Search Results (35)

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought indices, historical trend analyses, and the climatological water balance. Fourteen municipalities across the biome were analyzed. According to the Köppen classification, most municipalities were identified as Aw (tropical with dry winters) and Am (tropical monsoon), with Dourados, MS, and Sapezal, MT, alternating between Am and Aw. The standardized precipitation index (SPI) revealed changes in rainfall distribution. The Mann–Kendall test detected rising air temperatures in 13 of the 14 municipalities, with Sen’s slope ranging from 0.0156 to 0.0605 °C per year. Rainfall decreased in seven municipalities, with decreases from −4.54 to −12.77 mm per year. The climatological water balance supported the observed decrease in precipitation. The results indicated a clear warming trend and declining rainfall in most of the Brazilian savanna, highlighting potential challenges for water availability in the face of ongoing climate change. Full article

In semi-arid regions, seasonally dry tropical forests are essential for regulating the surface energy balance, which can be analyzed by examining air heating processes and water availability control. The objective of this study was to evaluate the ability of the Brazilian Developments on the Regional Atmospheric Modelling System (BRAMS) model in simulating the seasonal variations of the energy balance components of the Caatinga biome. The surface measurements of meteorological variables, including air temperature and relative humidity, were also examined. To validate the model, we used data collected in situ using an eddy covariance system. In this work, we used the BRAMS model version 5.3 associated with the Joint UK Land Environment Simulator (JULES) version 3.0. The model satisfactorily represented the rainfall regime over the northeast region of Brazil (NEB) during the wet period. In the dry period, however, the coastal rainfall pattern over the NEB region was underestimated. In addition, the results showed that the surface fluxes linked to the energy balance in the Caatinga were impacted by the effects of rainfall seasonality in the region. The assessment of the BRAMS model’s performance demonstrated that it is a reliable tool for studying the dynamics of the dry forest in the region, providing valuable support for sustainable management and conservation efforts. Full article

Understanding how assemblages are structured is important for ecology, especially in tropical regions that exhibit high biodiversity and are currently experiencing high rates of loss and modification of natural environments caused by anthropogenic impacts. Understanding the structuring of assemblages across different regions at different spatial scales allows us to comprehend how environmental modifications can affect biodiversity on a local and regional scale. The objective of this study was to evaluate the biodiversity of Odonata species using taxonomic diversity metrics (richness and composition) in areas of Cerrado, Atlantic Forest, and Caatinga and to evaluate which sets of local and spatial environmental variables are associated with these assemblages among the different areas evaluated. The study was conducted in the state of Bahia, where 49 streams were sampled, including 17 in the Atlantic Forest, 18 in the Caatinga, and 15 in the Cerrado. Our results demonstrate a high diversity of Odonata species, with 95 species collected. We found a similar species richness among the regions sampled. However, each region presented a distinct composition, with greater similarity between the Cerrado and the Caatinga. Spatial predictors along with some environmental variables were associated with the Caatinga and Cerrado. Some environmental variables, such as the amount of riparian vegetation and aquatic vegetation, were associated with the Cerrado. The results highlighted that each of the evaluated regions are fundamental for maintaining and conserving the regional dragonfly biodiversity. The lack of conservation of aquatic ecosystems in the different regions leads to local species loss and, consequently, to a loss of regional Odonata biodiversity. Full article

