The Guanabara Bay (GB) is an estuarine system in the metropolitan region of Rio de Janeiro (Brazil), with a surface area of $∼346km2$ threatened by anthropogenic pressure. Remote sensing can provide frequent data for studies and monitoring of water quality parameters, such as $chlorophyll-a$ concentration ($Chl-a$). Different combination of Medium Resolution Imaging Spectrometer (MERIS) remote sensing reflectance band ratios were used to estimate $Chl-a$. Standard algorithms such as Ocean Color 3-band, Ocean Color-4 band, fluorescence line height, and maximum chlorophyll index were also tested. The MERIS $Chl-a$ estimates were statistically compared with a dataset of in situ$Chl-a$ (2002 to 2012). Good correlations were obtained with the use of green, red, and near-infrared bands. The best performing algorithm was based on the red (665 nm) and green (560 nm) band ratio, named “RG3” algorithm ($r2=0.71$, $chl-a=62,565*x1.6118$). The RG3 was applied to a time series of MERIS images (2003- to 2012). The GB has a high temporal and spatial variability of $Chl-a$, with highest values found in the wet season (October to March) and in some of the most internal regions of the estuary. Lowest concentrations are found in the central circulation channel due to the flushing of ocean water masses promoted by pumping tide.

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.

The Santos Basin (SB) is the main petroliferous basin in the Brazilian continental margin and one of the most studied marine areas in Brazil. However, historical data suggest that new efforts should be carried out to acquire quantitative biological data, especially in the deep sea, to establish the baseline of essential ocean variables in different ecosystems for future monitoring programs. The Brazilian energy company Petrobras planned and executed 24 oceanographic cruises over a period of 2 years to assess the benthic (SANSED cruise) and pelagic (SANAGU cruise) systems of the SB (356 days at sea in 2019 and 2021/2022). These efforts were part of the Santos Project, which comprised a comprehensive environmental study aimed at investigating benthic and pelagic variables to characterize ecology, biogeochemistry, thermohaline properties of water masses, and ocean circulation patterns, geomorphology, and sedimentology, as well as organic and inorganic chemistry. Here we present the detailed sampling designs and the field methods employed on board, during the SB scientific cruises. All sampling protocols were based on standardized approaches. For the benthos analyses, triplicate sediment samples were performed using a GOMEX-type box corer (0.25 m²) or a large modified Van Veen grab (0.75 m²) at 100 stations ranging from 25 to 2400 m depth. At each station, 25 geochemical and physico-chemical parameters were analyzed in addition to micro-, meio-, and macrofauna and living foraminifera samples. For the pelagic system, 60 stations were selected to investigate the plankton community, ranging in size from pico- to macroplankton, through vertical, horizontal, and oblique net hauls (20, 200, and 500 μm mesh size), as well as 25 biogeochemical parameters collected with an aid of a CTD-rosette sampler. Part of this scientific information also serves the Regional Environmental Characterization Project (PCR-BS) in support of Petrobras’ Santos Basin drilling licensing process led by the Brazilian Environmental Agency – IBAMA. This project contributes to the sustainable development of the SB, in line with the guidelines of the United Nations Decade of Ocean Science for Sustainable Development.

Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km² mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R²(calibration) = 0.89, R²(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha⁻¹, and RMSE(validation) = 14.80 t·ha⁻¹). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

During the last quarter of 2019, the beaches, mangroves, and estuaries of Northeast Brazil received an unprecedented volume of crude oil from the sea, which became the worst environmental disaster ever to reach the Brazilian coast. The oil, having reached the shores completely unnoticed, left both society and government agents completely clueless on (i) where the oil was coming from; (ii) how much oil was still in the ocean to reach the shorelines; and (iii) which beaches were going to be affected next! By exploring remote sensing data and ocean numerical modeling, along with oil dispersion chemistry on sea water, this study investigates the possible origin and path of the spill and whether it could have been detected from space. The oil dispersion modeling simulations performed for this investigation revealed a possible region and timing of the oil spill, also indicating the likelihood of it being advected toward the shoreline under the ocean surface.