Natural calamities such as floods are a severe menace, causing huge socio-environmental threats. Remote sensing technologies have proven to be a boon in precisely mapping the spatiotemporal effects and spread of floods, allowing remedial measures to be implemented on time. This paper aims to demarcate the extent of flooded areas in the study area by the application of remote sensing techniques that involve combining temporal images acquired during the flood (crisis images) with images acquired before the flood (archive images). In disaster mapping applications, the image that is acquired before the event takes place is referred to as the archive image, while the image that is acquired at the time of the event is referred to as the crisis image. The research objectives were achieved through the analysis of freely available Sentinel-1A data to delineate the extent of flooded areas in the European Space Agency's-Sentinel Applications Platform (ESA-SNAP) environment. Multi-looking, radiometric calibration, and range doppler terrain correction (geometric) were applied to the temporal images for better visualization and distinction and for projecting the pixels onto the proper map system. Later, the archive and crisis images were overlaid to form a Red Green Blue (RGB) composite that showed the extent and spread of floods in north Bihar, where each color represented areas of different significance. Further, the flood map was overlaid onto the Google Earth optical layer for better visualization and comparison. The work demonstrated the applicability and use of remote sensing and GIS technology to quickly gain insight into the spatial and temporal distribution of floods in a given region and could be used as a precursor for efficient flood management and relief measures.

Land Use/Land Cover (LU/LC) change detection was performed in the Solani river watershed area using multi-temporal remote sensing images (Landsat 8 ETM+ image of year 2014 and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) image of year 2021). Image classification and change detection were carried out for both images using Arc Geographic Information System (GIS) 10.1 and Earth Resources Data Analysis System (ERDAS) Imagine 2016 software. High-resolution Google Earth imagery and Land Remote-Sensing Satellite (LANDSAT) images were used for the accuracy assessment of the classification. The results showed major increments of agricultural fallow land and build-up land of 25.19% and 20.69%, respectively, with the highest decrease in forest cover of 29.27%. Also, to analyze the impact of varying spatial resolution on the Topographic Wetness Index (TWI), two digital elevation models (DEMs) of different spatial resolutions (SRTM, 90m, and Cartosat, 30 m) were used. The results of the study indicated that the mean TWI value increases with an increase in grid size.

The main objectives of oil-well cementing are to restrict the movement of fluids from one zone to another and to provide a stable position for the casing string. Achieving greater compressive strength, tensile strength, and lower permeability are the main features for increasing the effectiveness of cement jobs. The conventional cementing job lacks the ability to attain these properties even with the usage of advanced materials like self-healing agents, fibers, and polymeric materials in cement slurry. However, a scope of improvement is required in providing better zonal isolation. According to studies, the inclusion of nanoparticles would improve cement efficiency by achieving sufficient compressive strength and durability, reducing potential maintenance costs and environmental effects. With the addition of nanoparticles to cement, slurry increases compressive strength, decreases settling time, and increases density by reducing the porosity and permeability of the cement sheath. This study provides an explanation of the alterations in oil-well cement properties with the addition of nanaparticles at different temperatures and incubation periods.

No abstract

No abstract