
考虑潮位校正的乐清湾潮滩面积近30年变化遥感研究
Remote sensing study of tidal flat area change in Yueqing Bay in recent 30 years considering tidal level correction
潮滩是滨海湿地的重要组成部分,监测其演变对于蓝碳增汇具有重要意义。卫星遥感是目前应用最广泛的潮滩监测手段之一,但受潮位影响,遥感提取的潮滩面积往往被低估。本文利用2016—2021年Sentinel-2卫星影像提取乐清湾潮滩瞬时面积,建立了潮滩面积与潮位的定量关系模型,从而实现潮滩面积的潮位校正。在此基础上,将建立的校正模型应用于Landsat卫星影像,获取了1988—2020年期间7个时期的乐清湾潮滩面积,并分析了乐清湾潮滩面积近30年的变化及主要驱动因素。结果表明,乐清湾潮滩面积在1988—1994年期间显著增加,在1994—2010年期间急剧减少,在2010—2015年期间少量增长,而在2015—2020年期间再次减少。人类活动对潮滩的开发利用是乐清湾潮滩面积变化的主要驱动因素。
Tidal flat is an important part of coastal wetland, and is of great significance for blue carbon. Remote sensing is the most widely used method for tidal flat monitoring, but due to the influence of tides, the area of tidal flat extracted by remote sensing is often greatly underestimated. In this study, Sentinel-2 satellite images from 2016 to 2021 were used to extract the instantaneous area of tidal flats in Yueqing Bay, and the quantitative relationship between tidal flats area and tidal level or the tidal level correction model for tidal flats area was established. On this basis, Landsat satellite images in 7 years from 1988 to 2020 were selected to extract the instantaneous tidal flat area, and the established correction model was used to obtain the corrected multi-year tidal flat area at low tide level, and the change of tidal flat area in Yueqing Bay in recent 30 years and the main driving factors were analyzed. In recent 30 years, the tidal flat area of Yueqing Bay showed a trend of significant increase from 1988 to 1994, sharp decrease from 1994 to 2010, slight increase from 2010 to 2015, and decrease again from 2015 to 2020. The development and utilization of tidal flat by human activities is the main driving factor of tidal flat area change in Yueqing Bay.
潮滩面积 / 乐清湾 / 卫星遥感 / 潮位校正 / 潮滩演变 / 驱动因素
tidal flat area / Yueqing Bay / satellite remote sensing / tidal level correction / tidal flat evolution / driving factor
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Coastal tidal flats are important ecological resources. As the dividing line between marine and terrestrial ecosystems, tidal flats provide a large number of ecosystem services. However, with the excessive development of coastal areas, tidal flat resources have been drastically reduced, leading to the deterioration of coastal ecosystems. There is an urgent need to acquire accurate information on the changes in tidal flat resources. This research proposes a tidal flat extraction model (RF-W model) that combines the random forest (RF) method and waterline method, which aims to improve the accuracy of tidal flat extraction. This method can effectively eliminate the shortcomings of the RF method in determining the lower boundary of tidal flats and those of the waterline method in distinguishing river channels and tidal flats. The tidal flat extraction of Qingdao Jiaozhou Bay in 2020 is performed as an example of the model. The results show that the user’s and producer’s accuracies of the RF-W model were both the highest, indicating that the improved model can accurately extract tidal flat information. Then, we used the RF-W model to extract tidal flat information for Jiaozhou Bay in seven periods (1990, 1995, 2000, 2005, 2010, 2015, and 2020) and to study the spatiotemporal changes in the tidal flats and influencing factors from 1990 to 2020. The tidal flat area of Jiaozhou Bay showed an overall downward trend before 2015, and the area decreased by 21.9 km2, with a reduction in the rate of approximately 1.1%/year. After 2015, the tidal flat area rebounded slightly. The overall change in Jiaozhou Bay showed reclamation and expansion toward the sea. The reduction in the sand content of the rivers entering the sea, reclamation and cultivation, and land development were the main factors contributing to the reduction in the tidal flat area in Jiaozhou Bay. In addition, sea level rise due to climate warming is a long-term potential factor.
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