Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets

Alexander M Melancon,Lori A Schultz,Robert E Griffin,John R Mecikalski,Jordan R Bell,Andrew L Molthan

doi:10.3390/rs13245098

Abstract

In response to Hurricane Florence of 2018, NASA JPL collected quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument, observing record-setting river stages across North and South Carolina. Fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging, stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study used random forest classification to generate maps of inundation from L-band UAVSAR imagery processed using the Freeman–Durden decomposition method. An average overall classification accuracy of 87% is achieved with this methodology, with areas of both under- and overprediction for the focus classes of open water and inundated forest. Fuzzy logic operations using hydrologic variables are used to reduce the number of small noise-like features and false detections in areas unlikely to retain water. Following postclassification refinement, estimated flood extents were combined to an event maximum for societal impact assessments. Results from the Hurricane Florence case study are discussed in addition to the limitations of available validation data for accuracy assessments.

Highlights

Accepted: 9 December 2021Flooding is a common occurrence across the United States and the world
The random forest (RF) classification was implemented on a total of fifteen Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data swaths over four flight tracks sampled during the 18 to 23 September 2018 observation period
This study demonstrated the unique capabilities of L-band SAR that make it a potentially reliable source of inundation detection in forested or vegetated areas

Summary

Introduction

Flooding is a common occurrence across the United States and the world. Reports that tropical cyclones and inland flooding were the second and third most frequent out of 290 billion-dollar disasters in the United States from 1980 to 2020. Tropical cyclones have caused the most damage (USD 1034 billion), have the highest average event cost (USD 19.9 billion), and are responsible for the highest number of deaths out of all disaster types (6593) [1]. Creating flood maps is an essential part of understanding the magnitude of a particular event and estimating impacts of a future occurrence. A broad audience, including government agencies and contractors, insurance agents, land developers, and community planners, views flood maps an estimated 30 million times each year for land management as well as mitigation, risk assessment, and disaster response purposes [2]

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Discussion

Conclusion