The Urban Heat Island (UHI) effect is a function of excess heating of man-made impermeable surfaces and structures. Using Landsat satellite imagery along with its Thermal-Infrared (TIR) band, the UHI of Starkville, MS; Birmingham, AL; and Atlanta, GA were analyzed. Unsupervised classification of the Landsat imagery and temperature extraction from the TIR band revealed city size and amount of high-density urban land use are directly related to UHI intensity and higher than average surface temperatures. Vegetation analysis within the three study area cities, however, revealed an average surface temperature reduction of 2 °C with only 15% forest coverage within a 1km2 area. Results obtained can be useful as a potential monitoring tool that can characterize relationships between amount and percentage of urban tree cover and surface temperature. The information can be utilized by city planners and others who are interested in mitigating UHI effects in the ever- increasing urban America.

This study looks at the changes to NDVI value in the Weeks Bay Reserve following the impact by Hurricane Katrina. Four Landsat images from March 24, 2005 (Pre-Katrina), September 16, 2005/ April 26, 2006 (Post-Katrina) and August 7, 2002 (Control) were classified into different landcover types and run with the NDVI vegetation index. Those images were compared against each other and showed that the September image had a NDVI value drop of 49% and the April image had a 47% drop as compared to the previous March. The emergent vegetation surrounding the shoreline was most susceptible to changes in NDVI value and recovered the slowest of the tested landcover types. Swift tracks, bay areas, and rivers in the study area where tested and showed that the rivers are the most susceptible change in NDVI value and recovered the slowest.

The current study has been conducted in response to the growing problem of water scarcity and the need for more effective methods of irrigation water management. Remote sensing techniques have been used to match spatially and temporally distributed crop water demand to water application rates. Remote sensing approaches using Landsat imagery have been applied to estimate the components of a soil water balance model for an agricultural field by determining daily values of surface/root-zone soil moisture, evapotranspiration rates, and losses and by developing a forecasting model to generate optimal irrigation application information on a daily basis. Incompatibility of coarse resolution Landsat imagery (30m by 30m) with heterogeneities within the agricultural field and potential underestimation of field variations led the study to its main objective, which was to develop models capable of representing spatial and temporal variations within the agricultural field at a compatible resolution with farming management activities. These models support establishing real-time management of irrigation water scheduling and application. The AggieAirTM Minion autonomous aircraft is a remote sensing platform developed by the Utah Water Research Laboratory at Utah State University. It is a completely autonomous airborne platform that captures high-resolution multi-spectral images in the visual, near infrared, and thermal infrared bands at 15cm resolution. AggieAir flew over the study area on four dates in 2013 that were coincident with Landsat overflights and provided similar remotely sensed data at much finer resolution. These data, in concert with state-of-the-art supervised learning machine techniques and field measurements, have been used to model surface and root zone soil volumetric water content at 15cm resolution. The information provided by this study has the potential to give farmers greater precision in irrigation water allocation and scheduling.

