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 pomyslnaszycie.com 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 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 tourettebrewing.com data, which opens new avenues for glacier monitoring, and the development of an 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.