Vegetation drought monitoring from MODIS imagery and soil moisture data in Oklahoma Mesonet sites

Diana Ximena Vanegas; Xiangming Xiao; Jeffrey Basara

doi:10.18270/rt.v13i2.1879

Autores/as

Diana Ximena Vanegas Universidad El Bosque
Xiangming Xiao University of Oklahoma
Jeffrey Basara University of Oklahoma

DOI:

https://doi.org/10.18270/rt.v13i2.1879

Palabras clave:

MODIS, vegetation drought monitoring, grassland, land surface water index

Resumen

Drought is a normal and recurrent climatic phenomenon, and is considered one of the most costly natural disasters in the United States. Grassland vegetation is sensitive to weather and climate, and persistent drought impacts goods and ecological services that grasslands provide (e.g., wildlife habitats, feedstock for the livestock industry, and recreational services). Droughts have extremely large spatial and temporal variations in areal coverage and intensity making drought monitoring a challenging task. Using soil and atmospheric data from the Oklahoma Mesonet and surface reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, this study examined the hypothesis that the satellite-derived Land Surface Water Index (LSWI) is sensitive to drought conditions and can potentially be used as an indicator or tool for drought monitoring. The sensitivity of LSWI to summer drought was first analyzed at 10 Mesonet sites that are homogeneous and representative of different types of grassland vegetation, soils and climate across Oklahoma. A summer drought event is defined, based on threshold values of LSWI and the Fractional Water Index (FWI) derived from soil moisture data at each site.

Secondly, the LSWI-based drought algorithm was evaluated at
103 Oklahoma Mesonet sites. Finally, the LSWI-based drought
algorithm was used to map spatial patterns and temporal dynamics
of drought-affected land surface during 2001-2010 across
Oklahoma. The results from this study demonstrated the potential
of LSWI-based drought algorithm for tracking and mapping
drought-affected grassland vegetation in Oklahoma with 3%
commission error in the Oklahoma Mesonet sites during 2001-2010.

La sequía es un fenómeno climático normal y recurrente, y es considerado uno de los desastres naturales más costosos en Estados Unidos. La vegetación de pastizales es sensible al estado del tiempo y el clima, y la persistencia de la sequía afecta a los bienes y servicios ecológicos que proporcionan los pastizales (por ejemplo, son hábitats de vida silvestre, proveen materia prima para la industria ganadera, así como servicios de esparcimiento). La cobertura de área e intensidad de las sequías presentan grandes variaciones espaciales y temporales, haciendo que el monitorea de sequías sea una tarea difícil. Usando datos atmosféricos y de suelos de la Oklahoma Mesonet, y datos de reflectancia de la superficie terrestre del espectrorradiómetro de imágenes de resolución moderada (MODIS, por sus siglas en inglés) a bordo de los satélites Terra y Aqua, este estudio examinó la hipótesis de que el índice de agua de la superficie del terreno (LSWI, por sus siglas en inglés) es sensible a condiciones de sequía y potencialmente puede utilizarse como un indicador o herramienta para la monitoreo de sequías. La sensibilidad del LSWI a la sequía estival se analizó inicialmente en 10 sitios Mesonet que son homogéneos y representativos de los diferentes tipos de vegetación de pastizales, los suelos y el clima a través de Oklahoma. Un evento de sequía estival se define, en base a los valores de umbral de LSWI y el Índice de Agua fraccional (FWI) derivado de los datos de humedad del suelo en cada sitio. Posteriormente, el algoritmo de sequía basado en LSWI se evaluó en 103 sitios Oklahoma Mesonet. Por último, se utilizó el algoritmo de sequía basado en LSWI para mapear los patrones espaciales y la dinámica temporal de la superficie de la tierra afectada por la sequía durante 2001-2010 a través de Oklahoma. Los resultados de este estudio demostraron el potencial del algoritmo de sequía basado en LSWI para el seguimiento y la cartografía de vegetación de pradera afectada por la sequía en Oklahoma con un 3% de error de comisión en los sitios Oklahoma Mesonet durante 2001-2010

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Biografía del autor/a

Diana Ximena Vanegas, Universidad El Bosque

Environmental Engineer, El Bosque University (2006), Masters in Geoinformatics, University of
Oklahoma – USA (2011). Work experience as the Environmental Engineer at a Local mayor office
in Bogota DC (2006 - 2008); professional research assistant at Earth Observation and Modeling
Facility (http://eomf.ou.edu/), the University of Oklahoma (2009-2012) and virtual teacher of the
Environmental Engineering program at El Bosque University (2012-2014).

Xiangming Xiao, University of Oklahoma

Is a Professor of Ecology and Remote Sensing at the Department of Microbiology and Plant Biology
(http://mpbio.ou.edu). His research interest include remote sensing, land use and land cover change,
carbon cycle, and infectious disease ecology. He has published 150 papers and book chapters.

Jeffrey Basara, University of Oklahoma

Jeffrey Basara is an Associate Professor in the School of Meteorology (http://weather.ou.edu) at the
University of Oklahoma and the Director of Research at the Oklahoma Climatological Survey. His
research interests include the physical processes which impact land-atmosphere interactions, the
planetary boundary layer, drought, flash floods, and urban meteorology.

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Vegetation drought monitoring from MODIS imagery and soil moisture data in Oklahoma Mesonet sites

Autores/as

DOI:

Palabras clave:

Resumen

Descargas

Biografía del autor/a

Diana Ximena Vanegas, Universidad El Bosque

Xiangming Xiao, University of Oklahoma

Jeffrey Basara, University of Oklahoma

Referencias bibliográficas

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