Rapid satellite-based flood inundation mapping and delivery of flood inundation maps during a flood event can provide crucial information for planners and decision makers to prioritize relief and rescue operations. The present study is undertaken to optimize the threshold ranges for the classification of flood water in Synthetic Aperture Radar (SAR) images (of 20° to 49° incidence angles) for quick flood inundation mapping and response during flood disasters. This is done through assessing the signature of flood water in Horizontal transmit and Horizontal received (HH), Horizontal transmit and Vertical received (HV), Vertical transmit and Horizontal received (VH), and Vertical transmit and Vertical received (VV) polarization radar data. The mean backscattering signature profiles of various water bodies were analyzed to discriminate flood water from other water bodies. The study shows that there is better demarcation of land-water surface in HH polarization. VV polarization has the potential to identify partially submerged features, which can be useful in flood damage assessments. The backscatter of flood water in HV and VH is the same and both HV and VH polarizations are adequate for the mapping of flood water. At near range to far range, −8 to −12 dB, −15 to −24 dB, and −6 to −15 dB can be used as optimum ranges for the classification of flood water in HH, HV, and VV polarizations. These optimum threshold ranges can be applied to the automation of flood mapping using SAR images in near-real time, where much time was often spent on finding the thresholds in order to produce flood inundation maps in a short time from the onset of flood disasters and deliver such maps to the concerned agencies.
The state of Jammu and Kashmir in North India experienced one of the worst floods in the past 60 years, during the first week of September 2014. In the present study, multi-temporal synthetic aperture radar (SAR) satellite images acquired from Indian Remote Sensing (IRS) satellite RISAT-1 and Canadian satellite Radarsat-2 during the peak flood period (08thÀ23rd September 2014) are used for extraction of flood disaster footprints, mapping spatial and temporal dynamics of flood inundation and assessing the disaster impact. With the aid of pre-and post-flood satellite images, coupled with hydro-meteorological data, the unprecedented flood situation is analyzed. It is estimated that about 557 km 2 of the Kashmir Valley's geographical area was inundated. Bandipora, Pulwama, Srinagar, Baramulla and Budgam were the worst flood affected districts, having more than 50 km 2 of their area affected by flood waters. Of the total inundated area, about 80% of the area under agricultural activity was submerged, followed by built-up areas constituting about 12% of geographical area. About 22 lakh people in 287 villages were affected by floods. The flood waters persisted in the northern and central part of the valley for more than two weeks.
The catchments of Rishiganga and then Dhauliganga valleys in the Chamoli district of Uttarakhand were impacted by a catastrophic flood triggered due to a massive rockslide, caused by wedge failure on 7th February, 2021. It is estimated that the massive rockslide of * 23 million cubic meter volume containing base rock, deposited ice, and snow got detached from the northern slopes of the Trishul mountain range near Ronti Glacier and created a vertical fall of almost 1700 m before severely impacting the Ronti Gad valley located at 1.5 km downstream of Ronti Glacier snout. The huge detached mass of rock and ice (GLIMS ID: G079733E30381N) swiftly moved downstream through the glaciated valley entraining snow, debris, mud on its way, caused rapid fluidization, created massive water/slush waves, and washed away partially or completely the hydel power projects and bridges in its route. It is estimated that * 0.93 Peta Joules of potential energy led to the generation of a significant amount of kinetic and thermal energy, good enough to trigger above-mentioned processes. Post-event analysis of high-resolution satellite data shows flood water marks in the valley and on the rock outcrops reaching up to * 80-150 m height on the way to Raini Village. The mud and the slush produced through this process led to the formation of a dammed lake and temporarily blocked one of the tributaries of the Rishiganga joining from the northeast. This study provides an insight into the sequence of events as they unfolded, through multi-temporal satellite image analysis, aerial survey, seismological data in conjunction with various other geo-spatial and geo-visualization tools for unraveling the flood event that has happened on February 7, 2021. We also discuss the potential cause of rockslide and the process mechanism of this unique event, causing loss of lives and property besides widespread devastation.
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