Land-use and land-cover (LULC) changes in the Amazon biome are key processes that influence the environment and societies at local, national, and global scales. Numerous studies have already relied on land-cover and land-use maps to analyze change processes. This study presents a new dataset created by calculating the time required for deforested areas to transition to agriculture (annual and permanent crops) in the Brazilian Amazon biome. The calculations were performed over MapBiomas land-cover data (version 7), which range from 1985 to 2021, at a spatial resolution of 30 m. The method consists of basic algebraic operation and recursion to identify every conversion from forest to agriculture between 1985 and 2021. The results show a correlation between environmental policies and the time required for the conversion to be completed, such as the adoption of the soy moratorium and the New Forest Code, that were followed by a search for old cleared areas for the establishment of new agricultural sites. The new data can be useful in interdisciplinary studies focused on land-use and land-cover change analysis in Brazil, such as planning of forest restoration initiatives, and the evaluation of carbon stocks according to conversion length. Our accuracy assessment shows an opportunity to improve conversion length calculations by reducing errors in the classification of agriculture establishment. The major innovation of this study is the establishment of explicit links between the deforestation year of a given pixel and its respective year of agriculture establishment, which can provide new insights into understanding long-term land-use conversion processes in tropical ecosystems. Full article

Tropical forests have high species richness, being considered the most diverse and complex ecosystems in the world. Research on the variation and maintenance of biodiversity in these ecosystems is important for establishing conservation strategies. The main objective of this study was to test the Spectral Variation Hypothesis through associations between species diversity and richness measured in the field and hyperspectral data collected by a Remotely Piloted Aircraft (RPA) in areas with secondary tropical forest in the Brazilian Atlantic Forest biome. Specific objectives were to determine which dispersion measurements, standard deviation (SD) or coefficient of variation (CV), estimated for the n pixels occurring within each sampling unit, better explains species diversity; the effects of pixel size on the direction and intensity of this relationship; and the effects of shaded pixels within each sampling unit. The spectral variability hypothesis was confirmed for the Atlantic Forest biome, with R² of 0.83 for species richness and 0.76 and 0.69 for the Shannon and Simpson diversity indices, respectively, using 1.0 m illuminated pixels. The dispersion (CV and SD) of hyperspectral bands were most strongly correlated with taxonomic diversity and richness in the red-edge and near-infrared (NIR) regions of the electromagnetic spectrum. Pixel size affected R² values, which were higher for 1.0 m pixels (0.83) and lower for 10.0 m pixels (0.71). Additionally, illuminated pixels had higher R² values than those under shadow effects. The main dispersion variables selected as metrics for regression models were mean CV, CV for the 726.7 nm band, and SD for the 742.3 and 933.4 nm bands. Our results suggest that spectral diversity can serve as a proxy for species diversity in the Atlantic Forest. However, factors that can affect this relationship, such as taxonomic and spectral diversity metrics used, pixel size, and shadow effects in images, should be considered. Full article

This study develops a structure for mapping native vegetation in a transition area between the Brazilian Cerrado and the Atlantic Forest from integrated spatial information of Sentinel-1 and Sentinel-2 satellites. Most studies use integrated data to improve classification accuracy in adverse atmospheric conditions, in which optical data have many errors. However, this method can also improve classifications carried out in landscapes with favorable atmospheric conditions. The use of Sentinel-1 and Sentinel-2 data can increase the accuracy of mapping algorithms and facilitate visual interpretation during sampling by providing more parameters that can be explored to differentiate land use classes with complementary information, such as spectral, backscattering, polarimetry, and interferometry. The study area comprises the Lobo Reservoir Hydrographic Basin, which is part of an environmental conservation unit protected by Brazilian law and with significant human development. LULC were classified using the random forest deep learning algorithm. The classifying attributes were backscatter coefficients, polarimetric decomposition, and interferometric coherence for radar data (Sentinel-1), and optical spectral data, comprising bands in the red edge, near-infrared, and shortwave infrared (Sentinel-2). The attributes were evaluated in three settings: SAR and optical data in separately settings (C1 and C2, respectively) and in an integrated setting (C3). The study found greater accuracy for C3 (96.54%), an improvement of nearly 2% compared to C2 (94.78%) and more than 40% in relation to C1 (55.73%). The classification algorithm encountered significant challenges in identifying wetlands in C1, but performance improved in C3, enhancing differentiation by stratifying a greater number of classes during training and facilitating visual interpretation during sampling. Accordingly, the integrated use of SAR and optical data can improve LULC mapping in tropical regions where occurs biomes interface, as in the transitional Brazilian Cerrado and Atlantic Forest. Full article

