Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study over Adyar watershed, India.

Urban floods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban floods are frequently observed in many parts of the world. Flood events in Chennai city are a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover are focused in the present study. The present study aims at mapping flooded region using Sentinel 1A datasets over Adyar watershed. Series of Sentinel 1A image is collected before, during and after floods for mapping the extent of flood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of flooded extent from the SAR datasets. Analysis performed over the Adyar watershed provided promising results in the extraction of flooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of flooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of flooded pixels.

Modeling, mapping and analysis of urban floods in India-a review on geospatial methodologies.

An increasing trend of urban floods in India from past several years causes major damages on Indian cities. By 2050, more than half of the population in the developing countries like India are expected to migrate to urban regions. Urbanization is triggered in developing countries as people migrate to cities in search of employment opportunities resulting in formation of new slums. With high density of population concentration in cities, urban floods are triggered leading to a significant impact of human life and economy of the country. The review focuses on addressing the urban flood occurrence in India and its relationship with population growth climate change. The study also describes the impact of urban floods to the environment and integrated methodologies adopted over decades for the prediction and effective mitigation and management during a disaster event.

Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India.

Brahmaputra is one of the perennial rivers in India which causes floods every year in the north-east state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and non-flooded water. From this method, the range of backscatter values for normal water are identified as - 18 to - 30 dB and the range is identified as - 19 to - 24 dB for flooded water. The results showed that the method is able to separate the flooded and non-flooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km2 on peak flood date for the chosen study area.

Identification of paddy crop phenological parameters using dual polarized SCATSAT-1 (ISRO, India) scatterometer data.

Paddy crop is one of the foremost food crops in the world. Human consumption accounts for 85% of total production of paddy. Paddy delivers 21% of human per capita energy and 15% of per capita protein. The present study focused on estimating the crop phenological parameters. The phenological parameters were estimated using soil moisture active passive (SMAP), MODIS NDVI, and SCATSAT-1 scatterometer data. The statistical models adopted in the study are two-parameter Gaussian distribution and two-parameter logistic distributions. The puddling stage is the first phenological stage, and it is estimated by the application of soil wetness index (SWI) and anomaly method. The transplanting stage is estimated using the anomaly method. The heading stages are estimated using statistical models, and it is found that Gaussian distribution is the best-fitted model. The harvesting stage is identified using SCATSAT-1 scatterometer and MODIS NDVI data. A chi-square test and degrees of freedom are used to identify the performance and comparison of statistical models. Chi-square test measure is equal to 80.561 and corresponding tabulated chi-square value with N-K-1 degrees of freedom that is equal to 117 is 151.929. The null hypothesis is not rejected.

Insights into structure and function of 30S Ribosomal Protein S2 (30S2) in Chlamydophila pneumoniae: A potent target of pneumonia.

The gene 30S ribosomal protein S2 (30S2) is identified as a potential drug and vaccine target for Pneumonia. Its structural characterization is an important to understand the mechanism of action for identifying its receptor and/or other binding partners. The comparative genomics and proteomics studies are useful for structural characterization of 30S2 in C. Pneumoniae using different bioinformatics tools and web servers. In this study, the protein 30S2 structure was modelled and validated by Ramachandran plot. It is found that the modelled protein under most favoured "core" region was 88.7% and overall G-factor statistics with average score was -0.20. However, seven sequential motifs have been identified for 30S2 with reference codes (PR0095, PF0038, TIGR01012, PTHR11489, SSF52313 and PTHR11489). In addition, seven structural highly conserved residues have been identified in the large cleft are Lys160, Gly161and Arg162 with volume 1288.83Å3 and average depth of the cleft was 10.75Å. Moreover, biological functions, biochemical process and structural constituents of ribosome are also explored. The study will be helped us to understand the sequential, structural, functional and evolutionary clues of unknown proteins available in C. Pneumoniae.