Browsing by Author "Heather Cheshire, Committee Chair"
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- Conifer Discrimination in the Sandhills of North Carolina Using High Spectral Resolution Data(2008-08-12) Otwell, Dwight Woodard; Heather Cheshire, Committee Chair; Stacy Nelson, Committee Member; Gary Blank, Committee MemberWe investigated techniques to discriminate long leaf pine (Pinus palustris) and loblolly pine (Pinus taeda) in 126 band HyMap imagery with a 4 meter spatial resolution. Field assessment provided stand composition information, and crowns of known species were selected in the imagery to represent species types for model construction. A Quadratic Discriminant Analysis used with a likelihood ratio test was able to identify southern yellow pine with a producer's accuracy of 98% and a user's accuracy of 96%. The same test identified loblolly pine with a producer's accuracy of 80% and a user's accuracy of 49%. Longleaf pine identification had a producer's accuracy of 60% and a user's accuracy of 76%. Price of image acquisition, the relatively low accuracy of discrimination between longleaf and loblolly pine crowns, and inherent bias in the approach make this particular method unreliable as an option for targeting potential sites for RCW habitat restoration.
- Developing a new fuel load mapping strategy using: USDA FS Forest Inventory and Analysis (FIA) protocols, Digital Photogrammetry, International Classification of Ecological Communities and Disturbance History(2004-01-13) Rosenfeld, Brian Jay; Heather Cheshire, Committee Chair; James Vose, Committee Member; Hugh Devine, Committee MemberFire behavior models require many variables including above ground biomass or fuel load. Fuel loads include the amount of woody debris, duff, and litter that can carry a fire. The collection of these data can be very time consuming and expensive. The purpose of this project was to develop a quantitative, multi-purpose strategy for mapping fuel loads. Study sites were located in the coastal plain (Alligator River National Wildlife Refuge and Dare County Bombing Range) and the mountains (Coweeta Hydrologic Laboratory) of North Carolina. The approach was based on classifying vegetation types based on categories at the association level of the International classification of Ecological Communities (ICEC), using digital stereo imagery. The vegetation classes were combined with field measurements of biomass volumes obtained from USDA Forest Service Forest Inventory and Analysis Phase 3 (FIA P3) plots. The methodology of using modified ICEC association level vegetation maps created from digital photogrammetry, disturbance history, and FIA P3 data, show promise as an approach to fuel mapping for the following reasons: (1) Softcopy photogrammetry coupled with ground truthing provides a high level of accuracy for mapping to the association level of the ICEC system. (2) Fuel loads generated from the FIA P3 plots in the field differ from fuel loads estimated by standard fire models. (3) Fuel loads within fuel size classes vary by vegetation type, and fuel size classes for some of the modified association level classifications had distinctive fuel loads. Disturbance history appears to play a significant role in explaining why fuel loads differ within associations and will help in creating more accurate fuel maps. The ultimate goal of this research would be to use all FIA P3 plot data from across the country to generate an index of fuel load by ICEC association level vegetation classification and disturbance history. This could lead to a valuable multi-purpose tool for both land managers and researchers to predict, prevent and manage forest biomass for wildfire.
- The effect of spatial resolution on an object-oriented classification of downed timber(2007-08-10) Swain, Jeff B; Heather Cheshire, Committee Chair; Stacy Nelson, Committee Member; George Hess, Committee Member
- Examination of Inter-relationships among Meteorological Transport Patterns, Ozone Concentrations, and Human Health Endpoints in New York State.(2010-10-15) Garcia, Valerie Cover; Heather Cheshire, Committee Chair; Viney Aneja, Committee Chair; Stacy Arnold Nelson, Committee Member; S. Rao, Committee Member; Viney Aneja, Committee Member
- Large-scale analysis of sustainable forest management indicators: assessments of air pollution, forest disturbance, and biodiversity(2004-06-28) Coulston, John Wesley; William D Smith, Committee Member; Kurt Riitters, Committee Member; Heather Cheshire, Committee Chair; Marcia Gumpertz, Committee MemberAs the doubling time of the global human population decreases, increasing emphasis is placed on sustainable development by both policy makers and scientists. Sustainable forest management is one part of the overall picture of sustainable development. One method to assess sustainable forest management is through the use of criteria and indicators. Criteria represent sustainable management goals. Indicators are measurable quantities that designate whether the goals are being met. The maintenance of forest health and vitality is a criterion of the Montrêal Process Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests. Measures of air pollution, forest disturbance, and change in ecological integrity provide indicators of how well forest health and vitality are being maintained. Using national databases, I assess air pollution in the United States, demonstrate the use of epidemiological approaches to examine forest disturbances, and develop an analytical technique to identify gaps and target priorities in reserve networks. The analyses in this dissertation offer new approaches to large-scale analysis of Montrêl Process Criteria and Indicators. The results can be summarized as follows. (1) From 1994 through 2000 air pollution was highest in the northeastern United States and the oak-hickory and loblolly-shortleaf forest type groups were consistently exposed to more air pollution than other forest types. Conversely, the western white pine and larch forest type groups were consistently exposed to less air pollution than all other forest types. (2) Examination of the southeastern United States revealed high rates of forest fragmentation in the piedmont and coastal plain region. In the Pacific North west, insect and pathogen activity was analyzed and recurring clusters of high rates of activity were identified. (3) Although protected areas of the Douglas-fir forest type group occurred throughout much of the species range, most existed in colder and drier parts of the range. To conserve representative habitats, future conservation efforts would be most effective in warmer and wetter areas of western Oregon, northwestern Washington, and northwestern California.
