TITLE:
A Statistical Approach for Predicting Grassland Degradation in Disturbance-Driven Landscapes
AUTHORS:
Anne Jacquin, Michel Goulard, J. M. Shawn Hutchinson, Thomas Devienne, Stacy L. Hutchinson
KEYWORDS:
Fire Regime, Spatial Statistics, GLM Model, Grassland, Remote Sensing
JOURNAL NAME:
Journal of Environmental Protection,
Vol.7 No.6,
May
23,
2016
ABSTRACT: 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.