage of the main functional dimensions involved (stress tolerance, resource use, regeneration and dispersal) is needed, and modelling approaches must be developed that explicitly account for: trait coordination in a hierarchical context; trait variability in space and time and its relationship with exposure; and the effect of biotic interactions in an ecological community context.

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%-18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost.

Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Tropical forests face increasing climate risk1,2, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, [Formula: see text]50) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk3-5, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters [Formula: see text]50 and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both [Formula: see text]50 and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM50 forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon6,7, with strong implications for the Amazon carbon sink.

Integrating plant physiology into simulation of fire behavior and effects

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Global review and state-of-the-art of biomass and carbon stock in the Amazon

Understanding how studies have been carried out in the region helps to understand the Amazon rainforest potential in mitigating climate change. In addition, evaluating scientific production is essential to verify the main methods and places researched. Thus, this study objective was to build an overview and identify the main gaps regarding research related to biomass and carbon stocks with the coverage limit of the Amazon rainforest. Therefore, an analysis of the publications indexed in the Scopus database was developed until 2020, performing a bibliometric analysis and a systematic and state-of-the-art review. Initially 2042 publications were obtained, of which 786 met the inclusion criteria. The first work indexed in the Scopus database related to the theme was published in 1982. Over time, it was possible to observe an increasing behavior in relation to the interest in the theme. Natural environments were the most researched and change in the land use and occupation of the Amazon Rainforest is still poorly evaluated. Brazil is the country with the highest number of studies, followed by Peru and Colombia. Guyana and Suriname appear as possible scientific gaps and potential environments to be studied. Studies preferentially explore the biomass carbon, with the soil being little evaluated when compared to the total amount of papers found. When observing only the biomass studies, the aboveground biomass is the most evaluated, while the roots and necromass are little studied. The main variables used in biomass equations were diameter at 1.3 m above ground and total tree height. The biomass to carbon conversion factor has been widely used, but it can generate unreliable results. It is recommended to carry out local assessments of the carbon content, especially using the dry combustion method, which generates less waste, with more precise results and shorter execution time of the analyses. Such assessments present values for the location that can avoid false or misinterpretations of the biomass and carbon stocks in the Amazon Rainforest.

The drivers and impacts of Amazon forest degradation

Approximately 2.5 × 10⁶ square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year^-1), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year^-1). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.

Future land-use competition constrains natural climate solutions

Natural climate solutions (NCS) are an essential complement to climate mitigation and have been increasingly incorporated into international mitigation strategies. Yet, with the ongoing population growth, allocating natural areas for NCS may compete with other socioeconomic priorities, especially urban development and food security. Here, we projected the impacts of land-use competition incurred by cropland and urban expansion on the climate mitigation potential of NCS. We mapped the areas available for implementing 9 key NCS strategies and estimated their climate change mitigation potential. Then, we overlaid these areas with future cropland and urban expansion maps projected under three Shared Socioeconomic Pathway (SSP) scenarios (2020-2100) and calculated the resulting mitigation potential loss of each selected NCS strategy. Our results estimate a substantial reduction, 0.3-2.8 GtCO₂ yr^-1 or 4-39 %, in NCS mitigation potential, of which cropland expansion for fulfilling future food demand is the primary cause. This impact is particularly severe in the tropics where NCS hold the most abundant mitigation potential. Our findings highlight immediate actions prioritized to tropical areas are important to best realize NCS and are key to developing realistic and sustainable climate policies.

Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

Burns are common practices in Brazil and cause major fires, especially in the Legal Amazon. This study evaluated the dynamics of the fire foci in the Legal Amazon in Brazil and their consequences on environmental degradation, particularly in the transformation of the forest into pasture, in livestock and agriculture areas, mining activities and urbanization. The fire foci data were obtained from the reference satellites of the BDQueimadas of the CPTEC/INPE for the period June 1998–May 2022. The data obtained were subjected to descriptive and exploratory statistical analysis, followed by a comparison with the PRODES data during 2004–2021, the DETER data (2016–2019) and the ENSO phases during the ONI index for the study area. Biophysical parameters were used in the assessment of environmental degradation. The results showed that El Niño’s years of activity and the years of extreme droughts (2005, 2010 and 2015) stand out with respect to significant increase in fire foci. Moreover, the significant numbers of fire foci indices during August, September, October and November were recorded as 23.28%, 30.91%, 15.64% and 10.34%, respectively, and these were even more intensified by the El Niño episodes. Biophysical parameters maps showed the variability of the fire foci, mainly in the south and west part of the Amazon basin referring to the Arc of Deforestation. Similarly, the states of Mato Grosso, Pará and Amazonas had the highest alerts from PRODES and DETER, and in the case of DETER, primarily mining and deforestation (94.3%) increased the environmental degradation. The use of burns for agriculture and livestock, followed by mining and wood extraction, caused the degradation of the Amazon biome.

Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning

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.

Climate Change Risks to Global Forest Health: Emergence of Unexpected Events of Elevated Tree Mortality Worldwide

Recent observations of elevated tree mortality following climate extremes, like heat and drought, raise concerns about climate change risks to global forest health. We currently lack both sufficient data and understanding to identify whether these observations represent a global trend toward increasing tree mortality. Here, we document events of sudden and unexpected elevated tree mortality following heat and drought events in ecosystems that previously were considered tolerant or not at risk of exposure. These events underscore the fact that climate change may affect forests with unexpected force in the future. We use the events as examples to highlight current difficulties and challenges for realistically predicting such tree mortality events and the uncertainties about future forest condition. Advances in remote sensing technology and greater availably of high-resolution data, from both field assessments and satellites, are needed to improve both understanding and prediction of forest responses to future climate change.

Removing climbers more than doubles tree growth and biomass in degraded tropical forests

Huge areas of tropical forests are degraded, reducing their biodiversity, carbon, and timber value. The recovery of these degraded forests can be significantly inhibited by climbing plants such as lianas. Removal of super‐abundant climbers thus represents a restoration action with huge potential for application across the tropics. While experimental studies largely report positive impacts of climber removal on tree growth and biomass accumulation, the efficacy of climber removal varies widely, with high uncertainty as to where and how to apply the technique. Using meta‐analytic techniques, we synthesize results from 26 studies to quantify the efficacy of climber removal for promoting tree growth and biomass accumulation. We find that climber removal increases tree growth by 156% and biomass accumulation by 209% compared to untreated forest, and that efficacy remains for at least 19 years. Extrapolating from these results, climber removal could sequester an additional 32 Gigatons of CO₂ over 10 years, at low cost, across regrowth, and production forests. Our analysis also revealed that climber removal studies are concentrated in the Neotropics (N = 22), relative to Africa (N = 2) and Asia (N = 2), preventing our study from assessing the influence of region on removal efficacy. While we found some evidence that enhancement of tree growth and AGB accumulation varies across disturbance context and removal method, but not across climate, the number and geographical distribution of studies limits the strength of these conclusions. Climber removal could contribute significantly to reducing global carbon emissions and enhancing the timber and biomass stocks of degraded forests, ultimately protecting them from conversion. However, we urgently need to assess the efficacy of removal outside the Neotropics, and consider the potential negative consequences of climber removal under drought conditions and for biodiversity.

Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data

Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon.

The Effects of Environmental Changes on Plant Species and Forest Dependent Communities in the Amazon Region

We review the consequences of environmental changes caused by human activities on forest products and forest-dependent communities in the Amazon region—the vast Amazonas River basin and the Guiana Shield in South America. We used the 2018 and 2021 Intergovernmental Panel on Climate Change reports and recent scientific studies to present evidence and hypotheses for changes in the ecosystem productivity and geographical distribution of plants species. We have identified species associated with highly employed forest products exhibiting reducing populations, mainly linked with deforestation and selective logging. Changes in species composition along with a decline of valuable species have been observed in the eastern, central, and southern regions of the Brazilian Amazon, suggesting accelerated biodiversity loss. Over 1 billion native trees and palms are being lost every two years, causing economic losses estimated between US$1–17 billion. A decrease in native plant species can be abrupt and both temporary or persistent for over 20 years, leading to reduced economic opportunities for forest-dependent communities. Science and technology investments are considered promising in implementing agroforestry systems recovering deforested and degraded lands, which could engage companies that use forest products due to supply chain advantages.

