Impactos da Elevação do Nível Médio do Mar sobre o Ecossistema Manguezal: A Contribuição do Sensoriamento Remoto e Modelos Computacionais

O processo de mudança climática resultante das atividades humanas é muito provável e dentre suas consequências, a elevação do nível médio do mar é a maior preocupação para as zonas costeiras. Devido sua posição entre o mar e o continente, o ecossistema manguezal é particularmente vulnerável às variações do mar. No Brasil, há um grande desafio no que se refere ao entendimento do padrão de resposta do manguezal a elevação do nível do mar, uma vez que a Amazônia Legal tem a maior área continua de manguezal do planeta e o país ainda apresenta a segunda maior área continua de manguezal do mundo. O presente estudo tem como objetivo discutir duas abordagens metodológicas que podem ser úteis para estudar os impactos da elevação do nível médio do mar sobre o ecossistema manguezal. As ferramentas metodológicas discutidas são: o sensoriamento remoto e modelos computacionais. Respectivamente, estas metodologias contribuem para mapeamento de florestas de mangues e simulação do padrão de resposta do manguezal a eventuais cenários de elevação do nível do mar. Ambas as abordagens metodológicas possuem vantagens e desvantagens, e podem ser utilizadas de forma conjunta para uma melhor compreensão dos impactos ao ecossistema manguezal

Lobster (Panulirus argus) captures and their relation with environmental variables obtained by orbital sensors for Cuban waters (1997-2005)

Dados de captura da lagosta Panulirus argus na plataforma cubana foram comparados com concentrações de clorofila (Chl a) e valores de Temperatura de Superfície do Mar (TSM) obtidos pelos sensores Sea Viewing Wide Field of view Sensor (SeaWIFS) e Advanced Very High Resolution Radiometer (AVHRR), respectivamente. Uma análise de correlação cruzada foi realizada entre as anomalias padronizadas das variáveis ambientais (Chl a e TSM) e as anomalias padronizadas de capturas da lagosta para cada zona de pesca no período 1997-2005. Para as águas profundas adjacentes às zonas de pesca não foi observada uma sazonalidade evidente da Chl a. De forma geral, os menores valores de Chl a ocorreram ao sul da Ilha. Na maioria das zonas de pesca, a captura da lagosta apresentou os maiores coeficientes de correlação com valores de Chl a com defasagem de dois e três anos. Já em relação à análise com dados de TSM, os coeficientes de correlação cruzada apresentaram valores significativos apenas a partir de uma defasagem de 1,5 anos para praticamente todas as zonas de pesca. Neste estudo confirma-se que, em águas cubanas, correlações cruzadas significativas entre estas duas variáveis ambientais medidas por satélite e as capturas da lagosta espinhosa ocorrem principalmente durante o ciclo de vida planctônico desta espécie.Chlorophyll concentrations (Chl a) data obtained from the Sea Viewing Wide Field of View Sensor (SeaWIFS) ocean color monthly images, Sea Surface Temperature (SST) pathfinder data obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensors, and lobster (Panulirus argus) captures at the Cuban shelf were examined in order to analyze their spatial and temporal variability. A cross-correlation analysis was made between the standardized anomalies of the environmental variables (Chl a and SST) and the standardized anomalies of lobster captures for each fishery zones for the period between 1997 and 2005. For the deep waters adjacent to the fishing zones it was not observed a clear Chl a seasonality and on average the lowest values occurred south of the Island. It is with the three years lag that Chl a had the greatest numbers of significant correlation coefficients for almost all fishing zones. However, the cross-correlation coefficients with SST showed higher values with 1,5 year lag at all zones. Since the two environmental variables obtained by satellite sensors (SST and Chl a) influence the lobsters mainly during the planktonic life cycle, the cross-correlation with lobster captures begin to show significant indexes with lags of 1.5 years or more

COMPARTIMENTAÇÃO E CARACTERIZAÇÃO BIO-ÓPTICA POR SATÉLITE DAS ÁGUAS DE SUPERFÍCIE DO RESERVATÓRIO DE ITUMBIARA (GO).

