Search Results (29)

The Cerrado domain, one of the richest on Earth, is among the most threatened in South America due to human activities, resulting in biodiversity loss, altered fire dynamics, water pollution, and other environmental impacts. Monitoring this domain is crucial for preserving its biodiversity and ecosystem services. This study aimed to apply machine learning techniques to classify the main vegetation formations of the Cerrado within the IBGE Ecological Reserve, a protected area in Brazil, using high-resolution PlanetScope imagery from 2021 to 2024. Three machine learning methods were evaluated: Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). A post-processing process was applied to avoid misclassification of forest in areas of savanna. After performance evaluation, the SVM method achieved the highest classification accuracy (overall accuracy of 97.51%, kappa coefficient of 0.9649) among the evaluated models. This study identified five main classes: grassland (GRA), savanna (SAV), bare soil (BS), samambaião (SAM, representing the superdominant species Pteridium esculentum), and forest (FOR). Over the three-year period (2021–2024), SAV and GRA formations were dominant in the reserve, reflecting the typical physiognomies of the Cerrado. This study successfully delineated areas occupied by the superdominant species P. esculentum, which was concentrated near gallery forests. The generated maps provide valuable insights into the vegetation dynamics within a protected area, aiding in monitoring efforts and suggesting potential new areas for protection in light of imminent anthropogenic threats. This study demonstrates the effectiveness of combining high-resolution satellite imagery with machine learning techniques for detailed vegetation mapping and monitoring in the Cerrado domain. Full article

Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020). Full article

The Cerrado has high plant and vertebrate diversity and is an important biome for conserving species and provisioning ecosystem services. Volume equations in this biome are scarce because of their size and physiognomic diversity. This study was conducted to develop specific volumetric models for the phytophysiognomies Gallery Forest, Dry Forest, Forest Savannah, and Savannah Woodland, a generic model and a model for Cerrado forest formation. Twelve 10 m × 10 m (100 m²) (National Forest Inventory) plots were used for each phytophysiognomy at different sites (regions) of the Federal District (FD) where trees had a diameter at breast height (DBH; 1.30 m) ≥5 cm in forest formations and a diameter at base height (Db; 0.30 m) ≥5 cm in savanna formations. Their diameters and heights were measured, they were cut and cubed, and the volume of each tree was obtained according to the Smalian methodology. Linear and nonlinear models were adjusted. Criteria for the selection of models were determined using correlation coefficients, the standard error of the estimates, and a graphical analysis of the residues. They were later validated by the chi-square test. The resultant models indicated that fit by specific phytophysiognomy was ideal; however, the generic and forest formation models exhibited similar performance to specific models and could be used in extensive areas of the Cerrado, where they represent a high potential for generalization. To further increase our understanding, similar research is recommended for the development of specific and generic models of the total volume in Cerrado areas. Full article

Dryland regions around the world are facing intricate challenges due to climate change and human activities. The Caatinga biome in Brazil, an exceptional dryland ecosystem covering approximately 86.3 million hectares, is particularly impacted by human influence. We conducted an extensive study analyzing changes in land use and land cover within the Caatinga region over a span of 35 years, from 1985 to 2019. This study leverages collective knowledge and collaborative effort with the MapBiomas project to provide valuable insights into the biome’s landscape. It maps eight principal land cover classes using Landsat Collection 1 Tier 1 data normalized to top-of-atmosphere reflectance. All data processing was carried out within the Google Earth Engine platform, and the graphics were generate using R version 3.6.2. This study achieved an impressive 80% global accuracy in the time series of Caatinga land use and land cover (LULC) changes, with allocation and area discrepancies of 11.6% and 8.5%, respectively. The extensive 35-year LULC dataset reveals a substantial 11% reduction in natural vegetation in the Caatinga biome, translating to a loss of 6.57 million hectares. This decline is primarily attributed to the expansion of cattle ranching and agriculture; all types of natural vegetation have experienced decreases, with Savanna Formation (SF) areas declining by 11% and Forest Formation (FF) areas declining by 8%. In contrast, pasturelands expanded by 62% and agricultural land expanded by 284% during this period. With their urgent and significant real-world for informing social, economic, and environmental policy decisions within the Caatinga and other dryland regions globally, these findings underscore the importance and immediacy of our research. Full article

