Debris-covered glaciers present significant challenges for accurately mapping and monitoring glacier dynamics, particularly in regions like the Eastern Pamir Plateau.This study shows a new hybrid ensemble classifier that uses random forest and decision tree algorithms to make mapping debris-covered glaciers more accurate using data from multiple satellites.The method leverages features derived from the SDGSAT-1, Sentinel-2, ASTER GDEM, and ITS_LIVE datasets, including color, texture, topography, land surface temperature, and velocity data.
Conventional sukrensi.com glacier mapping techniques often misclassify debris-covered areas due to their spectral similarity to the surrounding terrain, making this work crucial in addressing these limitations.To improve the accuracy of recognition between debris-covered glaciers and non-glaciated areas by capitalizing on the strengths of multiple machine-learning algorithms and diverse data sources.The hybrid ensemble classifier did better than single-classifier models, with an overall accuracy of 92% and a Kappa coefficient of 0.
885.It successfully delineated debris-covered glacier boundaries that closely matched established glacier inventories while offering a more detailed mapping of debris extent.Key innovations in this research include integrating SDGSAT-1 data, which opens new avenues for glacier monitoring, and the development of an tokidoki hello kitty blind box advanced feature selection strategy that enhances classification accuracy.
Further, the study introduces new spectral indices and temperature-based metrics specifically designed for debris-covered glacier identification.This was a significant step forward from previous work in the area.