Este trabalho tem como objetivo caracterizar a bio-óptica e a compartimentação das águas de superfície do reservatório hidrelétrico de Itumbiara (GO), do sistema FURNAS. Dados in situ de turbidez e concentração de clorofila coletados concomitantes com dados do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) foram utilizados para gerar modelos empíricos de estimativa destas variáveis limnológicas. A aplicação destes modelos e de testes estatísticos permitiu compartimentar o reservatório em 3 regiões: Corpo do reservatório, Braço do rio Araguari e uma terceira formada pelos braços dos rios Corumbá e Paranaíba. Uma série temporal de 18 meses de imagens MODIS médias de 8 dias foi analisada pela técnica de Modelo Linear de Mistura Espectral (MLME) de modo a diferenciar os componentes opticamente ativos (COA) nas águas do reservatório. Os resultados obtidos por esta análise indicaram que no período de seca há dominância de matéria orgânica dissolvida colorida e no período de cheia houve variação da dominância dos COA. O uso de técnicas de sensoriamento remoto para estimativas indiretas de variáveis limnológicas como turbidez e clorofila se mostrou eficiente para a caracterização e compartimentação das águas superficiais de um reservatório tropical

APLICAÇÃO DA TÉCNICA DE MODELO LINEAR DE MISTURA ESPECTRAL PARA O MAPEAMENTO DA PLUMA DO RIO AMAZONAS

This paper aims to verify the applicability of Spectral Mixture Analysis (SMA) for mapping the plume of the Amazonas River, a feature of great importance for the coastal dynamics at the South-American Northeastern coast. Remote sensing reflectance data acquired by Sea-viewing Wide Field-of-view Sensor (SeaWiFS) were utilized to identify 5 water masses with different spectral and color characteristics. Through one SeaWiFS image were identified 5 water masses with different spectral and color characteristics, of what mean spectral signatures were obtained. The 5 types of water masses were classified according to its spectral characteristics, of what demonstrated typical behavior of waters with (i) suspended sediment, (ii) dissolved organic matter, (iii) oceanic water, and (iv and v) with different chlorophyll concentration. The mean spectral signatures were applied as endmembers in the SMA resulting in 5 fraction images. The fraction image related to the oceanic water allowed the best classification and mapping of the plume. The mapped area in the image shows the great extension (510 x 103 km2) that the plume can reach in the Northwestern direction from the Amazonas River mouth and into the Equatorial Atlantic, driven by the North Equatorial Counter Current and the North Brazil Current, respectively. Key words: Spectral mixture analysis. Amazon River plume. Remote sensing, SeaWiFS.Este artigo tem como objetivo verificar a aplicação da técnica de Modelo Linear de Mistura Espectral (MLME) para o mapeamento da pluma do Rio Amazonas, uma feição de grande importância na dinâmica costeira da região nordeste da América do Sul. Foram utilizados dados de reflectância de sensoriamento remoto obtidos pelo sensor Sea-viewing Wide Field-of-view Sensor (SeaWiFS) para identificar 5 massas de água com características espectrais e de cor distintas, das quais se obtiveram assinaturas espectrais médias. Os 5 tipos de massas de água foram classificados de acordo com suas características espectrais, sendo estas, típicas de águas com (i) sedimentos em suspensão, (ii) matéria orgânica dissolvida, (iii) água oceânica e (iv e v) com diferentes concentrações de concentrações de clorofila. As assinaturas espectrais médias foram utilizadas como endmembers no MLME o que resultou em 5 imagens fração. A imagem fração referente à água oceânica foi a que possibilitou a melhor identificação e mapeamento da pluma. A área mapeada na imagem mostrou a grande extensão (510 x 103 km2) que a pluma alcança na direção noroeste da desembocadura do Rio Amazonas e para o Oceano Atlântico sob o efeito da Contra Corrente Norte Equatorial e Corrente Norte do Brasil, respectivamente. Palavras chave: Modelo linear de mistura espectral. Pluma do Rio Amazonas. Sensoriamento remoto. SeaWiFS

