Google Earth Engine e sua aplicabilidade na gestão de recursos hídricos

  • Fernanda Mota UFPEL
  • Matheus Gonçalves FURG
  • Marilton Aguiar UFPEL
  • Diana Adamatti FURG

Resumo


Os recursos e serviços hídricos desempenham um papel crucial no crescimento econômico e na sustentabilidade ambiental. Devido a isso, precisamos melhorar a coleta de dados hidrológicos, sua análise e o entendimento dos processos físicos da água. Este artigo tem como objetivo principal apresentar as funcionalidades da plataforma Google Earth Engine (GEE), tendo como objetivos específicos identificar e avaliar como a plataforma pode auxiliar no contexto de análise de dados em recursos hídricos. O GEE propicia a integração das tecnologias presentes em sistemas de informação geográficas, o que a torna interessante para o desenvolvimento de aplicações no âmbito da área ambiental. Este trabalho tem como estudo de caso o gerenciamento de recursos hídricos da bacia hidrográfica da Lagoa Mirim e Canal São Gonçalo. A análise resultante deste estudo pode auxiliar o Comitê de Gerenciamento das Bacias Hidrográficas na análise de dados das Bacias na região sul do Brasil.

Palavras-chave: Recursos hídricos, Google Earth Engine, bacias hidrográficas

Referências

Cann, K., Thomas, D. R., Salmon, R.,Wyn-Jones, A., and Kay, D. (2013). Extreme waterrelated weather events and waterborne disease. Epidemiology & Infection, 141(4):671– 686.

Che, X., Feng, M., Sexton, J., Channan, S., Sun, Q., Ying, Q., Liu, J., and Wang, Y. (2019). Landsat-based estimation of seasonal water cover and change in arid and semiarid central asia (2000–2015). Remote Sensing, 11(11):1323.

Dang, T. D., Cochrane, T. A., and Arias, M. E. (2018). Quantifying suspended sediment dynamics in mega deltas using remote sensing data: a case study of the mekong floodplains. International journal of applied earth observation and geoinformation, 68:105–115.

Dang, T. D., Cochrane, T. A., Arias, M. E., Van, P. D. T., and de Vries, T. T. (2016). Hydrological alterations from water infrastructure development in the mekong floodplains. Hydrological processes, 30(21):3824–3838.

Deng, Y., Jiang, W., Tang, Z., Ling, Z., and Wu, Z. (2019). Long-term changes of opensurface water bodies in the yangtze river basin based on the google earth engine cloud platform. Remote Sensing, 11(19):2213.

Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., and Moore III, B. (2016). Mapping paddy rice planting area in northeastern asia with landsat 8 images, phenology-based algorithm and google earth engine. Remote sensing of environment, 185:142–154.

Feyisa, G. L., Meilby, H., Fensholt, R., and Proud, S. R. (2014). Automated water extraction index: A new technique for surface water mapping using landsat imagery. Remote Sensing of Environment, 140:23–35.

Fisher, A., Flood, N., and Danaher, T. (2016). Comparing landsat water index methods for automated water classification in eastern australia. Remote Sensing of Environment, 175:167–182.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202:18–27.

Hakdaoui, S., Emran, A., Pradhan, B., Qninba, A., Balla, T. E., Mfondoum, A. H. N., Lee, C.-W., and Alamri, A. M. (2020). Assessing the changes in the moisture/dryness of water cavity surfaces in imlili sebkha in southwestern morocco by using machine learning classification in google earth engine. Remote Sensing, 12(1):131.

Hardy, A., Ettritch, G., Cross, D. E., Bunting, P., Liywalii, F., Sakala, J., Silumesii, A., Singini, D., Smith, M., Willis, T., et al. (2019). Automatic detection of open and vegetated water bodies using sentinel 1 to map african malaria vector mosquito breeding habitats. Remote Sensing, 11(5):593.

Hird, J. N., DeLancey, E. R., McDermid, G. J., and Kariyeva, J. (2017). Google earth engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote sensing, 9(12):1315.

Khandelwal, A., Karpatne, A., Marlier, M. E., Kim, J., Lettenmaier, D. P., and Kumar, V. (2017). An approach for global monitoring of surface water extent variations in reservoirs using modis data. Remote sensing of Environment, 202:113–128.

