Search

Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon.

Abstract Studies showed that Brazilian Amazon indigenous territories (ITs) are efficient models for preserving forests by reducing deforestation, fires, and related carbon emissions. Considering the importance of ITs for conserving socio-environmental and cultural diversity and the recent climb in the Brazilian Amazon deforestation, we used official remote sensing datasets to analyze deforestation inside and outside indigenous territories within Brazil's Amazon biome during the 2013–2021 period. Deforestation has increased by 129% inside ITs since 2013, followed by an increase in illegal mining areas. In 2019–2021, deforestation was 195% higher and 30% farther from the borders towards the interior of indigenous territories than in previous years (2013–2018). Furthermore, about 59% of carbon dioxide (CO2) emissions within ITs in 2013–2021 (96 million tons) occurred in the last three years of analyzed years, revealing the magnitude of increasing deforestation to climate impacts. Therefore, curbing deforestation in indigenous territories must be a priority for the Brazilian government to secure these peoples' land rights, ensure the forests' protection and regulate the global climate.

Fires are among the main drivers of forest degradation in Amazonia, causing multiple socioeconomic and environmental damages. Although human-ignited sources account for most of the fire events in Amazonia, extended droughts may magnify their occurrence and propagation. The southwestern Amazonia, a transnational region shared by Brazil, Peru, and Bolivia and known as the MAP region, has been articulating coordinated actions to prevent disasters, including fire, to reduce their negative impacts. Therefore, to understand the fire patterns in the MAP region, we investigated their main drivers and the changes in the suitability of fire occurrence for the years 2005, 2010, 2016, and 2020. We used a maximum entropy (MaxEnt) model approach based on active fire data from satellites, climatic data, and land use and land cover mapping to spatially quantify the suitability of fire occurrence and its drivers. We used the year 2015 to calibrate the models. For climatic data and active fire count, we only considered grid cells with active fire count over the third quartile. All our models had a satisfactory performance, with values of the area under the curve (AUC) above 0.75 and p < 0.05. Additionally, all models showed sensitivity rates higher than 0.8 and false positive rates below 0.25. We estimated that, on average, 38.5% of the study region had suitable conditions for fire occurrence during the study period. Most of the fire-prone areas belong to Acre, representing approximately 74% of the entire MAP region. The percentage of deforested areas, productive lands, forest edges, and high temperatures were the main drivers of fire occurrence in southwestern Amazonia, indicating the high vulnerability of fragmented landscapes extreme climatic conditions to fire occurrence. We observed that the modeling approach based on Maxint is useful for useful for evaluating the implications of climatic and anthropogenic variables on fire distribution. Furthermore, because the model can be easily employed to predict suitable and non-suitable locations for fire occurrence, it can to prevent potential impacts associated with large-scale wildfire in the future at regional levels.

Vegetation fires represent a major disturbance in the tropics, with South America notable for having both fire-sensitive (e [...]

This study uses regional and global remote sensing data to assess the regrowth of secondary forests in the Brazilian Amazon biome. The authors find differences of regrowth rates due to climate, forest fires and deforestation actions and further quantify their carbon capture potential.

Humans place strong pressure on land and have modified around 75% of Earth’s terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world.

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.

MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life.

El Niño years are characterized by a high sea surface temperature anomaly in the Equatorial Pacific Ocean, which leads to unusually warm and dry conditions over many fire-prone regions globally. This can lead to an increase in burned area and emissions from fire activity, and socio-economic, and environmental losses. Previous studies using satellite observations to assess the impacts of the recent 2015/16 El Niño found an increase in burned area in some regions compared to La Niña years. Here, we use the dynamic land surface model JULES to assess how conditions differed as a result of the El Niño by comparing simulations driven by observations from the year 2015/16 with mean climatological drivers of temperature, precipitation, humidity, wind, air pressure, and short and long-wave radiation. We use JULES with the interactive fire module INFERNO to assess the effects on precipitation, temperature, burned area, and the associated impacts on the carbon sink globally and for three regions: South America, Africa, and Asia. We find that the model projects a variable response in precipitation, with some areas including northern South America, southern Africa and East Asia getting drier, and most areas globally seeing an increase in temperature. As a result, higher burned area is simulated with El Niño conditions in most regions, although there are areas of both increased and decreased burned area over Africa. South America shows the largest fire response with El Niño, with a 13% increase in burned area and emitted carbon, corresponding with the largest decrease in carbon uptake. Within South America, peak fire occurs from August to October across central-southern Brazil, and temperature is shown to be the main driver of the El Niño-induced increase in burned area during this period. Combined, our results indicate that although 2015/16 was not a peak year for global total burned area or fire emissions, the El Niño led to an overall increase of 4% in burned area and 5% in emissions compared to a “No El Niño” scenario for 2015/16, and contributed to a 4% reduction in the terrestrial carbon sink.

