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

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Date Issued:
2014
Summary:
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.
Title: Mapping wetland vegetation with LIDAR in Everglades National Park, Florida, USA.
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Name(s): De Stoppelaire, Georgia H., author
Xie, Zhixiao, Thesis advisor
Florida Atlantic University, Degree grantor
Charles E. Schmidt College of Science
Department of Geosciences
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2014
Date Issued: 2014
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 166 p.
Language(s): English
Summary: 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.
Identifier: FA00004276 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2014.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Everglades National Park (Fla.)--Environmental conditions.
Geographic information systems--Florida--Everglades Region.
Ecosystem management--Florida--Everglades Region.
Vegetation monitoring--Florida--Everglades National Park.
Wetland management--Florida--Everglades National Park.
Coastal zone management--Remote sensing--Florida--Everglades National Park.
Environmental mapping--Florida--Everglades National Park.
Environmental monitoring--Remote sensing--Florida--Everglades National Park.
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00004276
Use and Reproduction: Copyright © 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.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.