INTEGRAÇÃO DE DADOS TM/LANDSAT E MEDIDAS IN SITU PARA ESTIMATIVA DE SEDIMENTOS EM SUSPENSÃO EM RIOS AMAZÔNICOS: UM ESTUDO DE VIABILIDADE

A base de dados in situ de concentração de sedimentos em suspensão (CSS) nos rios amazônicos possui baixa densidade de amostras (2,72 coletas por ano) distribuídas de modo não sistematizado. O uso de imagens de satélite pode aumentar essa densidade devido à relação direta entre CSS e a reflectância espectral da água na região do visível-infravermelho próximo. O objetivo desse trabalho é, portanto, avaliar a viabilidade de integração de dados do sensor TM/Landsat 5 (TM-5) e dados sedimentológicos para gerar modelos de estimativa da CSS de rios amazônicos. Analisam-se as limitações espaciais e radiométricas do sensor TM-5 e seu impacto sobre os modelos para diferentes tipos de água. Avalia-se também a frequência de dados in situ e orbitais e o ganho de informação com o uso de dados de sensoriamento remoto. Os resultados mostram que apenas 35 das 97 estações de coleta de CSS podem ser utilizadas. Apesar do reduzido número de amostras, os resultados mostram que nos casos mais restritivos de cobertura de nuvens, poderia haver um aumento de até 127% na base de dados se fossem desenvolvidos modelos empíricos baseados em imagens de satélite. Conclui-se que existem dados suficientes para testar o desenvolvimento de modelos empíricos e semi-empíricos baseados na integração de dados TM-5 e medidas in situ de modo a aumentar a densidade de dados de CSS dos rios da bacia amazônica. A próxima etapa dessa pesquisa é então o desenvolvimento e teste desses modelos

Sunglint correction in airborne hyperspectral images over inland waters

This study assessed sunglint effects in airborne high spatial and high spectral resolution images acquired by the SpecTIR sensor under different view-illumination geometries over the Brazilian Ibitinga reservoir (Case II waters). These effects were corrected using the Goodman et al. (2008) and the Kutser et al. (2009) methods, and a variant that used the continuum removal technique to calculate the oxygen absorption band depth. The performance of each method to removing sunglint effects was evaluated by a quantitative analysis of pre- and post-sunglint correction reflectance values (residual reflectance images). Furthermore, the analysis was supported by inspection of the reflectance differences along transects placed over homogeneous masses of waters or over specific portions of the scenes affected and non-affected by sunglint. Results showed that the algorithm of Goodman et al. (2008) produced better results than the other two methods, as it approached to zero the amplitude of the reflectance values between homogenous water masses free and contaminated by sunglint. The Kutser et al. (2009) method had also good performance, except for the most contaminated sunglint portions of the scenes. When the continuum removal technique was incorporated to the Kutser et al. (2009) method, results varied with the scene and were more sensitive to atmospheric correction artifacts and instrumental signal-to-noise ratio

Expressive fluxes over Amazon floodplain revealed by 2D hydrodynamic modelling

Water fluxes in the Amazon River floodplain affect hydrodynamic and ecological processes from local to global scales, but they remain poorly understood due to difficult accessibility and limited data. We characterized the hydrodynamics of eight floodplain units of the central Amazon River (40000 km2) using the 2D hydraulic model HEC-RAS. High resolution modelling (∼400 m) improved the representation of river and floodplain discharge, water surface elevation (77 cm accuracy) and flood extent (∼80% - high water period, ∼52% - low water period) compared to past modelling studies. Our results show that floodplain flows during floods are very intense with upstream inflow and downstream outflow of the floodplain units. These gross flows are much larger than the net flows, with values of up to 20% of the Amazon River discharge and residence time around 6 days during floods (several months during low water period). Water extent did not show strong interannual variability during floods as the volume stored in the floodplain did, possibly due to topographic constraints. Significant hysteresis in flood extent and volume, and active and storage zones on the floodplain highlight the complexity of floodplain hydrodynamics. Extreme floods strongly impacted the onset and duration of the flood by up to one month and, consequently on duration of high water renewal period with the river. Our characterization is important to assess the effects of extreme floods on riverine communities, understand nutrient and sediment variations in the floodplain, and characterize the export of water, sediment, and carbon flux to the ocean from the world's largest hydrological system

