Browsing by Author "Dr. Hugh A. Devine, Committee Chair"

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Geographic Modeling of El Nino Southern Oscillation Influence on Remotely Sensed Global Nutrient Distribution Patterns - Applications to Science and Geographic Information Systems Education
(2002-04-24) Jason, Allyson Lynne; Dr. Hugh A. Devine, Committee Chair; Dr. Daniel L. Kamykowski, Committee Member; Dr. Yu-Fai Leung, Committee Member
The objective of the study was to geographically model the effects of the El Niño Southern Oscillation (ENSO) influence on remotely sensed global nutrient distribution patterns. The result was a system of digital maps communicating the impact of ENSO on the physical and biological components of the ocean. These maps compare modeled phytoplankton biomass distribution over the ENSO extremes. Chlorophyll a, Aerosol Optical Thickness, and Sea Surface Temperature data, all obtained from remotely sensed sources, were used to develop these predictions. Areas of iron deposition and phytoplankton presence (chlorophyll a < 0.1 μg 1¯-1) were combined with nutrient distributions (based on the temperature-nutrient relationship) to create a sixteen-category composite phytoplankton ecological factor distribution map for each month in the study. The months included in the study were January, February, March of 1998, an El Niño year, and January, February, March of 1999, a La Niña year. Finally, an educational multimedia tool (CD-ROM) was created based on the research in the study for use in grades 7-16 classrooms. The tool was designed and tested to utilize Geographic Information Systems and the Internet to apply inquiry-based learning to science education.
An Integrated Spatial and Attribute Data Structure to Support National Park Service Management.
(2005-03-17) Colson, Thomas Payton; Dr. James Gregory , Committee Member; Dr. Hugh A. Devine, Committee Chair; Dr. Stacey Nelson, Committee Member
The National Park Service (NPS) maintains and supports various databases relevant to its mission. These include archeological sites, cultural objects, and exotic species inventories, to name a few. Data associated with each of these domains forms the basis on which NPS policy is formulated. However these databases often exist in independently maintained systems, often at different locations. Additionally these domains often contain spatial data in which data objects can possess spatial characteristics and location information. Currently most National Park Service non spatial attribute databases (e.g. ASMIS, CLAIMS, FMSS, and LCS) are centralized at regional or national headquarters, whereas most spatial information is generally created and maintained at the park. It is left to the data user to connect to these data entities, and this is at best cumbersome and at times, impossible. This project is a feasibility study that analyzes the use of GIS to integrate of park service attribute databases with locally collected park spatial data. The core procedure focuses on the application of computer aided software engineering tools to National Park Service GIS database design. This includes modeling database relationships and generating the database schema. The resultant prototype database was tested in two National Parks, Valley Forge National Historic Park and Appomattox Court House National Historic Park. In both instances, locally generated spatial data were imported successfully into a standardized ESRI geodatabase and integrated with other enterprise, non local data through the use of reusable objects.
Predicting Fuel Models and Subsequent Fire Behavior from Vegetation Classification Maps
(2003-09-02) Smith, Mark Preston; Dr. Stacy A. C. Nelson, Committee Member; Dr. E. Carlyle Franklin, Committee Member; Dr. Hugh A. Devine, Committee Chair
The recent trends in wildland fires have created a level of motivation that requires natural resource managers to predict fires through the use of computer based simulation programs. Using vegetation maps delineated from large-scale aerial photography and fuel loading values collected from fieldwork, I simulated how fire would react to changes in fuel model assignments for Booker T. Washington National Monument (BOWA) and George Washington Birthplace National Monument using FARSITE, a fire simulation program. The environments for these fires were based on weather and fuel conditions found during the summer and fall months for each area. Sample points, stratified by vegetation formation, were selected. Then, field measurements using Brown's transect lines and Burgan and Rothermel ocular procedures were used to calculate the amount of fuel loading in tons/acre for each sample point. These values were then used to assign a fuel load to each vegetation formation class. Then each vegetation polygon on the map was assigned one of the thirteen National Fire Fuel Laboratory fuel models based on fuel load, vegetation type, and overall structure of the surrounding area. The sampling results showed a one to one correspondence of fuel model to vegetation formation. The sensitivity of FARSITE was tested by changing fuel model layers within FARSITE while holding all other variables constant (e.g., weather, moisture, etc.). Rate of spread and fire line intensity were used to compare the differences between the simulations using different fuel models. The results from the simulations showed that there was little sensitivity to changes in the assignment of fuel models for forested vegetation for these sites. The rate of spread and fire line intensity for grass fuel models showed sensitivity to changes in fuel model assignment.
Remote Sensing Procedures to Update Forested Geospatial Datasets after a Landscape Altering Event
(2006-02-15) Shedd, Justin McEachern; Dr. Hugh A. Devine, Committee Chair; Dr. Heather Cheshire, Committee Member; Dr. Stacy Nelson, Committee Member
The creation of accurate geospatial datasets like vegetation and fire fuel loads is a time consuming effort and these datasets are routinely used by resource managers. Therefore the accuracy of these datasets is vital. Vegetation and fire fuel load datasets often represent a dynamic landscape and landscape altering events such as a wildland fire or a hurricane can drastically change that landscape. The goal of this research is to investigate the use of automated change detection techniques that can not only indicate areas of change but also quantify the magnitude of change that occurred as well. Hurricane Isabel did extensive damage to the forest landscapes of central Virginia in September of 2003, specifically Petersburg National Battlefield. The Rocky Top Fire occurred in July of 2002 in Shenandoah National Park, resulting in a mosaic pattern of burns, covering roughly 1500 acres. The objective of this research was to test the use of remote sensing procedures to update vegetation and fire fuel load spatial datasets. First, using digital orthorectified photomosaics, the automated feature extraction technique Visual Learning System's Feature Analyst, was employed to delineate forest damage following Hurricane Isabel. Second, the satellite based remote sensing technique Normalized Burn Ratio, was utilized to delineate and quantify burn severity on vegetation after the Rocky Top Fire. A third objective was to estimate fire behavior differences between the existing pre-event and the remotely sensed post-event fuel load datasets using the FARSITE model, thereby cataloging the potential need for vegetation and fuel load updates. The results of this research show that, 1) VLS Feature Analyst is an excellent indicator of downed woody debris, 2) the Normalized Burn Ratio is the best technique available for indicating and quantifying the effects of a wildland fire on the landscape, 3) changes in assigned Fuel Models, especially in the Logging Slash group, affect FARSITE outcomes, and 4) Fuel Models should be assigned based on expected fire behavior, not on the total fuel loading.
Resolution Assessment and Spatial Characterization of Airborne LIDAR Data: Assateague Island National Seashore.
(2004-07-12) Shroyer, William James; Dr. Hugh A. Devine, Committee Chair
The purpose of this research was to determine the effect of spatial data resolution change on the representation of barrier island topographic surfaces. A study area of approximately three kilometers of the northern section of the Maryland portion of Assateague Island National Seashore (ASIS) was chosen. Utilizing the Level 4 LIDAR data, provided by the United States Geological Survey in GeoTiff format, the data was converted into raster elevation Grids at four resolutions (0.5 m, 1 m, 2 m, and 10 m). Using the 1999 and 2001 LIDAR surveys the grids were processed as Digital Elevation Models (DEM's) at the varying resolutions and compared. An interpretation of volumetric change was established to determine the effect of resolution on representing change in topography. Subtraction of the elevation grids for 1999 and 2001 was used to estimate the volumetric change. Slope and hillshade surfaces were also used as a means to determine the variations in spatial distribution that occur when changing resolution. Although the amount of change in volume varied between resolutions the spatial distribution of change in each surface was consistent between resolutions. Using an analysis of variance (ANOVA) of mean elevation change, revealed no significant difference with resolution. There was also very little slope change observed when comparing each resolution. Subtracting the slope surfaces and reclassifying the values into slope increase, slope decrease and no change provided insight into the spatial distribution of slope change. Throughout the varying resolutions the position in which these slope classifications were represented was consistent. The visual comparison of hillshade surfaces and topographic profiles represented the generalization that takes place when resampling to coarser resolutions. As the cell size increased with each resolution, the profile of the topography represented less relief. This was also illustrated in the hillshade surfaces of each resolution as the topographic features became more apparent from the 0.5 m to the 10 m sizes. Constantly interrupted by coastal processes, the barrier island system exists in a continued flux of change. This constant change presents coastal scientists with a difficult challenge in measuring and monitoring the barrier island topography. Dependant upon application, the use of precision LIDAR surveys allows for an assessment of the continuing process of barrier island evolution.