A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil.

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.

Análise de múltiplas imagens para detecção de bordas do Reservatório de Sobradinho, Bahia / Analysis of multiple images for edge detection of Sobradinho Reservoir, in Bahia, Brazil

RESUMO Neste trabalho, foram realizados testes de mapeamento de borda do reservatório de Sobradinho, Bahia, Brasil, com imagens TM LANDSAT-5, MDE-SRTM, borda do SRTM-SWBD e ASTER-GDEM-VNIR. A segmentação da imagem mosaicada da banda 4 do sensor TM serviu como referência comparativa de borda do reservatório de Sobradinho em relação às outras imagens; observou-se que o mais próximo era a borda SRTM-SWBD da NASA/NGA. Foram elaborados perfis altimétricos, entre as bordas MDE-SRTM, e radiométricos, na banda B4 segmentada. Constatou-se: baixa penetração do imageamento do MDE-SRTM nas reentrâncias das bordas do reservatório. Foram constatadas discrepâncias entre a borda SRTM-SWBD (SWB) e a segmentação da banda 4.

ABSTRACT Edge mapping tests of Sobradinho reservoir, Bahia, Brazil, were carried out on this work with TM Landsat-5 and MDE-SRTM images and SRTM-SWBD and ASTER-GDEM-VNIR edges. Mosaic of image segmentation band 4 TM sensor aided as comparative reference edge of the Sobradinho reservoir in relation to other images, and it was observed that the closest was the SRTM-SWBD edge of the NASA/NGA. Altimetry profiles were developed between MDE-SRTM edges, and also radiometric profiles, on the targeted band B4. It was found: low penetration of MDE-SRTM imaging in recessed edges of the reservoir, and discrepancies between the SRTM-SWBD edge (SWB) and the segmentation of the band 4.

Geotecnologias na gestão de reservatórios: uma revisão e uma proposta de integração / Geotechnologies on water reservoir management: a review and an integration proposal

RESUMO Este trabalho teve como foco apresentar uma visão geral dos sistemas de informações geográficas (SIGs) e do sensoriamento remoto (SR), com ênfase nas suas aplicações para obtenção de informações da massa d'água. Essas informações são integradas ao Quadro da Diretiva da Água, e nesse sentido, busca-se propor a ligação dessas informações à Resolução nº 357/2005, de forma a servir de ferramenta para a tomada de decisão em uma gestão estratégica da qualidade da água, principalmente em grandes reservatórios. Assim, é realizada uma revisão integrando as geotecnologias à ciência limnológica e à gestão de reservatórios, ressaltando os possíveis potenciais da interação interdisciplinar. Por fim, é esquematizado um modelo integrando SIGs, SR e limnologia para o monitoramento e a gestão dos padrões de qualidade da água conforme a Resolução nº 357/2005.

ABSTRACT An overview of geographical information system (GIS) and remote sensing (RS) is the main focus of this work, that emphasizes their application to obtain water body information. This information is integrated to the Framework Directive, and in this way, seeks to propose the linkage of these information to the Resolution 357/2005, in order to serve as a tool for decision making in strategic management of water quality mainly in large reservoirs. Thus, a review that integrates geotechnology to limnological science and to reservoirs management was done, highlighting the possible potential of interdisciplinary integration. At the end, an integration model of GIS, RS and limnology is schematized for monitoring and management of water quality standards according to the Resolution 357/2005.

Comportamento espacial da clorofila-a no reservatório de Itaparica, rio São Francisco / Spatial behavior of chlorophyll-a in Itaparica reservoir, São Francisco River

RESUMONa análise do estado trófico de um corpo hídrico torna-se fundamental o conhecimento da concentração de clorofila-a. Esse trabalho tem como objetivo determinar e avaliar o comportamento da clorofila-a no reservatório de Itaparica, localizado no sub-médio São Francisco. Para esse fim, utilizou-se de imagem Landsat-TM, na qual foram utilizadas as bandas de 1 a 5 e 7. O modelo aplicado foi escrito em linguagem LEGAL-SPRING 5.2. A partir da imagem da clorofila-a realizou-se o fatiamento do corpo hídrico em seis classes concentração. O valor mínimo foi de <1 mg/L e o maior foi de 249,5 mg/L. As classes que obtiveram maior área foram de 0 a 5 mg/L com 27,4%, seguida da classe 5 a 10 mg/L com 24,6% da área total do reservatório, conforme observado por histograma. Por meio da análise gráfica de pontos situados ao longo do reservatório verifica-se que a concentração da clorofila-a aumenta da região fluvial para região lacustre e a partir dos contatos dos riachos com o reservatório. Em futuros estudos há necessidade de se validar os valores encontrados com dados de campo de forma a verificar a precisão do mapeamento para o reservatório em estudo, observando dia e horário da passagem do sensor.

ABSTRACTIn the analysis of trophic state of the water body is fundamental to know chlorophyll-a concentration. Thus, this work has as main aim to determinate and to assess the behavior of chlorophyll-a in the Itaparica reservoir, São Francisco river. This way, we used Landsat-TM imagery, in which it was used bands from 1 to 5 and 7. The algorithm used was written in LEGAL/SPRING 5.2. From the chlorophyll-a result was held slicing the water body in six concentration classes. As observed by histogram, the minimum value of Chl-a was < 1 mg/L and the highest was 249.5 mg/L. The classes that had the biggest area were Classe 01 (0-5 mg/L) with 27.4%, followed by Classe 02 (5-10 mg/L) with 24.6% of the total area of the study area. Through graphical analysis of points located along the reservoir it was possible to verify that chlorophyll concentration augmented from fluvial to lacustrine region and from the contact of streams with reservoir. In the next studies there is a need to validate the values with field data in order to verify the mapping accuracy in this reservoir, taking into account the day and also the transit time of the sensor.

Image segmentation of ovitraps for automatic counting of Aedes Aegypti eggs.

The Aedes Aegypti mosquito is the vector of the most difficult public health problems in tropical and semi-tropical world: the epidemic proliferation of dengue, a viral disease that can cause human beings death specially in its most dangerous form, dengue haemorrhagic fever. One of the most useful methods for mosquito detection and surveillance is the ovitraps: special traps to collect eggs of the mosquito. It is very important to count the number of Aedes Aegypti eggs present in ovitraps. This counting is usually performed in a manual, visual and non-automatic form. This work approaches the development of automatic methods to count the number of eggs in ovitraps images using image processing, particularly color segmentation and mathematical morphology-based non-linear filters.