A fully automated TerraSAR-X based flood service

Martinis, Sandro; Kersten, Jens; Twele, André

doi:10.1016/j.isprsjprs.2014.07.014

Cited by 251 publications

(190 citation statements)

References 25 publications

Supporting

Mentioning

160

Contrasting

Unclassified

Order By: Relevance

“…The detection of smaller water surfaces has been reported also in [18,20,35,51]. They can be detected by the use of images with higher spatial resolution, such as X band SAR data [20,29,31,32,36]. Although the scene size and associated coast do not allow the study of large areas for ecological researches at regional scale, they can be used as a complement to focus on particular area of interest with strong issues in terms of biodiversity and environmental management.…”

Section: Grassland Floods (Intra-field Scale)mentioning

confidence: 99%

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Cazals¹,

Rapinel

Frison³

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Abstract:In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

show abstract

Section: Grassland Floods (Intra-field Scale)mentioning

confidence: 99%

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Cazals¹,

Rapinel

Frison³

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In contrast to methods for the extraction of open water surfaces for flood detection [4,45,46], there is little research for the detection of the entire flood extent comprising TFV besides open water areas [11,25,29]. An example for automatic flood detection of open water in near-real time (NRT) by Sentinel-1 data is given by Twele et al [47].…”

Section: Introductionmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract:The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

show abstract

“…Since the launch of the high resolution SAR satellite systems TerraSAR-X, RADARSAT-2 and the COSMO-SkyMed constellation, a limited number of automatic image processing algorithms has been developed to derive open flood surfaces from SAR data [3][4][5][6][7]. Only few fully operational SAR-based flood mapping services have been presented in the literature so far based on TerraSAR-X [4] and Envisat ASAR [8,9] data.…”

Section: Introductionmentioning

confidence: 99%

“…Only few fully operational SAR-based flood mapping services have been presented in the literature so far based on TerraSAR-X [4] and Envisat ASAR [8,9] data. Automatic flood mapping algorithms generally only focus on open water areas and do not consider partially submerged vegetation areas.…”

Section: Introductionmentioning

confidence: 99%

Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany

2015

Self Cite

View full text Add to dashboard Cite

Abstract:In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as forests and agricultural fields. The test area is located at River Saale, Saxony-Anhalt, Germany, which is covered by a time series of 39 TerraSAR-X data acquired within the time interval December 2009 to June 2013. The data set is supplemented by ALOS PALSAR L-band and RADARSAT-2 C-band data. The time series covers two inundations in January 2011 and June 2013 which allows evaluating backscatter variations between flood periods and normal water level conditions using different radar wavelengths. According to the results, there is potential in detecting flooding beneath vegetation in all microwave wavelengths, even in X-band for sparse vegetation or leaf-off forests.

show abstract

A fully automated TerraSAR-X based flood service

Cited by 251 publications

References 25 publications

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Backscatter Analysis Using Multi-Temporal and Multi-Frequency SAR Data in the Context of Flood Mapping at River Saale, Germany

Contact Info

Product

Resources

About