المراقبة الديناميكية عالية الدقة لخطوط شواطئ الأنهار والبحيرات استنادًا إلى Swin-UNet

  • role: First author第一作者
  • Affiliation:

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email:chenhao235@mails.ucas.ac.cn
  • Introduction:陈浩,研究方向为水环境遥感。E-mail:chenhao235@mails.ucas.ac.cn
CHEN Hao123,  
  • role: Corresponding author通信作者
  • Affiliation:

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

  • Email:wuyh@aircas.ac.cn
  • Introduction:吴艳红,研究方向为遥感水文。E-mail:wuyh@aircas.ac.cn
WU Yanhong12*,  
  • Affiliation:

    College of Marine Technology, Ocean University of China, Qingdao 266100, China

ZHENG Siqi4,  
  • Affiliation:

    College of Marine Technology, Ocean University of China, Qingdao 266100, China

CHI Haojing4,  
  • Affiliation:

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

TENG Xuankai123,  
  • Affiliation:

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LI Junsheng123

ملخص

تعد خطوط شواطئ الأنهار والبحيرات مناطق التقاء المياه الداخلية بالأرض، وتلعب دورًا بيئيًا هامًا مثل تنظيم الفيضانات والحفاظ على التوازن البيئي. إن متابعة التغيرات الديناميكية لخطوط الشواطئ أمر حيوي للوقاية من الفيضانات والتخفيف من الكوارث ودعم بناء وحماية البيئة المائية. نظرًا لنقص الدقة في التعرف على المياه وخطوط الشاطئ على المستوى الإقليمي وعدم كفاية فهم سمات التغير الديناميكي طويل الأمد، تناولت هذه الدراسة منطقة تشنغتشو، مستخدمة صور الأقمار الصناعية عالية الدقة وصور Sentinel-2، وبنيت طريقة التعرف على المياه وخطوط الشواطئ القائمة على نموذج تعلم عميق Swin-UNet، حيث تم استخراج خطوط الشواطئ للفترة من 2015 إلى 2025، وكُشف عن سمات التغير الديناميكي لخطوط الشواطئ في المنطقة المدروسة. أظهرت النتائج أن دقة التعرف على المياه باستخدام نموذج Swin-UNet تفوق 90٪؛ متوسط مساحة المسطحات المائية في تشنغتشو يبلغ حوالي 314.7 كم²، وطول خطوط الشواطئ حوالي 7306.4 كم، ونتائج صور الأقمار الصناعية عالية الدقة وصور Sentinel-2 متوافقة جيدًا؛ التذبذب السنوي لطول خطوط الشواطئ في تشنغتشو (معامل التغير = 12.8٪) أعلى من التذبذب بين السنوات (معامل التغير = 9.0٪)، ويمتاز الشاطئ الإصطناعي باستقرار أكبر مقارنة بالشاطئ الطبيعي، كما شهدت آخر عشر سنوات تحول هيكل خطوط الشواطئ من شبه طبيعي-شبه اصطناعي إلى شواطئ اصطناعية مع استقرار نسبي للشواطئ الطبيعية. في المجمل، فإن طريقة التعرف على المياه وخطوط الشواطئ القائمة على نموذج Swin-UNet يمكن أن تحسن دقة واستقرار استخراج خطوط الشواطئ التلقائية في المناطق المعقدة، وتوفر دعمًا تقنيًا لرصد التغيرات الديناميكية طويلة الأجل لخطوط الشواطئ في المنطقة. يمكن أن تقدم نتائج هذه الدراسة مرجعًا لإدارة موارد خطوط شواطئ الأنهار والبحيرات، وحماية البيئة المائية وإعادة تأهيل النظام الإيكولوجي المكاني في تشنغتشو.

مفهوم

خطوط شواطئ الأنهار والبحيرات;المراقبة الديناميكية;Swin-UNet;القمر الصناعي عالي الدقة;Sentinel-2;تشنغتشو

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