Deforestation carbon emissions from the Brazilian Amazon have declined steeply, but how much drought-induced forest fire emissions add to this process is still unclear. Here the authors show that gross emissions from forest fires are more than half as great as those from deforestation during drought years.

Fire is widely used in the Amazon as a ubiquitous driver of land management and land cover change. Regardless of their purpose, fires release a considerable amount of pollutants into the atmosphere, with severe consequences for human health. This paper adds to the extant literature by measuring the causal effect of fires on hospitalizations, using the approach of instrumental variables, whose validity is assessed with multiple statistical tests. A wide range of confounders are added as covariates, seizing on the accuracy enhancement potential of a broad and fine-grained dataset that covers 14 years of the whole Amazon territory at a municipal–monthly level. The results reveal a positive effect of fire on hospitalizations due to respiratory illnesses in general, and particularly in those due to asthma. A 1% increase in pollution concentration would increase hospitalizations by 0.14% at a municipality–monthly level. A total of 5% of respiratory hospitalizations were estimated to be attributable to fire-induced pollution, corresponding to 822 cases per month. The analysis demonstrates that the coupling of econometrics and remote sensing data is a promising avenue towards the assessment of impacts caused by fires, which may be applied to other regions of the world subjected to anthropogenic fires.

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 km2 (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 km2 (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 km2 (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 km2 (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 CO2 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 CO2, with 0.23 Pg CO2 in 2010, 0.15 Pg CO2 in 2015 and 0.09 Pg CO2 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 CO2 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.

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

The 2019 fire crisis in Amazonia dominated global news and triggered fundamental questions about the possible causes behind it. Here we performed an in-depth investigation of the drivers of active fire anomalies in the Brazilian Amazon biome. We assessed a 2003–2019 time-series of active fires, deforestation, and water deficit and evaluated potential drivers of active fire occurrence in 2019, at the biome-scale, state level, and local level. Our results revealed abnormally high monthly fire counts in 2019 for the states of Acre, Amazonas, and Roraima. These states also differed from others by exhibiting in this year extreme levels of deforestation. Areas in 2019 with active fire occurrence significantly greater than the average across the biome had, on average, three times more active fires in the three previous years, six times more deforestation in 2019, and five times more deforestation in the five previous years. Approximately one-third of yearly active fires from 2003 to 2019 occurred up to 1 km from deforested areas in the same year, and one-third of deforested areas in a given year were located up to 500 m from deforested areas in the previous year. These findings provide critical information to support strategic decisions for fire prevention policies and fire combat actions.

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.

Aircraft atmospheric profiling is a valuable technique for determining greenhouse gas fluxes at regional scales (104–106 km2). Here, we describe a new, simple method for estimating the surface influence of air samples that uses backward trajectories based on the Lagrangian model Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). We determined “regions of influence” on a quarterly basis between 2010 and 2018 for four aircraft vertical profile sites: SAN and ALF in the eastern Amazon, and RBA and TAB or TEF in the western Amazon. We evaluated regions of influence in terms of their relative sensitivity to areas inside and outside the Amazon and their total area inside the Amazon. Regions of influence varied by quarter and less so by year. In the first and fourth quarters, the contribution of the region of influence inside the Amazon was 83–93% for all sites, while in the second and third quarters, it was 57–75%. The interquarter differences are more evident in the eastern than in the western Amazon. Our analysis indicates that atmospheric profiles from the western sites are sensitive to 42–52.2% of the Amazon. In contrast, eastern Amazon sites are sensitive to only 10.9–25.3%. These results may help to spatially resolve the response of greenhouse gas emissions to climate variability over Amazon.

Apesar da diminuição das taxas de desmatamento na Amazônia, as queimadas têm aumentado devido à associação entre práticas de manejo da terra e aumento da frequência de estiagens mais intensas. No presente trabalho mensurou-se o tipo de uso e cobertura da terra que mais queimou em um ano de seca extrema (2010) em comparação com dois anos de precipitação normais (2008 e 2012). Em seguida foi avaliada a associação entre os focos de calor em floresta e quais os tipos de uso e cobertura da terra que mais influenciam na propagação do fogo para a floresta. A área de estudo selecionada foi o município de São Félix do Xingu - Pará. Os resultados obtidos demonstraram que a classe pastagem apresentou maior influência na proximidade com focos de calor em florestas. Além disso, o maior déï¬cit hídrico de 2010 contribuiu para a intensiï¬cação das queimadas associado às fontes humanas de propagação do fogo.