We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021. Full article

The distribution of species of agro-extractivist interest and their ecological relationship with the physical environment geomorphological and bioclimatic allow supporting strategies aimed at socioeconomic and environmental development. We evaluated the contribution of high spatial resolution topographic variables in ecological niche models and the relationship of the distribution of five tree species with the geomorphological units and bioclimatic variables. The variables related to temperature variation and water availability proved to be important in predicting the areas of occurrence of the target species, with increased suitability of occurrence in regions with higher isothermality, located in the plateau and table geomorphological units. The predictions showed a significant difference when high spatial resolution variables were used, generating a more conservative scenario in the indication of suitable regions for the occurrence of species, important for local scale studies. The geomorphological units of plateau and tableland showed high suitability of occurrence, while the fluvial plains and dissected depressions did not present suitability for the occurrence of the species. The results allow us to strategically define areas with the greatest productive potential and prioritize areas for conservation, management, ecological restoration of forests, and targeting areas for the implementation of community agro-industries, essential for territorial planning within traditional communities. Full article

Adaptations to climate change rely on understanding the dynamics of plant biomass stocks on the planet. The high levels of deforestation in Cerrado have transformed this biome into the second-largest Brazilian source of carbon emissions. The objective of this study was to develop a method to accurately estimate aboveground and total biomass values among shrublands, savannas, and forests located in the Cerrado biome using an allometric equation adjusted from canopy height obtained through optical and laser sensors. The results show similarity between the estimates employed by our method and the data found in the literature review for different phytophysiognomies in the Cerrado biome. Shrubland formations showed higher biomass estimation uncertainties due to the discontinuity of isolated trees and the lower canopy height when compared to more clustered tree canopies in savannas and taller canopies in forests. Aboveground biomass estimates are related to expansion factors, and specific maps were developed for each compartment by root, litter, and necromass. The sum of these compartments is presented in the aboveground and below forest biomass map. This study presents, for the first time, the mapping of total biomass in 10 m pixels of all regions of the Cerrado biome. Full article

In order to assess the influencing factors of the presence of mercury in a river within the Savanna biome (Cerrado), we surveyed total mercury (THg) in bottom sediment from 50 lakes along 750 km of the Middle Araguaia floodplain. The sampling sites included non-urban and urban surroundings over three distinct geomorphologies. We measured water physicochemical parameters at each site and tested statistically if land use nested within the geological formation influenced the THg concentration in bottom sediments and related water parameters. Multivariate results indicate that the interaction between geological groups and land use is statistically significant (p < 0.05). Nested ANOVA and Tukey HSD tests confirmed that the geological formation with its nested land use influences the THg, pH, DO, conductivity, and TDS (p < 0.05). THg was significantly lower in Quaternary terrains (p < 0.05) and differed significantly between non-urban and urban areas in Neoproterozoic terrains (p = 0.02). The spatial projections of the THg eigenvector on the main axes with the scoring factors of the Neoproterozoic/Paleoproterozoic terrains, and urban/non-urban, confirmed the spatial correlations. These results indicate that the association of land use and geology could be the main driver of THg in the bottom sediments of lakes from the Middle Araguaia floodplain. Full article

Competition is an important biological filter that can define crucial features of species’ natural history, like survival and reproduction success. We evaluated in the Brazilian tropical savanna whether two sympatric and congenereric species, Qualea multiflora Mart. and Q. parviflora Mart. (Vochysiaceae), compete for pollinator services, testing whether there is a better competitor or whether plants present any anti-competitive mechanism. Additionally, we investigated the breeding system, pollinators, and flowering phenology of both species. The results showed that Q. multiflora and Q. parviflora are dependent on pollinators for fruit formation, as they exhibited a self-incompatible and non-agamospermic breeding system. These plants shared the same guild of pollinators, which was formed by bees and hummingbirds, and an overlap in the flower visitation time was observed. Each plant species had different pollinator attraction strategies: Q. multiflora invested in floral resource quality, while Q. parviflora invested in resource quantity. The blooming time showed a temporal flowering partition, with highly sequential flowering and no overlap. Qualea parviflora bloomed intensely from September to October, while Q. multiflora bloomed from November to January, with the flowering peak occurring in December. The two Qualea species have morphologically similar flowers, are sympatric, and share the same pollinator community, with overlapping foraging activity during the day. However, they do not compete for pollinator services as they exhibit an anti-competitive mechanism mediated by temporal flowering partition. Full article

