Remote sensing techniques have proven utility to a wide range of planning problems. This research explores the adoption and application of remote sensing techniques by Virginia planning agencies. All planning agencies within the Commonwealth were sent a Remote Sensing Utilization Questionnaire. Survey results indicate that adoption of remote sensing products is low at all 1evels of planning; that most agency characteristics, e.g. staff size, budget, are not correlated with utilization; and, the lack of remote sensor adoption and use appears to be a result of the lack of remote sensor education among Virginia planners. Research results suggest also that the demand for information concerning remote sensor applications to planning problems is high among Virginia planners at all levels of planning. A high proportion of respondents at all planning levels stated that they would like additional remote sensing/planning applications information; would like more remote sensing/planning applications articles in professional planning journals; and, would send a representative to a remote sensing/planning applications conference if the conference were held within the state. Research results define major programmatic roles for federal and state agencies involved with remote sensor technologies and Virginia colleges and universities with remote sensing capabilities. / D.E.D.P.

The improved spatial resolution of the data from the SPOT satellite provides a substantially better basis for monitoring urban land use and growth with remote sensing than Landsat data. The purpose of this study is to delineate the road network in 20-m resolution SPOT-images of urban areas automatically. The roads appear as linear features. However, most edge and line detectors are not effective in detecting roads in these images because of the low signal to noise ratio, low contrast and blur in the imagery. For the automatic recognition of roads, a new line detector based on surface modelling is developed. A line can be approximated by a piecewise straight curve composed of short linear line-elements, called linels, each characterized by a direction, a length and a position. The approach to linel detection is to fit a directional surface that models the ideal local intensity profile of a linel in the least square sense. A Gaussian surface with a direction of invariance forms an adequate basis for modelling the ideal local intensity profile of the roads. The residual of the least squares fit as well as the parameters of the fit surface characterize the linel detected. The reliable performance of this line operator makes the problems of linking linels more manageable. / Master of Science

In this study we applied the asymmetric Gaussian, double-logistic, and Savitzky-Golay filters to MODIS time-series NDVI data to compare the capability of smoothing algorithms in noise reduction for improving land cover classification in the Great Lakes Basin, and providing groundwork to support cyanobacteria and cyanotoxin monitoring efforts. We used inter-class separability and intra-class variability, at varying levels of pixel homogeneity, to evaluate the effectiveness of three smoothing algorithms. Based on these initial tests, the algorithm which returned the best results was used to analyze how image stratification by ecoregion can affect filter performance. MODIS 16-day 250m NDVI imagery of the Great Lakes Basin from 2001-2013 were used in conjunction with National Land Cover Database (NLCD) 2006 and 2011 data, and Cropland Data Layers (CDL) from 2008 to 2013 to conduct these evaluations. Inter-class separability was measured by Jeffries-Matusita (JM) distances between selected land cover classes (both general classes and specific crops), and intra-class variability was measured by calculating simple Euclidean distance for samples within a land cover class. Within the study area, it was found that the application of a smoothing algorithm can significantly reduce image noise, improving both inter-class separability and intra-class variability when compared to the raw data. Of the three filters examined, the asymmetric Gaussian filter consistently returned the highest values of interclass separability, while all three filters performed very similarly for within-class variability. The ecoregion analysis based on the asymmetric Gaussian dataset indicated that the scale of study area can heavily impact within-class separability. The criteria we established have potential for furthering our understanding of the strengths and weaknesses of different smoothing algorithms, thereby improving pre-processing decisions for land cover classification using time-series data. / Master of Science

