Unraveling impacts on carbon, water and energy exchange of Pinus plantations in South American temperate ecosystems.

Tree plantations are expanding in southern South America and their effects on ecosystem services, particularly climate regulation, are still not well understood. Here, we used remote sensing techniques and a paired design to analyze ≈33,000 ha of Pinus plantations along a broad geographical and environmental gradient (26-43° South latitude, 54-72° West longitude). Radiation interception, surface temperature, evapotranspiration, and albedo were assessed both in tree plantations stands and in adjacent uncultivated areas. Additionally, the climatic impact of tree plantations was quantified by analyzing changes in atmospheric radiative forcing and its carbon (C) equivalent. Tree plantations intercepted more radiation when replacing steppes, grasslands, and shrublands but not when replacing forests. The control exerted on radiation interception by precipitation decreased in both space and time after tree plantation. Furthermore, evapotranspiration notably increased in tree plantations. The lower albedo of tree plantations compared to uncultivated adjacent areas induces global warming through the biophysical pathway. Thus, the climate benefits of afforestation through C sequestration can be counteracted by 18 to 83 % due to albedo changes. It is necessary to fully consider the biophysical effects and water footprint of tree plantations in public policies that promote them, as well as in international carbon accounting mechanisms.

Mapping heavy mineral deposits on the coast of the state of Rio Grande do Sul (Brazil) using orbital and proximal remote sensing.

Heavy mineral deposits occur in several coastal areas of the world, formed over a long period due to variations in mean sea level, wave action, and winds. These are the main sources of ilmenite (FeTiO3), which in turn is the source of more than 80% of the TiO2 produced and applied in various industries, most recently in nanotechnology. The present study mapped heavy mineral deposits on the coast of Rio Grande do Sul in southern Brazil using integrated proximal and orbital thermal infrared (TIR) remote sensing techniques. Mineral groups, such as oxides and silicates, have spectral features in the TIR wavelengths. Using laboratory spectroscopy at TIR using Nicolet 6700 Thermo Scientific Spectrometer, we measured the spectral signature of the local sample of heavy minerals (between 8 and 14 µm) and identified a diagnostic spectral feature at 10.75 µm. The signature was resampled to be compatible with the Advanced Spaceborne Thermal Emission Radiometer (ASTER) sensor bandwidth values and used as a reference endmember for the Spectral Angle Mapper (SAM) and Linear Spectral Unmixing (LSU) digital image classification algorithms. Thus, we identified the presence of the reference endmember (heavy minerals) in the pixels of the ASTER scene. In pixels classified by SAM as the presence of heavy minerals, LSU was applied to estimate the surface concentration within the pixel. The results showed a concentration of up to 20% of heavy minerals, with the highest concentration on the beach and dune fields. Opaque minerals such as ilmenite do not have spectral reflectance features in visible, near-infrared, and short-wave infrared, which makes their identification by remote sensing difficult. The present study showed that the integration of proximal and orbital as well as hyperspectral and multispectral thermal data can be considered as an alternative for detecting and mapping heavy minerals in coastal areas.

Remote sensing in maize: effect of vegetal biostimulants application in three stages of development.

Maize is a crop of global economic importance and is widely cultivated throughout the Brazilian territory. The use of biostimulants can increase yield and improve crop yield. Unmanned aerial vehicles can be employed in arable areas, allowing their use in an economically way. This study to evaluate the use of biostimulant and the best application timing using photogrammetric indexes in maize, and indicate the most suitable plant index for yield increase through a Pearson's correlation. The DJI Drone coupled with RGB camera was used, and the images were processed through the AgisoftPhotoscan® software to generate the orthomosaic, and the QGIS® software version 3.4.15 with GRASS was used to generate thematic maps with the classification of the indexes of vegetation (NGRDI, EXG, SAVI, TGI, GLI, RI). A matrix of Pearson correlation coefficients between the variables was also created, and the results were analyzed with the R software. In general, the products Pyroligneous Extract (PE) and the hormonal product (HP) were the best for the two seasons studied. However, the HP was the best product to mitigate plant water stress in the dry period. Application at phenological stage V3 showed the lowest growth in the rainy season and in application to the seeds in the dry season. Dose 4 of the pyroligneous extract increased productivity in the rainy season and level 3.4 for the hormone product. Among the indexes evaluated, only the SAVI index showed significant differences between the others and showed significance for productivity in the two periods.

Assessing soil CO2 emission on eucalyptus species using UAV-based reflectance and vegetation indices.

Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO2. Thus, assessing the amount of CO2 released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO2) flux (FCO2), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO2 data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO2 for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO2 emission and the eucalypt species, providing a distinction of values between the species in the statistical data.

Artificial Neural Network and Remote Sensing combined to predict the Aboveground Biomass in the Cerrado biome.

