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Boletín de Geología

Print version ISSN 0120-0283On-line version ISSN 2145-8553

Abstract

MOLANO, Sergio Mauricio et al. Assessment of glacier retreat in the Sierra Nevada del Cocuy, Colombia based on multisensor image classification. Bol. geol. [online]. 2022, vol.44, n.1, pp.49-73.  Epub Jan 25, 2022. ISSN 0120-0283.  https://doi.org/10.18273/revbol.v44n1-2022002.

The Andean glaciers represent one of the most important water sources in South America. They have been significantly reduced in recent decades because of climate change and climate variability. The most extensive snow-capped peak in the Colombian Andes Mountains corresponds to the Sierra Nevada del Cocuy (SRC), a mountain range located toward the northeast of the Eastern Cordillera with snow at altitudes ranging from approximately 4800 to 5345 meters above sea level (masl). From Landsat-4 (1987), Landsat-5 (1991, 1997, 2009), Landsat-7 (2000, 2003), Landsat-8 (2014, 2016, 2017), and Sentinel-2 (2019, 2021) satellite imagery, a pixel-oriented classification was performed using the PCI Geomatics software, defining four cover types: glacier area, soil-rock, vegetation, and water. For accuracy validation, high spatial resolution satellite imagery (Google Earth ~1.0 m and Planet’s high-resolution, analysis-ready mosaics of the world’s tropics ~4.7 m) and field control points were used as reference data. Overall accuracy values (all coverages) ranged from 86-99%, with accuracy for glacier area coverage between 97-100%. The decrease in the glacier area is of 1099.59 ha over 34 years (1987-2021). This analysis revealed that the glacier area decreased by approximately 37.92% regarding the first scene (1987). According to this trend, the SRC glacier would be extinct by 2048. The rate of glacier retreat is mainly influenced by factors related to global warming, such as the increase in mean annual temperature and the decrease in precipitation rates and climate variability factors such as the El Niño phenomenon.

Keywords : Andes; Climate change; Climate variability; Accuracy; Supervised classification.

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