A comparative study of atmospheric and water column correction using various algorithms on Landsat imagery to identify Coral Reefs

Abstract

Coral Reefs are vital ecosystems as they support a vast population of aquatic life and help in maintaining biodiversity. Currently, these ecosystems are under severe threat due to rising ocean temperatures and contaminated surroundings. Hence, Coral Reefs need to be monitored regularly to study their health and protect them in a better way. Remote detection and assessment of Coral Reefs using satellite data would significantly enhance the reach and ensure continuous Coral Reef monitoring. The spectral signature of the Coral Reefs captured by the satellite sensor could be a great asset in this process. Still, these signatures are heavily dopped with several unwanted noise signals coming from various surface and atmospheric features. It is essential to remove these noise signals from the satellite data. This paper studied the effectiveness of different available atmospheric correction methods such as QUAC, 6S, and FLAASH Atmospheric correction tools (ACT) in removing atmospheric noise and studied the impact of sun glint and water column correction on improving remote detection of Coral Reefs. As a case study, these processes were applied on Landsat ETM+ to study the abundant Coral habitat near the North Reef Island at the Andaman Islands. The results indicated that FLAASH is the best atmospheric correction tool among the selected algorithms as FLAASH corrected image facilitated better identification of Coral Reefs. Hence this image was further enhanced using the Hedley sun glint and Lyzenga water column correction methods which improved Coral Reef detectability significantly. The detection of Corals from the image was done using ISO-DATA unsupervised classification to evaluate the role of each process in enhancing the Coral detection. Visual inspection was used to assess the classifier accuracy towards Coral reef detection. The results showed that a combination of FLAASH, sun glint removal, and water column correction significantly improved Coral Reef identification from the remote sensing data.