Debris-covered glaciers in High Mountain Asia are important indicators of climatic variability. The present study proposed an improved glacier mapping technique based on a TIRS/(RED/SWIR) band ratio that integrates thermal infrared (TIR), visible red, and shortwave infrared (SWIR) reflectance information with slope parameter to map debris-covered areas. The object-based machine learning technique comprising a hybrid feature selection model and a decision tree classifier was adopted to classify the glacierized region. The mapping results stated that the proposed band ratio combined with the slope parameter has better differentiated supraglacial debris from other glacier surfaces in comparison with the existing debris-covered glacier mapping approaches. The resulted debris-covered glacier boundaries were also validated with reference glacier inventories. The supraglacial debris-covered area was mapped with a high user’s accuracy of ≈98%. In addition, a high overall classification accuracy in the range of 99.59–99.84% was achieved with the proposed technique that overcomes the challenges of the previous noteworthy studies, confirming that this technique is effective in detecting debris-covered glaciers.

亚洲高山碎屑覆盖的冰川是气候变化的重要指标。本研究提出了一种基于 TIRS/(RED/SWIR) 波段比的改进冰川测绘技术，该技术将热红外 (TIR)、可见红光和短波红外 (SWIR) 反射率信息与坡度参数相结合，以绘制碎片覆盖的区域。采用基于对象的机器学习技术（包括混合特征选择模型和决策树分类器）对冰川区域进行分类。测绘结果表明，与现有的碎屑覆盖冰川测绘方法相比，所提出的带比与坡度参数相结合可以更好地区分冰川上碎屑与其他冰川表面。由此产生的碎片覆盖的冰川边界也通过参考冰川清单进行了验证。冰上碎片覆盖区域的绘制精度高达约 98%。此外，所提出的技术实现了 99.59-99.84% 的总体分类精度，克服了之前值得注意的研究的挑战，证实了该技术在检测碎片覆盖的冰川方面是有效的。