Gang Yang, Ke Huang, Lin Zhu, Weiwei Sun, Chao Chen, Xiangchao Meng, Lihua Wang, Yong Ge
{"title":"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","authors":"Gang Yang, Ke Huang, Lin Zhu, Weiwei Sun, Chao Chen, Xiangchao Meng, Lihua Wang, Yong Ge","doi":"10.5194/essd-2024-123","DOIUrl":null,"url":null,"abstract":"<strong>Abstract.</strong> 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.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":null,"pages":null},"PeriodicalIF":11.2000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatio-Temporal Changes in China’s Mainland Shorelines Over 30 Years Using Landsat Time Series Data (1990–2019)\",\"authors\":\"Gang Yang, Ke Huang, Lin Zhu, Weiwei Sun, Chao Chen, Xiangchao Meng, Lihua Wang, Yong Ge\",\"doi\":\"10.5194/essd-2024-123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Abstract.</strong> 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. 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Spatio-Temporal Changes in China’s Mainland Shorelines Over 30 Years Using Landsat Time Series Data (1990–2019)
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.
Earth System Science DataGEOSCIENCES, 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.