A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

Alexandre, Cyprien; Johary, Rosa; Catry, Thibault; Mouquet, Pascal; Révillion, Christophe; Rakotondraompiana, Solofo; Pennober, Gwenaëlle

doi:10.3390/rs12020252

Cited by 13 publications

(12 citation statements)

References 41 publications

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“…Costache et al [195] conducted research on flash flood susceptibility assessments using multi-criteria decision making and machine learning approaches based on SRTMand GIS techniques. With the open access of the RS time series for Sentinel-1 data these techniques are now widely implemented for flood detection and mapping [192,195,196]. Such techniques enabled the morphological characterization of the Kyagar glacier and the monitoring of glacier lake outburst floods based on a time series in 2018 Sentinel-1A data [197].…”

Section: Flood Events and Floodplain Risksmentioning

confidence: 99%

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

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The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

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Section: Flood Events and Floodplain Risksmentioning

confidence: 99%

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

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“…The first processing chain (Sen1Chain) integrates S1 time series in a change detection approach based on the Normalized Difference Ratio (NDR) for the mapping of floods. The operation and the results obtained with this processing chain have recently been published (Alexandre et al, 2020). In brief, the tool allows users to download and clip S1 images on S2 tiles or study area footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas.…”

Section: Processing Chainmentioning

confidence: 99%

“…Sen1Chain and Sen2Change have successfully been tested to assess recent cyclone impacts in various sites around the world. After very convincing results obtained on cyclones DORIAN (Bahamas) and IDAI (Mozambique) with Sen1Chain and S1 data (Alexandre et al, 2020), we focused here on two events that took place in Madagascar. The first study site is located around the city of Miandrivazo, chief town of his district, following the passage of cyclone AVA in January 2018.…”

Section: Study Areamentioning

confidence: 99%

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Sentinel-1 and Sentinel-2 Time Series Processing Chains for Cyclone Impact Monitoring in South West Indian Ocean

Mouquet¹,

Alexandre²,

Rasolomamonjy

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Monitoring the spatial footprint of cyclone impacts by remote sensing offers great potential for assessing the extent of damage and monitoring the resilience of the affected territories. For this purpose, as part of the Renovrisk-Impact project, we have developed two change detection processing chains based on optical (Sentinel-2) and SAR (Sentinel-1) data. These chains have been used to track different events in different regions of the world. In this article we focus on two study sites in Madagascar: the city of Miandrivazo, which was heavily affected by severe rainfall from Cyclone AVA in January 2018, and more recently the town of Marovoay which suffered a major disaster following the passage of tropical storm DIANE in January 2020. The obtained results were evaluated and compared with the Copernicus Emergency Mapping Service product, showing good consistency with this product and between them. These results confirm the potential of these Sentinel data and the developed processing chains for monitoring the impacts of cyclones, but also open up prospects for longer-term monitoring.

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“…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]. It is based on a normalized difference ratio comparing pre-event and post-event images, using both VV and VH (vertical transmit and horizontal receive) polarisations.…”

Section: Introductionmentioning

confidence: 99%

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

Hlaváčová

Kačmařík

Lázecký

et al. 2021

Water

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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.

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A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

Cited by 13 publications

References 41 publications

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Sentinel-1 and Sentinel-2 Time Series Processing Chains for Cyclone Impact Monitoring in South West Indian Ocean

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

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