Search

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome. [ABSTRACT FROM AUTHOR]

Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The Scopus database was used to evaluate the relevant scientific literature in this field, followed by a bibliometric analysis to extract additional information, such as architecture type, country collaboration, and most cited papers. The findings highlight significant advancements in oil detection at sea, with a strong correlation between technological evolution in detection methods and improved remote sensing data acquisition. Multilayer perceptrons (MLP) emerged as the most prominent neural network architecture in 11 studies, followed by a convolutional neural network (CNN) in 5 studies. U-Net, DeepLabv3+, and fully convolutional network (FCN) were each used in three studies, demonstrating their relative significance too. The analysis provides insights into collaboration, interdisciplinarity, and research methodology and contributes to the development of more effective policies, strategies, and technologies for mitigating the environmental impact of oil spills in OSDMDL. [ABSTRACT FROM AUTHOR]

The Southern Brazilian Continental Shelf (SBCS) is an area with great ecological and economic importance to Brazil. In this region can be observed the recurrent passage of frontal systems and extratropical cyclones, which are more frequent during the winter months of the southern hemisphere. These systems act on the ocean surface layers as direct driving forces, which may change the thermohaline structure of the water column and induce sea level perturbations. This study used the coupled ocean atmosphere regional model (COAWST) to evaluate the effect of the passage of a frontal system associated with an extra-tropical cyclone. The ocean and atmosphere models (ROMS and WRF) was configured with two nested grids, in order to solve the dynamic processes, at different scales, that comprise the energy transfer from the atmospheric system to the ocean. The simulation was based on a study case, occurred in September 2016, on the southwestern brazilian continental shelf. The model outputs were analyzed/compared to remote sensing data and 5 tide gauges from the Agricultural Research and Rural Extension (EPAGRI) in Santa Catarina state, Brazil. This comparison showed a correlation higher than 78% between sea level rise data and the model results, with average difference of less than 25cm. The use of low-pass Lanczos-Cosine filter made it possible to identify the meteorological component in the ocean model outputs. Our simulation also presents the sea level anomalies, associated with the crossing of the atmospheric frontal system, progressing to northward along the continental shelf at 480km.day-1, probably associated with the presence of a coastal-trapped wave. [ABSTRACT FROM AUTHOR]

A satellite synthetic aperture radar (SAR) data set facilitates investigation of the two-dimensional structure of internal solitary waves (ISWs) that propagate near the Amazon River mouth. Three distinct groups of waves are identified according to their propagation direction. While cross-shelf ISW propagation has been previously documented, it is found that the majority of the waves propagate along the shelf and upstream of the North Brazilian Current (NBC). These ISWs appear in packets with interpacket separations of ~4 km, mean crest lengths of 10 km, and intersoliton separations of ~500 m. They are observed throughout the entire semidiurnal and fortnightly tidal cycles, but their origin appears to result primarily from the steady NBC, modulated by tidal currents, as it flows over shallow bottom topography. A Froude number analysis considering mode-1 nonhydrostatic linear waves propagating upstream of the NBC is found to be consistent with a regime of transcritical generation in the study region. [ABSTRACT FROM AUTHOR]

The current study aimed to obtain dose conversion coefficients for marine animals due to an oil spill accident using two variables: crude oil activity concentration and organism depth. Thorium series presented a dose contribution twice that uranium series for similar conditions. Bi-214 and Tl-208 stood out for delivering a higher dose rate for uranium and thorium series, respectively. Results obtained can be used to assess the maximum exposure time for emergency oil control, removal, and mitigation in an oil spill accident., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

During the austral winter of 2008, thousands of penguins traveled to low latitudes along the South Atlantic coast of South America. The atmospheric and oceanic conditions from April to July 2008 may account for the penguins' unusual geographic distribution. During that period, South Atlantic coastal waters were cooler; the wind anomalies had northward and onshore components; the ocean's coastal region presented northward currents that favored the penguins to travel toward lower latitudes. This anomalous climate regime resulted from extreme meteorological frontal systems that occurred mainly during June 2008. Three consecutive extreme midlatitude cyclones produced strong wind shear that resulted in the northward oceanic flow along the South American eastern shoreline favoring the penguins to be spotted in northern tropical waters.