A socioeconomic and natural vulnerability index for oil spills in an Amazonian harbor: A case study using GIS and remote sensing

Andrade, Milena Marília Nogueira de; Szlafsztein, Cláudio Fabian; Souza-Filho, Pedro Walfir M.; Araújo, Adrilayne dos Reis; Gomes, Monique Kelly Tavares

doi:10.1016/j.jenvman.2010.04.016

Cited by 60 publications

(30 citation statements)

References 6 publications

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“…The mangroves have ecological and socioeconomic importance due to communities' livelihoods, and they are considered a protected ecosystem. The main activities are fishing, collecting shrimp and crabs (Andrade et al, 2010;Andrade et al, 2009). …”

Section: Remote Sensing and Coastal Environmental Sensitivity In Amazonmentioning

confidence: 99%

“…However, the use of Ikonos images is limited when cloud cover is higher than 25%. As a result the images are mostly inadequate between March and June (Andrade et al, 2010). On the other hand, biological and socioeconomic resources, risk areas and oil spill hazard zones of storage and platform transportation can be better identified and delimitated (Rodrigues & Szlafsztein, 2010;Andrade et al, 2009).…”

Section: Remote Sensing and Coastal Environmental Sensitivity In Amazonmentioning

confidence: 99%

“…Oil spill environmental sensitivity maps, adapted to the peculiarities of the Amazonian region (Souza Filho et al, 2004) were drawn at tactical scales through the use of Radarsat and Landsat sensors (Gonçalves et al, 2009;Teixeira & Souza Filho 2009;Boulhosa & Souza Filho, 2009), and operational scale through the use of High Resolution remote sensing (Andrade et al, 2010;Rodrigues & Szlafsztein 2010;Andrade et al, 2009). Over the past decade were reached advances in identification and assessment of sensitivity through spatial maps, the impacts to oil spill analyses, and oil spill risk in Amazonian coastal.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing and Environmental Sensitivity for Oil Spill in the Amazon, Brazil

Andrade¹,

Szlafsztein²

2012

Remote Sensing - Applications

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Section: Remote Sensing and Coastal Environmental Sensitivity In Amazonmentioning

confidence: 99%

Section: Remote Sensing and Coastal Environmental Sensitivity In Amazonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote Sensing and Environmental Sensitivity for Oil Spill in the Amazon, Brazil

Andrade¹,

Szlafsztein²

2012

Remote Sensing - Applications

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“…O mapeamento da ZCA tem sido abordado por sensores ópticos e SAR, com destaque aos estudos das unidades de paisagem com suscetibilidade natural a erosão e derramamento de óleo (DE ANDRADE et al, 2010;FILHO et al, 2009;GUIMARÃES, 2011b;SOUZA-FILHO, 2012); caracterização sedimentológica, espectral, textural e parâmetros biofísi-cos de alvos (COUGO et al, 2015;FRANÇA et al, 2007;GUIMARÃES, 2011a;SANTOS, 2006;TEIXEIRA, 2011); reconhecimento de ambientes úmidos costeiros e mensuração espacial de sistemas erosivos/deposicionais (BATISTA et al, 2007;FRANÇA;SOUZA-FILHO, 2003;NASCIMENTO et al, 2013;SANTOS, 2006;SOUZA-FILHO et al, 2011;SOUZA FILHO et al, 2006). Esses estudos permitiram inferir grandes extensões contínuas de manguezais, balanço sedimentar de acrescional a equilibrado e discriminação de ambientes, devido à boa performance por imagens ópticas multiespectrais, banda L polarizada e textura.…”

Section: Introductionunclassified

Aplicação De Dados Ers, Envisat E Sentinel Para Detecção De Mudanças Nos Ambientes Costeiros Amazônicos

Guimarães

Narvaes

Galo

2017

Rev. Bras. Geomorfol.

