利用MODIS、FY3A MERSI、HJ1A/B CCD、Landsat TM等4种遥感数据,在评估获取的最优监测算法基础上监测湄公河下游地区2011年洪灾淹没面积时序变化,并结合MODIS土地覆盖类型产品对3种重要地物类型进行受灾区域分析.结果表明:在湄公河地区,采用HJ1A/B CCD、FY3A MERSI数据监测洪灾淹没面积时建议优选NDWI算法;用Landsat TM、MODIS数据时优选NDVI算法.监测结果显示,2011年10月湄公河下游柬埔寨和三角洲地区洪灾灾情较重,最大淹没面积是正常水体面积的6.5倍.洞里萨河流域是重灾区,其河道扩宽约40倍.大量水体在洪灾初期汇流入洞里萨湖,洞里萨湖在洪灾初期起到蓄洪作用.利用多源遥感卫星数据监测洪灾变化,可获取更详尽的信息,提高洪灾监测效率.
Remote sensing monitoring of submerged areas is an effective method to measure flooding, directly indicating severity of the disaster. This study uses MODIS, FY3A MERSI, HJ1A/B CCD and Landsat TM data to monitor time series of the flood inundation area in the Mekong River downstream in 2011, based on an optimal algorithm obtained from experiments. We evaluate the flood area with different types of vegetation using the MODIS land cover data. From the results the following recommendations are made. NWDI is the best algorithm for HJ1A/B CCD and FY3A MERSI. NDVI is more suitable for Landsat TM and MODIS data compared with other three algorithms. Cambodia and the Mekong Delta region had serious flooding disaster in October 2011, with the inundated area 6.5 times larger than the normal area. The Tonle Sap River basin was the worstaffected area, with the river widened by about 40 times. At the beginning of the flood, a large amount of water flowed into Tonle Sap Lake, which played an important role in storing flood water. Therefore, by combining advantages of multi-source remote sensing satellite data to monitor changes in the floods, we can acquire more detailed information and improve efficiency of flood detection.
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