Study of point and diffuse pollution in the Funil reservoir hydropower plant contribution basin using spatially distributed Geographic Information System modeling

This study evaluated the potential polluter of the Funil Reservoir Contribution Basin (BCRF), located in the Paraíba do Sul River basin, considering the generation of nutrient loading, nitrogen (N) and phosphorus (P), due to point and diffuse sources from a distributed modeling using Geographic Information System (GIS). Loads and annual average concentrations of these nutrients were generated from the coupling of empirical equations, in GIS, considering spatial information such as land use/cover, population living in the basin and long period average annual flow obtained by equations of rainfall runoff. The results indicated that 80% of the total load of nitrogen was generated from point sources and 20% from diffuse sources, while 89.1% were originated from point sources and 10.9% from diffuse sources for the corresponding total load of phosphorus. The model estimated adequately the concentration when compared to the ​​observed values and it was able to detect the trend of changes in nutrient concentrations along different sections of the BCRF highlighting significant correlations between the observed and simulated concentrations of phosphorus and nitrogen with R²=0.96 (p<0.01) and R²=0.70 (p<0.01), respectively.Este estudo avaliou o potencial poluidor da bacia de contribuição do reservatório de Funil (BCRF), localizado na bacia hidrográfica do rio Paraíba do Sul, considerando a geração da carga de nutrientes, nitrogênio (N) e fósforo (P), por fontes pontuais e difusas, a partir de uma modelagem distribuída utilizando Sistema de Informação Geográfica (SIG). As cargas e concentrações médias anuais desses nutrientes foram geradas a partir do acoplamento de equações empíricas, em SIG, considerando informações espaciais de uso e cobertura do solo, população residente na bacia e vazão média anual de longo período, obtida por equações do tipo chuva vazão. Os resultados indicaram que 80% da carga total de nitrogênio foram provenientes de fontes pontuais e 20% de fontes difusas, enquanto que, da carga total de fósforo, 89,1% foram originadas de fontes pontuais e 10,9% de fontes difusas. As concentrações de nutrientes estimadas pelo modelo empírico apresentaram bons ajustes em relação aos valores observados de fósforo e de nitrogênio no rio Paraíba do Sul, com R²=0,96 (p<0,01) e R²=0,70 (p<0,01), respectivamente. Dessa forma, o modelo foi capaz de detectar, de forma significativa, a tendência das variações nas concentrações de nutrientes ao longo de diferentes trechos da BCRF

Simulation of spectral bands of the MERIS sensor to estimate chlorophyll-a concentrations in a reservoir of the semi-arid region

Nowadays, the monitoring of water is essential for the sustainability and better management of water resources. The use of remote sensing data is important, since it allows evaluation of dynamic problems in aquatic systems, such as the eutrophication of bodies of water and suspended sediment. The aim of this study was to estimate chlorophyll-a concentrations in a reservoir of the semi-arid region of Brazil using simulated orbital-sensor data, as an aid in the management of water resources. The study area corresponded to the Orós reservoir, in the State of Ceará, Brazil. Water samples for analysis of the chlorophyll-a and measurements of the spectral radiance of the aquatic system were collected from 20 points. The radiance was measured by spectroradiometer. The data were collected in June and August of 2011. The model using three bands of the MERIS sensor (7, 9 and 10) presented an R2 of 0.84. For the two-band model (7 and 9), the value of R2 was 0.85. The waters of the Orós reservoir were all classified as eutrophic. The main optically active component in modelling the shape of the spectra was chlorophyll-a. The models showed a mean absolute error (MAE) of 3.45 and 3.61 μg L-1 for the three- and two-band models respectively. The models displayed high coefficients of determination, i.e. the simulations show the feasibility of estimating chlorophyll-a concentration from the data of the MERIS orbital sensor

Amazon hydrology from space : scientific advances and future challenges

As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite-based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin-scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes-Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology-oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space-time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio