Image-based machine learning for monitoring the dynamics of deltaic islands in the Atchafalaya River Delta Complex between 1991 and 2019

Yang, Jiayi; Dai, Zhijun; Lou, Yaying; Mei, Xuefei; Sergio, Fagherazzi

doi:10.1016/j.jhydrol.2023.129814

Cited by 6 publications

(1 citation statement)

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“…In 2020, the Nile Delta (Egypt) was monitored to detect coastal changes from 1984 to 2018 by using Landsat images, detecting areas with higher erosion than other coastal regions [7]. In 2023, the Deltaic Islands in Louisiana (USA) were analyzed from 1991 to 2019 by using remote sensing with the platform GEE [8]. In 2023, Sentinel-2a images were used to measure the width of the beach in the southern part of the Baltic Sea, finding some limitations due to cloud cover in satellite data; however, some erosion rates and breach points point to flooding [9].…”

Section: Introductionmentioning

confidence: 99%

Detection of Coastal Erosion and Progradation in the Colombian ‘Atrato River’ Delta by Using Sentinel-1 Synthetic Aperture Radar Data

Vásquez-Salazar,

Cardona-Mesa,

Valdés-Quintero

et al. 2024

Remote Sensing

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This paper presents a methodology to detect the coastal erosion and progradation effects in the ‘Atrato River’ delta, located in the Gulf of Urabá in Colombia, using SAR (Synthetic Aperture Radar) images. Erosion is the physical–mechanical loss of the soil that affects its functions and ecosystem services while producing a reduction in its productive capacity. Progradation is the deposition of layers in the basinward direction while moving coastward. Other studies have investigated these two phenomena using optical images, encountering difficulties due to the persistent presence of clouds in this region. In order to avoid the cloud effects, in this study, we used 16 Sentinel 1 SAR images with two different polarizations between 2016 and 2023. First, each image was rescaled from 0 to 255, then the image was despeckled by a deep learning (DL) model. Afterwards, a single RGB image was composed with the filtered polarizations. Next, a classifier with 99% accuracy based on Otsu’s method was used to determine whether each pixel was water or not. Then, the classified image was registered to a reference one using Oriented FAST and Rotated BRIEF (ORB) descriptor. Finally, a multitemporal analysis was performed by comparing every image to the previous one to identify the studied phenomena, calculating areas. Also, all images were integrated to obtain a heatmap that showed the overall changes across eight years (2016–2023) in a single image. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. In addition, a comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons.

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Section: Introductionmentioning

confidence: 99%