...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Ocean & coastal management >Automatic extraction of aquaculture ponds based on Google Earth Engine
【24h】

Automatic extraction of aquaculture ponds based on Google Earth Engine

机译：基于Google地球发动机的水产养殖池自动提取

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Aquaculture is one of China's fastest-growing animal food production sectors. It accounts for the largest share in the world, mainly distributed in coastal areas. Due to the depletion of offshore resources and increasing domestic demand for aquatic products, more and more land, including newly reclaimed land, is gradually being used to build aquaculture ponds. Understanding the location, spatial pattern, scale, and other properties is critical for China's food and protein security. However, until recently, how to detect, monitor and map the aquaculture ponds with remote sensing is still a problem, which hinders the understanding of its magnitude and value, and interferes sustainable management of coastal ecosystems. Here we proposed a framework for extracting aquaculture ponds by integrating existing multi-source remote sensing data on the Google Earth Engine platform. Taking Shanghai as a study area, the Multi-threshold Connected Component Segmentation and Random Forest algorithm method were used to extract aquaculture ponds automatically. The results show that this method can effectively generate the maps of Shanghai's aquaculture ponds from 2016 to 2019, and the overall accuracy of the classification results in 2018 can reach 91.8%. This method can greatly improve the efficiency of extracting aquaculture ponds, and has a good performance in detecting non-intensive aquaculture pond areas. It can also be easily used and has high spatio-temporal transferability with the help of the Google Earth Engine platform.
机译：水产养殖是中国增长最快的动物生产领域之一。它占世界上最大的份额，主要分布在沿海地区。由于海上资源的消耗，增加了对水产品的需求，越来越多的土地，包括新开垦的土地，逐渐被用来建造水产养殖池塘。了解地点，空间模式，规模和其他属性对于中国的食物和蛋白质安全至关重要。然而，直到最近，如何检测，监测和映射水产养殖池塘，遥感仍然是一个问题，阻碍了对其幅度和价值的理解，并干扰了沿海生态系统的可持续管理。在这里，我们提出了一种通过在Google地球发动机平台上集成现有的多源遥感数据来提取水产养殖池的框架。将上海作为研究区，多阈值连接的组件分割和随机林算法方法用于自动提取水产养殖池。结果表明，该方法可以有效地从2016年到2019年生成上海水产养殖池地图，2018年分类结果的整体准确性可达91.8％。这种方法可以大大提高提取水产养殖池的效率，在检测非密集水产养殖池区具有良好的性能。在Google地球发动机平台的帮助下，它也可以轻松使用并具有高的时空可转换性。

著录项

  • 来源
    《Ocean & coastal management》 |2020年第12期|105348.1-105348.10|共10页
  • 作者

    Xia Zilong; Guo Xiaona; Chen Ruishan;

  • 作者单位

    East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China|East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China;

    East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China|East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China;

    East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China|East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China|Acad Plateau Sci & Sustainabil Xining 810016 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Google earth engine; Aquaculture; Time series; Image segmentation; Sentinel-2; Shanghai;

    机译：谷歌地球发动机;水产养殖;时间序列;图像分割;Sentinel-2;上海;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号