Search Results (30)

Forest fires release various chemical compounds that directly degrade air quality and endanger human health. This study examines the occurrence of forest fires in six Brazilian biomes over a 22-year period (1999–2021). The primary purpose is to identify significant meteorological predictors for the monthly number of hot spots using a standardized statistical framework. Fire hotspots were identified using satellite thermal sensors (AVHRR and MODIS), and we employed a standardized negative binomial regression modeling approach to analyze the relationship between meteorological variables and fire hotspots in all six Brazilian biomes simultaneously, providing a comprehensive comparative perspective often lacking in studies focused on isolated regions. The results show that the Amazon and Cerrado biomes have the highest absolute number of fires, which is consistent with their size and vegetation structure. To avoid bias associated with biome size, fire occurrence was additionally estimated using hotspot density normalized by biome area (hotspots per km²). Using these models, significant factors for fire occurrence were identified, namely the main meteorological variables—temperature, precipitation and wind speed. By comparing the performance of the models in different biomes, we aimed to better understand regional fire dynamics. The model’s ability to predict the expected number of fires based on these variables provides a key tool for preventive air quality monitoring. Such a predictive model serves as a basis for developing early warning systems, assessing potential health risks for the population, and adopting targeted fire management policies. Full article

Wildfire regimes are undergoing rapid transformation under anthropogenic climate change, with major implications for biodiversity, carbon cycling, and ecosystem resilience. This systematic review synthesizes findings from 42 studies across global, continental, and regional scales to assess emerging patterns in fire frequency, intensity, and seasonality, and to identify climatic, ecological, and anthropogenic drivers shaping these changes. Across biomes, evidence shows increasingly fire-conducive conditions driven by rising temperatures, vapor-pressure deficit, and intensifying drought, with climate model projections indicating amplification of extreme fire weather this century. Boreal ecosystems show heightened fire danger and carbon-cycle vulnerability; Mediterranean and Iberian regions face extended fire seasons and faster spread rates; tropical forests, particularly the Amazon, are shifting toward more flammable states due to drought–fragmentation interactions; and savannas display divergent moisture- and fuel-limited dynamics influenced by climate and land use. These results highlight the emergence of biome-specific fire–climate–fuel feedback that may push certain ecosystems toward alternative stable states. The review underscores the need for improved attribution frameworks, integration of fire–vegetation–carbon feedback into Earth system models, and development of adaptive, regionally tailored fire-management strategies. Full article

The protection of Indigenous Territories - ITs in the state of Maranhão, located in the Northeast region of Brazil, represents a major challenge at the intersection of environmental conservation and territorial rights. Situated between the Amazon and Cerrado biomes and within the MATOPIBA agricultural frontier, the state faces increasing anthropogenic pressures that accelerate land use changes, intensify fire regimes, and increase greenhouse gas emissions. This study assessed the temporal dynamics of land use and land cover and their relationship with fire in officially recognized Indigenous Territories from 1985 to 2023 using remote sensing, geoprocessing, and spatial analysis in Google Earth Engine. Indigenous Territories lost 185,327 ha of native vegetation, of which 66.9% corresponded to forest and 33.1% to savanna, yet still retained 2028.755 ha in 2023, with 81.2% classified as forest. Fire recurrence reached up to 37 events per pixel, with Araribóia, Kanela, and Porquinhos dos Canela Apãnjekra exhibiting the highest frequencies. During the 2015–2016 El Niño, Araribóia recorded the largest fire episode, with 200,652 ha burned (48.5%). Between 2013 and 2023, total greenhouse gas emissions reached approximately 709 Mt CO₂eq, with 85% originating from fires and 15% from deforestation. The findings highlight the need to integrate traditional knowledge, territorial governance, and Integrated Fire Management strategies to strengthen the protection of Indigenous Territories and support the preservation of Indigenous livelihoods in Maranhão. Full article

