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Methodology to create geospatial MODIS dataset

Por: Álvarez Carranza, Geraldine [autora].
Lazcano Hernández, Hugo Enrique [autor].
Tipo de material: Capítulo de libro en línea Capítulo de libro en línea Tipo de contenido: Texto Tipo de medio: Computadora Tipo de portador: Recurso en líneaTema(s): Sargassum | Datos espaciales | Sensores remotos | Espectrorradiómetro de imágenes de resolución moderadaTema(s) en inglés: Sargassum | Spatial data | Remote sensing | Moderate resolution imaging spectroradiometerNota de acceso: Disponible para usuarios de ECOSUR con su clave de acceso En: Telematics and computing: 8th international congress, WITCOM 2019 Merida, Mexico, November 4–8, 2019 proceedings / Miguel Felix Mata-Rivera, Roberto Zagal-Flores, Cristian Barría-Huidobro (Eds.). Cham, Switzerland : Springer Nature Switzerland AG, 2019. páginas 25–33. --ISBN: 978-3-030-33228-0, 978-3-030-33229-7 (eBook)Número de sistema: 59938Resumen:
Inglés

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 forearth 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, semiautomatic and automatic stages. The present study proposes a methodology for the construction of a geospatial dataset using MODIS sensordata. 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.

Recurso en línea: http://dx.doi.org/10.1007/978-3-030-33229-7_3
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Disponible para usuarios de ECOSUR con su clave de acceso

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 forearth 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, semiautomatic and automatic stages. The present study proposes a methodology for the construction of a geospatial dataset using MODIS sensordata. 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. eng

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