Author(s)

Anne Jacquin¹, Michel Goulard², J. M. Shawn Hutchinson³, Thomas Devienne¹, Stacy L. Hutchinson⁴

¹Université de Toulouse, INPT, Ecole d'Ingénieurs de Purpan, UMR 1201 DYNAFOR, 75, voie du TOEC, Toulouse, France.
²Institut National pour la Recherche Agronomique, UMR 1201 DYNAFOR, 24 Chemin de Borde Rouge-Auzeville, Castanet Tolosan, France.
³Department of Geography, Kansas State University, Manhattan, KS, USA.
⁴Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS, USA.

Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non-military sites in the Kansas Flint Hills. The response variable was the long-term linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non-military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the non-military site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage.

Fire Regime, Spatial Statistics, GLM Model, Grassland, Remote Sensing

Jacquin, A. , Goulard, M. , Hutchinson, J. , Devienne, T. and Hutchinson, S. (2016) A Statistical Approach for Predicting Grassland Degradation in Disturbance-Driven Landscapes. Journal of Environmental Protection, 7, 912-925. doi: 10.4236/jep.2016.76081.