Forest degradation, characterized by the gradual loss of the forest’s ecological and ecosystem functions, has been happening rapidly in the Amazon. Its main anthropogenic vectors are deforestation, forest fragmentation, selective logging, forest fires, and the edge effect. Impacts on the forest canopy and biomass can be estimated using satellite images and field data. The present study examines the dynamics of edges created annually by forest clearing and the effects of these edges considering the annual extent and loss of forest biomass between 2007 and 2023 in the municipality of Rorainópolis, located in the southern portion of the state of Roraima, in the far north of the Brazilian Amazon. We (i) delimited the edge areas created annually by deforestation between 2007 and 2023; (ii) tested the hypothesis of the existence of a spatial gradient for forest degradation using the increasing distance from the edge as a reference and the spectral behavior of three vegetation indices (NDVI, NBR, and NDWI) at the pixel level from average values of images from the Landsat-5/8 and Sentinel-2 satellites; and (iii) estimated the biomass exposed to deforestation and the edge effect and the consequent loss of biomass due to these processes. The loss of biomass in the study area due to deforestation totaled 17.1 × 10⁶ Mg in 2023, and the forest edge areas totaled 244.9 km², containing 10.5 × 10⁶ Mg of biomass. During 2023, we estimated a cumulative loss of 0.92 × 10⁶ Mg (8.73%). Analysis of the three vegetation indices showed that there is a gradient of forest degradation, characterized by an increase in the pixel index value from the edge to the interior of the forest. Forest degradation due to the edge effect is an important source of carbon emissions and should be included in national reports on greenhouse gas emissions. Full article

Deploying thousands of sensors across remote and challenging environments—such as the Amazon rainforest, Californian wilderness, or Australian bushlands—is a critical yet complex task for forest fire monitoring, while our backyard emulation confirmed the feasibility of small-scale deployment as a proof of concept, large-scale scenarios demand a scalable, efficient, and fault-tolerant network design. This paper proposes a Hierarchical Wireless Sensor Network (HWSN) deployment strategy with adaptive head node selection to maximize area coverage and energy efficiency. The network architecture follows a three-level hierarchy as follows: The first level incorporates cells of individual sensor nodes that connect to dynamically assigned cell heads. The second level involves the aggregated clusters of such cell heads, each with an assigned cluster head. Finally, dividing all cluster heads into regions, each with a region head, directly reports all the collected information from the forest floor to a central control sink room for decision making analysis. Unlike traditional centralized or uniformly distributed models, our adaptive approach leverages a greedy coverage maximization algorithm to dynamically select head nodes that contribute to the best forest sensed data coverage at each level. Through extensive simulations, the adaptive model achieved over 96.26% coverage, using significantly fewer nodes, while reducing node transmission distances and energy consumption. This facilitates the real-world deployment of our HWSN model in large-scale, remote forest regions, with very promising performance. Full article

Fires drive global ecosystem change, impacting carbon dynamics, atmospheric composition, biodiversity, and human well-being. Biomass burning, a major outcome of fires, significantly contributes to greenhouse gas and aerosol emissions. Among these, fine particulate matter (PM_2.5) is particularly concerning due to its adverse effects on air quality and health, and its substantial yet uncertain role in Earth’s energy balance. Variability in emission factors (EFs) remains a key source of uncertainty in emission estimates. This study evaluates PM_2.5 emission sensitivity to EFs variability in Brazil’s Amazon and Cerrado biomes over 2002–2023 using the 3BEM_FRP model implemented in the PREP-CHEM-SRC tool. We updated the EFs with values and uncertainty ranges from Andreae (2019), which reflect a more comprehensive literature review than earlier datasets. The results reveal that the annual average PM_2.5 emissions varied by up to 162% in the Amazon (1213 Gg yr⁻¹ to 3172 Gg yr⁻¹) and 184% in the Cerrado (601 Gg yr⁻¹ to 1709 Gg yr⁻¹). The Average peak emissions at the grid-cell level reached 5688 Mg yr⁻¹ in the “Arc of Deforestation” region under the High-end EF scenario. Notably, the PM_2.5 emissions from Amazon forest areas increased over time despite shrinking forest cover, indicating that Amazonian forests are becoming more vulnerable to fire. In the Cerrado, savannas are the primary land cover contributing to the total PM_2.5 emissions, accounting for 64% to 80%. These findings underscore the importance of accurate, region-specific EFs for improving emission models and reducing uncertainties. Full article

In the Amazon region, the impact of fire on the region’s biogeochemical processes remains poorly understood. In this study, we examined the relationship of seasonal fire on greenhouse gas (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. Results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early- and middle-dry-season fires (July–September). We found a strong positive correlation (r² = 0.71) between NO₂ and burned biomass when a small percentage of burned biomass (around ≤ 0.5%) is released during a fire. However, in the event of a large percentage of burned biomass (>0.8%), the correlation coefficient between NO₂ and burned biomass was weak (r² = 0.41). New models need to be developed that incorporate the substantial existing knowledge on the seasonal dynamics of fire-derived GHG emissions. This information should be utilized to make effective decisions about how to manage fire in the Amazon ecosystems and to drive further data collection campaigns and modelling initiatives. Full article

