遥感卫星具有覆盖范围广、连续观测等特点,被广泛应用于海雾识别相关研究。本文首先借助能够穿透云层,获取大气剖面信息的星载激光雷达(cloud-aerosol LiDAR with orthogonal polarization,CALIOP)对中高云、低云、海雾、晴空海表样本...遥感卫星具有覆盖范围广、连续观测等特点,被广泛应用于海雾识别相关研究。本文首先借助能够穿透云层,获取大气剖面信息的星载激光雷达(cloud-aerosol LiDAR with orthogonal polarization,CALIOP)对中高云、低云、海雾、晴空海表样本进行了标注。然后结合葵花8号卫星(Himawari-8)多通道数据提取了各类样本的亮温特征与纹理特征。最后根据海雾监测的需求,抽象出海雾监测的推理决策树,并据此建立深度神经决策树模型,实现了高精度监测夜间海雾的同时具备较强的可解释性。选择2020年6月5日夜间Himawari-8每时次连续观测数据进行测试,监测结果能够清晰地展现此次海雾事件的动态发展过程。同时本文方法海雾监测平均命中率(probability of detection,POD)为87.32%,平均误判率(false alarm ratio,FAR)为13.19%,平均临界成功指数(critical success index,CSI)为77.36%,为海上大雾的防灾减灾提供了一种新方法。展开更多
Mangroves are woody plant communities in the intertidal zone of tropical and subtropical coasts that play an important role in these zones. The infrared wave band is one of the key bands in the remote sensing identifi...Mangroves are woody plant communities in the intertidal zone of tropical and subtropical coasts that play an important role in these zones. The infrared wave band is one of the key bands in the remote sensing identification of mangrove forest, and ALI（advanced land imagery） has a large number of infrared bands. Two angle indices were proposed based on liquid water absorption at band 5p and band 5 of EO-1 ALI, denoted as β1.25 and β1.65 respectively. A decision tree method was adopted to identify mangrove forest using remote sensing techniques for β1.25–β1.65 and NDVI（normalized difference vegetation index） for EO-1 ALI imagery acquired at Shenzhen Bay. The results showed that the reflectance of mangrove forests at band 5p and band 5 was significantly lower than that of terrestrial vegetation due to the characteristics of coastal wetlands of mangrove forests. This resulted in a greater β1.25–β1.65 value for mangrove forest than terrestrial vegetation. The decision tree method using β1.25–β1.65 and NDVI effectively identifies mangrove forest from other land cover categories. The misclassification and leakage rates were 4.29% and 5.11% respectively. ALI sensors with many infrared bands could play an important role in discriminating mangrove forest.展开更多
基金National Natural Science Foundation of China(41201461)
文摘Mangroves are woody plant communities in the intertidal zone of tropical and subtropical coasts that play an important role in these zones. The infrared wave band is one of the key bands in the remote sensing identification of mangrove forest, and ALI（advanced land imagery） has a large number of infrared bands. Two angle indices were proposed based on liquid water absorption at band 5p and band 5 of EO-1 ALI, denoted as β1.25 and β1.65 respectively. A decision tree method was adopted to identify mangrove forest using remote sensing techniques for β1.25–β1.65 and NDVI（normalized difference vegetation index） for EO-1 ALI imagery acquired at Shenzhen Bay. The results showed that the reflectance of mangrove forests at band 5p and band 5 was significantly lower than that of terrestrial vegetation due to the characteristics of coastal wetlands of mangrove forests. This resulted in a greater β1.25–β1.65 value for mangrove forest than terrestrial vegetation. The decision tree method using β1.25–β1.65 and NDVI effectively identifies mangrove forest from other land cover categories. The misclassification and leakage rates were 4.29% and 5.11% respectively. ALI sensors with many infrared bands could play an important role in discriminating mangrove forest.
