LAUSR

Abstract Mapping and monitoring of the glacier changes over different regions of Earth surface is a challenging task due to regional rugged topography and climate conditions. This study focused on the monitoring of snow or ice cover changes over Chhota-Shigri glacier, Western Himalaya, India. A subpixel-based change detection (SCD) approach is proposed, aiming to identify the transition zones (mixed pixels) between the two class categories. The SCD approach involves the integration of subpixel classification and change vector analysis (CVA) to define the changes in the form of magnitude and direction between two multitemporal dates at the subpixel level. To check the efficacy of proposed SCD, experimental outcomes have also been compared with existing neural-network (NN) based SCD (NN‒SCD). The result analysis has shown that proposed SCD achieved better accuracy (84.80%) as compared to NN‒SCD (78.80%). In addition, a time series data was acquired using the Landsat series (Landsat 5, 7 and 8 as per availability) to perform the trend analysis over Chhota-Shigri glacier, during the period 2001–2019. This study offers the effective way of estimating the bi-temporal snow/ice changes especially over rugged terrains around the globe.