Cerrado is the second largest biome in Brazil, and it is responsible for providing us several ecosystem services, including the functions of storing Carbon and biodiversity conservation. In this study, we developed a modeling approach to predict the Aboveground biomass (AGB) in Cerrado vegetation using Artificial Neural Networks (ANNs), vegetation indices retrieved from RapidEye satellite imagery, and field data acquired within the Federal District territory, Brazil. Correlation testing was performed to identify potential vegetation index candidates to be used as input in the AGB modeling. Several ANNs were trained to predict the AGB in the study area using vegetation indices and field data. The optimum ANN was selected according to criteria of mean error of the estimate, correlation coefficient, and graphical analysis. The best performing ANN showed a predictive power of 90% and RMSE less than 17%. The validation tests showed no significant difference between the observed and ANN-predicted values. We estimated an average AGB of 16.55± 8.6 Mg.ha-1 in shrublands in the study area. Our study results indicate that vegetation indices and ANNs combined could accurately estimate the AGB in the Cerrado vegetation in the study area, showing to be a promising methodological approach to be broadly applied throughout the Cerrado biome.

Monitoring agricultural drought using different indices based on remote sensing data in the Brazilian biomes of Cerrado and Atlantic Forest.

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.

Dispersion analysis of the 2017 Persian Gulf oil spill based on remote sensing data and numerical modelling.

Oil spills, detected by SAR sensors as dark areas, are highly effective marine pollutants that affect the ocean surface. These spills change the water surface tension, attenuating capillary gravitational waves and causing specular reflections. We conducted a case study in the Persian Gulf (Arabian Sea to the Strait of Hormuz), where approximately 163,900 gal of crude oil spilled in March 2017. Our study examined the relationship between oil weathering processes and extracted backscatter values using zonal slices projected over SAR-detected oil spills. Internal backscatter values ranged from -22.5 to -23.5, indicating an oil chemical binding and minimal interaction with seawater. MEDSLIK-II simulations indicated increased oil solubilization and radar attenuation rates with wind, facilitating coastal dispersion. Higher backscatter at the spill edges compared to the core reflected different stages of oil weathering. These results highlight the complex dynamics of oil spills and their environmental impact on marine ecosystems.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

Combining multisensor images and social network data to assess the area flooded by a hurricane event.

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon.

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA's shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

Implementation of remote sensing algorithms to estimate TOC, Chl-a, and TDS in a tropical water body; Sanalona reservoir, Sinaloa, Mexico.

The present study implements a methodology to estimate water quality values using statistical tools and remote sensing techniques in a tropical water body Sanalona. Linear regression models developed by Box-Cox transformations and processed data from LANDSAT-8 imagery (bands) were used to estimate TOC, TDS, and Chl-a of the Sanalona reservoir from 2013 to 2020 at five sampling sites measured every 6 months. A band discriminant analysis was carried out to statistically fit and optimize the proposed algorithms. Coefficients of determination beyond 0.9 were obtained for these water quality parameters (r2TOC = 0.90, r2TDS = 0.95, and r2Chl-a = 0.96). A comparison between the estimated and observed water quality was carried out using different data for validation. The validation of the models showed favorable results with R2TOC = 0.8525, R2TDS = 0.8172, and R2Chl-a = 0.9256. The present study implemented, validated, and compared the results obtained by using an ordered and standardized methodology proposed for the estimation of TOC, TDS, and Chl-a values based on water quality parameters measured in the field and using satellite images.

Combined use of SAR images and numerical simulations to identify the source and trajectories of oil spills in coastal environments.

Remote sensing data and numerical simulation are important tools to rebuild any oil spill accident letting to identify its source and trajectory. Through these tools was identified an oil spill that affected Oaxacan coast in October 2022. The SAR images were processed with a standard method included in SNAP software, and the numerical simulation was made using Lagrangian transport model included in GNOME software. With the combining of these tools was possible to discriminate the look-alikes from true oil slicks; which are the main issue when satellite images are used. Obtained results showed that 4.3m3 of crude oil were released into the ocean from a punctual point of oil pollution. This oil spill was classified such as a small oil spill. The marine currents and weathering processes were the main drivers that controlled the crude oil displacement and its dispersion. It was estimated in GNOME that 1.6 m3 of crude oil was floating on the sea (37.2 %), 2.4 m3 was evaporated into the atmosphere (55.8 %) and 0.3 m3 reached the coast of Oaxaca (7 %). This event affected 82 km of coastline, but the most important touristic areas as well as turtle nesting zones were not affected by this small crude oil spill. Results indicated that the marine-gas-pump number 3 in Salina Cruz, Oaxaca, is a punctual point of oil pollution in the Southern Mexican Pacific Ocean. Further work is needed to assess the economic and ecological damage to Oaxacan coast caused by this small oil spill.

