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How much green space is available for individuals is a major question that city planners are generally interested in, and the present study aimed to address this issue in the context of Vellore, India, through two approaches, namely, the per capita and the geographical area approach. In existing studies, urban green space (UGS) was only calculated at the macro level, i.e., for the city as a whole. Micro-or ward-level analysis was not attempted before, and the present study carried out the same to get a clear picture of the amount of greenery available in each ward of a city. For this purpose, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland, and cropland were spectrally different. Then, classification techniques such as ISODATA, maximum likelihood, support vector machine (SVM), and objectbased methods were applied to the GE images. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that when considering the city as a whole, the amount of UGS available is 42% of the total area, which is more than the recommended range of 20-40%. Similarly, the available UGS per person is 97.84 m2, which is far above the recommended 12 m2/person. However, the micro-level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area, though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at the ward level rather than the city level as the former gives a closer look at the surplus and deficit areas. The results of terrestrial LiDAR survey at individual tree level revealed that if trees are located adjacent to buildings or roads, it results in fewer heat islands compared to the case where there are no trees. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]