Mozambique Flood (2019) Caused by Tropical Cyclone Idai Monitored From Sentinel-1 and Sentinel-2 Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Chengming Li¹  Yujie Luan²  Jinyun Guo²  Xin Liu²  Zhen Li²  Xiaotao Chang³ 

[1] Chinese Academy of Surveying and Mapping, Beijing, China;College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China;Land Satellite Remote Sensing Application Center of MNR, Beijing, China;

关键词: Decision tree algorithm;    flood;    Mozambique;    object-based image analysis (OBIA);    Sentinel-1;    Sentinel-2;   

DOI  :  10.1109/JSTARS.2021.3107279

来源: DOAJ

【 摘 要 】

The tropical cyclone Idai caused severe floods in Mozambique in March 2019. Sentinel-1 and Sentinel-2 images are processed to monitor the flood disaster in Pungue and Buzi rivers by combining the object-based image analysis approach and the decision tree algorithm. Water is preliminarily extracted from Sentinel-1 image, and shadows of mountain and buildings in the study area are extracted from Sentinel-2 image. Mountain shadow is extracted based on the decision tree classification rules constructed by the digital elevation model and the index model, while building shadow is extracted by constructing the decision tree classification rules using the features of objects. Water extraction results are combined with shadows to eliminate confusing shadows in the preliminary extraction to obtain the accurate flood extent. Sentinel-2 images are used to classify land use types in multiple periods before and after the flood and analyze the disaster by combining with the change information of the water. Land use type changes are analyzed and predicted with the CA-Markov model. The results show that the maximum submerged area was 3.5 times of the normal water area, and that most areas were submerged for more than 10 d. The area between Pungue and Buzi rivers were severely affected, where a large number of crops and villages were submerged. Grassland and cropland were the most seriously submerged. The overall accuracy of extracted water results ranges from 86% to 92%. The results indicated that the combining method can effectively suppress the influence of shadows on water extraction results.

【 授权许可】

Unknown