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Methodology to Create Geospatial MODIS Dataset

  • Geraldine Álvarez-Carranza
  • Hugo E. Lazcano-HernándezEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1053)

Abstract

Training and testing of algorithms used in computing for application in several studies, require datasets previously validated and labeled. In the case of satellite remote sensing, there are several platforms with large volumes of open source data. Aqua and Terra satellite platforms have available the sensor MODIS (Moderate-Resolution Imaging Spectroradiometer) which has available open access data for earth observation. Despite the facilities offered by the MODIS data platform, extracting data from a particular region for the construction of useful dataset requires an arduous work that includes manual, semi-automatic and automatic stages. The present study proposes a methodology for the construction of a geospatial dataset using MODIS sensor data. This methodology has been successfully implemented in the construction of dataset for the analysis of physical and biological variables in the Caribbean Sea, highlighting its application in the monitoring of Sargasso along the coastline of the state of Quintana Roo. Its application can be extended to any of the data and products offered by the MODIS sensor.

Keywords

MODIS dataset Geospatial dataset Sargasso MODIS processor 

Notes

Acknowledgments

Geraldine Álvarez thanks ECOSUR for her research assistant fellowship. We thank the NASA Ocean Biology Processing Group (OBPG) for providing all MODIS raw data used in this study. Finally the authors are grateful to the reviewers for their contribution to improve this manuscript.

References

  1. 1.
    Nathan, F.: Simulating transport pathways of pelagic Sargasso from the Equatorial Atlantic into the Caribbean Sea. Prog. Ocean. 165, 205–214 (2018).  https://doi.org/10.1016/j.pocean.2018.06.009CrossRefGoogle Scholar
  2. 2.
    Schell, J.M., Goodwin, D.S., Siuda, A.N.: Recent Sargassum inundation events in the Caribbean: shipboard observations reveal dominance of a previously rare form. Oceanography 28(3), 8–11 (2015)CrossRefGoogle Scholar
  3. 3.
    Gower, J.: Satellite images suggest a new Sargassum source region in 2011. Remote Sens. Lett. 4(8), 764–73 (2013)CrossRefGoogle Scholar
  4. 4.
    Wang, M.: Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations. Remote Sens. Environ. 15(183), 350–67 (2016)CrossRefGoogle Scholar
  5. 5.
    Wang, M.: Predicting Sargassum blooms in the Caribbean Sea from MODIS observations. Geophys. Res. Lett. 44(7), 3265–73 (2017)CrossRefGoogle Scholar
  6. 6.
    García-Mora, T., Jean-Francois, M.: Aplicaciones del sensor MODIS para el monitoreo del territorio, 1st edn. Secretaría de Medio Ambiente y Recursos Naturales (Semarnat), México (2011)Google Scholar
  7. 7.
    SeaDAS. https://seadas.gsfc.nasa.gov/. Accessed June 2019
  8. 8.
    Lance MODIS. https://lance-modis.eosdis.nasa.gov/. Accessed June 2019
  9. 9.
    Ocean Color. https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua. Accessed June 2019
  10. 10.
    Arellano-Verdejo, J.: ERISNet: deep neural network for Sargasso detection along the coastline of the Mexican Caribbean. PeerJ, 7, e6842, 1–19. (2019).  https://doi.org/10.7717/peerj.6842CrossRefGoogle Scholar
  11. 11.
    MODIS-Aqua, M.-A.O.C.: NASA goddard space flight center, ocean ecology laboratory, ocean biology processing group. Moderate-resolution imaging spectroradiometer (MODIS) Aqua l0 Data; NASA OB.DAAC, Greenbelt, MD, USA (2018). Accessed 01 Nov 2018Google Scholar
  12. 12.
    Van Tussenbroek, B.: Severe impacts of brown tides caused by Sargasso spp. On near-shore caribbean seagrass communities. Mar. Pollut. Bull. 122(1–2), 272–281 (2017)CrossRefGoogle Scholar
  13. 13.
    Rodríguez-Martínez, R.E.: Afluencia masiva de sargazo pelágico a la costa del Caribe mexicano (2014–2015). In: Florecimientos algales nocivos en México, CICESE, Ensenada, Mexico, pp. 352–365 (2016)Google Scholar
  14. 14.
    Google earth. https://www.google.com/intl/es/earth/. Accessed June 2019

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.El Colegio de la Frontera Sur, Estación para la Recepción de Información Satelital ERIS-ChetumalChetumalMexico
  2. 2.Cátedras CONACYT-El Colegio de la Frontera SurChetumalMexico

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