Tracking short-term seasonally flooded areas to understand the dynamics of the Coatzacoalcos River in Veracruz, Mexico

López‐Caloca, Alejandra A.; Navarro, Juan Carlos Valdiviezo; Tapia-Silva, Felipe Omar

doi:10.1117/1.jrs.15.042405

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“…Although some previous studies have used SAR image analysis and processing methods to monitor recurrent floods in the southern part of the country, traditional classification techniques were used in most investigations. Some of them used Radarsat-2 and Sentinel-1 SAR images to track flooding using single and dual polarizations; others evaluated the Sentinel-1 data for describing the spatial and temporal variability of water bodies [27][28][29][30]. The objective of this research was to evaluate and compare the performance of two assembly algorithms: gradient boosting (GB) and random forest (RF) for different combinations of Sentinel-1 SAR images, Sentinel-2 optical images, indexes to detect water bodies, and DEMs for monitoring the extent and depth of flooding generated by excess rainfall.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%