Nesting in the Clouds: Evaluating and Predicting Sea Turtle Nesting Beach Parameters from Lidar Data
Date of Award
1-1-2012
Document Type
Dissertation
Degree Name
Ph.D.
Organizational Unit
Geography and the Environment
First Advisor
Paul C. Sutton, Ph.D.
Second Advisor
Rebecca Powell
Third Advisor
Sharolyn Anderson
Fourth Advisor
Donald Sullivan
Keywords
Light detection and ranging, Remote sensing, Sea turtle
Abstract
Humans' desire for knowledge regarding animal species and their interactions with the natural world have spurred centuries of studies. The relatively new development of remote sensing systems using satellite or aircraft-borne sensors has opened up a wide field of research, which unfortunately largely remains dependent on coarse-scale image spatial resolution, particularly for habitat modeling. For habitat-specialized species, such data may not be sufficient to successfully capture the nuances of their preferred areas. Of particular concern are those species for which topographic feature attributes are a main limiting factor for habitat use. Coarse spatial resolution data can smooth over details that may be essential for habitat characterization.
Three studies focusing on sea turtle nesting beaches were completed to serve as an example of how topography can be a main deciding factor for certain species. Light Detection and Ranging (LiDAR) data were used to illustrate that fine spatial scale data can provide information not readily captured by either field work or coarser spatial scale sources. The variables extracted from the LiDAR data could successfully model nesting density for loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) sea turtle species using morphological beach characteristics, highlight beach changes over time and their correlations with nesting success, and provide comparisons for nesting density models across large geographic areas. Comparisons between the LiDAR dataset and other digital elevation models (DEMs) confirmed that fine spatial scale data sources provide more similar habitat information than those with coarser spatial scales. Although these studies focused solely on sea turtles, the underlying principles are applicable for many other wildlife species whose range and behavior may be influenced by topographic features.
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Kristina H. Yamamoto
Provenance
Received from ProQuest
File Format
application/pdf
Language
en
File Size
111 p.
Recommended Citation
Yamamoto, Kristina H., "Nesting in the Clouds: Evaluating and Predicting Sea Turtle Nesting Beach Parameters from Lidar Data" (2012). Electronic Theses and Dissertations. 955.
https://digitalcommons.du.edu/etd/955
Copyright date
2012
Discipline
Geography