Assessment of random forest method in pixel-based snow cover classification in Alpine region, Tatra mountains and Kaçkar mountains

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2022-11-21

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Aksu, Cansu

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For most countries in the Northern Hemisphere, the amount of usable water throughout the year is roughly determined by the amount of snow. Climate change and increasing demand on drinking and industrial water due to population growth make the monitoring of snow cover even more crucial than it was in the past. Today, to observe the amount of snow cover, different algorithms are being used on remote sensing data for classification of snow, aside from in-situ data collection techniques. This study presents the evaluation of the performance of Random Forest (RF) algorithm for snow cover classification on Sentinel-2 imagery over three selected mountainous regions: Alpine Region, Tatra Mountains, and Kaçkar Mountains, with different input combinations as independent variables (i.e., predictors). The combinations have been evaluated for three different times of the year for a much better assessment – to observe the differences in when snow cover starts to form (November to December), the time with roughly the maximum amount of snow is observed (January to March), and the time when it starts to melt (April to June). The confusion matrices, overall accuracy (OA) and Kappa coefficient were used for accuracy assessment. Overall, principle component bands combination (Pca) yielded the most accurate results. Pca combination also provided the shortest computation time out of all combinations, excluding the process of obtaining principal components, as the combination has the least amounts of input to the RF model, as compared to the other combinations. The overall results revealed that RF algorithm works well with appropriate numbers of principal component bands with NDSI, NDVI and NDWI indices for complex terrains over mountainous areas.

Subject Keywords

Sentinel-2, Remote sensing of snow, Classification, Machine learning, Snow hydrology, Random forest

URI

https://hdl.handle.net/11511/101226

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

C. Aksu, “Assessment of random forest method in pixel-based snow cover classification in Alpine region, Tatra mountains and Kaçkar mountains,” M.S. - Master of Science, Middle East Technical University, 2022.