地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 690-698.doi: 10.3724/SP.J.1047.2016.00690

• 遥感大数据协同计算综合应用 • 上一篇    下一篇

基于协同计算的白洋淀湿地时序水体信息提取

沈占锋1(), 李均力2, 于新菊1   

  1. 1. 中国科学院遥感与数字地球研究所,北京 100101
    2. 中国科学院新疆生态与地理研究所,乌鲁木齐 830020
  • 收稿日期:2015-12-15 修回日期:2016-02-22 出版日期:2016-05-10 发布日期:2016-05-10
  • 作者简介:

    作者简介:沈占锋(1977-),男,黑龙江大庆人,研究员,研究方向为遥感信息提取与高性能计算。E-mail:shenzf@radi.ac.cn

  • 基金资助:
    国家高分辨率对地观测系统重大专项(03-Y30B06-9001-13/15-01);国家高技术研究发展计划项目(2015AA123901);国家自然科学基金项目(41301473)

Water Information Extraction of Baiyangdian Wet Land Based on the Collaborative Computing Method

SHEN Zhanfeng1,*(), LI Junli2, YU Xinju1   

  1. 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    2. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830020, China
  • Received:2015-12-15 Revised:2016-02-22 Online:2016-05-10 Published:2016-05-10
  • Contact: SHEN Zhanfeng E-mail:shenzf@radi.ac.cn

摘要:

白洋淀湿地是华北平原仅存的为数极少的湖泊型湿地之一,具有改善生态环境、保护生物多样性等功能。通过遥感手段进行白洋淀地区湿地变化研究,可为景观格局变化、生态环境分析及湿地保护等提供重要的信息支撑,具有非常重要的意义。本文在分析遥感大数据特点的基础上,对遥感应用中的大数据信息提取这一重要环节进行了分析,并以遥感信息计算为切入点,深入分析并总结了遥感大数据计算过程中的多种协同计算问题。结合白洋淀地区长时相遥感湿地水体提取与变化分析的应用需求,本文提出了基于协同计算方式下的白洋淀水体提取技术路线,并详细分析了水体信息计算过程中的几种重要的协同计算问题,提高了水体信息提取的精度。最后,根据白洋淀地区43期(1973-2015年)精确的水体提取信息,统计了白洋淀历史时期水体面积的变化,并指出该区域自1973年以来水体面积经历了“减少-增加-再减少-再增加”的变化过程。

关键词: 遥感大数据, 信息提取, 协同计算, 白洋淀, 水体提取, 并行计算

Abstract:

Baiyangdian wetland is one of the very few remaining wetlands in north China, which has the functions of improving the ecological environment, maintaining the conservation of biological diversity and so on. It has the very prominent significance to researches that are focusing on the wetland changes in this area, because these researches can provide an important information support under the assistance of remote sensing to investigate the change of landscape pattern and conduct the environmental analysis. Remote sensing big data has become a trend for the development of remote sensing technology. Based on the characteristics of remote sensing big data, this paper analyzes the big data extraction technology, which is a critical part in the remote sensing applications. In addition, starting with the calculation of remote sensing information, this paper summarizes various collaborative computing problems encountered during the process of remote sensing big data calculation. Considering the application requirements of wetland water body extraction and change analysis over a long time period in Baiyangdian, this paper proposes a method based on the collaborative computing technology to extract the water body in Baiyangdian. The method firstly computes the initial NDWI threshold in its histogram and finds the possible lakes within the region, and then it computes the suitable GNDWI for every lake to implement the precise lake extraction one by one. And at last, several typical types of collaborative computing problems are analyzed in this procedure. According to the water extraction result from our analysis on a period between 1973 and 2015, we study the historical water area changes of Baiyangdian, and the results show that the water area in this region has experienced a “decrease-increase-another decrease-another increase” changing pattern.

Key words: remote sensing big data, information extraction, collaborative computing, Baiyangdian, water extraction, parallel computing