地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (7): 1004-1013.doi: 10.12082/dqxxkx.2018.180065

• 遥感科学与应用技术 • 上一篇    下一篇

基于多源卫星遥感的暴雨灾情时空动态信息的提取

苏亚丽1,3(), 郭旭东1,*(), 雷莉萍2, 汪晓帆1, 吴长江2   

  1. 1. 中国土地勘测规划院 国土资源部土地利用重点实验室,北京 100035
    2. 中国科学院遥感与数字地球研究所 数字地球重点实验室,北京 100094
    3. 西安科技大学研究生院,西安 710054
  • 收稿日期:2018-01-22 修回日期:2018-03-23 出版日期:2018-07-20 发布日期:2018-07-13
  • 通讯作者: 郭旭东 E-mail:syl1501235615@163.com;sam9560@vip.sina.com
  • 作者简介:

    作者简介:苏亚丽(1994-),女,硕士生,研究方向耕地暴雨洪水灾害多源卫星遥感监测方法研究。E-mail:syl1501235615@163.com

  • 基金资助:
    国家重点研发计划项目(2016YFB0501505);国土资源部土地利用重点实验室开放基金项目(KLLU201604)

Spatio-temporal Dynamics of the Impacts of Rainstorm Disaster on Crop Growing Using Multi-satellites Remote Sensing

SU Yali1,3(), GUO Xudong1,*(), LEI Liping2, WANG Xiaofan1, WU Changjiang2   

  1. 1. Key Laboratory of Land Use, Ministry of Land and Resources, China Land Surveying and Planning Institute, Beijing 100035, China
    2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
    3. Xi'an University of Science and Technoligy Graduate School, Xi' an 710054, China
  • Received:2018-01-22 Revised:2018-03-23 Online:2018-07-20 Published:2018-07-13
  • Contact: GUO Xudong E-mail:syl1501235615@163.com;sam9560@vip.sina.com
  • Supported by:
    The National Key Research and Development Program of China, No.2016YFB0501505;Ministry of Land and Resources Key Laboratory of Land Use Open Funding, No.KLLU201604

摘要:

强暴雨淹没耕地形成灾害的同时,对耕地作物的生长也产生着极大的影响,而暴雨灾害对耕地作物生长的影响是一个渐变过程,需要由时空动态的观测进行监测。多源卫星遥感观测技术具有捕捉地面瞬间状态和刻画过程的优势。论文利用Terra/MODIS、Landsat和Sentinel卫星观测数据,挖掘多源卫星遥感观测数据,提出了一种利用NDVI变化的特征值进行灾情动态信息提取方法;并以2016年发生暴雨灾害的巢湖地区为实验区进行了方法的应用和讨论。结果表明,基于MODIS多时相NDVI变化结果提取的信息能够获得受灾害影响开始时期和持续时长等丰富的时空动态信息,根据这些信息可以统计得出大范围区域中受灾害影响的面积。另外,结合利用30 m和10 m的Landsat和Sentinel观测数据提取的水淹区,可为在暴雨致灾范围方面提供准确的参考信息。多源遥感作为评估灾情信息的依据之一,其获取的灾情动态信息能够为灾后耕地的恢复情况以及国家灾后损失评估和救助决策提供科学的数据依据。

关键词: 多源卫星, 灾害, 暴雨, 耕地, 灾情

Abstract:

The heavy rain may induce flood disaster inundating crop lands while the long period of the continues heavy rainfall may strongly affect the growth of crops even not evolving into a flood disaster. The impact of heavy rainfall on the growth of crops is a gradual process due to the long period of time needed for soil to become saturated. Multi-satellites remote sensing observations can capture and characterize the ground conditions over large area in multiply time. To develop the potential applications of the multi-satellites remote sensing observations, this paper proposes a method of extracting dynamic information of heavy rain disaster and its impacts on the growth of crops using multi-satellites data including Terra/MODIS, Landsat and Sentinel. We implemented the application of the proposed method in the studying area around Chaohu lake, where the heavy rainfall started from the end of June and continued to August in 2016, and a heavier rain in July brought about the flood in large crop areas. The beginning period and the duration of the heavy rainfall, leading to the impacts on the growth of crops, were identified using a dynamic threshold method of multi-temporal NDVI derived from MODIS. Based on this information, the area with crop fields impacted by heavy rainfall and flooded were obtained. On the other hand, the dynamic information of flooded lands was extracted by using Landsat observing data in July and Sentinel observation data in August, respectively. These results provide more accurate area of flooded crop fields, and can be used to modify the area derived from MODIS although they have only few temporal data available. In conclusion, multi-satellites remote sensing, as one of the tools for monitoring and assessing the influences of heavy rainfall, can obtain the dynamic information of the heavy rainfall impacts on the growth of crops in addition to flooded land area and the recovery of the farmland, which provide the supporting scientific data for the assessment of the loss caused by the disaster and making the disaster relief policy.

Key words: multi-satellites, disaster, rainstorm, farmland, disaster situation