The 204-million-hectare Brazilian tropical savanna (Cerrado biome), located in the central part of Brazil, constitutes the main region of food and natural fiber production in the country. An important part of this production is based on center pivot irrigation. Existing studies evaluating the spatio-temporal dynamics of center pivots in Brazil do not consider their retraction. This study aimed to evaluate the expansion and retraction of center pivots in the Cerrado biome in the period 1985–2020. We relied on the data produced by the MapBiomas Irriga project. In this period, the area occupied by center pivots increased from 47 thousand hectares in 1985 to 1.2 million hectares in 2020, mostly concentrated in the states of Minas Gerais, Goiás, São Paulo, and Bahia, confirming previous reports available in the literature. Among the 13 irrigation poles recognized by the National Water Agency (ANA), the Oeste Baiano (Bahia State) and the São Marcos (Goiás State) presented the largest areas of center pivots (173,048 ha and 101,725 ha, respectively). We also found that 76% of the center pivots are concentrated in the regions with low water availability (0.01–0.45 mm day⁻¹). Within this 16-year period (2005–2020), more than 10% of center pivots found in 2005 were either abandoned or converted into rain-fed crop production. The results of this study can provide an important foundation for public policies directed toward the sustainable use of water resources by different consumers. Full article

Cerrado, constituting native Brazilian vegetation in the tropical and subtropical grasslands, savannas, and shrublands biome, has been extensively replaced by crop and pastureland, resulting in reduced water recycling to the atmosphere via evapotranspiration (ET). Re-introducing trees via integrated land-use systems potentially restores soil health and water-related processes; however, field data are scarce. During two years, we monitored soil moisture dynamics of natural Cerrado (CER), continuous pasture (COP), integrated crop-livestock (ICL), and integrated crop-livestock-forestry (ICLF) systems across 100 cm soil depth. Across years, mean soil moisture was highest for ICL, followed by COP and lowest in systems with trees (ICLF and CER). However, seasonal and spatial analyses revealed pronounced differences between soil layers and systems. COP and ICL mainly lost water from upper soil layers, whereas in ICLF, the strongest water depletion was observed at 40–100 cm depth, almost reaching a permanent wilting point during the dry season. CER was driest in the upper 40 cm, but water storage was highest below 60 cm depth. Our results suggest that compared to conventional land-use practices, integrated systems, including trees, increase water recycling to the atmosphere via ET and potentially compensate for the loss of key ecological functions of degraded or replaced Cerrado. Full article

This study uses a combined research approach based on remote-sensing and numerical modeling to quantify the effects of burned areas on the surface climate in the two Brazilian biomes most affected by fires: the tropical savanna and the Amazon rainforest. Our estimates indicate that between 2007 and 2020, approximately 6% of the savanna and 2% of the rainforest were burned on average. Non-parametric regressions based on 14-year climate model simulations indicate that latent heat flux decreases on average by approximately 0.17 W m⁻² in the savanna and 0.60 W m⁻² in the rainforest per each 1 km² burned, with most of the impacts registered during the onset of the wet season. Sensible and ground heat fluxes are also impacted but at less intensity. Surface air is also warmer and drier, especially over rainforest burned sites. On average, fire reduced gross primary production in the savanna and rainforest by 12% and 10%, respectively, in our experiments. Full article

