A statistical approach for predicting grassland degradation in disturbance-driven landscapes

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 longterm 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 nonmilitary 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

Time Series Analysis of Long-Term Vegetation Trends, Phenology, and Ecosystem Service Valuation for Grasslands in the U.S. Great Plains

Doctor of PhilosophyDepartment of GeographyJ. M. Shawn HutchinsonGrasslands are one of the largest, most biodiverse, and productive terrestrial biomes but they receive very low levels of protection. The temperate grasslands in the United States are one of the most threatened grassland ecosystems. Every year, a significant portion of grasslands in the Great Plains are converted to agricultural use, with almost 96% of the historical extent lost. Other factors that affect existing grassland health include significant climatic changes, invasion of woody, non-native species, fragmentation, lack or inadequate burning, and excessive grazing. The impact of the combination of these factors on grasslands in the US Great Plains is still unknown. The goal of this research is to investigate the long-term grassland vegetation conditions using a well-known indicator (greenness) and assesses its impact on the provision of select grassland ecosystem services within the US Great Plains from 2001 to 2017. The above goal was achieved with three objectives addressed in three chapters. In Chapter 3, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 6) was performed to assess long-term trends in vegetation greenness across the Great Plains ecoregion of the United States. The Breaks for Additive Season and Trend (BFAST) decomposition method was applied to a time series of images from 2001 to 2017 to derive spatially explicit estimates of gradual interannual change. Results show more 'greening' trends than 'browning' and 'no change' trends during the study period. Comparing the trend results from both vegetation indices suggests that EVI is more suitable for this analysis in the study area, especially in areas with high biomass. In Chapter 4, a time-series analysis of Moderate Resolution Imaging Spectrometer (MODIS) 16-day maximum value composite Enhanced Vegetation Index (EVI) data (MOD13Q1 Collection 5) is used to explore spatial patterns of vegetation phenology and to assess long-term phenology trends across the region. The program TIMESAT was used to extract key measures of vegetation phenological development from 2001 to 2017, including the phenometrics (1) season length, (2) start of growing season, (3) end of growing season, (4) middle of the growing season, (5) maximum NDVI value, (6) small integral, (7) left derivative, and (8) right derivative. Results show important variation in phenological patterns across the region such as a shift to a later start, earlier end, and shorter the growing season length, especially in the southern parts of the region. As shown in the small integral and maximum EVI, vegetation productivity appears to have increased over many areas in the Great Plains ecoregion. Finally, Chapter 5 focuses on developing a methodological improvement to the widely used Invest ecosystem services model that uses remotely sensed inputs to capture the interannual spatio-temporal dynamics of grassland vegetation on the provision of grassland ecosystem services across the US Great Plains. A selected set of grassland ecosystem services was quantified (economic and biophysical values) for the period between 2001 and 2017. This exploratory study will be a basis for highlighting the role grasslands play in providing essential ecosystem services and how improved long-term vegetation monitoring can benefit land-use decisions locally and regionally

Time series analysis of phenometrics and long-term vegetation trends for the Flint Hills ecoregion using moderate resolution satellite imagery

Master of ArtsDepartment of GeographyJ. M. Shawn HutchinsonGrasslands of the Flint Hills are often burned as a land management practice. Remote sensing can be used to help better manage prairie landscapes by providing useful information about the long-term trends in grassland vegetation greenness and helping to quantify regional differences in vegetation development. Using MODIS 16-day NDVI composite imagery between the years 2001-10 for the entire Flint Hills ecoregion, BFAST was used to determine trend, seasonal, and noise components of the image time series. To explain the trend, 4 factors were considered including hydrologic soil group, burn frequency, and precipitation deviation from the 30 year normal. In addition, the time series data was processed using TIMESAT to extract eight different phenometrics: Growing season length, start of season, end of season, middle of season, maximum value, small integral, left derivative, and right derivative. Phenometrics were produced for each year of the study and an ANOVA was performed on the means of all eight phenometrics to assess if significant differences existed across the study area. A K-means cluster analysis was also performed by aggregating pixel-level phenometrics at the county level to identify administrative divisions exhibiting similar vegetation development. For the study period, the area of negatively and positively trending grassland were similar (41-43%). Logistic regression showed that the log odds of a pixel experiencing a negative trend were higher in sites with clay soils and higher burning frequencies and lower for pixels having higher than normal precipitation and loam soils. Significant differences existed for all phenometrics when considering the ecoregion as a whole. On a phenometric-by-phenometric basis, unexpected groupings of counties often showed statistically similar values. Similarly, when considering all phenometrics at the same time, counties clustered in surprising patterns. Results suggest that long-term trends in grassland conditions warrant further attention and may rival other sources of grassland change (e.g., conversion, transition to savannah) in importance. Analyses of phenometrics indicates that factors other than natural gradients in temperature and precipitation play a significant role in the annual cycle of grassland vegetation development. Unanticipated, and sometimes geographically disparate, groups of counties were shown to be similar in the context of specific phenology metrics and this may prove useful in future implementations of smoke management plans within the Flint Hills