Performance of Landsat-8 and Sentinel-2 Surface Reflectance Products for River Remote Sensing Retrievals of Chlorophyll-A and Turbidity

Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the more than 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to shipborne, underway in situ validation measurements. The land surface product shows the best agreement (4 percent Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36 percent higher reflectance values in the visible bands compared to aquatic methods (ACOLITE (Atmospheric Correction for OLI (Operational Land Imager) 'lite') and SeaDAS (Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System)) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 per steraradian) compared to Landsat-8 (0.001 per steraradian). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59 percent and 35 percent respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 531 percent depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance

Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic

Comparisons between in situ measurements of surface chlorophyll-a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic. In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3MRAD), Ocean Chlorophyll 4 bands (OC4v4RAD), and Ocean Chlorophyll 2 bands (OC2v4RAD). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3MSAT, and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01SAT), and CarderSAT. In the present work, MODIS, lidar and in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg/m3. In general, the empirical algorithms applied to the in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m3 (OC2v4RAD). The semi-analytical GSM01 algorithm applied to MODIS data performed better (rmse 0.28, rmse-L 0.08, mean diff. -0.01 mg/m3) than the Carder and the empirical OC3M algorithms (rmse 1.14 and 0.36, rmse-L 0.34 and 0.11, mean diff. 0.17 and 0.02 mg/m3, respectively). We find that rmsd values between MODIS relative to the in situ radiometric measurements are < 26%, i.e., there is a trend towards overestimation of RRS by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm

A compilation of global bio-optical in situ data for ocean-colour satellite applications - version three

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022)

A compilation of global bio-optical in situ data for ocean colour satellite applications – version three

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022)

PHYTOPLANKTON PRIMARY PRODUCTION AND BIOMASS ESTIMATE WITH OCEAN COLOUR REMOTE SENSING AND IN SITU DATA ALONG THE SOUTHEASTERN BRAZILIAN COAST