- Spatial Tools for Managing the Hemlock Woolly Adelgid in the Southern Appalachians(2005-12-06) Koch, Frank Henry Jr.; Heather Cheshire, Committee Chair; Hugh Devine, Committee Member; Fred Hain, Committee Member; George Hess, Committee MemberNative to Asia, the hemlock woolly adelgid (Adelges tsugae) has recently spread into parts of the southern Appalachian region. This insect pest attacks both native hemlock species (Tsuga canadensis and T. caroliniana), has no natural enemies, and can kill hemlock trees within just a few years. While biological control displays promise for combating the pest, such counter-measures are significantly hampered because neither adelgid nor hemlock distribution patterns have been detailed explicitly. We developed a spatial management system to better target control efforts. The system has two components: (1) a protocol for mapping hemlock stands, and (2) a technique to map areas at risk of imminent hemlock woolly adelgid infestation. To map hemlock stands, we utilized topographically normalized satellite imagery from Great Smoky Mountains National Park. Because hemlocks are difficult to distinguish using just satellite data, we constructed a decision tree classifier that supplemented the imagery with a suite of topographic, environmental, and proximity variables. We then implemented the classifier in a geographic information system and generated hemlock distribution maps. Our final decision tree had 27 terminal nodes and nine variables, with elevation, image band ratios, topographic relative moisture index, and distance to the closest stream among the most important variables. Accuracy assessment—based on field data and aerial photos—of the maps resulting from this tree yielded an overall thematic accuracy of 90% for one study area and 75% accuracy in capturing hemlocks in a second study area. To map areas at risk, we combined known first-year infestation locations from Great Smoky Mountains National Park and the Blue Ridge Parkway with points from uninfested hemlock stands, recording a suite of environmental variables for each point. We applied four different techniques (discriminant analysis, k-nearest neighbor, logistic regression, and decision tree) to generate models from these data in order to predict locations at high risk of imminent hemlock woolly adelgid infestation. We then used the resulting models to generate risk maps of the study region. All techniques performed well, accurately capturing 70-90% of training and validation samples. Discriminant analysis was the most accurate technique, but logistic regression yielded a more practical map from a management standpoint, with large, discrete risk zones. In any case, our results suggest that roads, major trails, and riparian corridors provide an important degree of connectivity enabling long-distance dispersal of the hemlock woolly adelgid, probably by humans or birds. Both components of our hemlock woolly adelgid management system are built on readily available or easily calculable spatial data. Furthermore, they are constructed generally enough that they should be applicable throughout the southern Appalachians. Overlay of derived maps will allow forest managers to prioritize hemlock stands and allocate resources more efficiently.
- Using GIS and LIDAR to Map Headwaters Stream Networks in the Piedmont Ecoregion of North Carolina(2005-03-03) Garcia, Valerie Cover; James Gregory, Committee Member; George Hess, Committee Member; Heather Cheshire, Committee ChairA large percentage of nonpoint source pollution found in our Nation's waterbodies is suspected to occur through first- and second-order (headwaters) streams. Such streams drain a much greater proportion of watershed area and have a much greater length of riparian zone interaction with the land than the higher-order streams typically studied for nonpoint source water quality problems. The State of North Carolina and the U.S. EPA are interested in examining the contribution of lowerorder streams to the overall nonpoint source pollution problem; however, the mapping of first- and second-order streams is extremely poor. The recent availability of fine resolution Light Detection and Ranging (LIDAR) data for portions of the State of North Carolina provides the opportunity for developing improved methods of mapping lower-order streams using Geographic Information System (GIS) approaches. In this study, I investigated the state-of-science for mapping topography and extracting headwaters stream networks using LIDAR data and GIS approaches. I applied these techniques to map headwaters streams at a study site in the Piedmont Ecoregion of North Carolina. I found that LIDAR produced more accurate elevation maps (elevation accuracy within 1.2') than currently available maps, such as the USGS 7.5 minute Digital Elevation Models (elevation accuracy within 49'). The Triangulated Irregular Network (TIN) produced the best topographic maps, but the Digital Elevation Model (DEM) was better for automatically extracting headwaters streams. The best headwaters stream maps were derived by using a hydro-enforced TIN for generating the base DEM,and extracting the stream network from this base DEM using ArcHydro and the AGREE algorithm. These improved headwaters stream maps will enable decision-makers to assess and mitigate nonpoint source water quality problems.