Tropical extreme droughts drive long-term increase in atmospheric CO2 growth rate variability

The terrestrial carbon sink slows the accumulation of carbon dioxide (CO2) in the atmosphere by absorbing roughly 30% of anthropogenic CO2 emissions, but varies greatly from year to year. The resulting variations in the atmospheric CO2 growth rate (CGR) have been related to tropical temperature and water availability. The apparent sensitivity of CGR to tropical temperature ([Formula: see text]) has changed markedly over the past six decades, however, the drivers of the observation to date remains unidentified. Here, we use atmospheric observations, multiple global vegetation models and machine learning products to analyze the cause of the sensitivity change. We found that a threefold increase in [Formula: see text] emerged due to the long-term changes in the magnitude of CGR variability (i.e., indicated by one standard deviation of CGR; STDCGR), which increased 34.7% from 1960-1979 to 1985-2004 and subsequently decreased 14.4% in 1997-2016. We found a close relationship (r2 = 0.75, p < 0.01) between STDCGR and the tropical vegetated area (23°S - 23°N) affected by extreme droughts, which influenced 6-9% of the tropical vegetated surface. A 1% increase in the tropical area affected by extreme droughts led to about 0.14 Pg C yr-1 increase in STDCGR. The historical changes in STDCGR were dominated by extreme drought-affected areas in tropical Africa and Asia, and semi-arid ecosystems. The outsized influence of extreme droughts over a small fraction of vegetated surface amplified the interannual variability in CGR and explained the observed long-term dynamics of [Formula: see text].

Fires Drive Long-Term Environmental Degradation in the Amazon Basin

The Amazon Basin is undergoing extensive environmental degradation as a result of deforestation and the rising occurrence of fires. The degradation caused by fires is exacerbated by the occurrence of anomalously dry periods in the Amazon Basin. The objectives of this study were: (i) to quantify the extent of areas that burned between 2001 and 2019 and relate them to extreme drought events in a 20-year time series; (ii) to identify the proportion of countries comprising the Amazon Basin in which environmental degradation was strongly observed, relating the spatial patterns of fires; and (iii) examine the Amazon Basin carbon balance following the occurrence of fires. To this end, the following variables were evaluated by remote sensing between 2001 and 2019: gross primary production, standardized precipitation index, burned areas, fire foci, and carbon emissions. During the examined period, fires affected 23.78% of the total Amazon Basin. Brazil had the largest affected area (220,087 fire foci, 773,360 km2 burned area, 54.7% of the total burned in the Amazon Basin), followed by Bolivia (102,499 fire foci, 571,250 km2 burned area, 40.4%). Overall, these fires have not only affected forests in agricultural frontier areas (76.91%), but also those in indigenous lands (17.16%) and conservation units (5.93%), which are recognized as biodiversity conservation areas. During the study period, the forest absorbed 1,092,037 Mg of C, but emitted 2908 Tg of C, which is 2.66-fold greater than the C absorbed, thereby compromising the role of the forest in acting as a C sink. Our findings show that environmental degradation caused by fires is related to the occurrence of dry periods in the Amazon Basin.

The “New Transamazonian Highway”: BR-319 and Its Current Environmental Degradation

The Brazilian government intends to complete the paving of the BR-319 highway, which connects Porto Velho in the deforestation arc region with Manaus in the middle of the Amazon Forest. This paving is being planned despite environmental legislation, and there is concern that its effectiveness will cause additional deforestation, threatening large portions of forest, conservation units (CUs), and indigenous lands (ILs) in the surrounding areas. In this study, we evaluated environmental degradation along the BR-319 highway from 2008 to 2020 and verified whether highway maintenance has contributed to deforestation. For this purpose, we created a 20 km buffer adjacent to the BR-319 highway and evaluated variables extracted from remote sensing information between 2008 and 2020. Fire foci, burned areas, and rainfall data were used to calculate a drought index using statistical tests for a time series. Furthermore, these were related to data on deforestation, CUs, and ILs using principal component analysis and Pearson’s correlation. Our results showed that 743 km2 of forest was deforested during the period evaluated, most of which occurred in the last four years. A total of 16,472 fire foci were identified. Both deforestation and fire foci occurred mainly outside the CUs and ILs. The most affected areas were close to capital cities, and after resuming road maintenance in 2015, deforestation increased outside the capital cities. Current government policy for Amazon occupation promotes deforestation and will compromise Brazil’s climate goals of reducing greenhouse gas (GHG) emissions and deforestation.