Between 1998 and 2017, climate-related disasters represented 91% of all occurrences worldwide, causing approximately US$ 2.245 billion of direct economic losses. In the Amazon region, fire is used as a widely spread technique for land clearing, agricultural management, hunting, and religious rituals. However, over the past 20 years, severe droughts caused a major amplification of fire occurrences, leading to several socioeconomic and environmental impacts. Particularly in Acre state, located in the southwestern Brazilian Amazon, the occurrence of extensive fires, associated with extreme climatic events, has been reported since 2005. However, fire dynamics, land tenure relationships, and associated impacts are poorly quantified. In this study, we aim to investigate the following: (1) The spatiotemporal variability of fire dynamics during anomalously dry and regular climate conditions; (2) the attribution of fire occurrence and land tenure relationship, and (3) the environmental, social, and economic impacts caused by fires and its consequences for Acre’s economy. We analyzed information on the spatial patterns of fire, its direct impacts on land use and land cover, carbon stocks, CO2 emissions, the indirect impact on human illness, and finally the costs of these impacts from 2008 to 2012. During the studied period, burned areas were concentrated around the major cities and roads, forming polygons up to 0.6 km2. However, in 2010, an extremely dry year, fires spread to remote areas, impacting protected private areas and sustainable-use conservation areas. In 2010, the total area affected by forest fires was approximately 16 times greater than in meteorologically normal years. The total economic loss estimated in 2010 was around US$ 243.36 ± 85.05 million and for the entire period, US$ 307.46 ± 85.41 million. These values represent 7.03 ± 2.45% and 9.07 ± 2.46% of Acre’s gross domestic product (GDP), respectively.

Amazonian forests are extremely heterogeneous at different spatial scales. This review intends to present the large-scale patterns of the ecosystem properties of Amazonia, and focuses on two parts of the main components of the net primary production: the long-lived carbon pools (wood) and short-lived pools (leaves). First, the focus is on forest biophysical properties, and secondly, on the macro-scale leaf phenological patterns of these forests, looking at field measurements and bringing into discussion the recent findings derived from remote sensing dataset. Finally, I discuss the results of the three major droughts that hit Amazonia in the last 15 years. The panorama that emerges from this review suggests that slow growing forests in central and eastern Amazonia, where soils are poorer, have significantly higher above ground biomass and higher wood density, trees are higher and present lower proportions of large-leaved species than stands in northwest and southwest Amazonia. However, the opposite pattern is observed in relation to forest productivity and dynamism, which is higher in western Amazonia than in central and eastern forests. The spatial patterns on leaf phenology across Amazonia are less marked. Field data from different forest formations showed that new leaf production can be unrelated to climate seasonality, timed with radiation, timed with rainfall and/or river levels. Oppositely, satellite images exhibited a large-scale synchronized peak in new leaf production during the dry season. Satellite data and field measurements bring contrasting results for the 2005 drought. Discussions on data processing and filtering, aerosols effects and a combined analysis with field and satellite images are presented. It is suggested that to improve the understanding of the large-scale patterns on Amazonian forests, integrative analyses that combine new technologies in remote sensing and long-term field ecological data are imperative.

The objective of this paper is to provide near real time information about deforestation detection (DETER) in the entire Brazilian Amazon using MODIS high temporal resolution images. It is part of the operational deforestation monitoring project to estimate the annual deforestation rate in the Brazilian Amazon (PRODES). A rapid deforestation detection method was designed to support land use policies in this region. In order to evaluate the proposed method a test site was selected covering a Landsat ETM+ scene (227/68) located in Mato Grosso State. For this purpose a multitemporal series of MODIS surface reflectance images (MOD09) and the corresponding ETM+ images from June to October 2002 were analyzed. It was found that small deforested areas (lower than 15 ha) were detected by MODIS images with lower accuracy when compared with ETM+ images. As the deforested areas increase MODIS and ETM+ results tend to converge. This procedure showed to be adequate to operationally detect and monitor deforested areas and has been used since 2004 as part of a government plan to control the Amazon deforestation.

Amazonia is the world largest tropical forest, playing a key role in the global carbon cycle. Thus, understanding climate controls of photosynthetic activity in this region is critical. The establishment of the relationship between photosynthetic activity and climate has been controversial when based on conventional remote sensing-derived indices. Here, we use nine years of solar-induced chlorophyll fluorescence (ChlF) data from the Global Ozone Monitoring Experiment (GOME-2) sensor, as a direct proxy for photosynthesis, to assess the seasonal response of photosynthetic activity to solar radiation and precipitation in Amazonia. Our results suggest that 76% of photosynthesis seasonality in Amazonia is explained by seasonal variations of solar radiation. However, 13% of these forests are limited by precipitation. The combination of both radiation and precipitation drives photosynthesis in the remaining 11% of the area. Photosynthesis tends to rise only after radiation increases in 61% of the forests. Furthermore, photosynthesis peaks in the wet season in about 58% of the Amazon forest. We found that a threshold of ≈1943 mm per year can be defined as a limit for precipitation phenological dependence. With the potential increase in the frequency and intensity of extreme droughts, forests that have the photosynthetic process currently associated with radiation seasonality may shift towards a more water-limited system.

n/a