Assessing over decadal biomass burning influence on particulate matter composition in subequatorial Amazon: literature review, remote sensing, chemical speciation and machine learning application.

A study on aerosols in the Brazilian subequatorial Amazon region, Tangará da Serra (TS) and Alta Floresta (AF) was conducted and compared to findings in an additional site with background characteristics (Manaus, AM). TS and AF counties suffer from intense biomass burning periods in the dry season, and it accounts for high levels of particles in the atmosphere. Chemical characterization of fine and coarse particulate matter (PM) was performed to quantify water-soluble ions (WSI) and black carbon (BC). The importance of explanatory variables was assessed using three machine learning techniques. Average concentrations of PM in AF and TS were similar (PM2.0, 17±10 µg m-3 (AF) and 16±11 µg m-3 (TS) and PM10-2.0, 13±5 µg m-3 (AF) and 11±7 µg m-3 (TS)), but higher than the background site. BC and SO4 2- were the prevalent components as they represented 27%-68% of particulates chemical composition. The combination of the machine learning techniques provided a further understanding of the pathways for PM concentration variability, and the results highlighted the influence of biomass burning for key sample groups and periods. PM2.0, BC, and most WSI presented higher concentrations in the dry season, providing further support for the influence of biomass burning.

Remote sensing evidence for population growth of isolated indigenous societies in Amazonia.

Isolated indigenous societies who actively avoid sustained peaceful contact with the outside world are critically endangered. Last year, "Tanaru", the lone surviving man of his tribe for at least 35 years, died in Southwest Amazonia, marking the latest cultural extinction event in a long history of massacres, enslavement, and epidemics. Yet in the upper reaches of the Amazon Basin, dozens of resilient isolated tribes still manage to survive. Remote sensing is a reliable method of monitoring the population dynamics of uncontacted populations by quantifying the area cleared for gardens and villages, along with the fire detections associated with the burning of those clearings. Remote sensing also provides a method to document the number of residential structures and village fissioning. Only with these longitudinal assessments can we better evaluate the current no-contact policies by the United Nations and governments, along with the prospects for the long-term survival of isolated tribes. While the world's largest isolated indigenous metapopulation, Pano speakers in Acre, Brazil, appears to be thriving, other smaller isolated populations disconnected from metapopulations continue to be extremely vulnerable to external threats. Our applied anthropological conservation approach is to provide analyses of publicly available remote sensing datasets to help inform policies that enhance the survival and well-being of isolated cultural groups.

Quantitative assessment of soil condition, basic environmental factors and productivity of Linum usitatissimum in the steppe zone of Kazakhstan using the remote sensing method.

The influence of environmental factors, such as lack of water and uneven rainfall, depletion of nutrients in the soil and reduced soil fertility, planting patterns and plant density, uneven growth stages, are the main limiting factors that hinder the growth of agricultural production in arid regions. The aim of the study was to assess the potential of Sentinel-2 to quantify soil conditions, which can improve the understanding of spatiotemporal dynamics in organic agriculture in the steppe zone of Kazakhstan and improve productivity management of Linum usitatissimum. In the course of the research, the influence of individual factors of the general environmental impact, such as the influence of humidity, meteorological conditions, the content of individual nutrient components of the soil on the yield, was studied. The meteorological conditions in this region in 2021 and the data of agrochemical analysis of the soil on which the oilseed crop was grown were evaluated. Sentinel-2 satellite images were used to determine the NDVI and GNDVI indices. A high content of nitrate nitrogen (12.3-16.2 mg/kg), a very low level of available phosphorus (3-10 mg/kg), and a high content of potassium (289-420 mg/kg) were found in the soil. A low content of humus (2.68-3.31%) and sulfur (1.1-4.9 mg/kg) was found. A study of the NDVI growth index showed that the highest value was reached by the period of July 20, 2021. After this period, a decrease in the vegetation index was observed. In conditions of severe drought, this change occurred earlier than under favorable conditions, and correlated with low flax yield (1.6-6.9 c/ha). This study demonstrates the potential of Sentinel-2 for quantifying soil conditions, which not only improves our understanding of spatial-temporal dynamics and environmental components in organic agriculture in the steppe zone of Kazakhstan, but also improves the management of Linum usitatissimum productivity.

A biomass map of the Brazilian Amazon from multisource remote sensing.

The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha-1, a mean of 174 Mg ha-1, and a standard deviation of 102 Mg ha-1. This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions.

