Geoinformation Service for Flood Monitoring Using Satellite Data

Skakun, Serhiy

doi:10.15407/scin6.04.029

Cited by 7 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HEC-RAS, Sobek, Mike12, XP-SWMM, TUFLOW, and OpenFlows FLOOD are widely used for hydrodynamic modelling of floods generally [61]. Satellite imagery is used commonly with many software tools, including mapping, hydrodynamic modelling, hydrometeorological modelling, and risk assessments [62][63][64][65][66][67][68][69][70][71][72]. Different studies have used AS-TER, MODIS, Proba-V, Landsat, Sentinel-2, Synthetic Aperture Radar (SAR) data collected by Sentinel-1, ERS-2/SAR, ENVISAT/ASAR, RADARSAT-I, Light Detection And Ranging (LiDAR) elevation data, TerraSAR-X, SPOT, and SRTM DEM for flood mapping.…”

Section: Flood Mapping Practicesmentioning

confidence: 99%

An Evaluation of Risk-Based Agricultural Land-Use Adjustments under a Flood Management Strategy in a Floodplain

2021

View full text Add to dashboard Cite

Agricultural damage due to floods in the Indus basin’s fertile land has been the most damaging natural disaster in Pakistan so far. Earthen dikes are protecting the vast areas of the floodplain from regular flooding. However, the floodplain is attractive to farmers due to its fertility and experiences regular crop production within and out of the dike area. This paper evaluates the flood risk in a floodplain of the Chenab river in Pakistan and recommends land-use changes to reduce the flood risk for crops and associated settlements within the study area. The objective of the land-use change is not just to reduce flood losses but also to increase the overall benefits of the floodplain in terms of its Economic Rent (ER). This preliminary study analyses the economic impacts of the risk-based land-use improvements on existing floodplain land uses. Expected Annual Damage (EAD) maps were developed using hydrodynamic models and GIS data. The developed model identified the areas where maize can be economically more productive compared to rice under flood conditions. Promising results were obtained for the settlement relocations. It was also observed that the infra-structure, running parallel to the river, plays a significant role in curtailing the extent of floods. The results show that a combination of structural and non-structural measures proves more effective. The study also recommends the inclusion of social and environmental damages as well as other types of non-structural measures to develop the most effective flood management strategy.

show abstract

Section: Flood Mapping Practicesmentioning

confidence: 99%

An Evaluation of Risk-Based Agricultural Land-Use Adjustments under a Flood Management Strategy in a Floodplain

2021

View full text Add to dashboard Cite

show abstract

“…Over the past few years, machine learning methods have been in prevalence for flood mapping. Techniques like Bayesian network fusion (Li et al 2019b), self-organized maps (Skakun 2010 ), and support vector machines (Insom et al 2015) have been applied for extraction of flooded areas from optical as well as SAR satellite images, although there have been limited studies in this domain due to the lack of large-scale labeled flood event datasets. Deep learning methods represented by convolutional neural networks have proven to be effective in the field of flood damage assessment (Bai et al 2021;Ghosh et al 2020) and have enabled development of new methods for automated extraction of flood extent from SAR images (Zhang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Ghosh,

Garg,

Motagh

et al. 2024

PFG

View full text Add to dashboard Cite

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

show abstract

“…It should be noted, that many years researchers were dealing with the problem of searching the optimal solution for counting losses due to various natural disasters [Imamaliyeva 2011, Kussul 2011, Samoylenko et al 2009, Skakun 2010. But even as of today, an elaborate technological scheme that would become the standard, has still not been found.…”

Section: Introductionmentioning

confidence: 99%

“…But even as of today, an elaborate technological scheme that would become the standard, has still not been found. In the literature, a great deal of consideration is paid to the problem of areas damaged due to flooding [Samoylenko et al 2009, Skakun 2010 and/or fires and less attention is focused on the destruction of urban facilities due to earthquakes. The aim of this work is to evaluate the degree of destruction of buildings due to the earthquake in L' Aquila.…”

Section: Introductionmentioning

confidence: 99%

Determining the influence of the earthquake on the change(s) of objects using remote sensing data

Chetverikov

Babiy

Dorozhynskyy

2013

Geomatics, Landmanagement and Landscape

View full text Add to dashboard Cite

SummaryTwo methods of determining changes of objects (structures) using remote sensing data obtained in different times are considered in this article. The comparison of the percentage of destroyed buildings as a consequence of the earthquake in L' Aquila city (Italy) was implemented. For comparison two ways of data collection were used: obtaining data by the digitalization of objects before and after the disaster (using MapInfo software) and by automatic image classification using a special module Delta Cue (Erdas software).

show abstract

Geoinformation Service for Flood Monitoring Using Satellite Data

Cited by 7 publications

References 8 publications

An Evaluation of Risk-Based Agricultural Land-Use Adjustments under a Flood Management Strategy in a Floodplain

An Evaluation of Risk-Based Agricultural Land-Use Adjustments under a Flood Management Strategy in a Floodplain

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

Determining the influence of the earthquake on the change(s) of objects using remote sensing data

Contact Info

Product

Resources

About