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黄河冰凌灾害遥感动态监测模式及冰情信息提取模型研究

The Research on the RS Dynamic Monitoring Mode of the Yellow River Icicle Hazard and the Ice Regime Information Extraction Model

【作者】 杨中华

【导师】 刘少峰;

【作者基本信息】 中国地质大学(北京) , 地学信息工程, 2006, 博士

【摘要】 黄河的洪水问题一直是中华民族的心腹大患,而黄河冰凌灾害又是影响因素最多、成灾机理最复杂的一种灾害,从“凌汛决口,河官无罪”及“伏汛好抢,凌汛难防”的古谚语即可见一斑。因此,研究黄河冰凌灾害成灾机理,探讨冰情信息的快速检测提取模型,制定、完善并形成一套成功且可操作性强的冰凌灾害遥感动态监测模式对防灾减灾及国民经济和社会可持续发展都具有十分巨大的意义。 主要工作和研究内容:1、系统全面地研究了黄河冰凌灾害的形成机理、演变过程、影响因素及成灾特点,并对传统的冰凌观测预报模型及应用情况进行了深入的分析研究;2、在对现有在轨运行卫星的背景、特点及应用情况进行了深入分析、比较的基础上,深入研究了适合黄河冰凌灾害遥感动态监测的数据组合模式;3、全面系统地对MODIS、CBRS-02等卫星数据的背景、处理、应用进行了研究,并在充分理解MODIS各种产品算法的基础上,对基于MODIS数据的自动几何配准算法、亮度温度算法、云检测算法、冰雪检测算法等进行了深入研究;4、研究了冰雪自动检测模型建立的原理和实现方法,并在实践中进行了检验;5、对中巴资源卫星02星在黄河冰凌灾害监测中的应用进行了研究探讨。 主要研究结论: 1、黄河冰凌灾害是一个十分复杂的现象,即表现在它具有自然属性,也表现在它具有社会属性,只有采取工程措施和非工程措施相结合的手段才可能使其影响减至最小,GIS与遥感相结合的动态监测措施是现阶段行之有效的非工程措施之一。 2、综合现有在轨运行卫星特点的“四星三源”遥感动态监测模式是现阶段进行黄河冰凌灾害遥感动态监测的最佳数据组合,并被实践证明是行之有效的。 3、提出并建立的黄河冰凌信息提取模型具有较高的灵敏度,能较好的实现对黄河冰凌灾害的实时(准实时)动态监测。 4、中巴资源卫星02星数据能较好的用于黄河冰凌的精细监测,特别是其侧摆数据接收功能,能快速实现对突发灾害区域的动态监测。 主要创新点: 1、首次提出了基于MODIS、ETM+、CBERS-02、RADARSAT的“四星三源”遥感动态监测模式,并通过几年的应用取得了很好的效果。 2、首次提出并建立了基于MODIS数据的以NDSI、第2通道反射率、地26通道反射率及封冻指数为主要参数的黄河冰凌信息提取模型,并被实践证明行之有效。 3、首次应用CBERS-02的侧摆接收功能成功实现了对突发冰凌灾害的动态监测,为国产卫星的应用开辟了一个新领域。

【Abstract】 The flood of Yellow River is always the serious trouble of Chinese, and ice flood disaster have the maximum influencing factors and the most complicated flooded theory, it was concluded with ancient adage "the ice flood burst and official innocence" and " estival flood easily prevented and ice flood hardly defended". So, it is very significative that researching flooded theory of ice flood of Yellow River and studying quickly monitoring model of disaster condition of ice flood and excogitating a set of dynamic RS monitoring pattern of ice flood disaster for national economy and social continuable develop.Central works and researchful content : 1、I roundly studied developed theory and evolutive process and influencing factor and flooded feature of ice flood disaster of Yellow River; 2、Based on background and feature and applications of secondary planet, I excogitated a kind of reasonable dynamic RS monitoring pattern of Yellow River; 3 、 I discussed background and method of process and application of MODIS and CBERS-02 data, and I understood arithmetic of MODIS productions, then I studied automatic geometrical register arithmetic and lighteness or temperature arithmetic and cloud detection arithmetic and ice detection arithmetic based on MODIS data; 4、I researched constitutional theory and finished method of automatic ice detection model; 5、 I discussed application of CBERS-02 on the ice flood monitoring of Yellow River.Central conclusions :1. The ice flood disaster of Yellow River is very complicated phenomenon, in other words, It has natural and social feature, the influence of this disaster would reduce rock bottom if we combined project measure with non-project measure, the dynamic RS monitoring with GIS is effective one of non-project measure.2. "four secondary planets and three sources" dynamic RS monitoring pattern is the present best combination of data, It was proved that it was effective.3. This model had high delicacy and effectively finished Real-time(quasi-Real-time) dynamic monitoring of ice flood disaster of Yellow River.4. Application of CBERS-02 data on elaborate monitoring of the ice flood of Yellow River is very good, specially side-swing of CBERS-02 could quickly finish dynamic monitoring of unexpected disaster area.Central innovations :1. I first brought forward "four secondary planets and three sources" dynamic RS monitoring pattern based on MODIS、ETM+、CBERS-02、 RADARSAT, and actual applications of the model achieved favorable effect.2. I first brought forward extracting model of information of ice flood of Yellow River, It was proved that it was effective.3. I successfully realized dynamic monitoring ice flood of Yellow River using side-swing of CBERS-02 for the first time, it opened up a new field on homemade satellitic application.

  • 【分类号】P237;TV873
  • 【被引频次】10
  • 【下载频次】823
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