A Review of Satellite Remote Sensing Techniques of River Delta Morphology Change

River deltas are important coastal depositional systems that are home to almost half a billion people worldwide. Understanding morphology changes in deltas is important in identifying vulnerabilities to natural disasters and improving sustainable planning and management. Satellite remote sensing has shown to be a useful technology for analyzing these morphology changes owing largely to its capability to provide spatially continues observations. In this paper, we critically review the literature about satellite remote sensing techniques that were used to study delta morphology changes.
We identify and categorize the techniques reported in ...  more

River deltas are important coastal depositional systems that are home to almost half a billion people worldwide. Understanding morphology changes in deltas is important in identifying vulnerabilities to natural disasters and improving sustainable planning and management. Satellite remote sensing has shown to be a useful technology for analyzing these morphology changes owing largely to its capability to provide spatially continues observations. In this paper, we critically review the literature about satellite remote sensing techniques that were used to study delta morphology changes.
We identify and categorize the techniques reported in the literature into 3 major classes: 1) One-step change detection, 2) Two-step change detection, and 3) Ensemble Classifications. In total we offer a review of 18 techniques within these categories. Example studies, the strengths and caveats in relation to the deltaic environment are discussed for each technique. Our synthesis of the literature reveals that sub-pixel-based algorithms perform better than pixel-based ones. Machine learning techniques rank second to sub-pixel techniques although an ensemble of techniques can be used just as effectively to achieve high feature detection accuracies.
We evaluate the 7 most commonly used techniques in literature (Conventional Techniques: (1) Modified Normalized Difference Water Index (MNDWI), 2) Normalized Difference Water Index (NDWI), 3) PCA analysis, 4) Unsupervised Classification, and 5) Supervised Classification)]. Machine Learning techniques: 6) Random Forest Classifier, and 7) Support Vector Machine) on a sample of global deltas, for delta morphological feature extraction performance. Findings show the Unsupervised Classification significantly outperforms the others and is recommended as a first order feature extraction technique in previously unknown, or, data sparse deltaic territories.
We propose four pathways for future advancement in satellite remote sensing of delta morphology: 1) utilizing new high-resolution imagery and development of more efficient data mining techniques, 2) moving toward universal applicability of algorithms and their transferability across satellite platforms, 3) improvement of the availability and use of ancillary data in image processing algorithms, and 4) development of a global-scale repository of deltaic data for the sharing of scientific knowledge across regions and disciplines.