@conference {KNZ001715, title = {A statistical approach for predicting grassland degradation in disturbance-driven landscapes}, booktitle = {2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp)}, year = {2015}, publisher = {IEEE}, organization = {IEEE}, address = {Annecy, France}, abstract = {

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.

}, keywords = {LTER-KNZ}, doi = {10.1109/Multi-Temp.2015.7245759}, url = {https://ieeexplore.ieee.org/document/7245759}, author = {Jacquin, Anne and Goulard, Michel and Hutchinson, J.M.S. and Hutchinson, S.L.} } @article {KNZ001258, title = {Connectivity of the American agricultural landscape: Assessing the national risk of crop pest and disease spread}, journal = {Bioscience}, volume = {59}, year = {2009}, pages = {141 -151}, abstract = {

More than two-thirds of cropland in the United States is devoted to the production of just four crop species\—maize, wheat, soybeans, and cotton\—raising concerns that homogenization of the American agricultural landscape could facilitate widespread disease and pest outbreaks, compromising the national food supply. As a new component in national agricultural risk assessment, we employed a graph-theoretic approach to examine the connectivity of these crops across the United States. We used county crop acreage to evaluate the landscape resistance to transmission\—the degree to which host availability limits spread in any given region\—for pests or pathogens dependent on each crop. For organisms that can disperse under conditions of lower host availability, maize and soybean are highly connected at a national scale, compared with the more discrete regions of wheat and cotton production. Determining the scales at which connectivity becomes disrupted for organisms with different dispersal abilities may help target rapid-response regions and the development of strategic policies to enhance agricultural landscape heterogeneity.

}, keywords = {LTER-KNZ, geographic information systems, graph theory, Invasive species, landscape connectivity, networks}, doi = {https://doi.org/10.1525/bio.2009.59.2.7}, url = {https://academic.oup.com/bioscience/article/59/2/141/228297}, author = {Margosian, M.L. and Garrett, K.A. and Hutchinson, J.M.S. and With, K.A.} } @article {KNZ00973, title = {Seasonal, topographic and burn frequency effects on biophysical/spectral reflectance relationships in tallgrass prairie}, journal = {Int. J. Remote Sensing}, volume = {25}, year = {2004}, pages = {5429 -5445}, abstract = {Application of remote sensing relies on understanding how the physical properties of surfaces (especially vegetated surfaces) control spectral reflectance. Empirical investigation of links between canopy properties/processes and spectral response have generally consisted of univariate modelling of one spectral response variable in terms of one canopy property, or less frequently, in terms of two or more canopy variables. While this approach has been fruitful, it cannot account for multivariate interactions of spectral and surface properties in determining canopy response across the spectrum. In this study, two closely related multivariate analysis techniques, canonical correlation and redundancy analysis, are used to investigate the relationship between a series of tallgrass prairie canopy biophysical properties and spectral reflectance measured in situ using a portable radiometer. To capture a variety of different conditions within the tallgrass canopy, data were collected at two times during the 2002 growing season (28 May and 18 August), from two different slope/aspect situations, located on one frequently burned and one infrequently burned watershed. Results suggest that canopy structure (canopy height, greenness fraction) is the most consistent influence on spectral reflectance during both data collection periods. Canopy optical properties also emerge as an important control in August. Neither soil moisture nor plant physiology/biochemistry systematically influenced spectral reflectance. The relative importance of the various canopy variables shows some dependence on burn frequency and topographic setting.}, keywords = {LTER-KNZ}, doi = {10.1080/01431160412331270821}, author = {Goodin, D.G. and Gao, J. and Hutchinson, J.M.S.} } @phdthesis {KNZ00737, title = {Estimating tallgrass prairie soil moisture using active satellite microwave imagery and optical sensor inputs}, volume = {PhD Dissertation}, year = {2000}, pages = {1 -246}, school = {Kansas State University}, type = {Ph.D. Thesis}, address = {Manhattan, KS}, abstract = {

Recent advances in active microwave remote sensing techniques provide the potential for monitoring soil moisture conditions at the spatial and temporal scales required for detailed local modeling efforts. The goal of this research was to produce accurate and spatially distributed estimates of soil moisture using a time series of ERS-2 images for the Konza Prairie, a tallgrass environment in northeast Kansas. The methods used in this research involve field data collection of soil moisture, digital image interpretation of optical (NOAA AVHRR and LANDSAT TM) and radar (ERS-2) imagery, and environmental modeling in a raster GIS environment

}, keywords = {LTER-KNZ, tallgrass prairie}, author = {Hutchinson, J.M.S.} } @mastersthesis {KNZ00595, title = {Radar backshatter soil-moisture relationships over tallgrass prairie}, volume = {MS Thesis}, year = {1997}, pages = {1 -54}, school = {Kansas State University}, type = {M.S. Thesis}, address = {Manhattan, KS}, keywords = {LTER-KNZ, tallgrass prairie}, author = {Hutchinson, J.M.S.} }