TY  - JOUR
AV  - none
TI  - Assessing the impacts of rising sea level on coastal morpho-dynamics with automated high-frequency shoreline mapping using multi-sensor optical satellites
ID  - scholars14509
KW  - Beaches; Erosion; Image segmentation; Population statistics; Remote sensing; Shore protection; Tide gages; Uncertainty analysis
KW  -  Anthropogenic activity; High frequency data; High population density; Optical satellites; Remote sensing data; Sediment transportation; Supervised image classifications; Temporal resolution
KW  -  Sea level
N2  - Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same temporal scale as sea-level records. Using a Google Earth Engine (GEE)-enabled Python toolkit, this study conducted shoreline dynamic analysis using high-frequency data sampling to analyze the impact of sea-level rise on the Malaysian coastline between 1993 and 2019. Instantaneous shorelines were extracted from a test site on Teluk Nipah Island and 21 tide gauge sites from the combined Landsat 5â??8 and Sentinel 2 images using an automated shoreline-detection method, which was based on supervised image classification and sub-pixel border segmentation. The results indicated that rising sea level is contributing to shoreline erosion in the study area, but is not the only driver of shoreline displacement. The impacts of high population density, anthropogenic activities, and longshore sediment transportation on shoreline displacement were observed in some of the beaches. The conclusions of this study highlight that the synergistic use of multi-sensor remote-sensing data improves temporal resolution of shoreline detection, removes short-term variability, and reduces uncertainties in satellite-derived shoreline analysis compared to the low-frequency sampling approach. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
IS  - 18
N1  - cited By 7
PB  - MDPI
SN  - 20724292
Y1  - 2021///
VL  - 13
JF  - Remote Sensing
A1  - Adebisi, N.
A1  - Balogun, A.-L.
A1  - Mahdianpari, M.
A1  - Min, T.H.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114683497&doi=10.3390%2frs13183587&partnerID=40&md5=f3c46ac285665a1c91a8a6fadda950eb
ER  -