{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T13:16:04Z","timestamp":1788959764758,"version":"build-2803163510"},"reference-count":51,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Coordination of Scientific Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km<jats:sup>2<\/jats:sup>), followed by deciduous forest (1,591 km<jats:sup>2<\/jats:sup>) and sub-perennial forest (1,317 km<jats:sup>2<\/jats:sup>). About 1,608 km<jats:sup>2<\/jats:sup> represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km<jats:sup>2<\/jats:sup> of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km<jats:sup>2<\/jats:sup>, with the agricultural class as the most impacted (735 km<jats:sup>2<\/jats:sup>). Our analysis detected the saltmarsh class occupied 541 km<jats:sup>2<\/jats:sup>in the LULC map, and around 328 km<jats:sup>2<\/jats:sup> were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.<\/jats:p>","DOI":"10.7717\/peerj.17319","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T08:30:10Z","timestamp":1714379410000},"page":"e17319","source":"Crossref","is-referenced-by-count":4,"title":["Combining multisensor images and social network data to assess the area flooded by a hurricane event"],"prefix":"10.7717","volume":"12","author":[{"given":"Rafael","family":"Hern\u00e1ndez-Guzm\u00e1n","sequence":"first","affiliation":[{"name":"CONAHCYT - Instituto de Investigaciones sobre los Recursos Naturales, Universidad Michoacana de San Nicol\u00e1s de Hidalgo, Morelia, Michoac\u00e1n, 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