Agricultural ecosystems provide multiple services including food and fiber provision, nutrient cycling, soil retention and water regulation. Objectives of the study were to identify and quantify a selection of ecosystem services provided by agricultural land, using existing geospatial tools and preferably free and open source data, such as the Virginia Land Use Evaluation System (VALUES), the North Carolina Realistic Yield Expectations (RYE) database, and the land cover datasets NLCD and CDL. Furthermore I sought to model tradeoffs between provisioning and other services. First I assessed the accuracy of agricultural land in NLCD and CDL over a four county area in eastern Virginia using cadastral parcels. I uncovered issues concerning the definition of agricultural land. The area and location of agriculture saw little change in the 19 years studied. Furthermore all datasets have significant errors of omission (11.3 to 95.1%) and commission (0 to 71.3%). Location of agriculture was used with spatial crop yield databases I created and combined with models I adapted to calculate baseline values for plant biomass, nutrient composition and requirements, land suitability for and potential production of biofuels and the economic impact of agriculture for the four counties. The study area was then broadened to cover 97 counties in eastern Virginia and North Carolina, investigating the potential for increased regional grain production through intensification and extensification of agriculture. Predicted yield from geospatial crop models was compared with produced yield from the NASS Survey of Agriculture. Area of most crops in CDL was similar to that in the Survey of Agriculture, but a yield gap is present for most years, partially due to weather, thus indicating potential for yield increase through intensification. Using simple criteria I quantified the potential to extend agriculture in high yield land in other uses and modeled the changes in erosion and runoff should conversion take place. While the quantity of wheat produced though extensification is equal to 4.2 times 2012 production, conversion will lead to large increases in runoff (4.1 to 39.4%) and erosion (6 times). This study advances the state of geospatial tools for quantification of ecosystem services. / Ph. D.

In Jordan, the fourth driest country in the world, demand for water exceeds available water resources. The annual per capita water availability is 145 Cubic Meter (CM) per year, which is below the international poverty line of 500 CM/year. The increasing water deficit year-on-year poses serious future threat that will impact many sectors. Water quantity and quality are essential issues in Jordan and more efforts are needed to find new water resources, and to protect and improve the available resources. This research seeks to study the relationship between Landuse/cover change and water quality in reservoirs in Jordan. Landuse changes were detected by using Landsat Thematic Mapper (TM) images obtained for King Talal reservoir in 1990 and 2006 and for Karameh reservoir in 1998 and 2006. Geometric correction and supervised classification were utilized in ERDAS software. Turbidity levels within the two reservoirs were estimated by the chromaticity technique and were compared to measured data from previous reports for both reservoirs. Remote sensing was successful in detecting the changes in landuse in both areas. The estimated turbidity levels correlated moderately well with measured data from previous reports for the same reservoirs; it was difficult to directly relate a specific Landuse/cover for turbidity levels. Limitations were defined as data collection and quality problems, in addition to some theoretical issues about using Landsat for this study. / Master of Science

In 1987 a NASA panel recommended the creation of the Mission to Planet Earth. This mission was intended to apply to remote sensing experience of the space community to earth remote sensing to enhance the understanding of the climatalogical processes of our planet and to determine if, and to what extent, the hydrological cycle of Earth is being affected by human activity. One of the systems required for the mission was a wide scanning, high gain reflector antenna system for use in radiometric remote sensing from geostationary orbit. This work describes research conducted at Virginia Tech into techniques for beam scanning offset Cassegrain reflector antennas by subreflector translation and rotation. Background material relevant to beam scanning antenna systems and offset Cassegrain reflector antenna system is presented. A test case is developed based on the background material. The test case is beam scanned using two geometrical optics methods of determining the optimum subreflector position for the desired scanned beam direction. Physical optics far-field results are given for the beam scanned systems. The test case system is found to be capable of beam scanning over a range of 35 half-power beamwidths while maintaining a 90% beam efficiency or 50 half-power beamwidths while maintaining less than 1 dB of gain loss during scanning. / Master of Science

As attenuation in silica based fibers approaches its ultimate theoretical limit, investigation is in progress to develop new materials which exhibit lower losses than silica. These bulk materials could then be used to fabricate ultralow loss optical fibers which operate farther out in the infrared than do silica fibers. Such infrared fibers could be used in long, repeaterless telecommunications links, the transmission of <i>CO</i> and <i>CO</i>₂ laser power, and in several sensing mechanisms. This thesis presents an overview of these new fibers and how they can be applied in noncontact temperature measurement. Fiber optic temperature sensing is thus reviewed, and an optical fiber pyrometer is discussed. / Master of Science

