Stuart E. Advised by Marsh
Cynthia S.A. Wallace
2002
<p>The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape <span class="hit">temporal </span>dynamics and <span class="hit">spatial</span> structure <span class="hit">from</span> <span class="hit">remotely</span> <span class="hit">sensed</span> <span class="hit">data</span> <span class="hit">for</span> the purpose of <span class="hit">mapping</span> <span class="hit">wildlife</span> <span class="hit">habitat</span>. By coupling the landscape measures gleaned <span class="hit">from </span>the <span class="hit">remotely</span> <span class="hit">sensed</span> <span class="hit">data</span> with various sets of animal sightings and population <span class="hit">data</span>, effective models of <span class="hit">habitat</span> preference were created.</p><p>Measures of <span class="hit">temporal</span> dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific <span class="hit">habitat</span> of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo <span class="hit">habitat</span> in Arizona. Various measures that capture different aspects of the <span class="hit">temporal</span> dynamics of the landscape were derived <span class="hit">from</span> AVHRR Normalized Difference Vegetation Index composite <span class="hit">data</span> using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn <span class="hit">habitat</span> models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using <span class="hit">data</span> that represent detailed knowledge of ground-condition.</p><p>Measures of <span class="hit">temporal</span> dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite <span class="hit">data</span>. Such relationships, once verified and established, can be used to help indirectly monitor the population.</p><p>Measures of landscape <span class="hit">spatial</span> structure derived <span class="hit">from</span> IKONOS high <span class="hit">spatial</span> resolution (1-m) satellite <span class="hit">data</span> using geostatistics effectively map details of Sonoran pronghorn antelope <span class="hit">habitat</span>. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the <span class="hit">spatial</span> autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations.</p><p>The synoptic view of the landscape provided by satellite <span class="hit">data</span> can inform resource management efforts. The ability to characterize the <span class="hit">spatial</span> structure and <span class="hit">temporal</span> dynamics of <span class="hit">habitat</span> using repeatable remote sensing <span class="hit">data</span> allows closer monitoring of the relationship between a species and its landscape.</p>
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The University of Arizona
Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson
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