基于高分一号影像的武汉市洪涝遥感监测与分析
Characteristic Analysis of Flood Monitoring in Wuhan City Based on GF-1 Remote Sensing Data
中文摘要：
2016年6月30日—7月6日，强降雨引发了武汉及其邻近地区多处的城市内涝及河堤溃坝等严重洪涝灾害。以洪涝前后高分一号（GF-1）WFV影像和2015年30 m土地覆盖类型图为数据源，在分析典型地物与洪涝期不同水体的GF-1光谱曲线特征的基础上，对比NDWI阈值法与面向对象分析法提取研究区各类水体信息的适宜性，采用最优算法对武汉市洪涝灾害范围进行识别和判定。结果显示，面向对象分类法对洪涝期不同城市水体类型的遥感提取结果总体上要优于水体指数阈值法提取结果；城市郊区由河水漫堤和河堤溃坝等原因导致的洪涝淹没范围可以通过GF-1 WFV数据有效地进行识别。该研究成果可以对国产高分一号影像在城市洪涝灾害监测气象业务服务提供科学参考。
中文关键词：
高分一号，水体识别，NDWI，面向对象，洪涝监测
KeyWords：
GF-1 WFV, flood monitoring, NDWI, object-oriented, water identification
Abstract：
Severe flooding and river embankment dam collapse in Wuhan City was caused by heavy rainfall from June 30 to July 6, 2016. In this study, GF-1 WFV images before and after the floods were used to analyze the spectral characteristics of water bodies during the flooding period and to compare the suitability of this data for extracting various types of information about the water bodies using NDWI thresholds and object-oriented classification. An optimal algorithm was adopted for monitoring the flood disaster. The results showed that the object-oriented classification method was superior to the NDWI threshold method for extracting the results of different types of urban water bodies during the flood season. The flood submergence area caused by flooding of rivers and river embankments was effectively identified from GF-1 WFV images using object-oriented classification methods. The results provide a reference for the application of GF-1 image analysis to monitor urban flood disasters.