The Pantanal biome—a tropical wetland area—has been suffering a prolonged drought that started in 2019 and peaked in 2020. This favored the occurrence of natural disasters and led to the 2020 Pantanal fire crisis. The purpose of this work was to map the burned area’s extent during this crisis in the Brazilian portion of the Pantanal biome using Sentinel-2 MSI images. The classification of the burned areas was performed using a machine learning algorithm (Random Forest) in the Google Earth Engine platform. Input variables in the algorithm were the percentiles 10, 25, 50, 75, and 90 of monthly (July to December) mosaics of the shade fraction, NDVI, and NBR images derived from Sentinel-2 MSI images. The results showed an overall accuracy of 95.9% and an estimate of 44,998 km² burned in the Brazilian portion of the Pantanal, which resulted in severe ecosystem destruction and biodiversity loss in this biome. The burned area estimated in this work was higher than those estimated by the MCD64A1 (35,837 km²), Fire_cci (36,017 km²), GABAM (14,307 km²), and MapBiomas Fogo (23,372 km²) burned area products, which presented lower accuracies. These differences can be explained by the distinct datasets and methods used to obtain those estimates. The proposed approach based on Sentinel-2 images can potentially refine the burned area’s estimation at a regional scale and, consequently, improve the estimate of trace gases and aerosols associated with biomass burning, where global biomass burning inventories are widely known for having biases at a regional scale. Our study brings to light the necessity of developing approaches that aim to improve data and theory about the impacts of fire in regions critically sensitive to climate change, such as the Pantanal, in order to improve Earth systems models that forecast wetland–atmosphere interactions, and the role of these fires on current and future climate change over these regions. Full article

New agricultural practices and land-use intensification in the Cerrado biome have affected the soil carbon stocks. A major part of the native vegetation of the Brazilian Cerrado, a tropical savanna-like ecoregion, has been replaced by crops, which has caused changes in the soil carbon (C) stocks. To ensure the sustainability of this intensified agricultural production, actions have been taken to increase soil C stocks and mitigate greenhouse gas emissions. In the last two decades, new agricultural practices have been adopted in the Cerrado region, and their impact on C stocks needs to be better understood. This subject has been addressed in a systematic review of the existing data in the literature, consisting of 63 articles from the Scopus database. Our review showed that the replacement of Cerrado vegetation by crop species decreased the original soil C stocks (depth 0–30 cm) by 73%, with a peak loss of 61.14 Mg ha⁻¹. However, when analyzing the 0–100 cm layer, 52.4% of the C stock data were higher under cultivated areas than in native Cerrado soils, with a peak gain of 93.6 Mg ha⁻¹. The agricultural practices implemented in the Brazilian Cerrado make low-carbon agriculture in this biome possible. Full article

Brazil has emerged as the world’s largest soybean producer and exporter in recent years. In the Brazilian Amazon Biome, the state of Pará has become a new agricultural frontier over the last two decades due to a significant increase in soybean cultivation throughout its territory. However, it is essential to understand the associated effects on the environment at every point in the supply chain. This research aims to measure the effects on the environment of the soybean supply chain of two production poles utilising openLCA software and the life cycle assessment (LCA) methodology in the northeast (Paragominas) and south (Redenção) of the state of Pará in Brazil. In addition, we determine which is the most efficient route between the shipment port and the ultimate destination. The Recipe Midpoint (H) and Intergovernmental Panel on Climate Change (IPCC) methods of environmental impact categories were used in accordance with the cradle-to-grave scope. The BRLUC regionalised model (v1.3) was used to quantify land use change (LUC). According to the observed results, LUC was primarily responsible (between 3.8 and 32.69 tCO₂ Eq·ha⁻¹·year⁻¹) for the global warming potential (GWP) of the soybean supply chain when rainforest-occupied land was converted into cropland. The soybean harvest in the Redenção pole is better loaded through the port of Itaqui (TEGRAM), which is in São Luis (state of Maranhão), due to the use of multiple modes of transport (lorry + train), allowing for better logistical performance and less impact on the environment, despite the longest distance (road + railway = 1306 km). Due to the short road distance (approximately 350 km) and consequently lower environmental impact, soybean harvested in the Paragominas pole is better loaded through the ports around Barcarena in the state of Pará. Full article