The state of Acre, located in the Western Amazon, has been more intensely affected in recent years by extreme weather events, especially those of a hydrological nature. These are rainy seasons with major floods and record water levels and, later in the same year, severe droughts that last for more months than is normal for the dry season. In this sense, remote sensing acts as an important tool for monitoring the meteorological variables involved in this dynamic, and for predicting future climate trends. Different temporal lengths reflect the availability of reliable data for each variable, and statistical methods were applied separately to ensure robust analyses despite these differences. Our research used data on rainfall (1982–2023), air temperature (2001–2020), fire foci, vegetation, and snow cover (2001–2023) for these purposes. Snow cover data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD10CM (MODIS/Terra Snow Cover Monthly L3 Global Climate Modeling Grid). The MOD10CM product was used to quantify snow cover in an area close to the state, connected to one of the main river basins in Acre. The results showed an increase in the amount of rainfall for the month of February and a reduction in the amount for months of the dry season, as well as an extension of the same. A reduction in the percentage of snow cover was also observed in the region, which may have a direct impact on water availability for several populations, including the city of Rio Branco. The Mann–Kendall test reinforced this reduction, with a Z index of −1.98 for the month of June. Principal Component Analysis (PCA) highlighted key relationships among variables. For the first principal component (PC1), rainfall, snow cover, maximum temperature, and minimum temperature had the strongest contributions, capturing over 56% of the total variance across all datasets. A negative relationship was observed between rainfall and minimum temperature, indicating that higher minimum temperatures are associated with reduced rainfall in the region. Conversely, the second principal component (PC2), which explained approximately 29% of the variance, revealed a strong positive relationship between fire foci and maximum temperature, suggesting that higher maximum temperatures significantly increase the number of fire foci. These results reinforce the role of climatic extremes in shaping environmental dynamics in Acre. The level of statistical significance (p-value) adopted for the data was up to 0.10. Full article

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r² = 0.30) due to the complex dynamics of plant moisture and associated CO and NO₂ emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r²~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article

Based on statistical analyses applied to official data from the Digital Atlas of Disasters in Brazil over the last 25 years, we evidenced a consistent intensification in the annual occurrence of natural disasters in the state of Pará, located in the eastern Brazilian Amazon. The quantitative comparison between the averages of the most intense period of disasters (2017 to 2023) and the earlier years (1999 to 2016) revealed a remarkable percentage increase of 473%. Approximately 81% of the state’s municipalities were affected, as indicated by disaster mapping. A clear seasonal pattern was observed, with Hydrological disasters (Inundations, Flash floods, and Heavy rainfall) peaking between February and May, while Climatological disasters (Droughts and Forest fires) were most frequent from August to October. The catastrophic impacts on people and the economy were documented, showing a significant rise in the number of homeless individuals and those directly affected, alongside considerable material damage and economic losses for both the public and private sectors. Furthermore, we conducted a comprehensive composite analysis on the tropical ocean–atmosphere dynamic structure that elucidated the various triggering mechanisms of disasters arising from Inundations, Droughts, and Forest fires (on seasonal scale), and Flash floods and Heavy rainfall (on sub-monthly scale) in Pará. The detailed characterization of disasters on a municipal scale is relevant in terms of the scientific contribution applied to the strategic decision-making, planning, and implementation of public policies aimed at early risk management (rather than post-disaster response), which is critical for safeguarding human well-being and strengthening the resilience of Amazonian communities vulnerable to climate change. Full article

Forest degradation and forest disturbance are distinct yet often conflated concepts, complicating their definition and monitoring. Forest degradation involves interrupted succession and a severe reduction in forest services over time, caused by factors like fires, illegal selective logging, and edge effects. Forest disturbance, on the other hand, refers to abrupt, localized events, natural or anthropogenic, such as legal selective logging, tropical blowdowns, storms, or fires, without necessarily leading to long-term degradation. Despite the varying intensity and scale of forest degradation and disturbance, systematic studies distinguishing its types and classes are limited. This study reviews anthropogenic impacts on forests in the Brazilian Amazon, analyzing 80 scientific articles using remote sensing techniques and data. Most research focuses on the “arc of deforestation,” characterized by intense human activity, showcasing methodological advancements but also revealing gaps in monitoring less-studied regions like the central and western Amazon. The findings emphasize the need for advanced remote sensing tools to differentiate degradation types, particularly in sustainable forest management (SFM) contexts. Expanding research to underrepresented regions and refining methodologies are crucial for better understanding forest dynamics and improving conservation strategies. These efforts are essential to support effective forest management and informed policy development across the Amazon. Full article

