Browsing by Subject "LiDAR"

Now showing 1 - 7 of 7

Open Access
A Comparison of Aboveground Biomass in Mature Old-Field Forests and Hardwood Forests of the Piedmont Using High Resolution LiDAR Data
(2015-12-07) Harrington, Mary
Airborne scanning LiDAR (Light Detection and Ranging) is a promising technique for efficient and accurate forest volume and biomass mapping due to its capacity for direct measurement of the three-dimensional vegetation structure. In this study, small-footprint, multiple return LiDAR data was collected over our 58 mi2 study site in western South Carolina. The area was heavily farmed for about 150 years until farmers abandoned the fields in the early 1900s. Today, mature old-field pine forests grow on the abandoned agricultural land. This study used LiDAR data to compare aboveground biomass (ABG) of old-field forests and neighboring reference hardwood stands. Metrics were derived from the LiDAR data and a step-wise multiple linear regression was calibrated with field measurements (R2 =0.722, F2,32 =45.23, p < 0.001). The resulting model was used to predict the distribution of AGB across the site. A paired t-test indicated that mean AGB was significantly higher in reference hardwood sites than in old-field forests (t=5.22, df= 21, p < 0.001).
Open Access
A Comparison of Remote Sensing Methods for Estimating Above-Ground Carbon Biomass at a Wetland Restoration Area in the Southeastern Coastal Plain
(2012-04-19) Riegel, Ben
Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented. Previous studies have shown that full-waveform LiDAR (light detection and ranging) is well suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of discrete-return LiDAR to model above-ground biomass in areas with relatively sparse vegetation. This study compared the abilities of discrete-return LiDAR and high-resolution optical imagery to model above-ground carbon biomass at a wetland restoration area in eastern North Carolina. The optical imagery model explained more of the overall variation in biomass at the study site than the LiDAR model did (R2 values of 0.36 and 0.19 respectively). Moreover, the optical imagery model was better able to detect high and low biomass areas than the LiDAR model. These results suggest that the ability of discrete-return LiDAR to model above-ground biomass is rather limited in areas with relatively small trees and that high spatial resolution optical imagery may be the better tool in such areas.
Open Access
Characterizing Spatial Pattern and Heterogeneity of Pine Forests in North Carolina’s Coastal Plain using LiDAR
(2009-04-24T18:49:24Z) Smart, Lindsey S.
Remote sensing tools that directly characterize canopy structure would be beneficial for management activities and conservation planning. LiDAR (Light Detection And Ranging) is such a tool, as an active remote sensing technology that provides fine-grained information about the three-dimensional structure of ecosystems across a broad spatial extent. This project assesses the feasibility of using the state-wide North Carolina Floodplain Mapping LiDAR dataset to differentiate between the structural components of evergreen forest types in North Carolina’s coastal plain. Vertical structure and spatial patterns of vertical structure were quantified using geospatial measures such as semivariogram/ correlograms, lacunarity analysis, and correlation length. LiDAR-derived metrics were also created for comparison with standard field-based measurements of stand structure. I found that LiDAR is capable of measuring canopy variation and can differentiate between the structural characteristics of evergreen forest types. Also, the N.C. LiDAR has potential for use as a surrogate for field measurements when collection is not feasible due to time, labor, or financial constraints. In addition, the project examined LiDAR’s use as a screening tool in the identification of suitable habitat for the federally endangered red-cockaded woodpecker (Picoides borealis). I used Maximum Entropy (Maxent), an inductive modeling algorithm for presence data only, to create a spatial species distribution model using LiDAR-derived variables in addition to more typical geospatial variables. The Area (AUC) under the Receiver Operating Characteristic (ROC) curve was analyzed for increases in predictive power with additions of variables. Results suggested that the addition of LiDAR-derived variables to habitat models improved their predictive power, resulting in a test AUC increase from 0.923 with standard spatial variables only, to a test AUC of 0.951 with LiDAR-derived variables added. The success of this project has important implications for natural resource management and conservation planning, especially given that the LiDAR dataset is publicly available and covers the entire state of North Carolina.
Open Access
Habitat preference and use by the cougar (Puma concolor)
(2019-04-24) Bischoff-Mattson, Sky
Cougars (Puma concolor) are widespread in the western US, penetrating even into the edges of inhabited and developed areas. Despite their widespread distribution, many aspects of their lives remain unquantified and poorly studied. To explain the factors that influence cougar behavior, I adapted methods used for the African Lion and field data and movement metrics from USGS to designate behavioral states of cougars to GPS locations. Fitting a Bayesian multinomial model, I explain cougar behavior based on vegetation and visibility- the amount of visible area at a point, accounting for the amount of daylight- as well as elevation and land cover. The model explains a third of the variation in the data, but considerable individual variation makes differentiating between behaviors difficult. Visibility likely plays a role in cougar behavior, but additional research is necessary to fully identify all the factors that drive cougar behavior.
