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Mapping wetland vegetation with LIDAR in Everglades National Park, Florida, USA

Knowledge of the geospatial distribution of vegetation is fundamental for resource management. The objective of this study is to investigate the possible use of airborne LIDAR (light detection and ranging) data to improve classification accuracy of high spatial resolution optical imagery and compare the ability of two classification algorithms to accurately identify and map wetland vegetation communities. In this study, high resolution imagery integrated with LIDAR data was compared jointly and alone; and the nearest neighbor (NN) and machine learning random forest (RF) classifiers were assessed in semi-automated geographic object-based image analysis (GEOBIA) approaches for classification accuracy of heterogeneous vegetation assemblages at
Everglades National Park, FL, USA. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Identiferoai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_30765
ContributorsDe Stoppelaire, Georgia H. (author), Xie, Zhixiao (Thesis advisor), Florida Atlantic University (Degree grantor), Charles E. Schmidt College of Science, Department of Geosciences
PublisherFlorida Atlantic University
Source SetsFlorida Atlantic University
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation, Text
Format166 p., application/pdf
RightsCopyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/

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