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

Abstract

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. In this paper, we critically review literature on satellite remote sensing techniques that were used to study delta morphology changes. We identify and categorize these techniques 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 with example studies, and strengths and caveats of each. Synthesis of 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 also evaluate the performance of the 7 most commonly used techniques in literature on a sample of global deltas. Findings show the unsupervised classification significantly outperforms the others, and is recommended as a first-order delta morphological feature extraction technique in previously unknown, or, data sparse deltaic territories. We propose four pathways for future advancement delta morphological remote sensing: (1) utilizing 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) 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 disciplines.