The study documents seagrass patch dynamics over large spatial extents in Tampa Bay, Florida. Using GIS techniques a set of fine scale seagrass maps was created within locations previously identified as "patchy" seagrass or areas of seagrass loss. Thirty randomly selected landscape windows of various extents were mapped for the years 2004, 2006, and 2008 by visualizing 0.3 m resolution color imagery on-screen at a digitizing scale of 1:500 using a minimum mapping unit of 1 m2. Characteristics of seagrass patches and patterns of seagrass change were quantified using area-based and time interval metrics including total seagrass area, percent change in seagrass area, seagrass percent cover, and number of patches. Patterns of change were then reviewed at multiple levels of spatial organization and multiple temporal scales. Results from seagrass mapping generated from the fine scale (1 m2 resolution) and previously-reported broad scale (2.02 ha resolution) mapping approaches were also compared. The study documented seagrass patches ranging in size from 1 m2 to greater than 10,000 m2. The fine scale mapping data reported a net increase in seagrass cover from 2004 to 2008. However, only 19 landscape windows were either stable in cover or contributed to the gains in seagrass documented during the study. The remaining 11 landscape windows exhibited various temporal patterns in seagrass loss where patch contraction, complete patch mortality, seagrass fragmentation, and seagrass gap formation were all documented. Results from fine scale mapping indicate that the amount of total seagrass patch area represented by locations categorized as "patchy" in broad scale mapping were, on average, 44 percent less than estimated by the broad scale maps. Together these findings provide new information on how different mapping techniques may produce variable views of seagrass dynamics.

Spring dead spot (SDS) is the most destructive disease of bermudagrass (Cynodon spp.) in Virginia. SDS infects bermudagrass in the fall with symptoms appearing in the spring when dormancy breaks. Patches are sporadically distributed but generally reoccur in the same location. Chemical control options are expensive with inconsistent results. Our objectives were to develop SDS incidence maps, investigate methods to analyze these maps, and evaluate suppression efficacy of incidence-map-based chemical applications. Methods were developed to build SDS incidence maps in 2016 and 2017. 2016 SDS incidence maps were compared for spatial accuracy to Digital Orthophoto Quarter Quadrangle (DOQQ), ground-validated differential GPS coordinates, and to 2017 SDS incidence maps, with average deviations of 1.3 m, 1.6 m, and 0.1 m, respectively. Digital Image Analysis (DIA) of aerial maps was compared to a point-intersect method for validation with a significant linear relationship (r2 = 0.77, P ≤ 0.0001). In the fall of 2016 and 2017, a site-specific penthiopyrad (SSP) treatment was evaluated against blanket, full-coverage applications of penthiopyrad (BP) and tebuconazole (BT), and an untreated control. Treatments were compared using DIA, post-treatment SDS patch count (PC), and SDS patch reduction (PR). Across all three metrics, the penthiopyrad treatments were statistically superior to both the tebuconazole and untreated. SSP compared favorably to BP for DIA, but BP had 2.57 fewer PC (LSD = 2.05) and a greater PR by 2.58 (LSD = 2.55). SSP using SDS incidence maps required 51% less fungicides in 2016 and 65% less in 2017 when compared to BP. / Master of Science in Life Sciences / Spring dead spot (SDS) is one of the most devastating diseases of bermudagrass in Virginia. Bermudagrass is utilized as a playing surface on golf courses and sports fields. During the fall, when the bermudagrass is preparing for winter dormancy, SDS can infect and reduce the turf’s cold tolerance. As a result, dead patches are present in the spring of the year. SDS ruins the integrity of playing surfaces and is slow to recover. The objectives of this research were to develop SDS incidence maps, investigate methods to analyze these maps, and evaluate site-specific chemical applications to control SDS, based on historical incidence. We developed methods for building SDS incidence maps in 2016 and 2017. Maps were evaluated for spatial accuracy as well as their ability to differentiate SDS from healthy bermudagrass. Digital Image Analysis (DIA) was used to calculate SDS coverage. DIA utilizes pixel color values to distinguish SDS from healthy turf. In the fall of 2016 and 2017, a site-specific penthiopyrad (SSP) treatment was evaluated against two full-coverage, blanket fungicides in penthiopyrad (BP) and tebuconazole (BT), as well as an untreated control. These programs were analyzed and across three metrics, DIA, Patch Count (PC) and Patch Reduction (PR), the penthiopyrad treatments were statistically superior to both the tebuconazole and untreated. SSP compared favorably to BP for DIA, but blanket applications were statistically superior when analysis by PC and PR. SSP required 51% less fungicides in 2016 and 65% less in 2017 when compared to BP.

Small Unmanned Aerial Vehicles (SUAVs) are an attractive choice for many surveillance tasks. However, video from an SUAV can be difficult to use in its raw form. In addition, the limitations inherent in the SUAV platform inhibit the distribution of video to remote users. To solve the problems with using SUAV video, we propose a system to automatically create geo-referenced mosiacs of video frames. We also present three novel techniques we have developed to improve ortho-rectification and geo-location accuracy of the mosaics. The most successful of these techniques is able to reduce geo-location error by a factor of 15 with minimal computational overhead. The proposed system overcomes communications limitations by transmitting the mosaics to a central server where there they can easily be accessed by remote users via the Internet. Using flight test results, we show that the proposed mosaicking system achieves real-time performance and produces high-quality and accurately geo-referenced imagery.

Miniature Aerial Vehicles (MAVs) are quickly gaining acceptance as a platform for performing remote sensing or surveillance of remote areas. However, because MAVs are typically flown close to the ground (1000 feet or less in altitude), their field of view for any one image is relatively small. In addition, the context of the video (where and at what orientation are the objects being observed, the relationship between images) is unclear from any one image. To overcome these problems, we propose a geo-referenced mosaicing method that creates a mosaic from the captured images and geo-references the mosaic using information from the MAV IMU/GPS unit. Our method utilizes bundle adjustment within a constrained optimization framework and topology refinement. Using real MAV video, we have demonstrated our mosaic creation process on over 900 frames. Our method has been shown to produce the high quality mosaics to within 7m using tightly synchronized MAV telemetry data and to within 30m using only GPS information (i.e. no roll and pitch information).