Kumar, L. and Mutanga, O. (2018). Google earth engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10):1509.

Kumar, L. and Mutanga, O. (2019). Google Earth Engine Applications. MDPI.

Li, L., Vrieling, A., Skidmore, A., Wang, T., and Turak, E. (2018). Monitoring the dynamics of surface water fraction from modis time series in a mediterranean environment. International journal of applied earth observation and geoinformation, 66:135–145.

Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., and Wang, S. (2018). Highresolution multi-temporal mapping of global urban land using landsat images based on the google earth engine platform. Remote sensing of environment, 209:227–239.

Lu, S., Jia, L., Zhang, L., Wei, Y., Baig, M. H. A., Zhai, Z., Meng, J., Li, X., and Zhang, G. (2017). Lake water surface mapping in the tibetan plateau using the modis mod09q1 product. Remote sensing letters, 8(3):224–233.

McCullough, I. M., Loftin, C. S., and Sader, S. A. (2013). Lakes without landsat? an alternative approach to remote lake monitoring with modis 250 m imagery. Lake and reservoir management, 29(2):89–98.

Mutanga, O. and Kumar, L. (2019). Google earth engine applications.

Nguyen, U. N., Pham, L. T., and Dang, T. D. (2019). An automatic water detection approach using landsat 8 oli and google earth engine cloud computing to map lakes and reservoirs in new zealand. Environmental monitoring and assessment, 191(4):235.

Ou, C., Yang, J., Du, Z., Liu, Y., Feng, Q., and Zhu, D. (2020). Long-term mapping of a greenhouse in a typical protected agricultural region using landsat imagery and the google earth engine. Remote Sensing, 12(1):55.

Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633):418–422.

Poortinga, A., Clinton, N., Saah, D., Cutter, P., Chishtie, F., Markert, K. N., Anderson, E. R., Troy, A., Fenn, M., Tran, L. H., et al. (2018). An operational before-aftercontrol- impact (baci) designed platform for vegetation monitoring at planetary scale. Remote Sensing, 10(5):760.

Schwatke, C., Scherer, D., and Dettmering, D. (2019). Automated extraction of consistent time-variable water surfaces of lakes and reservoirs based on landsat and sentinel-2. Remote Sensing, 11(9):1010.

Shami, S. and Ghorbani, Z. (2019). Investigating water storage changes in iran using grace and chirps data in the google earth engine system. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.

Sidhu, N., Pebesma, E., and Câmara, G. (2018). Using google earth engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing, 51(1):486–500.

Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L., and Shi, L. (2018). Mapping vegetation and land use types in fanjingshan national nature reserve using google earth engine. Remote Sensing, 10(6):927.

Tulbure, M. G. and Broich, M. (2019). Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of landsat satellite data. Science of The Total Environment, 658:1574–1585.

Wang, C., Jia, M., Chen, N., and Wang, W. (2018). Long-term surface water dynamics analysis based on landsat imagery and the google earth engine platform: A case study in the middle yangtze river basin. Remote Sensing, 10(10):1635.

Wood, E. F., Roundy, J. K., Troy, T. J., Van Beek, L., Bierkens, M. F., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., et al. (2011). Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring earth’s terrestrial water. Water Resources Research, 47(5).

Xia, H., Zhao, J., Qin, Y., Yang, J., Cui, Y., Song, H., Ma, L., Jin, N., and Meng, Q. (2019). Changes in water surface area during 1989–2017 in the huai river basin using landsat data and google earth engine. Remote Sensing, 11(15):1824.

Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty, R. B., Menarguez, M. A., Zhang, G., and Wang, J. (2018). Divergent trends of open-surface water body area in the contiguous united states from 1984 to 2016. Proceedings of the National Academy of Sciences, 115(15):3810–3815.
Publicado
30/06/2020
Como Citar

Selecione um Formato
MOTA, Fernanda; GONÇALVES, Matheus; AGUIAR, Marilton; ADAMATTI, Diana. Google Earth Engine e sua aplicabilidade na gestão de recursos hídricos. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 11. , 2020, Evento Online. Anais do XI Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Porto Alegre: Sociedade Brasileira de Computação, june 2020 . p. 161-170. ISSN 2595-6124. DOI: https://doi.org/10.5753/wcama.2020.11030.