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Conference Papers Year : 2012

Using spatial statistics tools on remote-sensing data to identify fire regime linked with grassland dynamic

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Abstract

Fire is acknowledged to be a factor for explaining the disturbance of vegetation dynamics interacting with other environmental factors. Depending on the fire regime, the amount of herbaceous biomass changes but depends on local conditions. Maintaining healthy vegetation and continuous vegetated cover on military training lands is important to provide for realistic soldier training experiences in a sustainable manner. The U.S. Army Integrated Training Area Management (ITAM) program is the organization responsible for ensuring training lands are available and accessible to meet Army operational needs now, and into the future, while attempting to minimize landscape degradation. At Fort Riley (Kansas), burning grassland is the main practice to prevent shrub encroachment and maintain accessible the training areas for the soldiers. One effort currently underway to assist the Fort Riley ITAM program is to clarify the importance and the role of fire on the dynamics of grassland vegetation in the training area. The image dataset is composed of one indicator related to vegetation activity changes and one indicator about fire regime that results from a combination of fire frequency and seasonality. All indicators were measured between 2000 and 2010 using remote sensing MODIS and Landsat time series. For the spatial statistic analysis, we implemented an approach in which a spatial GLM model is computed. Based on the spatial structure of the residuals, we built a stratification of the study area according to the spatial variations of the relationship established between vegetation activity changes and fire regime. The use of spatial statistical tools produces parsimonious models which we found to be consistent with expert knowledge.
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Dates and versions

hal-02806695 , version 1 (06-06-2020)

Identifiers

  • HAL Id : hal-02806695 , version 1
  • PRODINRA : 256562

Cite

Anne A. Jacquin, J.M. Shawn Hutchinson, Goulard Michel. Using spatial statistics tools on remote-sensing data to identify fire regime linked with grassland dynamic. Association of American Geographers Annual Meeting, Feb 2012, New-York, United States. 3 p. ⟨hal-02806695⟩
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