@article {KNZ001992, title = {Effects of fire and large herbivores on canopy nitrogen in a tallgrass prairie}, journal = {Remote Sensing}, volume = {11}, year = {2019}, chapter = {1364}, abstract = {

This study analyzed the spatial heterogeneity of grassland canopy nitrogen in a tallgrass prairie with different treatments of fire and ungulate grazing (long-term bison grazing vs. recent cattle grazing). Variogram analysis was applied to continuous remotely sensed canopy nitrogen images to examine the spatial variability in grassland canopies. Heterogeneity metrics (e.g., the interspersion/juxtaposition index) were calculated from the categorical canopy nitrogen maps and compared among fire and grazing treatments. Results showed that watersheds burned within one year had higher canopy nitrogen content and lower interspersions of high-nitrogen content patches than watersheds with longer fire intervals, suggesting an immediate and transient fire effect on grassland vegetation. In watersheds burned within one year, high-intensity grazing reduced vegetation density, but promoted grassland heterogeneity, as indicated by lower canopy nitrogen concentrations and greater interspersions of high-nitrogen content patches at the grazed sites than at the ungrazed sites. Variogram analyses across watersheds with different grazing histories showed that long-term bison grazing created greater spatial variability of canopy nitrogen than recent grazing by cattle. This comparison between bison and cattle is novel, as few field experiments have evaluated the role of grazing history in driving grassland heterogeneity. Our analyses extend previous research of effects from pyric herbivory on grassland heterogeneity by highlighting the role of grazing history in modulating the spatial and temporal distribution of aboveground nitrogen content in tallgrass prairie vegetation using a remote sensing approach. The comparison of canopy nitrogen properties and the variogram analysis of canopy nitrogen distribution provided by our study are useful for further mapping grassland canopy features and modeling grassland dynamics involving interplays among fire, large grazers, and vegetation communities.

}, keywords = {LTER-KNZ, fire, grassland canopy nitrogen, grassland dynamics, grazing history, Spatial heterogeneity, Ungulate grazing}, url = {https://www.mdpi.com/2072-4292/11/11/1364?fbclid=IwAR3lLrrJFA3JBzN5IcRlRx-Gn7S_f-9nclPRB4H7IdDHxYQe34Ric_mraDs}, author = {Ling, B. and Raynor, E.J. and Goodin, D.G. and Anthony Joern} } @article {KNZ001211, title = {Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment}, journal = {Remote Sensing of Environment}, volume = {112}, year = {2008}, pages = {2977 -2987}, abstract = {

Understanding the spatial scaling behavior of evapotranspiration and its relation to controlling factors on the land surface is necessary to accurately estimate regional water cycling. We propose a method for ascertaining this scaling behavior via a combination of wavelet multi-resolution analysis and information theory metrics. Using a physically-based modeling framework, we are able to compute spatially distributed latent heat fluxes over the tall-grass prairie in North-central Kansas for August 8, 2005. Comparison with three eddy-covariance stations and a large aperture scintillometer demonstrates good agreement, and thus give confidence in the modeled fluxes. Results indicate that the spatial variability in radiometric temperature (a proxy for soil moisture) most closely controls the spatial variability in evapotranspiration. Small scale variability in the water flux can be ascribed to the small scale spatial variance in the fractional vegetation. In addition, correlation analysis indicates general scale invariance and that low spatial resolution data may be adequate for accurately determining water cycling in prairie ecosystems.

}, keywords = {LTER-KNZ, Entropy, information theory, Konza Prairie, Latent heat, MODIS, Spatial heterogeneity, SVAT model, wavelets}, doi = {10.1016/j.rse.2008.02.002}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0034425708000655?via\%3Dihub}, author = {N. Brunsell and J.M. Ham and Owensby, C.E.} }