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International audience; The relationship between fire and long-term trends in tallgrass prairie vegetation was assessed at Fort Riley and Konza Prairie Biological Station (KPBS) in Kansas. Linear trends of surface greenness were previously estimated using BFAST and MODIS MOD13Q1 NDVI composite images from 2001 to 2010. To explain trends, fire frequency and seasonality (fire regime) was determined and each site was divided into spatial strata using administrative or management units. Generalized linear models (GLM) were used to explain trends by fire regime and/or stratification. Spatialized versions of GLMs were also computed address unexplained spatial components. Non-spatial models for FRK showed fire regime explained only 4% of trends compared to strata (7-26%). At KPBS, fire regime and spatial stratification explained 14% and 39%, respectively. At both sites, improvements in performance were minimal using both fire and strata as explanatory variables. Model spatialization resulted in a 5% improvement at FRK, but with weak spatial structure in the residuals, and was not necessary at KPBS as the existing stratification most of the spatial structure in model residuals. All models at KPBS performed better for each explanatory variable and combination tested. Fire has only a marginal effect on vegetation trends at FRK despite its widespread use as a grassland management tool to improve vegetation health, and explains much more of the trends at KPBS. Analysis of predictors from spatial models with existing stratification yielded an approach with fewer strata but similar performance and may provide insight about additional explanatory variables omitted from this analysis.

International audience; 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.

International audience; 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. One effort currently underway to assist the Fort Riley ITAM program their mission is the evaluation of a long-term time series of satellite imagery to identify trends in vegetation condition and, if possible, to correlate any trends detected with various landscape disturbances such a training exercises, prescribed burns, and incidents of wildfire. A total of 207 16 day composite normalized difference vegetation index (NDVI) images acquired by the Moderate Resolution Imaging Spectrometer (MODIS) Terra sensor from 2001 through 2009 were analyzed for using the Breaks for Additive Seasonal and Trend (BFAST) change detection algorithm. BFAST decomposes a time series dataset into trend, seasonal, and noise components that allow classification of different types of change (e.g., disturbances and phenological change). Preliminary results from this analysis at Fort Riley suggest this approach is very useful for identifying the location and spatial extent of statistically significant positive and negative trends in vegetation health and offers the additional benefit of quantifying the number of trends breakpoints in the time series and the year when the highest magnitude break (either positive or negative) was measured.