Open Journal Systems

MULTITEMPORAL ANALYSIS OF SAR IMAGES FOR DETECTION OF FLOODED AREAS IN PANTANAL

Gabriel Henrique De Almeida Pereira, Clóvis Cechim Júnior, Giovani Fronza, Flávio André Cecchini Deppe

Resumo


The Pantanal is one of the most important and preserved biomes in Brazil. This region is annually flooded due to episodes of precipitation along the Paraguay River and its tributaries. Understanding the dynamics of flooding is extreme important since it influences the entire Pantanal ecosystem. Remote Sensing data is an alternative to the identification of flooded areas and their changes in different periods. Among the possible sensors capable of mapping these flooded areas Radar sensor is one of the most attractive – mainly due to the low influence of cloud cover and atmospheric conditions, allowing imaging in dry or rainy seasons. For this work, Radar images from Sentinel 1 satellites for the years 2016, 2017, and 2018 were used. All available data from these years for the study area were used to generate images that represent the seasonality in the region for each year. In total, 1141 Sentinel 1 radar images were processed. The processing of such amount of data was possible through Google Earth Engine platform, which is capable of robust processing of a large amount of data, especially Remote Sensing data. At the end, it was possible to generate images that represent the seasonality of each year. It was also possible to compare the years, highlighting the differences between flooded areas indicating the periods of major precipitation.


Palavras-chave


Remote Sensing; image processing; time series images; seasonality; wetlands

Referências


ALLEN, G. H.; PAVELSKY, T. M. Global extent of rivers and streams. Science. Vol. 361, Issue 6402, pp. 585-588. 2018. doi: https://doi.org/10.1126/science.aat0636

ANTUNES, J.F.F.; ESQUERDO, J.C.D.M. Classificação sub-pixel de séries temporais de dados MODIS para a quantificação de áreas inundadas do Pantanal. Anais 5º Simpósio de Geotecnologias no Pantanal, Campo Grande, MS. Embrapa Informática Agropecuária. 2014.

CLIMATE ENGINE, 2016. Desert Research Institute, University of Ido. Disponível em: . Acesso em: 13 dez. 2018.

COLLECT EARTH, 2016. United Nations Food and Agriculture Organization. Disponível em: . Acesso em: 13 dez. 2018.

COLTIN, B.; MCMICHAEL, S.; SMITH, T.; FONG, T. Automatic boosted flood mapping from satellite data. Int. J. Remote Sensing, 37 (5), p. 993-1015, 2016.

DONG, J.; XIAO, X.; MENARGUEZ, M.A.; ZHANG, G.; QIN, Y.; THAU, D.; BIRADAR, C.; Moore, B. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing Environment, v.185, p. 142-154, 2016.

FREITAS, R. M.; ARAI, E.; ADAMI, M.; SOUZA, A. F.; SATO, F. Y.; SHIMABUKURO, Y. E.; ROSA, R. R.; ANDERSON, L. O.; RUDORFF, B. F. T. Virtual laboratory of remote sensing time series: visualization of MODIS EVI2 data set over South America. Journal of Computational Interdisciplinary Sciences (2011) 2(1):57-68. doi: 10.6062/jcis.2011.02.01.0032.

GORELICK, N.; HANCHER, M.; DIXON, M.; ILYUSHCHENKO, S.; THAU, D.; MOORE, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, v. 202, p. 18-27, 2017.

HANSEN, M.C.; POTAPOV, P.V.; MOORE, R.; HANCHER, M.; TURUBANOVA, S.A.; TYUKAVINA, A.; THAU, D.; STEHMAN, S. V.; Goetz, S. J.; LOVELAND, T. R.; KOMMAREDDY, A.; EGOROV, A.; CHINI, L.; JUSTICE, C. O.; TOWNSHEND, J. R. G. High-resolution global maps of 21st-century forest cover change. Science, v.342, p. 850-853, 2013.

LOBELL, D.; THAU, D.; SEIFERT, C.; ENGLE, E.; LITTLE, B. A scalable satellite-based crop yield mapper. Remote Sensing of Environment, v.164, p. 324-333, 2015.

MAPBIOMAS, 2015. MAPBIOMAS. Disponível em: . Acesso em: 22 jan. 2019.

MAP Of LIFE, 2016. Putting biodiversity on the map. Disponível em: . Acesso em: 13 dez. 2018.

NOVO, E.L.M; COSTA, M.P.F. Fundamentos e aplicações de radar no estudo de áreas alagáveis. In: SOUZA, R. B. Oceanografia por Satélites, 2ed. Oficina de Textos, 2009, 382 p.

PADOVANI, C.R. Dinâmica Espaço-Temporal das Inundações do Pantanal. 174p. Tese (Doutorado em Ecologia Aplicada) - Escola Superior de Agricultura, Luiz de Queiroz, Piracicaba, SP, 2010.

PATEL, N.; ANGIULI, E.; GAMBA, P.; GAUGHAN, A.; LISINI, G.; STEVENS, F.; TATEM, A.; TRIANNI, A. Multitemporal settlement and population mapping from Landsat using google earth engine. Int. J. Appl. Earth Obs. Geoinf., v.35, p. 199-208, 2015.

PATEL, N; ESQUERDO, J. .D; MAIA, A.H.N; PAZIANOTTO, R.A.A; SORIANO, B.M.A. Geohidro-Pantanal, portal de informações hidrológicas da bacia do Alto Paraguai-Pantanal. 2000. Disponível: https://www.embrapa.br/pantanal/. Acesso em: 26 de março de 2019.

PAZ, A. R.; COLLISCHONN, W.; TUCCI, C. E. M.; PADOVANI, C. R. Large-scale modelling of channel flow and floodplain inundation dynamics and its application to the Pantanal (Brazil). Hydrological Processes, v. 25, p. 1498-1516, 2011.

PEKEL, J.F.; COTTAM, A.; GORELICK, N.; BELWARD, A.S. High-resolution mapping of global surface water and its long-term changes, Nature Geoscience, v. 540 (7633), p. 418-122, 2016.

SOULARD, C.E.; ALBANO, C.M.; VILLARREAL, M.L.; WALKER, J.J. Continuous 1985–2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park, California. Remote Sensing, v.8 (5), p. 371, 2016.

STURROCK, H.J.; COHEN, J.M.; KEIL, P.; TATEM, A.J.; LE MENACH, A.; NTSHALINTSHALI, N.E.; HSIANG, M.S.; GOSLING, R.D. Fine-scale malaria risk mapping from routine aggregated case data. Malaria Journal, v.13 (1), p. 1-9, 2014.

ZHANG, Q.; LI, B.; THAU, D.; MOORE, R. Building a better urban picture: combining day and night remote sensing imagery. Remote Sensing, v.7 (9), p. 11887-11913, 2015.

ZHOU, T.; PAN, J.; ZHANG, P.; WEI, S.; HAN, T. Mapping winter wheat with multi-temporal SAR and optical images in an urban agricultural region. Sensors, v.17, p. 1-16, 2017.




DOI: http://dx.doi.org/10.5380/raega.v46i3.66988