Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Remote Sensing, Год журнала: 2024, Номер 16(3), С. 446 - 446

Опубликована: Янв. 23, 2024

Since 1971, remote sensing techniques have been used to map and monitor phenomena parameters of the coastal zone. However, updated reviews only considered one phenomenon, parameter, data source, platform, or geographic region. No review has offered an overview that can be accurately mapped monitored with data. This systematic was performed achieve this purpose. A total 15,141 papers published from January 2021 June 2023 were identified. The 1475 most cited screened, 502 eligible included. Web Science Scopus databases searched using all possible combinations between two groups keywords: geographical names in areas platforms. demonstrated that, date, many (103) (39) (e.g., coastline land use cover changes, climate change, urban sprawl). Moreover, authors validated 91% retrieved parameters, 39 1158 times (88% combined together other parameters), 75% over time, 69% several compared results each available products. They obtained 48% different methods, their 17% GIS model techniques. In conclusion, addressed requirements needed more effectively analyze employing integrated approaches: they data, merged

Язык: Английский

---

Fast high-fidelity flood inundation map generation by super-resolution techniques

Journal of Hydroinformatics, Год журнала: 2024, Номер 26(1), С. 319 - 336

Опубликована: Янв. 1, 2024

Abstract Flooding is one of the most frequent natural hazards and causes more economic loss than all other hazards. Fast accurate flood prediction has significance in preserving lives, minimizing damage, reducing public health risks. However, current methods cannot achieve speed accuracy simultaneously. Numerical can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks results a matter seconds, have shown low map generation by existing methods. This work combines strengths numerical builds framework that quickly accurately model inundation with detailed water depth information. In this paper, we employ U-Net generative adversarial network (GAN) models to recover lost physics information from ultra-fast, low-resolution simulations, ultimately presenting high-resolution, maps as end results. study, both GAN proven their ability reduce computation time for generating it 7–8 h down 1 min. Furthermore, notably high.

Язык: Английский

---

Impacts of DEM type and resolution on deep learning-based flood inundation mapping

Earth Science Informatics, Год журнала: 2024, Номер 17(2), С. 1125 - 1145

Опубликована: Фев. 7, 2024

Язык: Английский

---

Automatic Flood Detection from Sentinel-1 Data Using a Nested UNet Model and a NASA Benchmark Dataset

PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science, Год журнала: 2024, Номер 92(1), С. 1 - 18

Опубликована: Март 1, 2024

Abstract During flood events near real-time, synthetic aperture radar (SAR) satellite imagery has proven to be an efficient management tool for disaster authorities. However, one of the challenges is accurate classification and segmentation flooded water. A common method SAR-based mapping binary by thresholding, but this limited due effects backscatter, geographical area, surface characterstics. Recent advancements in deep learning algorithms image have demonstrated excellent potential improving detection. In paper, we present a approach with nested UNet architecture based on backbone EfficientNet-B7 leveraging publicly available Sentinel‑1 dataset provided jointly NASA IEEE GRSS Committee. The performance model was compared several other UNet-based convolutional neural network architectures. models were trained from Nebraska North Alabama USA, Bangladesh, Florence, Italy. Finally, generalization capacity architectures testing data varied regions such as Spain, India, Vietnam. impact using different polarization band combinations input capabilities also evaluated Shapley scores. results these experiments show that perform comparably UNet++ both well test cases. Therefore, it can inferred certain used detection areas, thus proving transferability models. effect still varies across cases around world terms performance; individual bands, VV VH, ratios gives best results.

Язык: Английский

---

Nonlinear Interactions of Sea‐Level Rise and Storm Tide Alter Extreme Coastal Water Levels: How and Why?

AGU Advances, Год журнала: 2024, Номер 5(2)

Опубликована: Март 18, 2024

Abstract Sea‐level rise (SLR) increasingly threatens coastal communities around the world. However, not all are equally threatened, and realistic estimation of hazard is difficult. Understanding SLR impacts on extreme sea level challenging due to interactions between multiple tidal non‐tidal flood drivers. We here use global hourly data show how why tides surges interact with mean (MSL) fluctuations. At most locations world, amplitude at least one constituent and/or residual have changed in response MSL variation over past few decades. In 37% studied locations, “Potential Maximum Storm Tide” (PMST), a proxy for dynamics, co‐varies variations. Over stations, median PMST will be 20% larger by mid‐century, conventional approaches that simply shift current storm tide regime up rate projected may underestimate flooding these factor four. Micro‐ meso‐tidal systems those diurnal generally more susceptible altered than other categories. The nonlinear captured statistics contribute, along SLR, estimated increase three‐fourth mid‐21st century. threshold captures components their co‐evolution time. Thus, this statistic can help direct assessment design critical infrastructure.

Язык: Английский

---