Dynamic Flood Vulnerability Mapping with Google Earth Engine

Abstract:

Satellites capture the rate and character of environmental change from local to global levels, yet integrating these changes into flood exposure models can be cost or time prohibitive. We explore an approach to global flood modeling by leveraging satellite data with computing power in Google Earth Engine to dynamically map flood hazards. Our research harnesses satellite imagery in two main ways: first to generate a globally consistent flood inundation layer and second to dynamically model flood vulnerability.

Accurate and relevant hazard maps rely on high quality observation data. Advances in publicly available spatial, spectral, and radar data together with cloud computing allow us to improve existing efforts to develop a comprehensive flood extent database to support model training and calibration. This talk will demonstrate the classification results of algorithms developed in Earth Engine designed to detect flood events by combining observations from MODIS, Landsat 8, and Sentinel-1. Our method to derive flood footprints increases the number, resolution, and precision of spatial observations for flood events both in the US, recorded in the NCDC (National Climatic Data Center) storm events database, and globally, as recorded events from the Colorado Flood Observatory database.

This improved dataset can then be used to train machine learning models that relate spatial temporal flood observations to satellite derived spatial temporal predictor variables such as precipitation, antecedent soil moisture, and impervious surface. This modeling approach allows us to rapidly update models with each new flood observation, providing near real time vulnerability maps.

We will share the water detection algorithms used with each satellite and discuss flood detection results with examples from Bihar, India and the state of New York. We will also demonstrate how these flood observations are used to train machine learning models and estimate flood exposure. The final stage of our comprehensive approach to flood vulnerability couples inundation extent with social data to determine which flood exposed communities have the greatest propensity for loss. Specifically, by linking model outputs to census derived social vulnerability estimates (Indian and US, respectively) to predict how many people are at risk.