Flooded Extent and Depth Analysis Using Optical and SAR Remote Sensing with Machine Learning Algorithms

Abstract

Recurrent flooding occurs in most years along different parts of the Gulf of Mexico coastline and the central and southeastern parts of Mexico. These events cause significant economic losses in the agricultural, livestock, and infrastructure sectors, and frequently involve loss of human life. Climate change has contributed to flooding events and their more frequent occurrence, even in areas where such events were previously rare. Satellite images have become valuable information sources to identify, precisely locate, and monitor flooding events. The machine learning models use remote sensing images pixels as input feature. In this paper, we report a study involving 16 combinations of Sentinel-1 SAR images, Sentinel-2 optical images, and digital elevation model (DEM) data, which were analyzed to evaluate the performance of two widely used machine learning algorithms, gradient boosting (GB) and random forest (RF), for providing information about flooding events. With machine learning models GB and RF, the input dataset (Sentinel-1, Sentinel-2, and DEM) was used to establish rules and classify the set in the categories specified by previous tags. Monitoring of flooding was performed by tracking the evolution of water bodies during the dry season (before the event) through to the occurrence of floods during the rainy season (during the event). For detection of bodies of water in the dry season, the metrics indicate that the best algorithm is GB with combination 15 (F1m = 0.997, AUC = 0.999, K = 0.994). In the rainy season, the GB algorithm had better metrics with combination 16 (F1m = 0.995, AUC = 0.999, Kappa = 0.994), and detected an extent of flooded areas of 1113.36 ha with depths of <1 m. The high classification performance shown by machine learning algorithms, particularly the so-called assembly algorithms, means that they should be considered capable of improving satellite image classification for detection of flooding over traditional methods, in turn leading to better monitoring of flooding at local, regional, and continental scales.

Combining multisensor images and social network data to assess the area flooded by a hurricane event

“…Notwithstanding the flooded areas are now mapped using sophisticated deep learning ( Bai et al., 2021 ; Katiyar, Tamkuan & Nagai, 2021 ) and machine learning algorithms ( Uddin, Matin & Meyer, 2019 ; Soria-Ruiz et al., 2022 ), some researchers acknowledge that the histogram thresholding technique employed in this study is the simplest and most common procedure for floods mapping from SAR data. This technique is a fast, reliable, and computationally less time-consuming method ( Liang & Liu, 2020 ; Levin & Phinn, 2022 ; Rossi et al., 2023 ).…”

Índices para el monitoreo de cuerpos de agua usando sensores remotos

“…El índice AWEI ha sido utilizado en México para detectar cambios en cuerpos de agua en el Lago de Chapala, Jalisco (Calvario et al, 2017), para el monitoreo (Wang et al, 2018) y discriminación de cuerpos de agua (Calvario et al, 2018), así como en la extracción (Arreola-Esquivel et al, 2019) y detección (Soria-Ruiz et al, 2022) de cuerpos de agua.…”

Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia

“…Mehmood et al [13] utilized the GEE cloud platform to detect floods using Landsat images. However, the limitation of optical images such as Landsat is that it is not possible to detect flood inundation during the rainy season [14]. Hence, in this study, Synthetic Aperture Radar (SAR) is utilized to detect flood inundation to gain the same advantage as previous studies.…”