Arctic coasts are transition zones influenced by terrestrial, marine, and cryospheric factors. Due to the degradation of the cryosphere exacerbated by climate change, many segments of Arctic coasts are characterized by severe erosions and thus resulting in many social-economic consequences. To assess the imminent coastal risks and increasing organic carbon fluxes released from Arctic erosional coasts, continuous monitoring of shoreline movement is necessary. Conventional studies employ spaceborne multi-spectral optical images to detect ample Arctic coasts' dynamics; nonetheless, the frequent cloud cover and Arctic haze limit the number of usable images. Thence, most studies merely utilize a few image pairs to estimate long-term rate changes, which deter statistically meaningful trend analysis and are likely biased by intra-annual variations. This study employs cross-mission synthetic aperture radar (SAR) images that are cloud-penetrating and weather-independent to depict 32-year spatiotemporal changes of Drew Point Coast along the Alaskan Beaufort Sea. To efficiently and robustly extract shorelines, a non-manual intervention-required and cross-SAR sensor applicable approach is proposed. Based on the automatically delineated time series shoreline positions, each coastal segment's position-time records are modeled with a statistic-based coastal dynamics classification scheme that enables constructing non-linear trends of inter-decadal recession rates. Results reveal that 83.7 % of the coast exhibits continuous erosion during 1992-2023. Dynamically, 48.6 % of coast demonstrates polynomial change patterns with an erosive rate higher than -6 m/yr. Remarkably, 22.5 % of the coast has been statistically significantly accelerating. For instance, the erosional rate nearly double (93.8 %) between Drew Point and McLeod Point, while between Lonely and Pitt Point, the most erosive segment in the study coast, the retreating rate increases 285.57 % from -5.92 to -22.81 m/yr. These findings exemplify the high heterogeneity of Arctic coastal changes and highlight the opportunities of using spaceborne SAR data to empower the management and conservation of Arctic coasts.
Keywords: Coastal resilience; Coastline change; Edge detection; GLCM; Radar data cube; Remote sensing change detection.
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