Pilocarpus microphyllus Stapf. ex Wardlew. (Rutaceae) is an endemic and threatened medicinal plant species from tropical Brazil. Popularly known as “jaborandi”, it is the unique natural source of pilocarpine, an alkaloid used to medical treat glaucoma and xerostomia. Based on Species Distribution Models (SDMs), we modeled the suitability of P. microphyllus’s geographical distribution considering three Global Circulation Models (GCMs) under two future climate change scenarios (SSP2-4.5 and SSP5-8.5). The quantitative analyses carried out using ten different SDM algorithms revealed that precipitation seasonality (Bio15) and precipitation of the driest month (Bio14) were the most important bioclimatic variables. The results evidenced four main key areas of continuous occurrence of the plant spreading diagonally over tropical Brazilian biomes (Amazon, Cerrado and Caatinga). The near-future (2020 to 2040) ensemble projections considering all GCMs and scenarios have indicated negative impacts for the potential loss or significant reduction in suitable habitats for P. microphyllus in the transition region between the Amazon and Cerrado into central and northern Maranhão state, and mainly in the Caatinga biome over the northern Piaui state. On the other hand, positive impacts of the expansion of the plant habitat suitability are projected over forest cover protected areas of the Amazon biome in the southeastern Pará state. Since the jaborandi is of socioeconomic importance for many families in the north/northeast Brazil, it is urgent to implement public policies for conservation and sustainable management, thus mitigating the impacts of global climate change. Full article

This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge structural complexity. The selection of the species was done based on the number of tree samples, which exist in the plot data and in the fact the UAV imagery does not acquire information below the forest canopy. Due to the complexity of the forest, only species that exist in the upper canopy of the remnant were included in the classification. A combination of hyperspectral UAV images and LiDAR point clouds were in the experiment. The hyperspectral images were photogrammetric and radiometric processed to obtain orthomosaics with reflectance factor values. Raw spectra were extracted from the trees, and vegetation indices (VIs) were calculated. Regarding the LiDAR data, both the point cloud—referred to as Peak Returns (PR)—and the full-waveform (FWF) LiDAR were included in this study. The point clouds were processed to normalize the intensities and heights, and different metrics for each data type (PR and FWF) were extracted. Segmentation was preformed semi-automatically using the superpixel algorithm, followed with manual correction to ensure precise tree crown delineation before tree species classification. Thirteen different classification scenarios were tested. The scenarios included spectral features and LiDAR metrics either combined or not. The best result was obtained with all features transformed with principal component analysis with an accuracy of 76%, which did not differ significantly from the scenarios using the raw spectra or VIs with PR or FWF LiDAR metrics. The combination of spectral data with geometric information from LiDAR improved the classification of tree species in a complex tropical forest, and these results can serve to inform management and conservation practices of these forest remnants. Full article

The present study aimed to contribute to the diagnosis and advance the knowledge of the impacts of land use change and climate change on the tropical longleaf forest biome at the continental scale in South America (Biome 1 according to the WWF classification) for realizing scientific progress in the search for convincing strategies and actions by different actors for the preservation of forests in the continent. The status and climate of the area, which harbors the tropical longleaf forests of South America, were assessed. Moreover, volumetric soil moisture (VSM) was evaluated through maps and simulation using the autoregressive integrated moving average model (ARIMA). Furthermore, future climate scenarios were predicted based on El Niño–Southern Oscillation phenomena, meteorological systems, and scientific evidence, such as the shared socioeconomic pathways (SSPs) and sociopolitical dynamics evident in the region from the case analysis of the Brazilian states of Acre and Rio de Janeiro. An increase was noted in the temperature and range of precipitation variation in the biome. ARIMA analysis indicated changes of up to 0.24 m³ m⁻³ and an increased range of future VSM values. The December–January–February (DJF) quarter recorded the highest VSM median with the measurement scale of 0.05 to 0.44 m³ m⁻³, while the June–July–August (JJA) quarter recorded the lowest value. The regions of the biome with the lowest VSM values included southern Amazon (Ecuador, Peru, and the Brazilian states of Acre, Mato Grosso, Pará, and Maranhão), Brazilian Atlantic Forest, Southeast Region, and the Brazilian state of Bahia. Full article