Flood Proxy Mapping with Normalized Difference Sigma-Naught Index and Shannon’s Entropy

Extraction and mapping of downpour impact and their Cumulonimbus origin, 20 May 2020, Vâlcea (Romania) via Sentinnel-1 SAR dual polarization

The aim of this work is to study the impact and characteristics of the meteorological phenomenon that occurred on May 20, 2020 in Vâlcea County, Romania. For this purpose, we used SAR radar images from the Sentinel-1 series at different dates, before, during and after the event. The methodology consists in exploiting and combining the two polarisations, VV and VH, of the recorded radar wave. The results obtained can be summarised as the extraction of areas completely covered by water and areas characterised by high roughness and very high humidity. The latter (roughness/humidity) can have two different origins. The first one corresponds to an area affected by downpours, giving a high roughness due to the interaction of water drops with the ground and also to the presence of hail, amplified by the wind factor. On the other hand, the second one coincides, quite simply, with the presence of a thundercloud, precisely a Cumulonimbus, which formed in that particular place as a result of the favourable geomorphological characteristics and meteorological conditions, giving a high humidity due to the high water content. We also determined the total impacted area of about 96.71 km², whose 60.17 km² of water covered area, which is 2.45% of the study area. The remaining 36.54 km² (1.49%) represents the affected rough surface, located in the plain, or the humid surface corresponding to the area of the Cumulonimbus head covering the plain.

Sentinel-1 Spatiotemporal Simulation Using Convolutional LSTM for Flood Mapping

The synthetic aperture radar (SAR) imagery has been widely applied for flooding mapping based on change detection approaches. However, errors in the mapping result are expected since not all land-cover changes are flood-induced, and those changes are sensitive to SAR data, such as crop growth or harvest over agricultural lands, clearance of forested areas, and/or modifications on the urban landscape. This study, therefore, incorporated historical SAR images to boost the detection of flood-induced changes during extreme weather events, using the Long Short-Term Memory (LSTM) method. Additionally, to incorporate the spatial signatures for the change detection, we applied a deep learning-based spatiotemporal simulation framework, Convolutional Long Short-Term Memory (ConvLSTM), for simulating a synthetic image using Sentinel One intensity time series. This synthetic image will be prepared in advance of flood events, and then it can be used to detect flood areas using change detection when the post-image is available. Practically, significant divergence between the synthetic image and post-image is expected over inundated zones, which can be mapped by applying thresholds to the Delta image (synthetic image minus post-image). We trained and tested our model on three events from Australia, Brazil, and Mozambique. The generated Flood Proxy Maps were compared against reference data derived from Sentinel Two and Planet Labs optical data. To corroborate the effectiveness of the proposed methods, we also generated Delta products for two baseline models (closest post-image minus pre-image and historical mean minus post-image) and two LSTM architectures: normal LSTM and ConvLSTM. Results show that thresholding of ConvLSTM Delta yielded the highest Cohen’s Kappa coefficients in all study cases: 0.92 for Australia, 0.78 for Mozambique, and 0.68 for Brazil. Lower Kappa values obtained in the Mozambique case can be subject to the topographic effect on SAR imagery. These results still confirm the benefits in terms of classification accuracy that convolutional operations provide in time series analysis of satellite data employing spatially correlated information in a deep learning framework.

An Overview of Flood Risk Analysis Methods

Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.