MULTISPECTRAL UAS BASED COASTAL CHANGE DETECTION METHODS

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.