Rapid urban flood damage assessment using high resolution remote sensing data and an object-based approach

Abstract

Torrential rainfall can generate landslides, flash floods, and debris flows which might become disasters, causing loss of life and damage to property and infrastructure. To respond opportunely to hydrometeorological hazards, it is necessary to assess, rapidly and accurately, damage to the affected area. This is commonly done through time-consuming reconnaissance visits to obtain detailed field information. This paper proposes a methodology which uses: i) high resolution satellite and RGB images from unmanned aerial vehicles (UAV), ii) digital elevation models (DEM), and iii) object-based image analysis (OBIA) for rapid urban flood damage assessment and estimation of the number of houses washed away, or with a total or partial roof collapse, by comparing pre- and post-event data. The case study was Tropical Storm Earl in 2016 that affected the town of Chicahuaxtla, Puebla, Mexico, due to the overflow of the Zempoloantongo River that cuts through the town causing several loss of life and severe property damage. The results indicate that the three-pronged approach proposed herein is able to discriminate changes before and after the event and improve image classification of washed-away or destroyed houses. The overall accuracy of the proposed automatic classification obtained with UAV data had a value of 97.4%. Structural damage was not assessed in this study.

Keywords

• remote sensing
• object-based image analysis
• obia
• rgb image
• valley-emphasis method