The suitability of the camera trap–retrap method was explored for identifying territories and studying the spatial distribution of leopards (Panthera pardus fusca) in the Jhalana Reserve Forest, Jaipur, India. Data from two years (November 2017 to November 2019, N = 23,208 trap-hours) were used to provide estimates of minimum home-range size and overlap. We conducted home-range analysis and estimation, using the minimum convex polygon (MCP) method with geographic information system (GIS) tools. We are aware of the limitations and advantages of camera trapping for long-term monitoring. However, the limitations of the research permit allowed only the use of camera traps to estimate the home ranges. A total of 25 leopards were identified (male = 8, female = 17). No territorial exclusivity was observed in either of the sexes. However, for seven females, we observed familial home-range overlaps wherein daughters established home ranges adjacent to or overlapping their natal areas. The median home range, as calculated from the MCP, was 305.9 ha for males and 170.3 ha for females. The median percentage overlap between males was 10.33%, while that between females was 3.97%. We concluded that camera trapping is an effective technique to map the territories of leopards, to document inter- and intraspecific behaviors, and to elucidate how familial relationships affect dispersal.

Vegetation indices and Temperature datasets are very crucial in remote sensing to identify the differences over the period of time on the particular landscape. Remotely sensed multi-spectral data from Landsat-8 is highly useful in vegetation change analysis based on which remote sensing indices and temperature parameters. NDVI (Normalized Difference Vegetation Index)-LST (Land Surface Temperature) relation is important to understand the climatological effects on vegetation on regional scales. Threshold based classification have been used to understand vegetation change in multi-temporal studies. Similarly, in this study NDVI based classification have been applied in order to understand change in the area covered by vegetation and waterbodies. Overall, there is weak negative correlation (r = -0.232) found between NDVI-LST. It is observed that our results based on correlation analysis reaffirms other findings previously done for LST-NDVI relations in semi-arid regions.

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Due to the negative consequences of climate change, the fragmentation of forest areas worldwide as a result of increased anthropogenic pressure has become a source of concern. The objective of this research study was to evaluate forest fragmentation analysis around the Greater Gir Landscape, Gujarat. The Fragmentation assessment was performed based on Land-use & Land-cover (LULC) analysis using the Landsat 8 OLI images of 2015 and 2019 as primary datasets for the study. Geographic Information System (GIS) techniques were employed for LULC mapping with seven classes showing increment in the agriculture and vegetation patches with the year 2019 in compare to year 2015 due to accumulative rainfall pattern. The Spatial Metric was performed with the use of FRAGSTATS software, where Landscape Metrics were quantified using Class level, Landscape level and Moving Window Analysis. The trend observed in all the metrics calculated indicates increasing of continuity in Greater Gir Landscape. The forest has not undergone severe degradation but a rise in the natural classes like agriculture, vegetation patches, and waterbodies has led to increase in the level of continuity which is leading to conversion of these land patches in homogeneity of the areas using geospatial techniques. These spatial metrics using FRAGSTATS helps in simplifying quantification of the complex spatial processes and can be used for generating a positive framework for forest conservation.