Open Access
Natural Resource Management at South Topsail Beach, NC
(2007-08-31T19:43:12Z) Wright, Katherine
The undeveloped southern tip of Topsail Island, NC, known as South Topsail Beach, has been accreting land and extending southwest into New Topsail Inlet at the rate of approximately 100 feet per year for the past decade, growing to its current size of roughly 135 acres. The dynamic coastal processes that dominate this landscape create habitat that the federally threatened shorebird the piping plover (Charadrius melodus), the loggerhead sea turtle (Caretta caretta), and the annual plant seabeach amaranth (Amaranthus pumilus) depend on for survival. Human disturbance and loss of habitat due to shoreline stabilization are among the biggest threats to success of these species throughout their habitat range. This Masters Project, in the form of a management plan, seeks to address the needs of these threatened species, while allowing for traditional and passive recreational uses at South Topsail Beach. In an effort to better understand shoreline change at this location, and to inform management recommendations for South Topsail Beach, a geospatial analysis using LIDAR (light detection and ranging) data was performed. Areas of erosion and accretion on both sides of New Topsail Inlet were identified and volumetric change was calculated for the years 1996 through 2005. Beach profiles were created to more closely examine spatial changes. Monitoring shoreline change over time can be used as a management tool to indicate habitat size and quality on a local level. On a broader scale, this type of analysis may be used to identify additional undeveloped dynamic inlet habitat appropriate for conservation.
Open Access
Pathogen Pollutant Loading Responses to Precipitation Dynamics and Land Cover
(2008-04-25T15:33:48Z) O'Banion, Ryan
The Newport River Estuary in Carteret County, North Carolina has been placed on the state’s 303D list for its inability to meet federally mandated surface water quality criteria. A pathogen pollutant Total Maximum Daily Load (TMDL) study with fecal coliform as an indicator species has therefore been undertaken by Kenneth H. Reckhow of Duke University. Integral to the completion of this TMDL is an understanding of the terrestrial pathogen pollutant loading responses to precipitation dynamics and land use within the Newport River Estuary. This masters project investigates pathogen pollutant loading by completing three primary objectives. Through visual analysis of sampled fecal coliform and flow data, the best available data are chosen for model fitting and creation. Geospatial analysis tools are then developed in Python and ArcGIS to accurately delineate coastal watersheds with Light Detection and Ranging (LIDAR) data. The data are then used to calibrate a model to predict fecal coliform loading responses to precipitation dynamics within the Newport River Estuary. The results of the three primary objectives illustrate the complicated relationship between fecal coliform loading and precipitation events. The geospatial analysis tools allow for the accurate delineation of coastal watersheds at scales previously unavailable to managers. Additionally, the calibrated model highlights problem areas for future modelers to address when attempting to quantify fecal coliform loading and precipitation dynamics.
Open Access
Site prioritization of current and potential red-cockaded woodpecker habitat in the Onslow Bight Landscape
(2023-04-27) Brockington, Laura
The red-cockaded woodpecker (RCW) has been listed as endangered for over 50 years. This is most notably due to a lack of habitat, as they prefer to roost in old-growth longleaf pine forests, a landscape that once covered the region, but is now extremely fragmented and sparse. The Onslow Bight Landscape is home to some of the largest remaining populations of RCW in North Carolina. This project aimed to (1) determine the likelihood of 55 parcels of interest to the North Carolina Coastal Land Trust (NCCLT) having suitable habitat for RCW, and (2) prioritize those parcels based on a set of four criteria to aid in the recovery of RCW through appropriate land acquisition. Thirty-five parcels were found to contain suitable habitat for RCW based on forest structure and soil type, but only 10 parcels had at least 10% of their total area as suitable. Two parcels were deemed as having a high RCW conservation value, while 40 received a moderate score, and 13 a low score. The results from this project provide NCCLT with an accurate estimate of current RCW habitat on their parcels of interest across the Onslow Bight landscape. These results will also help secure funding for the acquisition and continued management of these parcels, and provide a useful toolbox in understanding each parcel’s ecological structure to inform management decisions and help predict the presence of other important species in the future.