Primary productivity is a fundamental ecological process and an important measure of ecosystem response to environmental change. Currently, there is a considerable lapse in our understanding of primary productivity in hot and cold deserts, due to the difficulty of measuring production in cryptogam vegetation. However, remote sensing can provide long-term, spatially-extensive estimates of primary production and are particularly well suited to remote environments, such as in the McMurdo Dry Valleys (MDV) of Antarctica, where cyanobacterial communities are the main drivers of primary production. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of desert pavement. The cryptic nature of these communities, their often patchy spatial distribution, and their ability to survive desiccation make assessments of productivity challenging. I used field-based surveys of microbial mat biomass and pigment chemistry in conjunction with analyses of multispectral satellite data to examine the distribution and activity of microbial mats. This is the first satellite-derived estimate of microbial mat biomass for Antarctic microbial mat communities. I show strong correlations between multispectral satellite data (i.e., NDVI) and ground based measurements of microbial mats, including ground cover, biomass, and pigment chemistry. Elemental (C, N) and isotopic composition (15N, 13C) of microbial mats show that they have significant effects on biogeochemical cycling in the soil and sediment of this region where they occur. Using these relationships, I developed a statistical model that estimates biomass (kg of C) in selected wetlands in the Lake Fryxell Basin, Antarctica. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N cycling in the McMurdo Dry Valleys, Antarctica. / Master of Science / Primary productivity is an essential ecological process and a useful measure of how ecosystems respond to climate change. Primary production is more difficult to measure in polar desert ecosystems where there is little to no vascular vegetation. Polar regions are also ecosystems where we expect to see significant responses to a changing climate. Remote sensing and image analysis can provide estimates of primary production and are particularly useful in remote environments. For example, in the McMurdo Dry Valleys (MDV) of Antarctica, cyanobacterial communities are the main primary producers. These microbial communities form multi-layered sheets (i.e., microbial mats) on top of rocks and soil. These communities are cryptic, do not cover large areas of ground continuously, and are able to survive desiccation and freezing. All of these characteristics make assessments of productivity especially challenging. For my master’s research, I collected microbial mat samples in conjunction with the acquisition of a satellite image of my study area in the MDV, and I determined biological parameters (e.g., percent ground cover, organic matter, and chlorophyll-a content) through laboratory analyses using these samples. I used this satellite image to extract spectral data and perform a vegetation analysis using the normalized difference vegetation index (i.e., NDVI), which determines areas in the image that contain vegetation (i.e., microbial mats). By linking the spectral data to the biological parameters, I developed a statistical model that estimates biomass (i.e., carbon content) of my study areas. These are the first microbial mat biomass estimates using satellite imagery for this region of Antarctica. Additionally, I researched the importance of microbial mats on nitrogen cycling in Taylor Valley. Using elemental and isotopic analyses, I determined microbial mats have significant effects on the underlying soil and nutrient cycling. Overall, this research demonstrates the importance of terrestrial microbial mats on C and N fixation in Antarctic soil environments.

Remote sensing techniques are important for detecting disease within the turfgrass canopy. Herein, we look at two such techniques to assess their viability in detecting and isolating turfgrass diseases. First, thermal imagery is used to detect differences in canopy temperature associated with the onset of brown patch infection in tall fescue. Sixty-four newly seeded stands of tall fescue were arranged in a randomized block design with two runs with eight blocks each containing four inoculum concentrations within a greenhouse. Daily measurements were taken of the canopy and ambient temperature with a thermal camera. After five consecutive days differences were detected in canopy – ambient temperature in both runs (p=0.0015), which continued for the remainder of the experiment. Moreover, analysis of true colour imagery during this time yielded no significant differences between groups. A field study comparing canopy temperature of adjacent symptomatic and asymptomatic tall fescue and creeping bentgrass canopies showed differences as well (p<0.0492). The second project attempted to isolate spring dead spot from aerial imagery of bermudagrass golf course fairways using a Python script. Aerial images from unmanned aerial vehicle flights were collected from four fairways at Nicklaus Course of Bay Creek Resort in Cape Charles, VA. Accuracy of the code was measured by creating buffer zones around code generated points and measuring how many disease centers measured by hand were eclipsed. Accuracies measured as high as 97% while reducing coverage of the fairway by over 30% compared to broadcast applications. Point density maps of the hand and code points also appeared similar. These data provide evidence for new opportunities in remote turfgrass disease detection. / Master of Science in Life Sciences / Turfgrasses are ubiquitous, from home lawns to sports fields, where they are used for their durability and aesthetics. Disease within the turfgrass canopy can ruin these aspects of the turfgrass reducing its overall quality. This makes detection and management of disease within the canopy an important part of maintaining turfgrass. Here we look at the effectiveness of imaging techniques in detecting and isolating disease within cool-season and warm-season turfgrasses. We test the capacity for thermal imagery to detect the infection of tall fescue (Festuca arundenacea) with Rhizoctonia solani, the causal agent of brown patch. In greenhouse experiments, differences were detected in normalized canopy temperature between differing inoculation levels at five days post inoculation, and in field conditions we were able to observe differences in canopy temperature between adjacent symptomatic and non-symptomatic stands. We also developed a Python script to automatically identify and record the location of spring dead spot damage within mosaicked images of bermudagrass golf fairways captured via unmanned aerial vehicle. The developed script primarily used Hough transform to mark the circular patches within the fairway and recorded the GPS coordinates of each disease center. When compared to disease incidence maps created manually the script was able to achieve accuracies as high as 97% while reducing coverage of the fairway by over 30% compared to broadcast applications. Point density maps created from points in the code appeared to match those created manually. Both findings have the potential to be used as tools to help turfgrass managers.

