Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat

Inventing a robust road-vehicle flood level monitoring device for disaster mitigation

Flooding impedes road utility and the frequency has increased across countries of the world owing to global climate change phenomena. Global road flooding casualties have risen from 371,800 in 2015 to 842,000 in 2017 resulting to economic losses valued at approximately US$71 billion. Existing devices that offer warning signals on safe threshold during flooding are predictive in nature and based on complex technologies that are cumbersome and rather expensive thereby affecting the attractiveness to low-economy societies of developing countries. There is therefore a dare need for better inventions towards greater mitigation. This paper presents an adaptive, affordable, robust, efficient and effective road vehicle flood level monitoring device for detecting rising flood on roads above a user defined safe threshold to mitigate road flooding disasters. The device operates on the principle of level conductivity sensor. The developed device offers interoperability with Google map enabling the level of flood on road to be accessed by road users online when fully commercialized. By this, road users are aware of the dangerous levels of flood to enable them use alternative routes. The study therefore recommends for adoption of mandatory inclusion of this invention on roads towards averting the usual road flooding hazard in Africa.

Use of remote sensing techniques to assess water storage variations and flood-related inflows for the Hawizeh wetland

High spatial and temporal resolution remote sensing data are becoming readily available. This has made the use of remote sensing to monitor and quantify spatiotemporal changes in surface waters feasible and efficient. In this paper, remote sensing techniques based on spectral indices were used to assess the changes in submerged areas and water storage in the Hawizeh marsh (south of Iraq) during the 2019 flood. Two water indices, the Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI), were used for this purpose. Water indices have been frequently utilized to detect water bodies because of their particular spectral properties in the visible and infrared spectrum. Non-measured flood-related flows into the marsh have also been estimated by applying the water balance approach. The accuracy assessment of the water areas extracted by the remote sensing indices showed an acceptable degree of reliability, which reflected positively on the water inflow calculations. As the Hawizeh is a transboundary marsh shared by Iraq and Iran, remote sensing techniques allowed for the estimation of difficult-to-measure inflows from the Iranian side. The results of the water balance revealed that the inflows from the Iranian side to the marsh during the 5 months of the flood made up approximately 41.2% of the total water volume entering the marsh. The study demonstrated the feasibility of using uncomplicated water extraction methods that depend on remote sensing data to monitor hydrological changes in the Hawizeh wetland that lack sufficient data.

Towards a Flood Assessment Product for the Humanitarian and Disaster Management Sectors Based on GNSS Bistatic Radar Measurements

This manuscript focuses on the need for tailoring flood assessment products to decision making within the humanitarian sector. Decision-makers often struggle to extract all of the information contained in scientific products, either because they come from different fields of expertise or because they have different needs that are not captured in the results or the processing of the data. Here we define the key elements of a flood assessment product designed for the humanitarian sector. From a remote sensing perspective, in order to assess flooding, the measurement sampling properties, i.e., spatial resolution and temporal repeat, are key. We have therefore implemented a methodology through the processing and interpretation of the measurements from the Cyclone Global Navigation Satellite System (CYGNSS) mission. CYGNSS measurements are usually parametrized in various possible observables. Those observables are then linked to the surface characteristics, such as, in this case, the presence of inundation in the CYGNSS footprint. Our methodology includes the variability of the pixels in landscapes with infrastructure, rivers, agricultural fields, rural areas, and other elements characteristic of the agricultural-urban interface. We provide an original methodology that uses CYGNSS mission bistatic radar measurements and an artificial intelligence classification algorithm based on statistical properties of the land pixels through a k-means clustering strategy to detect and monitor flooding events, as well as to characterize the land surface prior to and post flooding events. The novel methodology to derive a flooding product is then evaluated towards the needs of the humanitarian sector by a cognizant link (a translator) between technologists or scientists and decision-makers. The inclusion of humanitarian needs into product development following the advice of a cognizant link is novel to the applications developed employing GNSS bistatic radar data.

Increased flooded area and exposure in the White Volta river basin in Western Africa, identified from multi-source remote sensing data

Accurate information on flood extent and exposure is critical for disaster management in data-scarce, vulnerable regions, such as Sub-Saharan Africa (SSA). However, uncertainties in flood extent affect flood exposure estimates. This study developed a framework to examine the spatiotemporal pattern of floods and to assess flood exposure through utilization of satellite images, ground-based participatory mapping of flood extent, and socio-economic data. Drawing on a case study in the White Volta basin in Western Africa, our results showed that synergetic use of multi-temporal radar and optical satellite data improved flood mapping accuracy (77% overall agreement compared with participatory mapping outputs), in comparison with existing global flood datasets (43% overall agreement for the moderate-resolution imaging spectroradiometer (MODIS) Near Real-Time (NRT) Global Flood Product). Increases in flood extent were observed according to our classified product, as well as two existing global flood products. Similarly, increased flood exposure was also observed, however its estimation remains highly uncertain and sensitive to the input dataset used. Population exposure varied greatly depending on the population dataset used, while the greatest farmland and infrastructure exposure was estimated using a composite flood map derived from three products, with lower exposure estimated from each flood product individually. The study shows that there is considerable scope to develop an accurate flood mapping system in SSA and thereby improve flood exposure assessment and develop mitigation and intervention plans.

Remote Sensing Methods for Flood Prediction: A Review

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country's economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology's practice and usage in flood prediction.