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Sikkim, a tiny Himalayan state situated in the north-eastern region of India, records limited research on the climate change. Understanding the changes in climate based on the perceptions of local communities can provide important insights for the preparedness against the unprecedented consequences of climate change. A total of 228 households in 12 different villages of Sikkim, India, were interviewed using eight climate change indicators. The results from the public opinions showed a significant increase in temperature compared to a decade earlier, winters are getting warmer, water springs are drying up, change in concept of spring-water recharge (locally known as Mul Phutnu), changes in spring season, low crop yields, incidences of mosquitoes during winter, and decrease in rainfall in last 10 years. In addition, study also showed significant positive correlations of increase in temperature with other climate change indicators viz. spring-water recharge concept (R (2) = 0.893), warmer winter (R (2) = 0.839), drying up of water springs (R (2) = 0.76), changes in spring season (R (2) = 0.68), low crop yields (R (2) = 0.68), decrease in rainfall (R (2) = 0.63), and incidences of mosquitoes in winter (R (2) = 0.50). The air temperature for two meteorological stations of Sikkim indicated statistically significant increasing trend in mean minimum temperature and mean minimum winter temperature (DJF). The observed climate change is consistent with the people perceptions. This information can help in planning specific adaptation strategies to cope with the impacts of climate change by framing village-level action plan.

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Land use and forest cover (LUFC) classification from satellite data in mountainous terrain offers challenge due to varied topography and complexities owing to different illumination conditions. Digital classification following supervised and/or unsupervised techniques in combination with/without ancillary information often does not provide acceptable level of accuracy. This chapter formulates and applies a hybrid approach for LUFC classification using moderate resolution satellite data. Both 'elimination' and 'fishing' approaches were used to classify the state of Sikkim into seventeen categories. The classification accuracy was estimated at 94.87% at 1:50,000 scale, which is suitable for utilization in further studies such as surface hydrological and energy fluxes. Further, the digital elevation model was utilized to derive the topographic units at 1000 m elevation steps, slope and aspect and their distribution across the seventeen LUFC classes. The distribution of various LUFC classes across different elevation, slope and aspects offers useful information for ecosystem planning and management.