Doctor of Philosophy (PhD)
First Committee Member
Number of Pages
One of the most critical issues associated with using satellite data-based products to study and estimate surface energy fluxes and other ecosystem processes, has been the lack of frequent acquisition at a spatial scale equivalent to or finer than the footprint of field measurements. In this study, we incorporated continuous field measurements based on using Normalized difference vegetation index (NDVI) time series analysis of individual shrub species and transect measurements within 625 m2 size plots equivalent to the Landsat-5 Thematic Mapper spatial resolution. The NDVI system was a dual channel SKR-1800 radiometer that simultaneously measured incident solar radiation and upward reflectance in two broadband red and near-infrared channels comparable to Landsat-5 TM band 3 and band 4, respectively. The two study sites identified as Spring Valley 1 site (SV1) and Snake Valley 1 site (SNK1) were chosen for having different species composition, soil texture and percent canopy cover; NDVI time-series of greasewood (Sarcobatus vermiculatus) from the SV1 site allowed for clear distinction between the main phenological stages of the entire growing season during the period from January to November, 2007. Comparison of greasewood NDVI values between the two sites revealed a significant temporal difference associated with early canopy development and early dry down of greasewood at the SNK1 site. NDVI time series values were also significantly different between sagebrush (Artemisia tridentata ) and rabbitbrush (Chrysothamnus viscidiflorus) at SV1 as well as between the two bare soil types at the two sites, indicating the ability of the ground-based NDVI to distinguish between different plant species as well as between different desert soils based on their moisture level and color. The difference in phenological characteristics of greasewood between the two sites and between sagebrush, rabbitbrush and greasewood within the same site were not captured by the spatially integrated Landsat NDVI acquired during repeated overpasses. Greasewood NDVI from the SNK1 site produced significant correlations with many of the measured plant parameters, most closely with chlorophyll index (r = 0.97), leaf area index (r = 0.98) and leaf xylem water potential (r = 0.93). Whereas greasewood NDVI from the SV1 site produced lower correlations ( r = 0.89, r = 0.73), or non significant correlations (r = 0.32) with the same parameters, respectively. Total percent cover was estimated at 17.5% for SV1 and at 63% for SNK1; Transect measurements provided detailed information with regard to the spectral properties of shrub species and soil types, differentiating the two sites, which was not possible to discern with the spatial resolution of Landsat. Correlation between transect NDVI data and Landsat NDVI produced an r of 0.79. While correlation between transect NDVI data and ground-based NDVI sensors produced an r of 0.73. The linear regression equation between daily ET measured by the eddy covariance method and Landsat NDVI yielded a strong relationship (r = 0.88) for data combined across the experimental period (May to September) and across the two sites. The ET prediction equation was improved (r2 = 0.86) by introducing net solar radiation (Rn) which was the meteorological variable that had the highest prediction of ET (r2 = 0.82). A high correlation was found between weighted ground-based sensor NDVI estimates and Landsat derived NDVI at the pixel scale (r = 0.97) for the two study sites combined over time. While results from this study in scaling ground-based NDVI measurements and estimating ET were very promising, further verification and improvement is needed to determine the performance level of this approach over larger heterogeneous areas and over extended time periods.
Dense; Environment; Evapotranspiration; Great Basin; Ground; Measurements; Ndvi; Predicting; Satel; Satellite; Sparse; Semiarid; Vegetation
Ecology; Botany; Remote sensing
University of Nevada, Las Vegas
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Baghzouz, Malika, "Predicting evapotranspiration from sparse and dense vegetation communities in a semiarid environment using Ndvi from satellite and ground measurements" (2008). UNLV Retrospective Theses & Dissertations. 2835.