@Article{GoltzBrToMaAdShFo:2007:UtÍnEs,
               author = "Goltz, Elizabeth and Brand{\~a}o, Daniela and Tom{\'a}s, 
                         L{\'{\i}}via and Mantelli, Luiz Rog{\'e}rio and Adami, Marcos 
                         and Shimabukuro, Yosio Edemir and Formaggio, Antonio Roberto",
          affiliation = "{} and {} and {} and {} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Utiliza{\c{c}}{\~a}o de {\'{\i}}ndices espectrais de 
                         vegeta{\c{c}}{\~a}o do sensor MODIS na determina{\c{c}}{\~a}o 
                         de {\'a}reas suscet{\'{\i}}veis a alagamento no pantanal 
                         sulmatogrossense / Use of MODIS Spectral Vegetation Indices to 
                         Determine Susceptible Flooding Area in the Pantanal 
                         Sulmatogrossense",
              journal = "Revista Brasileira de Cartografia",
                 year = "2007",
               volume = "59",
               number = "1",
                pages = "35--44",
                month = "abr.",
             keywords = "sensoriamento remoto, an{\'a}lise multitemporal, {\'{\i}}ndices 
                         de vegeta{\c{c}}{\~a}o, MODIS, Pantanal, remote sensing, 
                         multitemporal images, vegetation indices, MODIS, Pantanal.",
             abstract = "O Pantanal {\'e} a maior {\'a}rea {\'u}mida tropical do 
                         planeta, onde ocorrem inunda{\c{c}}{\~o}es sazonais pelo Rio 
                         Paraguai e seus afluentes. Altera{\c{c}}{\~o}es nesta 
                         din{\^a}mica influenciam diretamente o bioma. O objetivo deste 
                         trabalho {\'e} tentar determinar as {\'a}reas 
                         suscet{\'{\i}}veis de alagamento nas regi{\~o}es de 
                         Paiagu{\'a}s e Nhecol{\^a}ndia (Pantanal Sulmatogrossense), 
                         utilizando imagens multitemporais de {\'{\i}}ndices de 
                         vegeta{\c{c}}{\~a}o (NDVI e EVI) do sensor MODIS/TERRA. Foram 
                         adquiridas imagens do produto MOD13 ({\'{\I}}ndice de 
                         Vegeta{\c{c}}{\~a}o) do sensor MODIS entre os anos de 2000 e 
                         2005. A partir destas imagens foi realizada a m{\'e}dia mensal 
                         para cada {\'{\i}}ndice (EVI e NDVI), com o intuito de observar 
                         o comportamento ao longo do ano. Em seguida, selecionaram-se as 
                         imagens de m{\'{\i}}nimo e m{\'a}ximo (EVI e NDVI) para cada 
                         ano. Com este resultado, foram geradas as imagens diferen{\c{c}}a 
                         (m{\'a}ximos-m{\'{\i}}nimos). Observou-se que nestas imagens 
                         diferen{\c{c}}a, algumas {\'a}reas apresentavam valores 
                         negativos, isto {\'e}, nestas regi{\~o}es os valores dos 
                         {\'{\i}}ndices de vegeta{\c{c}}{\~a}o eram maiores na 
                         {\'e}poca da seca do que na {\'e}poca da cheia. Desta forma, 
                         deduziu-se que estas {\'a}reas (valores negativos), durante a 
                         {\'e}poca da cheia se encontravam alagadas (valores dos 
                         {\'{\i}}ndices de vegeta{\c{c}}{\~a}o menores). Com estas 
                         informa{\c{c}}{\~o}es gerou-se uma imagem final real{\c{c}}ando 
                         as {\'a}reas prov{\'a}veis de alagamento para os dois 
                         {\'{\i}}ndices em cada ano. Al{\'e}m disso, notou-se que o EVI 
                         {\'e} mais sens{\'{\i}}vel {\`a}s mudan{\c{c}}as da cobertura 
                         e conseguiu destacar a drenagem na regi{\~a}o de estudo. 
                         ABSTRACT: Pantanal is the largest tropical wetland on the planet, 
                         where occurs seasonal flooding caused by Paraguay River and its 
                         tributaries. Changes in this dynamic directly influence the biome. 
                         The objective of this work is to try to determine susceptible 
                         flooding areas in the regions of Paiagu{\'a}s and 
                         Nhecol{\^a}ndia (Pantanal), using multitemporal vegetation index 
                         (NDVI and EVI) images provided by MODIS/TERRA sensor. Images from 
                         MOD13 product (vegetation index) acquired between the years 
                         2000-2005 were used in this study. With these images the monthly 
                         average for each index (EVI and NDVI) was computed, with the 
                         intention to observe the behavior along of the year. After that, 
                         the images of Maximum and Minimum (EVI and NDVI) were selected for 
                         each year. With these results, the difference images 
                         (Maximum-Minimum) were generated. In these difference images, it 
                         was observed that some areas presented negative values, i.e., in 
                         these regions the values of vegetation indices were greater in the 
                         dry season than in the flooding season. In this manner, it was 
                         implied that these areas (negative values), during the flooding 
                         season, were found flooded (lower vegetation index values). With 
                         these information it was generated a final image enhancing the 
                         susceptible flooding areas for both vegetation indices in each 
                         year. In addition, it was noticed that EVI is more sensible to the 
                         land cover changes and was able to enhance the drainage in the 
                         study region.",
                 issn = "0560-4613 and 1808-0936",
             language = "pt",
           targetfile = "59_01_5.pdf",
        urlaccessdate = "09 maio 2024"
}