Woody plant encroachment in grassy ecosystems is a widely reported phenomenon associated with negative impacts on ecosystem functions. Most studies of this phenomenon have been carried out in arid and semi-arid grasslands. Therefore, studies in tropical regions, particularly savannas, which are composed of grassland and woodland mosaics, are needed. Our objective was to evaluate the accuracy of woody encroachment classification in the Brazilian Cerrado, a tropical savanna. We acquired dry and wet season unmanned aerial vehicle (UAV) images using RGB and multispectral cameras that were processed by the support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers. We also compared two validation methods: the orthomosaic and in situ methods. We targeted two native woody species: Baccharis retusa and Trembleya parviflora. Identification of these two species was statistically (p < 0.05) most accurate in the wet season RGB images classified by the RF algorithm, with an overall accuracy (OA) of 92.7%. Relating to validation assessments, the in situ method was more susceptible to underfitting scenarios, especially using an RF classifier. The OA was higher in grassland than in woodland formations. Our results show that woody encroachment classification in a tropical savanna is possible using UAV images and field surveys and is suggested to be conducted during the wet season. It is challenging to classify UAV images in highly diverse ecosystems such as the Cerrado; therefore, whenever possible, researchers should use multiple accuracy assessment methods. In the case of using in situ accuracy assessment, we suggest a minimum of 40 training samples per class and to use multiple classifiers (e.g., RF and DT). Our findings contribute to the generation of tools that optimize time and cost for the monitoring and management of woody encroachment in tropical savannas. Full article

The upland lakes (ULs) in Carajás, southeastern Amazonia, have been extensively studied with respect to their high-resolution structural geology, geomorphology, stratigraphy, multielement and isotope geochemistry, palynology and limnology. These studies have generated large multiproxy datasets, which were integrated in this review to explain the formation and evolution of the ULs. These ULs evolved during the Pliocene–Pleistocene periods through several episodes of a subsidence of the lateritic crust (canga) promoted by fault reactivation. The resulting ULs were filled under wet/dry and warm/cool paleoclimatic conditions during the Pleistocene period. The multielement geochemical signature indicates that the detrital sediments of these ULs were predominantly derived from weathered canga and ferruginous soils, while the sedimentary organic matter came from autochthonous (siliceous sponge spicules, algae, macrophytes) and allochthonous (C3/C4 canga and forest plants and freshwater dissolved organic carbon) sources. Modern pollen rain suggests that even small ULs can record both the influence of canga vegetation and forest signals; thus, they can serve as reliable sites to provide a record of vegetation history. The integrated data from the sedimentary cores indicate that the active ULs have never dried up during the last 50 ka cal BP. However, subaerial exposure occurred in filled ULs, such as the Tarzan mountain range during the Last Glacial Maximum (LGM) and the Bocaína and S11 mountain ranges in the mid-Holocene period, due to the drier conditions. Considering the organic proxies, the expansion of C4 plants has been observed in the S11 and Tarzan ULs during dry events. Extensive precipitation of siderite in UL deposits during the LGM indicated drier paleoenvironmental conditions, interrupting the predominantly wet conditions. However, there is no evidence of widespread forest replacement by savanna in the Carajás plateau of southeastern Amazonia during the late Pleistocene and Holocene. Full article

