Tsai Ya-Lun S
Earth Observation and Remote Sensing Lab, Surveying and Geospatial Engineering Division, Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan; Center for Research on High Performance Remote Sensing and Urban Informatics, National Taiwan University, Taipei 10617, Taiwan.
Sci Total Environ. 2024 Mar 20;917:170389. doi: 10.1016/j.scitotenv.2024.170389. Epub 2024 Feb 1.
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.
北极海岸是受陆地、海洋和冰冻圈因素影响的过渡区域。由于气候变化加剧了冰冻圈的退化，北极海岸的许多地段都出现了严重侵蚀，从而导致了许多社会经济后果。为了评估迫在眉睫的海岸风险以及北极侵蚀海岸释放的有机碳通量增加的情况，有必要对海岸线移动进行持续监测。传统研究采用星载多光谱光学图像来检测北极海岸的丰富动态；然而，频繁的云层覆盖和北极霾限制了可用图像的数量。因此，大多数研究仅利用少数图像对来估计长期速率变化，这阻碍了具有统计意义的趋势分析，并且可能受到年内变化的偏差影响。本研究采用具有穿透云层和不受天气影响特性的跨任务合成孔径雷达（SAR）图像，来描绘阿拉斯加波弗特海沿岸德鲁角海岸32年的时空变化。为了高效且稳健地提取海岸线，提出了一种无需人工干预且适用于跨SAR传感器的方法。基于自动划定的时间序列海岸线位置，每个海岸段的位置-时间记录采用基于统计的海岸动力学分类方案进行建模，该方案能够构建年代际衰退速率的非线性趋势。结果显示，在1992 - 2023年期间，83.7%的海岸呈现持续侵蚀。从动态角度看，48.6%的海岸呈现多项式变化模式，侵蚀速率高于 -6米/年。值得注意的是，22.5%的海岸在统计上有显著加速。例如，德鲁角和麦克劳德角之间的侵蚀速率几乎翻倍（93.8%），而在研究海岸侵蚀最严重的地段——孤独角和皮特角之间，后退速率从 -5.92米/年增加到 -22.81米/年，增幅达285.57%。这些发现例证了北极海岸变化的高度异质性，并突出了利用星载SAR数据加强北极海岸管理和保护的机会。