Understanding current fire dynamics in the Amazon is vital for designing effective fire management strategies and setting a baseline for climate change projections. This study aimed to analyze recent fire probabilities and project future “fire niches” under global warming scenarios across the Legal Amazon, a scale chosen for its relevance in social and economic planning. Utilizing the maximum entropy method, this study combined a complex set of predictors with fire occurrences detected during 1985–2022. It allowed for the estimation of current fire patterns and projecting changes for the near future (2020–2040) under two contrasting socioeconomic pathways. The results showed strong model performance, with AUC values consistently above 0.85. Key predictors included “Distance to Farming” (53.4%), “Distance to Non-Vegetated Areas” (11.2%), and “Temperature Seasonality” (9.3%), revealing significant influences from human activities alongside climatic predictors. The baseline model indicated that 26.5% of the Amazon has “moderate” to “very high” fire propensity, especially in the southern and southeastern regions, notably the “Arc of Deforestation”. Future projections suggest that fire-prone areas may expand, particularly in the southern border regions and near the Amazon riverbanks. The findings underscore the importance of incorporating both ecological and human factors into fire management strategies to effectively address future risks. Full article

Recent times have witnessed wildfires causing harm to both ecological communities and urban–rural regions, underscoring the necessity to comprehend wildfire triggers and assess measures for mitigation. This research hones in on Cartagena del Chairá, diving into the interplay between meteorological conditions and land cover/use that cultivates a conducive environment for wildfires. Meteorologically, the prevalence of wildfires is concentrated during boreal winter, characterized by warm and dry air, strong winds, and negligible precipitation. Additionally, wildfires gravitate toward river-adjacent locales housing agriculture-linked shrubs, notably in the northern part of the zone, where a confluence of land attributes and meteorological factors synergize to promote fire incidents. Employing climate scenarios, we deduced that elevated temperature and reduced humidity augment wildfire susceptibility, while wind speed and precipitation discourage their propagation across most scenarios. The trajectory toward a warmer climate could instigate fire-friendly conditions in boreal summer, indicating the potential for year-round fire susceptibility. Subsequently, via machine-learning-driven sensitivity analysis, we discerned that among the scrutinized socio-economic variables, GINI, low educational attainment, and displacement by armed groups wield the most substantial influence on wildfire occurrence. Ultimately, these findings converge to shape proposed wildfire mitigation strategies that amalgamate existing practices with enhancements or supplementary approaches. Full article

The ability to measure the ecological effects of understory fire in the Amazon on a landscape scale remains a frontier in remote sensing. The Global Ecosystem Dynamics Investigation’s (GEDI) LiDAR data have been widely suggested as a critical new tool in this field. In this paper, we use the GEDI Simulator to quantify the nuanced effects of understory fire in the Amazon, and assess the ability of on-orbit GEDI data to do the same. While numerous ecological studies have used simulated GEDI data, on-orbit constraint may limit ecological inference. This is the first study that we are aware of that directly compares methods using simulated and on-orbit GEDI data. Simulated GEDI data showed that fire effects varied nonlinearly through the canopy and then moved upward with time since burn. Given that fire effects peaked in the mid-canopy and were often on the scale of 2 to 3 m in height difference, it is unlikely that on-orbit GEDI data will have the sensitivity to detect these same changes. Full article

This study identifies the spatial and temporal trends, as well as the drivers, of fire dynamics in the Bolivian Amazon basin. Bolivia ranks in the top ten countries in terms of total annual burnt, with fires affecting an estimated 2.3 million hectares of forest in 2020. However, in comparison to the Brazilian Amazon, there has been little research into the fire regime in Bolivia. The sparse research and the limited literature on the subject indicate that fire activity is higher in the Bolivian Amazon basin’s dry forests and flooded savanna zones, and that agriculture and drought are the primary causes of fire activity. In this study, trend analysis and emerging hotspot analysis are deployed to identify the spatial and temporal patterns of fire activity and boosted regression tree models to identify the drivers of forest fire within each ecoregion of the Bolivian Amazon basin. Comparable to most of the Brazilian literature, this study finds that fire activity and fire season length is higher in the flooded Beni Savanna, and Chiquitano seasonally dry tropical forests than in the Bolivian Amazon ecoregion. This study also identifies moisture stress and human activity as the main drivers of fire dynamics within the region. It is intended that this research will offer a foundation for future research and conservation activities aimed at better understanding the fire regime of the Bolivian Amazon basin. Full article