Spatio-Temporal Changes in China’s Mainland Shorelines Over 30 Years Using Landsat Time Series Data (1990–2019)

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Gang Yang, Ke Huang, Lin Zhu, Weiwei Sun, Chao Chen, Xiangchao Meng, Lihua Wang, Yong Ge
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Abstract

Abstract. Continuous monitoring of shoreline dynamics is essential to understanding the drivers of shoreline changes and evolution. A long-term shoreline dataset can describe the dynamic changes in the spatio-temporal dimension and provide information on the influence of anthropogenic activities and natural factors on coastal areas. This study, conducted on the Google Earth Engine platform, analyzed the spatio-temporal evolution characteristics of China’s shorelines, including those of Hainan and Taiwan, from 1990 to 2019 using long time series of Landsat TM/ETM+/OLI images. First, we constructed a time series of the Modified Normalized Difference Water Index (MNDWI) with high-quality reconstruction by the harmonic analysis of time series (HANTS) algorithm. Second, the Otsu algorithm was used to separate land and water of coastal areas based on MNDWI value at high tide levels. Finally, a 30-year shoreline dataset was generated and a shoreline change analysis was conducted to characterize length change, area change, and rate of change. We concluded the following: (1) China’s shoreline has shown an increasing trend in the past 30 years, with varying growth patterns across regions; the total shoreline length increased from 24905.55 km in 1990 to 25391.34 km in 2019, with a total increase greater than 485.78 km, a rate of increase of 1.95 %, and an average annual increasing rate of 0.07 %; (3) the most visible expansion has taken place in Tianjin, Hangzhou Bay, and Zhuhai for the three economically developed regions of the Bohai Bay-Yellow River Estuary Zone (BHBYREZ), the Yangtze River Estuary-Hangzhou Bay Zone (YRE-HZBZ) and the Pearl River Estuary Zone (PREZ), respectively. The statistics of shoreline change rate for the three economically developed regions show that the average end point rates (EPR) were 43.59 m/a, 39.10 m/a, and 13.42 m/a, and the average linear regression rates (LRR) were 57.40 m/a, 43.85 m/a, and 10.11 m/a, respectively. This study presents an innovative and up-to-date dataset and comprehensive information on the status of China’s shoreline from 1990 to 2019, contributing to related research and policy implementation, especially in support of sustainable development.
利用大地遥感卫星时间序列数据(1990-2019 年)分析 30 年间中国大陆海岸线的时空变化
摘要要了解海岸线变化和演化的驱动因素,就必须对海岸线动态进行连续监测。长期的海岸线数据集可以从时空维度描述海岸线的动态变化,提供人类活动和自然因素对海岸地区影响的信息。本研究在谷歌地球引擎平台上,利用 Landsat TM/ETM+/OLI 长时间序列影像,分析了 1990 年至 2019 年包括海南和台湾在内的中国海岸线的时空演变特征。首先,我们利用时间序列谐波分析算法(HANTS)构建了高质量重建的修正归一化差异水指数(MNDWI)时间序列。其次,利用大津算法,根据高潮位的 MNDWI 值来分离沿海地区的陆地和水域。最后,生成了 30 年海岸线数据集,并进行了海岸线变化分析,以确定长度变化、面积变化和变化率。我们得出以下结论(1)近 30 年中国海岸线呈增长趋势,各地区增长模式不同;海岸线总长度从 1990 年的 24905.55 km 增长到 2019 年的 25391.34 km,总增长大于 485.78 km,增长率为 1.95 %,年均增长率为 0.07 %;(3)渤海湾-黄河口区、长江口-杭州湾区和珠江口区三个经济发达地区中,天津、杭州湾和珠海的扩张最为明显,分别为渤海湾-黄河口区、长江口-杭州湾区和珠江口区。三个经济发达地区的海岸线变化率统计结果显示,平均端点变化率(EPR)分别为 43.59 m/a、39.10 m/a、13.42 m/a,平均线性回归率(LRR)分别为 57.40 m/a、43.85 m/a、10.11 m/a。本研究提供了一个创新的、最新的数据集和全面的信息,展示了 1990 年至 2019 年中国海岸线的状况,有助于相关研究和政策实施,特别是支持可持续发展。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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