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Monitoramento de inundações com imagens multifuente de sensoriamento remoto na plataforma GEE

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, China

    Key Laboratory of Integrative Prevention of Air Pollution and Ecological Security of Henan province, Kaifeng 475004, China

    School of Geography and Environmental Science, Henan University, Kaifeng 475004, China

  • Email：lxy@henu.edu.cn
  • Introduction：刘小燕， 研究方向为遥感信息提取与城乡生态效应。E-mail： lxy@henu.edu.cn
LIU Xiaoyan123,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, China

    Key Laboratory of Integrative Prevention of Air Pollution and Ecological Security of Henan province, Kaifeng 475004, China

    School of Geography and Environmental Science, Henan University, Kaifeng 475004, China

  • Email：cuiyp@lreis.ac.cn
  • Introduction：崔耀平，研究方向为遥感大数据和空间优化。E-mail： cuiyp@lreis.ac.cn
CUI Yaoping123*,  
  • Affiliation：

    Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, China

    School of Geography and Environmental Science, Henan University, Kaifeng 475004, China

SHI Zhifang13,  
  • Affiliation：

    Institute of Geology and Exploration, Henan Geology and Exploration Bureau, Zhengzhou 450001, China

FU Yiming4,  
  • Affiliation：

    Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, China

    School of Geography and Environmental Science, Henan University, Kaifeng 475004, China

RUN Yadi13,  
  • Affiliation：

    Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, China

    School of Geography and Environmental Science, Henan University, Kaifeng 475004, China

LI Mengdi13,  
  • Affiliation：

    Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Kaifeng 475004, China

    School of Geography and Environmental Science, Henan University, Kaifeng 475004, China

LI Nan13,  
  • Affiliation：

    Henan Institute of Geophysics and Spatial Information, Zhengzhou 450009, China

LIU Sujie5

Resumo

Devido às restrições meteorológicas durante o período de inundações, os dados de sensoriamento remoto usados na avaliação das inundações são principalmente imagens de radar ou dados de fotografia aérea, enquanto o papel de muitos dados de luz noturna e imagens ópticas na avaliação das inundações ainda precisa ser mais explorado. Este estudo teve como objeto de pesquisa a cidade de Fuyang nos meses de julho e agosto, baseado em dados de satélite Sentinel-1, Sentinel-2 e Landsat 8, utilizando a plataforma de big data de sensoriamento remoto GEE (Google Earth Engine) para extrair informações de corpos d’água, e utilizando o Índice Total de Luz Noturna TNL (Total Night-time Light) e o Índice Composto de Luz Noturna CNLI (Compounded Night Light Index) construídos com dados de luz noturna para explorar a relação entre a variação dos corpos d’água e a luz noturna, a fim de monitorar e avaliar a dinâmica das inundações. Os resultados mostraram: (1) alterações significativas na distribuição dos corpos d’água na parte sul de Fuyang em julho e agosto, especialmente um aumento notável na área de corpos d’água na zona de armazenamento de Mengwa, com a área atingindo o valor máximo de 323 km2 em 31 de julho, o que é seis vezes maior que a área antes da inundação; subsequentemente, a cobertura da água mostrou uma tendência de diminuição, que correspondeu ao tempo de abertura e alívio do reservatório Wangjiaba. (2) A análise combinada dos índices de luz noturna TNL e CNLI para as variações da luz noturna em Fuyang revelou que a tendência dos índices de luz é inversa à tendência das variações dos corpos d’água, indicando que os índices de luz noturna podem refletir efetivamente o processo de mudança das inundações. (3) A análise combinada dos corpos d’água e dos índices de luz noturna na parte leste de Fuyang, onde os dados são relativamente completos, reforçou que tanto os dados de luz noturna quanto os de corpos d’água podem ser usados para monitorar inundações. Usando como exemplo a inundação deste ano em Fuyang, este estudo expandiu o alcance de aplicação dos dados de luz noturna e imagens ópticas, e também confirmou que, após um rigoroso processamento de dados, dados de sensoriamento remoto multifuente, como imagens de radar do Sentinel-1, e imagens ópticas do Sentinel-2 e Landsat 8, podem monitorar efetivamente as mudanças das inundações, desempenhando um papel importante no monitoramento futuro de inundações.

Palavras-chave

Google Earth Engine (GEE); luz noturna; sensoriamento remoto multifuente; inundações; Sentinel; NPP-VIIRS DNB; Landsat

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