Remote sensing of vegetation characteristics and spatial analysis of pyric herbivory in a tallgrass prairie

Date

2018-08-01

Journal Title

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Abstract

Quantitative remote sensing provides an effective way of estimating and mapping vegetation characteristics over an extensive area. The spatially explicit distribution of canopy vegetative properties from remote sensing imagery can be further used for studies of spatial patterns and processes in grassland systems. My research focused on remote sensing of grassland vegetation characteristics and its applications to spatial analysis of grassland dynamics involving interactions between pyric herbivory and vegetation heterogeneity. In remote sensing of vegetation characteristics, (1) I estimated the foliar pigments and nutritional elements at the leaf level using hyperspectral data. The foliar pigments, chlorophylls and carotenoids, were retrieved by inverting the physical radiative transfer model, PROSPECT. The nutritional elements were modeled empirically using partial least squares (PLS) regression. Correlations were found between the leaf pigments and nutritional elements. This provided insight into the use of pigment-related vegetation indices as a proxy of the plant nutritional quality. (2) At the canopy level, I assessed the use of the broadband vegetation indices, normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI), in detecting vegetation quantity (LAI) and quality (leaf and canopy chlorophyll concentrations). The relationships between vegetation indices and vegetation characteristics were examined in the physical model, PROSAIL, and validated by a field dataset collected from a tallgrass prairie. NDVI showed high correlations with LAI and canopy chlorophylls. GRVI performed even better than NDVI in estimating LAI. A new index GNVI (green-red normalized vegetation index) that combined NDVI and GRVI was proposed to extract leaf chlorophyll concentration. These findings showed the potential of using broadband vegetation indices from multispectral remote sensors to monitor vegetation quantity and quality over a wide spatial extent. In the spatial analysis, I examined interactions between pyric herbivory and grassland heterogeneity at multiple scales from the remote sensing imagery. (3) At a coarse, watershed level, I evaluated effects of fire and large herbivores on the spatial distribution of canopy nitrogen. It was found that the interactive effects of fire and ungulate grazing were present in the watersheds burnt in spring, where a high level of ungulate grazing reduced vegetation density, but promoted canopy heterogeneity. Two grazer species, bison and cattle, were compared. Differences in the vegetation canopy between sites with bison and cattle were observed, which may be related to differences in the grazing intensity, forage behavior and habitat selection between the two grazer species. (4) At a fine, patch level (30 m), bison forage pattern was examined associated with canopy nitrogen heterogeneity. Bison preference for patches with high canopy nitrogen was evident in May. Later in June – September, bison tended to avoid sites with high canopy nitrogen. Vegetation heterogeneity showed significant influences on bison habitat selection in June. Bison preferred sites with low variance in canopy nitrogen, where the patch types were highly aggregated and equitably proportioned.

Description

Keywords

Remote sensing, Tallgrass prairie, Pyric herbivory, Grassland heterogeneity

Graduation Month

August

Degree

Doctor of Philosophy

Department

Department of Geography

Major Professor

Douglas Goodin

Date

2018

Type

Dissertation

Citation