Analysis of coastline extraction indices using Sentinel-2 and Google Earth Engine, case study in Bali, Indonesia

Abstract

Coastal zone is the most dynamic environment and requires regular monitoring to maintain sustainable coastal resource management. Employing remote sensing technology to monitor changes involves an essential process referred to as coastline extraction. This study aims to conduct a comparative analysis of five commonly employed techniques for coastline extraction: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), and Automated Water Extraction Index (AWEI). These methods are evaluated by automatically delineating sandy beaches and coastlines using highresolution imagery from the Sentinel-2 satellite for Bali, Indonesia. The Otsu algorithm is utilized to determine the optimal threshold value. Compared to the other indices, the results indicate that MNDWI proved to be the most effective in highlighting water bodies, with the average distance from the validated point of MNDWI being 12.9 m and a Mean Absolute Error (MAE) of 7.11, whereas NDVI demonstrated a high level of proficiency in detecting coastal vegetation. This study highlights the potential of utilizing both Sentinel-2 satellite imagery and the Google Earth Engine (GEE) platform for efficient coastline monitoring. This study also provides scientific evidence supporting the reliability and accuracy of coastline extraction through spectral water indices.