The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Potential and Limitations of Open Satellite Data for Flood Mapping

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps

river morphology monitoring using multitemporal sar data preliminary results

AbstractIn this paper, we test the capability of satellite synthetic aperture radar (SAR) images to enhance the monitoring of river geomorphological processes. The proposed approach exploits the recently introduced Level-α products. These products are bi-temporal RGB composites in which the association color-object, being physical-based, is stable whatever the scene is considered. This favors the detection of temporary rivers' characteristics for classification purposes in a change-detection environment. The case study was implemented on the Orco river (northwest Italy), where a set of 39 COSMO-SkyMed SAR stripmap images acquired from October 2008 to November 2014 was used to monitor channel planform changes. This preliminary study is devoted to assess the suitability of Level-α images for geomorphologist, with particular reference to the detection of phenomena of interest in river monitoring. This is prior for semi-automatic or automatic classification activities

Terrestrial applications: An intelligent Earth-sensing information system

For Abstract see A82-2214

Using Optically Stimulated Luminescence to Unravel Sedimentary Processes of the Usumacinta and Grijalva Rivers (SE Mexico)

This report provides an optically stimulated luminescence (OSL) chronology for sediment collected through terrace deposits of the Usumacinta and Grijalva rivers in SE Mexico. The Grijalva and Usumacinta rivers are susceptible to flooding during the hurricane season (between May and November), affecting the population of the state of Tabasco, and leaving many households at a flood risk. The present study was initiated to obtain an understanding of the sediment processes, rates and frequency of flood events in the past. The report summaries the initial luminescence profiling, using a SUERC PPSL system, and laboratory analysis, used to characterise the stratigraphy and interpret sedimentary processes in each profile, together with the quantitative quartz SAR dating used to define chronologies in each. Initial luminescence profiling revealed that the stratigraphy in each was complex, reflecting multiple cycles of deposition, with maxima, followed by a tail to lower intensities, possibly indicating deposition during extreme flood events, followed by a period in which the sediment was mixed and the luminescence signals reset. The laboratory profiling reproduced the apparent maxima/trends in the field profiling dataset. In the Grijalva section, the profiling samples encompass the full range of variations in the IRSL net signal intensities, re-affirming the complex stratigraphy. In the Usumacinta section, the profiling samples were positioned on the trend of a normal age-depth progression, which may indicate that the horizons sampled are well suited for quartz SAR dating. Given the nature of the sediment sampled, it is unsurprising that the equivalent dose distributions obtained for each of the dating samples showed considerable scatter, leading to some ambiguity in estimating a stored dose for age calculations. In each, a number of aliquots returned high equivalent dose values, implying residual luminescence signals (leading to higher apparent ages), and others, low values, implying re-setting of the luminescence signals in the modern environment. It is well recognised that fluvial sediment of this sort can enclose mixed-age populations. It has been argued elsewhere (Fuchs and Lang, 2001; Lepper et al., 2000; Olley et al., 1998; Olley et al., 1999) that the lowest population of dose(s) may best represent the burial dose of the youngest depositional component, and that an arbitrary value of say the lowest 5% be used in age calculations. However, if this method was instigated for the Mexican samples, it would include the low equivalent dose values thought to reflect contamination from the surface, by bioturbation or some other weathering process, leading to artificially young ages. Therefore, each sample was evaluated on an individual basis, where low equivalent doses were considered to represent contamination and rejected, along with high equivalent dose outliers and any aliquots which failed SAR acceptance criteria. The weighted mean and weighted standard deviation of the reduced set were used in age calculations. The dating results reported here provide a first chronology to interpret the changing fluvial dynamics of the Usumacinta and Grijalva rivers, and a means to quantify flood events through the historical period. The chronology established for the Grijalva section spans from the 6th century AD to the 12th century AD; and the chronology for the Usumacinta section from the 17th century AD to the 19th century AD