多源数据淮河流域水稻洪涝灾害遥感监测方法与应用研究

It has great significance to study quick monitoring of rice flood disaster and applying timely remedial measures in the disaster area. At present, artificial methods of flood disaster monitoring are time-consuming and poor effectiveness. Remote sensing technology has the characteristics of rapid, nondestructive to obtain object information in large scale. It has many advantages compared with traditional manual method, but existing methods of flood monitoring using remote sensing technology rarely consider the underlying surface is water and effects of sediment on the spectral characteristics of rice. Flood disaster assessment has never considered the effects of farmland environment, limiting the precision of rice flood disaster monitoring model. The object of this study is the rice in Huaihe district with high incidence of flood disaster. By extracting spectral characteristics of rice in flood stress with underlying surface being water, mathematical model that quantitative description of rice growing distribution in sediment water can be built. According to farmland environment characteristics in the study area, integrating GIS technology and agricultural knowledge, and considering multi-factor environment, monitoring model of rice growth affected by disaster can be built to achieve fast and accurate real-time monitoring of rice flood disaster. The results can provide a theoretical basis for rice research in different areas, post disaster rehabilitation and recovery. It can also provide a theoretical basis for the implementation of targeted remedial measures at the same time.

水稻洪涝受灾情况快速监测及针对受灾地区进行及时有效补救措施的研究具有重要意义。目前人工的水稻洪涝灾害灾情监测方法耗时耗力、时效性较差。遥感技术具有快速、无损大范围获取地物信息的特点，相对传统的人工方法有很多优势，但现有的洪涝遥感监测方法很少考虑下垫面为水体与水体中泥沙对水稻光谱特征的影响，并且洪涝灾情评估并未考虑农田环境中生境因子对水稻受灾程度的影响，限制了水稻洪涝灾情模型监测精度。本项目以洪涝灾害高发的淮河地区水稻为研究对象，研究水稻受洪涝灾害胁迫后，通过提取下垫面为浑浊水体光谱特征，建立定量描述水稻在泥沙水体中长势分布的数学模型，并根据研究区域农田环境特点，综合GIS技术与农学知识，考虑多因子环境下建立水稻受灾监测模型，实现水稻灾情快速准确实时监测。研究结果能为水稻不同地区性研究、灾后恢复提供有针对性的理论基础，同时也能为针对性补救措施的实施提供理论基础。

由于全球气候变化，国内水稻洪涝是历年来发生频度较高的灾害，并且损伤程度是严重的， 其灾损程度仅次于干旱，是一种毁灭性作物灾害。如何在大范围内水稻及时预测与实时监测灾情，一直是重要的研究内容。本项目围绕上述问题，主要开展了以下四个方面的研究工作。首先，（1）在详细计算长江中下游地区1971-2011年426个地面气象站标准化降水指数的基础上，从洪涝发生频率、洪涝站次比及洪涝强度3个方面分析了近40年长江中下游地区年度尺度与基于水稻物候期月尺度洪涝时空演变趋势。结果表明：基于结合研究区水稻物候期来看，三个指标均集中发生在6、7、8月份，影响范围最大，强度最强。（2）根据研究区气象站点近40年的气象资料，通过暴雨洪涝等级指标进行分省修订，构建了分省农业洪涝等级指标，且对比典型年份农业洪涝灾害的分布验证了指标合理性。（3）在快速获取区域尺度水稻洪涝监测灾情手段上，研究区通过获取MODIS反射率数据计算得出7种植被指数，通过主成分分析法快速获取反映区域尺度洪涝灾情程度的敏感植被指数，用包含最多植被信息的归一化植被指数（NDVI）与旬尺度的标准化降水指数（SPI）滞后反应进行分析。分析表明，NDVI对降水量变化确实存在一定滞后性，尤其是在营养生长时期时，表现的相关系数达0.1009*，同时在7月份延后10天达显著相关。（4）水稻发生洪涝后，实际获取参数比较困难，通过分析试验区能获取到的关键生育期实测数据，继而通过构建半经验模型进行受洪涝胁迫的水稻长势时间序列分析，进行水稻洪涝灾情评价，该模型经验证精度R2达到0.4251。在今后监测洪涝灾害时，结合气象数据与遥感监测更为精准的对灾情进行评价。