Fire occurrence affects the distribution of key resources for fauna in natural ecosystems worldwide. For fire management strategies adequate for biodiversity conservation, the understanding of how species respond to fire-induced changes is essential. In this study, we investigated the role of fire regimes on spaces used by medium and large mammals at multiple spatial scales (0.8 ha to 78.5 ha) in a fire-prone savanna ecosystem (Brazilian Cerrado). We sampled mammals using 60 camera traps distributed in 30 sampling units located in grassland and typical savanna formations. We applied single-species occupancy models and AIC-based model selection to assess how mammals use the space in response to pyrodiversity (both diversity of fire frequencies and diversity of fire ages), the proportion of recently burned area, and the proportion of long-unburned area while accounting for detectability. Our results showed that fire regime variables affected the study species differently. Deer species used the space regardless of mosaic pyrodiversity and the proportion of specific fire ages. Fire-related variables, however, affected space use by tapirs and maned wolves. Tapirs preferred to use fire mosaics with lower diversity of fire frequencies, whereas maned wolves more intensively used mosaics with high fire age diversity and a high proportion of recently burned areas. Based on our findings, we recommend that fire management targeting specific mammal species should not necessarily focus on maximum pyrodiversity. Instead, we suggest a management strategy combining “patch mosaic burning” with the maintenance of specific fire-age patches suitable for different species’ requirements. Full article

The classification of savanna woodland tree species from high-resolution Remotely Piloted Aircraft Systems (RPAS) imagery is a complex and challenging task. Difficulties for both traditional remote sensing algorithms and human observers arise due to low interspecies variability (species difficult to discriminate because they are morphologically similar) and high intraspecies variability (individuals of the same species varying to the extent that they can be misclassified), and the loss of some taxonomic features commonly used for identification when observing trees from above. Deep neural networks are increasingly being used to overcome challenges in image recognition tasks. However, supervised deep learning algorithms require high-quality annotated and labelled training data that must be verified by subject matter experts. While training datasets for trees have been generated and made publicly available, they are mostly acquired in the Northern Hemisphere and lack species-level information. We present a training dataset of tropical Northern Australia savanna woodland tree species that was generated using RPAS and on-ground surveys to confirm species labels. RPAS-derived imagery was annotated, resulting in 2547 polygons representing 36 tree species. A baseline dataset was produced consisting of: (i) seven orthomosaics that were used for in-field labelling; (ii) a tiled dataset at 1024 × 1024 pixel size in Common Objects in Context (COCO) format that can be used for deep learning model training; (iii) and the annotations. Full article

We identified a small, supernumerary marker chromosome (sSMC) in two phenotypically normal Asian elephants (Elephas maximus): a female (2n = 57,XX,+mar) and her male offspring (2n = 57,XY,+mar). sSMCs are defined as structurally abnormal chromosomes that cannot be identified by conventional banding analysis since they are usually small and often lack distinct banding patterns. Although current molecular techniques can reveal their origin, the mechanism of their formation is not yet fully understood. We determined the origin of the marker using a suite of conventional and molecular cytogenetic approaches that included (a) G- and C-banding, (b) AgNOR staining, (c) preparation of a DNA clone using laser microdissection of the marker chromosome, (d) FISH with commercially available human painting and telomeric probes, and (e) FISH with centromeric DNA derived from the centromeric regions of a marker-free Asian elephant. Moreover, we present new information on the location and number of NORs in Asian and savanna elephants. We show that the metacentric marker was composed of heterochromatin with NORs at the terminal ends, originating most likely from the heterochromatic region of chromosome 27. In this context, we discuss the possible mechanism of marker formation. We also discuss the similarities between sSMCs and B chromosomes and whether the marker chromosome presented here could evolve into a B chromosome in the future. Full article

A lactic-acid-producing strain was isolated from the Brazilian Cerrado soil (Brazilian savanna). Glycerol, a byproduct of the biodiesel industry, can be converted into various chemical intermediates of industrial value by biotechnological routes. Klebsiella pneumoniae can metabolize glycerol in environments with or without oxygen and bioconvert it into several chemicals with high value-added, such as lactic acid, 3-hydroxypropionic acid and 1,3 propanediol. The wild-type bacterial strain (2GPP) isolated from a soil sample from the Brazilian Cerrado was determined to be a K. pneumoniae complex that was capable of successfully metabolizing glycerol. Fermentations were performed with different temperatures, pH, and inoculum concentrations to evaluate the best lactic acid production. At first, 1,3-propanediol and L-(+)-lactic acid were produced in mini reactors. A lactic acid production of 3.8 g·L⁻¹ and a decrease in 1,3-propanediol output were observed. Thus, by adjusting process variables such as pH and temperature during fermentation, it was possible to maximize the production of lactic acid and decrease the formation of 1,3-propanediol by utilizing experimental design strategies. Full article