Remote sensing environmental indicators for monitoring spatial and temporal dynamics of weather and vegetation conditions: applications for Brazilian biomes.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm and the radiation use efficiency (RUE) model were coupled to test large-scale remote sensing environmental indicators in Brazilian biomes. MODIS MOD13Q1 reflectance product and gridded weather data for the year 2016 were used to demonstrate the suitability of the algorithm to monitor the dynamics of environmental remote sensing indicators along a year in the Brazilian biomes, Amazon, Caatinga, Cerrado, Pantanal, Atlantic Forest, and Pampa. Significant spatial and temporal variations in precipitation (P), actual evapotranspiration (ET), and biomass production (BIO) yielded differences on water balance (WB = P-ET) and water productivity (WP = ET/BIO). The highest WB and WP differences were detected in the wettest biomes, Amazon, Atlantic Forest, and Pampa, when compared with the driest biome, Caatinga. Rainfall distribution along the year affected the magnitude of the evaporative fraction (ETf), i.e., the ET to reference evapotranspiration (ET0) ratio. However, there was a gap between ETf and WB, which may be related to the time needed for recovering good soil moisture conditions after rainfalls. For some biomes, BIO related most to the levels of absorbed photosynthetically active radiation (Amazon and Atlantic Forest), while for others, BIO followed most the soil moisture levels, depicted by ETf (Caatinga, Cerrado, Pantanal, and Pampa). The large-scale modeling showed suitability for monitoring the water and vegetation conditions, making way to detect anomalies for specific periods along the year by using historical images and weather data, with strong potential to support public policies for management and conservation of natural resources and with possibilities for replication of the methods in other countries.

Remote Sensing and Ecological Variables Related to Influenza A Prevalence and Subtype Diversity in Wild Birds in the Lluta Wetland of Northern Chile.

The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index-NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers.

Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum.

The objectives of this study were to use machine learning algorithms to establish a model for estimating the evapotranspiration fraction (ETf) using two data input scenarios from the spectral information of the Sentinel-2 constellation, and to analyze the temporal and spatial applicability of the models to estimate the actual evapotranspiration (ETr) in agricultural crops irrigated by center pivots. The spectral bands of Sentinel 2A and 2B satellite and vegetation indices formed the first scenario. The second scenario was formed by performing the normalized ratio procedure between bands (NRPB) and joining the variables applied in the first scenario. The models were generated to predict the ETf using six regression algorithms and then compared with ETf calculated by the Simple Algorithm For Evapotranspiration Retrieving (SAFER) algorithm, was considered as the standard. The results possible to select the best model, which in both scenarios was Cubist. Subsequently, ETf was estimated only for the center pivots present in the study area and the classification of land use and cover was accessed through the MapBiomas product. Land use was necessary to enable the calculation of ETr in each scenario, in the center pivots with sugarcane and soybean crops. ETr was estimated using two ETo approaches (EToBrazil and Hargreaves-Samani). It was found that the Hargreaves-Samani equation overestimated ETr with higher errors mainly for center pivots with sugarcane, where systematic error (MBE) ranged from 0.89 to 2.02 mm d-1. The EToBrazil product, on the other hand, presented statistical errors with MBE values ranging from 0.00 to 1.26 mm d-1 for both agricultural crops. Based on the results obtained, it is observed that the ETr can be monitored spatially and temporally without the use of the thermal band, which causes the estimation of this parameter to be performed with greater temporal frequency.

Remote sensing monitoring of mining tailings in the fluvial-estuarine-coastal ocean continuum of the Lower Doce River Valley (Brazil).

Water clarity is a key parameter of aquatic ecosystems impacted by mining tailings. Tracking down tailings dispersion along the river basin requires a regional monitoring approach. The longitudinal fluvial connectivity, river-estuary-coastal ocean, and the lateral connectivity, river-floodplain-alluvial lakes are interconnected by hydrological flows, particularly during high fluvial discharge. The present study aims to track the dispersal of iron ore tailing spill, from the collapse of the Fundão dam (Mariana, MG, Brazil), on November 5, 2015, in the Lower Doce River Valley. A semi-empirical model of turbidity data, as a water clarity proxy, and multispectral remote sensing data (MSI Sentinel-2), based on different hydrological conditions and well-differentiated water types, yielded an accuracy of 92%. Five floods (> 3187m3 s-1) and five droughts (< 231m3 s-1) events occurred from 2013 to 2020. The flood of January 2016 occurred one month after the mining slurries reached the coast, intruding tailings on some alluvial and coastal plain lakes with highly turbid waters (> 400 NTU). A fluvial plume is formed in the inner shelf adjoining the river mouth on high flow. The dispersion of river plume was categorized as plume core (turbidity > 200 NTU), plume core and inner shelf waters (100-199 NTU), other shelf water (50-99 NTU), and offshore waters (< 50 NTU). Fluvial discharge and local winds are the main drivers for river plume dispersion and transport of terrigenous material along the coast. This work provides elements for evaluating the impact of mining tailings and an approach for remote sensing regional monitoring of surface water quality.