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.

中文翻译：

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使用基于亚像素的变化检测方法在印度喜马拉雅西部的 Chhota-Shigri 冰川上检测雪/冰覆盖变化

摘要 由于区域崎岖的地形和气候条件，绘制和监测地球表面不同区域的冰川变化是一项具有挑战性的任务。这项研究的重点是监测印度喜马拉雅西部 Chhota-Shigri 冰川上的雪或冰盖变化。提出了一种基于亚像素的变化检测 (SCD) 方法，旨在识别两类类别之间的过渡区域（混合像素）。SCD 方法涉及子像素分类和变化矢量分析 (CVA) 的集成，以在子像素级别定义两个多时态日期之间的幅度和方向形式的变化。为了检查所提出的 SCD 的功效，实验结果还与现有的基于神经网络 (NN) 的 SCD (NN-SCD) 进行了比较。结果分析表明，与 NN-SCD (78.80%) 相比，所提出的 SCD 实现了更好的准确度 (84.80%)。此外，使用 Landsat 系列（Landsat 5、7 和 8 根据可用性）获取时间序列数据，以在 2001-2019 年期间对 Chhota-Shigri 冰川进行趋势分析。这项研究提供了估算双时雪/冰变化的有效方法，尤其是在全球崎岖地形上。