TitleQuantifying three-dimensional vegetation structure and its responses to disturbances using laser altimetry in the New Jersey Pinelands
NameSkowronski, Nicholas (author), Xu, Ming (chair), Lathrop, Richard (internal member), Dighton, John (internal member), Hamerlynck, Erik (outside member), Rutgers University, Graduate School - New Brunswick,
SubjectEcology and Evolution,
Growth (Plants)--New Jersey--Pine Barrens,
Plant biomass--Carbon content,
Pine Barrens (N.J.)--Environmental conditions
DescriptionThe use of remotely sensed data to gain insight into large-scale ecological questions has intrigued researchers for decades. Recent technological advances have allowed for new types of data collection that add a third dimension to the realm of remotely sensed data. The work presented in this dissertation is linked by the use of laser altimetry data that allows scaling of field-based estimates of three-dimensional canopy structure to the landscape level. This dissertation strives to determine the repeatability of these measurements, develop robust methodologies for the application of new processing techniques to the data, application of these techniques to carbon accounting through high-resolution biomass mapping, and determine the ecological importance of these through the quantification of successional pathways and disturbance effects on canopy structure. Through examination of spatially-rectified Light Detection and Ranging (LiDAR) datasets, I found that intrinsic differences between LiDAR sensors affect their representation of canopy structure. However, I also found that statistical outputs from these sensors were highly correlated, and thus could be cross-calibrated strongly enough to allow for high resolution change detection of canopy height. Additionally, I calibrated and validated a methodology for processing raw LiDAR data allowing for the generation of high resolution (here 25 m horizontal resolution) maps of canopy height profiles, or canopy shape. I applied these methodologies to a large scale (ca. 1600 km2) scanning LiDAR dataset and developed maps of forest carbon storage and forest canopy shape. Forest carbon analyses illustrated the importance of wetlands systems and the contrasting effects of wildfire and prescribed fire on carbon storage in this system. Analysis of forest canopy shape showed distinct similarities, but also subtle differences, between cover types when compared over a height gradient. My results indicate that canopy shape is affected differently by contrasting fire regimes. This dissertation illustrates the potential for using LiDAR data for quantifying forest structure in complex landscape mosaics. The results here suggest that LiDAR technology can advance our measurement and understanding of forest structure and thereby improve our ability to estimate forest function, habitat suitability, wildfire risk, and other questions within many disciplines.
NoteIncludes bibliographical references
Noteby Nicholas Scott Skowronski
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.