Automatic flood detection using sentinel-1 images on the google earth engine

Moharrami, Meisam; Javanbakht, Mohammad; Attarchi, Sara

doi:10.1007/s10661-021-09037-7

Cited by 56 publications

(25 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In many studies, Sentinel 1 (SAR) was demonstrated suitable for mapping flood areas due to its ability to penetrate cloud forms among others. This result are in line and supports the findings of previously conducted researches (Uddin et al 2019;Moharrami et al 2021). Uddin et al, (2019) concluded that GEE algorithm performs well with an optimum accuracy of 96.44%.…”

Section: Resultssupporting

confidence: 91%

See 1 more Smart Citation

Satellite-based flood inundation and damage assessment

Mata

Balderama

Alejo

et al. 2022

Preprint

View full text Add to dashboard Cite

Because recurring floods in the Philippines have become more damaging throughout time, risk assessments, quantifying, and visualizing flood damages as accurately as possible become imperative. To deal with an up-to-date database and a practical assessment tool, a satellite imagery-based method was used which aimed to map flood inundation and estimate damages brought by the flood during Typhoon Ulysses. This paper presents a framework for an integrated flood risk management in a river basin context with the following components as follows: 1) collection of the comprehensive database containing information relevant for flood analysis; 2) use of a satellite-imagery based method for flood inundation map using Google Earth engine; 3) validation of map accuracy through quick post-flood participatory approach. Analysis of the recent flood inundation event in November 2020 in Cagayan Valley, Philippines showed the inundation of an extensive area of 620.88 km2 affecting the Cagayan province at 55.91% and Isabela province at 44% share of inundation. The flood severely affected approximately 614.05 km2 of the total croplands. Using a participatory validation approach, the overall accuracy of datasets used is 97.78% while flood extent is 95%. Through this study, the framework, approach, and methodology can be replicated in other locations in the Philippines and in other countries which recurrently experience flooding.

show abstract

Section: Resultssupporting

confidence: 91%

“…Uddin et al, (2019) concluded that GEE algorithm performs well with an optimum accuracy of 96.44%. Moharrami et al (2021) monitored flood events using multi-temporal Sentinel 1 images. The accuracy ranges from 92.8%-96.2% with an overall accuracy higher than 90%.…”

Section: Resultsmentioning

confidence: 99%

Satellite-based flood inundation and damage assessment

Mata

Balderama

Alejo

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We used the automated Otsu thresholding approach (Otsu, 1979) that finds optimum threshold values based on clusters found in the histograms of pixel values. This method is particularly useful for detecting inundated areas, because water bodies have considerably lower backscatter values compared to other land cover classes in a SAR image (Borah et al., 2018; Donchyts et al., 2016; Moharrami et al., 2021; Tiwari et al., 2020). The Otsu method assumes that there is a clear distinction between the two classes of interest in a bimodal histogram of the radar backscattering values, which is a valid assumption in this study (Fig.…”

Section: Methodsmentioning

confidence: 99%

Radar and optical remote sensing for near real‐time assessments of cyclone impacts on coastal ecosystems

Mondal

Dutta

Qadir

et al. 2022

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

Rapid impact assessment of cyclones on coastal ecosystems is critical for timely rescue and rehabilitation operations in highly human-dominated landscapes. Such assessments should also include damage assessments of vegetation for restoration planning in impacted natural landscapes. Our objective is to develop a remote sensing-based approach combining satellite data derived from optical (Sentinel-2), radar (Sentinel-1), and LiDAR (Global Ecosystem Dynamics Investigation) platforms for rapid assessment of post-cyclone inundation in nonforested areas and vegetation damage in a primarily forested ecosystem. We apply this multi-scalar approach for assessing damages caused by the cyclone Amphan that hit coastal India and Bangladesh in May 2020, severely flooding several districts in the two countries, and causing destruction to the Sundarban mangrove forests. Our analysis shows that at least 6821 sq. km. land across the 39 study districts was inundated even after 10 days after the cyclone. We further calculated the change in forest greenness as the difference in normalized difference vegetation index (NDVI) pre-and post-cyclone. Our findings indicate a <0.2 unit decline in NDVI in 3.45 sq. km. of the forest. Rapid assessment of post-cyclone damage in mangroves is challenging due to limited navigability of waterways, but critical for planning of mitigation and recovery measures. We demonstrate the utility of Otsu method, an automated statistical approach of the Google Earth Engine platform to identify inundated areas within days after a cyclone. Our radar-based inundation analysis advances current practices because it requires minimal user inputs, and is effective in the presence of high cloud cover. Such rapid assessment, when complemented with detailed information on species and vegetation composition, can inform appropriate restoration efforts in severely impacted regions and help decision makers efficiently manage resources for recovery and aid relief. We provide the datasets from this study on an open platform to aid in future research and planning endeavors.

show abstract

“…The authors in [5] tried to improve histogram thresholding using bilateral filtering as a smooth labelling method and tested the approach on three European events. The authors in [6] used the Otsu iterative thresholding algorithm applied to Sentinel-1 GDR data with single VV (vertical transmit-vertical receive) polarisation. Another approach is mentioned in [7].…”

Section: Introductionmentioning

confidence: 99%

“…The developed system was closely linked to other existing structures in the USA, such as the United States Geological Survey, a network of gauge stations, etc. Some other solutions, e.g., [6][7][8][9][10][11]13,14,17,18,24,26], relied on the use of computational intelligence tools, indexes and thresholding, or on specific auxiliary data sets. The computational intelligence tools used, such as neural networks, must always be trained on sample data first and only afterwards can be used to evaluate the current situation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery

Hlaváčová

Kačmařík

Lázecký

et al. 2021

Water

View full text Add to dashboard Cite

Many technical infrastructure operators manage facilities distributed over large areas. They face the problem of finding out if a flood hit a specific facility located in the open countryside. Physical inspection after every heavy rain is time and personnel consuming, and equipping all facilities with flood detection is expensive. Therefore, methods are being sought to ensure that these facilities are monitored at a minimum cost. One of the possibilities is using remote sensing, especially radar data regularly scanned by satellites. A significant challenge in this area was the launch of Sentinel-1 providing free-of-charge data with adequate spatial resolution and relatively high revisit time. This paper presents a developed automatic processing chain for flood detection in the open landscape from Sentinel-1 data. Flood detection can be started on-demand; however, it mainly focuses on autonomous near real-time monitoring. It is based on a combination of algorithms for multi-temporal change detection and histogram thresholding open-water detection. The solution was validated on five flood events in four European countries by comparing its results with flood delineation derived from reference datasets. Long-term tests were also performed to evaluate the potential for a false positive occurrence. In the statistical classification assessments, the mean value of user accuracy (producer accuracy) for open-water class reached 83% (65%). The developed solution typically provided flooded polygons in the same areas as the reference dataset, but of a smaller size. This fact is mainly attributed to the use of universal sensitivity parameters, independent of the specific location, which ensure almost complete successful suppression of false alarms.

show abstract

Automatic flood detection using sentinel-1 images on the google earth engine

Cited by 56 publications

References 58 publications

Satellite-based flood inundation and damage assessment

Satellite-based flood inundation and damage assessment

Radar and optical remote sensing for near real‐time assessments of cyclone impacts on coastal ecosystems

Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery

Contact Info

Product

Resources

About