A biomassa e a produtividade primária fitoplanctônica da costa sudeste foram estimadas através de sensoriamento remoto da cor do oceano e dados in situ. Foram realizados quatro cruzeiros hidrográficos sazonais de mesoescala nas regiões de plataforma e talude continental durante os verões e invernos de 2001 e 2002. Com a descrição das características hidrográficas e distribuições de nutrientes inorgânicos dissolvidos, complementada pela análise de imagens orbitais (AVHRR e SeaWiFS), foi possível determinar os padrões de circulação da Corrente do Brasil, monitorar ressurgências costeiras e de quebra de plataforma, meandramentos e vórtices de mesoescala, assim como, a intrusão de águas frias, menos salinas e ricas em nutrientes vindas de sul sobre a plataforma continental, no inverno. A área de estudo foi dividida nos domínios de plataforma, talude-verão e talude-inverno, baseados em análises estatísticas da biomassa e produção primária fitoplanctônica integradas na zona eufótica. O domínio de plataforma não apresentou diferença sazonal devido ao processo de intrusão da ACAS sobre a plataforma. Este foi apontado como o principal processo de fertilização da zona eufótica nas águas de plataforma e talude tanto no verão como no inverno. Imagens da produtividade primária oceânica foram geradas pela primeira vez para a costa brasileira a partir de imagens da cor do oceano (SeaWiFS), utilizando-se um algoritmo semi-analítico não espectral, verticalmente homogêneo e dados fotossintéticos in situ obtidos simultaneamente. O avanço científico decorrente do presente trabalho é significativo, pois as estimativas de biomassa e produtividade primária fitoplanctônica através de imagens da cor do oceano e dados in situ coletados simultaneamente, ainda não haviam sido realizados na Zona Econômica Exclusiva brasileira. Comparações entre temperaturas da superfície do mar obtidas pelo AVHRR e medidas in situ (CTD) mostraram diferenças menores que 0,5ºC. O algoritmo OC4 apresentou o melhor desempenho entre os algoritmos testados (OC2, GSM01 e NN) para estimar a concentração de clorofila a, a partir de dados SeaWiFS, em relação às medidas fluorimétricas in situ, subestimando as concentrações mais baixas e superestimando as mais altas. Os algoritmos semi-analíticos de produção primária por satélite testados concordaram com as estimativas in situ (14C) por um fator de 2, nos melhores casos. As análises de regressão múltipla mostraram uma relação linear entre a produção primária e a biomassa fitoplanctônica integradas na coluna dágua. Uma abordagem alternativa baseada em uma rede neural artificial multicamada perceptron (12-5-1) foi testada como um modelo não linear para estimar a produção primária integrada na coluna dágua. A produção primária média para o período 2001-2002 foi estimada a partir de dados SeaWiFS em 386 gC m-2 a-1 e a produção primária potencial para a plataforma continental sudeste brasileira (PCSE) foi estimada em 0,06 Gt C a-1. O limite superior da produção pesqueira foi estimado considerando-se uma cadeia trófica com 2,8 níveis e uma eficiência trófica média de 10%. O resultado obtido foi cerca de 90 vezes maior que a captura média entre 1991 e 2000. Porém, se a produção pesqueira estimada for um limite superior que será reduzido a 10% ou 20% devido à acessibilidade ambiental, os recursos pesqueiros estariam limitados por alimento na PCSE.The phytoplankton biomass and primary production of the Southeastern Brazilian coast are estimated using ocean color remote sensing and in situ data. This study is based on four seasonal hydrographic cruises carried out during the summer and winter of 2001 and 2002, along the continental shelf and slope waters. The hydrographic and dissolved inorganic nutrients in situ measurements were complemented by the sea surface temperature from the Advanced Very High Resolution Radiometer (AVHRR) sensor and chlorophyll a estimates from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) sensor to identify relevant features of the Brazil Current (BC) circulation related to the biological dynamics of this oceanic region. Several meanders and vortices were observed throughout the period along the inshore frontal zone of the BC in association with break shelf upwelling. Coastal upwelling events were observed to encompass a region larger than normally indicated in the literature and anomalous upwelling events were observed during the winter. Cold, less saline, and nutrient rich waters were observed flowing northward over the shelf during the winter. Based on statistical analysis of water column integrated chlorophyll and primary production the study area was divided into 3 domains: shelf, summer-slope, and winter-slope. The shelf domain did not present a significant seasonal difference due to the intrusion of South Atlantic Central Water over the shelf. This intrusion is the main process for the euphotic zone productivity enhancement in the shelf and slope waters during the summer and winter. A non-spectral and vertically homogeneous semi-analytical algorithm was applied to the SeaWiFS ocean color data, which incorporate simultaneously measured in situ photosynthetic parameters. This is the first time that the phytoplankton primary production and biomass estimation over the Brazilian Economic Exclusive Zone were done through remote sensed ocean colour and simultaneously acquired in situ data. Comparisons between in situ measurements and AVHRR based estimates of sea surface temperature have shown differences lower than 0.5ºC. The OC4 algorithm performed better then other chlorophyll retrieval algorithms (OC2, GSM01 and NN) when compared with in situ fluorometric data. However, it overestimates chlorophyll a at higher concentrations and underestimates at lower concentration. The best performing ocean color remote sensing primary productivity algorithms tested agreed with the 14C-based estimates within a factor of 2. A multiple regression analysis showed linear relation between the water column integrated primary production and the integrated chlorophyll. An alternative approach based on a backpropagation multilayer perceptron artificial neural network (12-5-1) was tested to estimate the water column integrated primary production for non-linear phytoplankton production modeling. The mean primary production for the 2 year period in the South Brazilian Bight (SBB) estimated from SeaWiFS is 386 gC m-2 yr-1, and the potential primary production is 0.06 Gt C yr-1. The upper bound for sustainable fish yield was estimated using a food chain of 2.8 links and an average trophic efficiency of 10%. The result was ~90 times larger than the observed fish catch from 1991 through 2000. If this fish yield is an upper bound that will be decreased to 10% or 20% by environmental accessibility, the fishery resources in the SBB are likely to be food-limited