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Terrestrial LiDAR in measuring forest understory structure and predicting understory growth
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Russell, Katarina. Terrestrial LiDAR in measuring forest understory structure and predicting understory growth. Retrieved from https://doi.org/doi:10.7282/t3-qb0m-b107

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TitleTerrestrial LiDAR in measuring forest understory structure and predicting understory growth
NameRussell, Katarina (author); Lathrop, Richard (chair); Aronson, Myla F (member); Grabosky, Jason C (member); Gallagher, Michael R (member); Rutgers University; School of Graduate Studies
Date Created2022
Other Date2022-05 (degree)
SubjectEcology, Remote sensing, Forest structure, Invasive plants, Regeneration, Terrestrial Laser Scanning, Terrestrial LiDAR
Extent70 pages : illustrations
DescriptionTerrestrial LiDAR (Light Detection and Ranging) is an increasingly accessible and valuable tool for research and monitoring, especially when used in conjunction with field data. First, this work seeks to use terrestrial LiDAR to corroborate the results of an increasingly common field technique for the measurement of understory density - the forest secchi board. The forest secchi index corroborated the LiDAR-derived estimate of vegetation surface area somewhat effectively (R2 = 0.71) when both measurements were taken during the summer growing season, but less effectively when there was a phenological mismatch between the two measurements (R2 = 0.51). Second, random forest modelling was used to test the ability of LiDAR-derived canopy metrics and vegetation height profiles to predict tree regeneration or the cover of invasive species. However, minimal variation among canopy characteristics lead to inconclusive random forest models.
NoteM.S.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-qb0m-b107
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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