The twospotted spider mite (TSSM), Tetranychus urticae Koch, is a common polyphagous pest in peanut agroecosystems. The mite has caused serious economic losses to peanut farmers in the Virginia-Carolina area, where approximately 20% of the peanuts are produced annually in the United States. Peanut farmers depend on pesticides to control mite populations. Because TSSM has developed resistance to many acaricides and there are restrictions on the use of pesticides, an alternative approach, such as precision pest management, is needed that would reduce the amount of pesticides that must be applied. This study was initiated to determine whether precision pest management is a feasible management strategy for use against TSSM populations in peanut. Two requirements of the precision management approach are that maps of the spatial distribution of TSSM populations can be developed and the pattern of distribution changes little over time to allow management strategies to be implemented. To this end, a study of four commercial peanut fields located in two counties of southeastern Virginia was conducted to characterize the spatial distribution of TSSM populations. Intensive sampling of TSSM populations was conducted within each of the fields. The results showed that there was a general increase in TSSM populations during the early phases of sampling. Fields with low densities of TSSM populations had a spatial distribution that was either uniform or random; in fields with relatively higher densities, TSSM populations usually were aggregated. Little or no change in the spatial distribution of TSSM occurred from week to week in all fields that were sampled. Where changes in the distribution were observed, these were apparently caused by the application of a pesticide by the grower. The study also looked at remote sensing technology as an alternative to intensive sampling within peanut fields. Research was conducted under laboratory conditions to determine whether damage caused by feeding TSSM could be detected spectrally before symptoms become visible. The study showed that after eight days leaves of peanut plants subjected to low soil moisture levels had significantly lower reflectance ratios (mean = 9.4766; a = 0.05) than plants given medium (mean = 10.0186) or high (mean = 10.5413) soil moisture levels. After 10 days, there were significant differences (P < 0.05) in the mean reflectance ratios of peanut leaves exposed to four levels of spider mite densities (0, 5, 10, 20 mites/leaf) and the three levels of soil moisture. However, no significant interaction was observed between soil moisture and spider mite density (P = 0.8710). The mean reflectance ratio for 20 TSSM per leaf was found to be significantly lower than 0, 5, and 10 TSSM per leaf at all levels of moisture (low, medium, and high). The results suggested that remote sensing could be used to detect and map plant damage caused by feeding of spider mites before visual symptoms of damage are observed. The study also attempted to develop a platform for using remote sensing technology in the field. An Unmanned Air Vehicle (UAV) was evaluated that carried a remote sensing system. The UAV remote sensing system was flown over peanut fields where it captured images, which were analyzed to show the spatial distribution of plant stress. Further studies are needed to relate the distribution of plant stress or damage observed by the UAV with the distribution of TSSM densities within peanut fields. Once this has been accomplished, low-altitude remote sensing could be used as an alternative to sampling for building maps of the spatial distribution of TSSM populations for precision pest management. / Master of Science