Date of Award
2014
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Xie, Zhixiao
Abstract
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.
Extent
166 p.
Recommended Citation
De, Stoppelaire Georgia H, "Mapping wetland vegetation with LIDAR in Everglades National Park, Florida, USA" (2014). Electronic Theses and Dissertations 2010 - 2019. 1047.
https://digitalcommons.fau.edu/etd_general_6/1047
Identifier
FA00004276
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2014.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.