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MULTISPECTRAL UAS BASED COASTAL CHANGE DETECTION METHODS
| Title: | MULTISPECTRAL UAS BASED COASTAL CHANGE DETECTION METHODS. |
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| Name(s): |
Rajkumar, Monica , author Nagarajan, Sudhagar, Thesis advisor Florida Atlantic University, Degree grantor Department of Civil, Environmental and Geomatics Engineering College of Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Created: | 2022 | |
| Date Issued: | 2022 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 177 p. | |
| Language(s): | English | |
| Abstract/Description: | Coastal landscape plays a vital role in reflecting various natural processes. Vegetation resource management improves the quality of life above the surface of the earth. Due to factors such as climatic change, urban development, and global warming, monitoring the coastal region as well as its vegetation has indeed become a challenge to mankind. The purpose of the study is to propose an effective low-cost methodology to monitor the 120- acre Jupiter Inlet Lighthouse Outstanding Natural Area (ONA) located in Jupiter, Florida (USA) using Unmanned Aerial Systems (UAS) Imagery deployed with RedEdge Micasense Multispectral sensor having five bands. Since, UAS provides high resolution imagery at lower altitudes, it has a lot of potential for variety of applications. This research aims to (1) Automate the extraction of shoreline and coastline through Modified Normalized Difference Index (MNDI), thereby comparing it with the manually digitized shoreline using transect-based analysis (2) Automate the volume change computation, as the area has been affected due to various natural and anthropogenic factors in the past few decades. (3) Perform shoreline change detection for the time period 1953 to 2021 (4) Develop an algorithm to differentiate ground and non-ground points along the shore region and generate Digital Terrain Model (DTM) (5) Land use and Land cover (LULC) mapping using different band combinations and compare its result using deep learning approach. | |
| Identifier: | FA00013931 (IID) | |
| Degree granted: | Thesis (MS)--Florida Atlantic University, 2022. | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): | Includes bibliography. | |
| Subject(s): |
Coastal zone management--Florida Jupiter Inlet Light (Fla.) Multispectral imaging Drone aircraft ArcGIS |
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| Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00013931 | |
| 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. |
