An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery

Sediment deposition causes the reduction of aquatic habitats and increase of water velocities within rivers, which negatively impacts the environment and the surrounding ecology. This makes the prediction of river sediment deposition a key factor for the protection of river environments. The prediction of sediment deposition in rivers through the integration of satellite imagery and unsupervised machine learning is beneficial and convenient, as it is less resource-intensive due to not requiring ground-truth data. The Terengganu River in Malaysia is used as a case study in this research. This study aims to discuss satellite imagery's key preparation processes, namely image correction and identification of determinant image bands through a correlation analysis. Satellite imagery of the Terengganu River between 1989 and 2019 is obtained from the United States Geological Survey (USGS). Image correction is successfully implemented on the available satellite imagery with the results shown in this study. Through the performed correlation analysis, the study finds that the determinant image bands for river sediment deposition prediction using unsupervised machine learning are the NST spectral bands, which consist of the NIR, SWIR, and TIR bands. This is due to the NST spectral bands exhibiting low correlations with respect to the RGB bands. It is found that correlation coefficients between the NIR band and red, green, and blue bands are generally the lowest, especially in 2009 with values of 0.1087, 0.2085, and 0.1252, respectively. This indicates that the NIR band is the most important determinant image band in predicting river sediment deposition. This study also identifies k-means, clustering large application (Clara), and hierarchical agglomerative clustering (HAC) as suitable unsupervised machine learning algorithms to be utilized in predicting river sediment deposition. Studies on the application of unsupervised machine learning algorithms on satellite imagery in the field of river sediment deposition prediction are currently scarce, possibly due to the gap of knowledge on the initial steps required for such application. Therefore, this study's novelty is the introduction and discussion on critical preliminary processes, specifically image correction and identification of determinant image bands, that are required for the successful implementation of unsupervised machine learning algorithms on satellite imagery for the prediction of river sediment deposition.