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This study was undertaken to determine the lifestyle, mental health and social support of the citizens living in India and Nepal during post-Covid-19.Two different areas were taken for the present study, i.e. India and Nepal. In Nepal, subjects were taken from the Bagmati district of Kathmandu and the Kapurthala district of Punjab. 100 subjects were taken (50 from Nepal and 50 from India) among Nepal subjects, the equal number of male and female subjects were taken, it was followed among Indian subjects in the same way. The subjects were also matched in age, education and occupation. All the subjects were very cooperative and free from any severe physical or mental illness. Mental Health Battery, Lifestyle Scale and Multidimensional Scale of Perceived Social Support were employed with each of the subjects in two sessions respectively.It revealed that the people of Nepal showed deterioration in mental health compared to the subjects of the Indian population. In the other two dimensions, like lifestyle scale and social support scale, Indian subjects showed less prominence compared to Nepal subjects. Covid pandemic was so unexpected that the people were in a state of severe shock, who survived.

Unmanned aerial vehicles (UAV) have emerged as new platforms for acquiring ultra-high resolution images, which are challenging for extraction of features using conventional image processing approaches. Tree canopies are required to be constantly monitored for better planning and management. UAV is currently one way to survey canopies over a large area for precisely estimating their geometry. Conventional segmentation techniques are extensively used for image feature extraction. However, they lack in accuracy and require high computational processing when used for ultra-high resolution UAV datasets. These issues can be handled by superpixel segmentation algorithms which have good boundary adherence and are computationally efficient. Simple linear iterative clustering (SLIC) is a subset of superpixel segmentation technique which uses minimum tuning parameters making it most efficient. As the random forest is known for handling multiple attributes and robustness, it can be used for classifying and extracting features from segmented image generated using SLIC. The present study mainly focuses on the automation for extraction of tree canopies along with their object-based attributes from the UAV dataset. The data acquisition was carried out using Trimble UX5 fixed-wing UAV which was further orthorectified at a spatial resolution of 13 cm. The ortho-image was further segmented using SLIC algorithm. Canopy segments are then identified and classified using random forest, which is then merged into trees objects on the basis of their proximity. Accuracy assessment was then carried out for extracted tree canopies and was found that the aforesaid approach could achieve 93% similarity index. The current study highlights the potential of using SLIC segmentation and random forest classification method for tree canopy extraction from the ultra-high resolution ortho-image derived from UAV platforms.