Arid lands cover approximately 30% of the earth’s surface. Due to the broadness, remoteness, and harsh condition of these lands, land condition assessment and monitoring using ground-based techniques appear to be limited. Remote sensing imagery with its broad areal coverage, repeatability, cost and time-effectiveness has been suggested and used as an alternative approach for more than three decades. This thesis evaluated the potential of different remote sensing techniques for assessing and monitoring land condition of southern arid lands of South Australia. There were four specific objectives: 1) to evaluate vegetation indices derived from multispectral satellite imagery for prediction of vegetation cover; 2) to compare vegetation indices and field measurements for detecting vegetation changes and assessing land condition; 3) to examine the potential of hyperspectral imagery for discriminating vegetation components that are important in land management using unmixing techniques; and 4) to test whether spatial heterogeneity in land surface reflectance can provide additional information about land condition and effects of management on land condition. The study focused on Kingoonya and Gawler Soil Conservation Districts that were dominated by chenopod shrublands and low open woodlands over sand plains and dunes. The area has been grazed predominately by sheep for more than 100 years and land degradation or desertification due to overgrazing is evident in some parts of the region, especially around stock watering points. Grazing is the most important factor that influences land condition. Four full scenes of Landsat TM and ETM+ multispectral and Hyperion hyperspectral data were acquired over the study area. The imagery was acquired in dry seasons to highlight perennial vegetation cover that has an important role in land condition assessment and monitoring. Slope-based, distance-based, orthogonal transformation and plant-water sensitive vegetation indices were compared with vegetation cover estimates at monitoring points made by state government agency staff during the first Pastoral Lease assessments in 1991. To examine the performance of vegetation indices, they were tested at two scales: within two contrasting land systems and across broader regional landscapes. Of the vegetation indices evaluated, selected Stress Related Vegetation Indices using red, nearinfrared and mid-infrared bands consistently showed significant relationships with vegetation cover at both land system and landscape scales. Estimation of vegetation cover was more accurate within land systems than across broader regions. Total perennial and ephemeral plant cover was predicted best within land systems (R2=0.88), while combined vegetation, plant litter and soil cryptogam crust cover was predicted best at landscape scale (R2=0.39). The results of applying one of the stress related vegetation indices (STVI-4) to 1991 TM and 2002 ETM+ Landsat imagery to detect vegetation changes and to 2005 Landsat TM imagery to discriminate Land Condition Index (LCI) classes showed that it is an appropriate vegetation index for both identifying trends in vegetation cover and assessing land condition. STVI-4 highlighted increases and decreases in vegetation in different parts of the study area. The vegetation change image provided useful information about changes in vegetation cover resulting from variations in climate and alterations in land management. STVI-4 was able to differentiate all three LCI classes (poor, fair and good condition) in low open woodlands with 95% confidence level. In chenopod shrubland and Mount Eba country only poor and good conditions were separable spectrally. The application of spectral mixture analysis to Hyperion hyperspectral imagery yielded five distinct end-members: two associated with vegetation cover and the remaining three associated with different soils, surface gravel and stone. The specific identity of the image end-members was determined by comparing their mean spectra with field reflectance spectra collected with an Analytical Spectral Devices (ASD) Field Spec Pro spectrometer. One vegetation end-member correlated significantly with cottonbush vegetation cover (R2=0.89), distributed as patches throughout the study area. The second vegetation end-member appeared to map green and grey-green perennial shrubs (e.g. Mulga) and correlated significantly with total vegetation cover (R2=0.68). The soil and surface gravel and stone end-members that mapped sand plains, sand dunes, and surface gravel and stone did not show significant correlations with the field estimates of these soil surface components. I examined the potential of a spatial heterogeneity index, the Moving Standard Deviation Index (MSDI), around stock watering points and nearby ungrazed reference sites. One of the major indirect effects of watering points in a grazed landscape is the development around them of a zone of extreme degradation called a piosphere. MSDI was applied to Landsat red band for detection and assessment of these zones. Results showed watering points had significantly higher MSDI values than non-degraded reference areas. Comparison of two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Perpendicular Distance vegetation index (PD54), which were used as reference indices, showed that the PD54 was more sensitive than NDVI for assessing land condition in this perennial-dominated arid environment. Piospheres were found to be more spatially heterogeneous in land surface reflectance. They had higher MSDI values compared to non-degraded areas, and spatial heterogeneity decreased with increasing distance from water points. The study has demonstrated overall that image-based indices derived from Landsat multispectral and Hyperion hyperspectral imagery can be used with field methods to assess and monitor vegetation cover (and consequently land condition) of southern arid lands of South Australia in a quick and efficient way. Relationships between vegetation indices, end-members and field measurements can be used to estimate vegetation cover and monitor its variation with time in broad areas where field-based methods are not effective. Multispectral vegetation indices can be used to assess and discriminate ground-based land condition classes. The sandy-loam end-member extracted from Hyperion imagery has high potential for monitoring sand dunes and their movement over time. The MSDI showed that spatial heterogeneity in land surface reflectance can be used as a good indicator of land degradation. It differentiated degraded from nondegraded areas successfully and detected grazing gradients slightly better than widely used vegetation indices. Results suggest further research using these remote sensing techniques is warranted for arid land condition assessment and monitoring in South Australia. / http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1295218 / Thesis (Ph.D.) -- School of Earth and Environmental Science, 2007

The first chapter of this thesis reviews applications of satellite remote sensing and geographical information systems (GIS) in wildlife studies. The simpler uses of remote sensing are for habitat mapping, often using satellite imagery classified for other natural resources. More sophisticated applications incorporate remotely sensed data into a GIS for the digital manipulation of data planes. The most advanced applications are those which use remote sensing and GIS in models predicting habitat quality or population levels. The second chapter reports how brightness values of six Landsat Thematic Mapper (TM) bands were used in multiple linear regressions to predict percent cover of six rangeland components. Regression equations were applied to TM imagery to create cover maps for live shrub, dead and live shrub, sagebrush, forb/grass, forb, and bare ground/rock. Accuracy was assessed at two levels and ranged from 55 to 90%. The third chapter presents results of sage grouse surveys used with satellite data and GIS to assess habitat use patterns. Habitats used by grouse were compared to availability in the landscape for continuous images of rangeland cover variables, for discrete images of rangeland classes, and for habitat diversity values. Overall, results were comparable to those in studies using traditional methods.

Despite the important values forests play in the tropics, sustainable forest management still remains a challenge as manifested through continued forest loss. The objective of this study was to provide information on the dynamics of forest cover and their drivers vital for enhancing sustainable forest management in the Lake Victoria crescent, Uganda. Several methodologies including remote sensing and Geographic Information Systems techniques, analysis of landscape patterns and various social science techniques were integrated in working towards the stated goal. Results showed that the Lake Victoria crescent, Uganda covering an area of about 1,509,228 ha, experienced a decline in forest cover from 9.0% in 1989 to 4.4% in 2009. This was in comparison with non-forest cover which increased from 58.7% in 1989 to 63.5% in 2009 while open water coverage generally remained unchanged averaging 32.3% from 1989 to 2009. Mean annual deforestation rate from 1989 to 2009 decreased with a weighted mean rate of 2.56%. Both deforestation and afforestation declined between 1989 and 2009 although deforestation still exceeded afforestation. In addition to deforestation, the Lake Victoria crescent also experienced forest fragmentation from 1989 to 2009. Forests greater than 100 ha in size were the most vulnerable to forest fragmentation yet they still constituted a big proportion of forest cover in 2009. Deforestation was a consequence of proximate causes which were triggered by a number of underlying drivers acting singly or in combination, with underlying drivers being more influential. In a bid to promote sustainable forest management, there is a need to continue with efforts to curb deforestation and forest fragmentation, especially amongst forests greater than 100 ha. This could be achieved through empowerment of local communities to take a core role in sustainable management of forest resources. / Ph. D.