Jupiter Inlet Light (Fla.)

For much of the 20th century, mariners in the United States were able to utilize the radio beacon system to aid in navigation; however, in spite of its importance in U.S. nautical history, there has been very little historical or archaeological research published about the system. The Jupiter Inlet Light Station Radio Beacon Building, located at what is today known as the Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA), was part of this coastal network of radio beacons. This thesis involves the methodologies of historical research and terrestrial laser scanning and serves several purposes: to provide JILONA with information about and a digital point cloud of the radio beacon building for future use in a planned museum onsite, to create a much-needed historical narrative of the U.S. radio beacon system, and to aid the Florida Atlantic University Department of Anthropology in future terrestrial laser scanner and modeling efforts. Because the project was undertaken at the request of JILONA, this thesis is to be considered a work of public archaeology.

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.