remote sensing Article
Assessment of Surface Inundation Monitoring and Drivers after Major Storms in a Tropical Island Mei Yu * and Qiong Gao Department of Environmental Sciences, University of Puerto Rico, Rio Piedras, San Juan, PR 00925, USA * Correspondence: meiyu@ites.upr.edu
Citation: Yu, M.; Gao, Q. Assessment of Surface Inundation Monitoring and Drivers after Major Storms in a Tropical Island. Remote Sens. 2024, 16, 503. https://doi.org/10.3390/
Abstract: Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson’s correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters.
rs16030503 Academic Editor: Yuriy Kuleshov
Keywords: coastal inundation; SAR; machine learning; discharges; major storms; Bayesian regression kriging
Received: 9 December 2023 Revised: 16 January 2024 Accepted: 23 January 2024
1. Introduction
Published: 28 January 2024
Extreme climate events are becoming more frequent and severe under the warming climate [1], and climate models have predicted increased extreme El Niño and extreme La Niña events [2,3]. The increase in tropical cyclones, both in frequency and in intensity, and the lengthy droughts, together with heat waves, are connected to large-scale mangrove mortality globally [4,5]. Coastal regions support the highest population density, yet they are more prone than inland areas to damage by tropical storms, as the lowlands in the coast are more likely to be inundated [6,7]. In coastal regions, destructive tropical cyclones have brought disastrous damage to ecosystems and threatened dense coastal communities through events such as widespread floods resulting from heavy rainfall, coastal storm surge, and rising sea level [8–10]. The exacerbation of more frequent and intense tropical
Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Remote Sens. 2024, 16, 503. https://doi.org/10.3390/rs16030503
https://www.mdpi.com/journal/remotesensing