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Ambientes costeiros são naturalmente vulneráveis a processos morfodinâmicos como erosão e deposição de sedimentos e essa condição se intensifica em condições tropicais úmidas. A costa amazônica é marcada pela elevada descarga de sedimentos e água doce sob influência do rio Amazonas e caracterizada por uma ampla plataforma continental, extensas planícies inundáveis e planaltos mais elevados formados em rochas sedimentares mais antigas. O presente estudo apresenta uma abordagem para mapeamentos morfológicos e detecção de mudanças em zonas costeiras, baseado em dados de radar de abertura sintética (Synthetic Aperture Radar-SAR) em banda C, em áreas com intensa dinâmica costeira e de constante cobertura de nuvens. O método se baseia na cadeia de processamento SAR em amplitude aplicada a um perfil temporal de dados ERS-1/2, ENVISAT e SENTINEL-1A entre 1992 a 2015 e fornecidos pela European Space Agency (ESA). Os ambientes costeiros foram discriminados pela informação primária do coeficiente de retroespalhamento, e dados auxiliares derivados da textura de Haralick e índice de forma. As mudanças costeiras detectadas no período se estenderam por uma área de 646,15 km2, com balanço sedimentar erosivo em -2,32 km2 e sob a taxa de -0,10 km2.ano-1. O perfil temporal de mudanças costeiras está em equilíbrio e apresentou condição erosiva no período de 1992 a 1993 e 1996 a 1999 e, enquanto que no período de 1993 a 2006 e 2006 a 2015 ocorreu uma condição de acresção. A acurácia do mapeamento dos ambientes e das mudanças costeiras resultaram, respectivamente, em exatidão global de 78,35% e 48,54%, e coeficiente Kappa de 0,65 e 0,32. Os dados ERS, ENVISAT e SENTINEL permitiram mapear satisfatoriamente as mudanças morfológicas no relevo plano e dinâmico da costa amazônica, devido à disponibilidade de uma série histórica de imagens SAR em banda C, com polarização vertical paralela e incidência de íngreme a rasante. Tal aplicação foi essencial ao monitoramento sistemático de linha de costa em escala espacial regional (1:250.000) e escala temporal eventual (anual a decadal)

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“…These areas are experiencing various environmental impacts and climate change associated with local, regional and global issues. These areas are highly vulnerable to threats from both natural processes and socio-economic activities (Andrade, et al, 2010;. Present research on vulnerability is focus on natural disasters and climate related impacts such as droughts, floods, see level rise and cyclones (Lankao, 2010;), but not on non-climatic parameters such as elevation, slope, aspects, vegetation and socio-economic activities (Nicholls, et.…”

Section: Introductionmentioning

confidence: 99%

Exposer Intensity, Vulnerability Index And Landscape Change Assessment In Olomouc, Czech Republic

Boori

Voženílek

Choudhary

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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THEME: DISA -Natural disasters monitoring, warning and response.KEY WORDS: Vulnerability Index, Exposer Index, Sensitivity Index, Adaptive Capacity Index ABSTRACT:The objective of this study is vulnerability and exposer intensity due to land use change in Olomouc, Czech Republic. Vulnerability assessment with exposer intensity to land use/cover change is an important step for enhancing the understanding and decision-making to reduce vulnerability. This study work includes quantification of Exposure Index (EI), Sensitivity Index (SI) and Adaptive Capacity Index (AI). EI is based on intensity of land use/cover change, SI and AI based on natural factors such as elevation, slope, vegetation and land use/cover. Vulnerability Index (VI) derived on the quantification of SI and AI and compared from 1991, 2001 and 2013. Comparing of EI and VI for last three decades, settlements have highest vulnerability index due to high socio-economic activities and water have lowest vulnerability index due to less human interferences. Agriculture has highest exposer index and second highest vulnerability, which show its high rate of exploitation and production. In the study areas, vulnerability tends to increase with the increase of exposure to land use change, but can peak off once the land use start to benefit socio-economically from development. Only in this way we can enhance the adaptive capacity of study area to use change of land.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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A socioeconomic and natural vulnerability index for oil spills in an Amazonian harbor: A case study using GIS and remote sensing

Cited by 60 publications

References 6 publications

Remote Sensing and Environmental Sensitivity for Oil Spill in the Amazon, Brazil

Remote Sensing and Environmental Sensitivity for Oil Spill in the Amazon, Brazil

Aplicação De Dados Ers, Envisat E Sentinel Para Detecção De Mudanças Nos Ambientes Costeiros Amazônicos

Exposer Intensity, Vulnerability Index And Landscape Change Assessment In Olomouc, Czech Republic

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