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Snow is a common global meteorological phenomenon known to be a critical component of the hydrological cycle and an environmental hazard. In South Africa, snow is commonly limited to the country’s higher grounds and is considered one of the most destructive natural hazards. As a result, mapping of snow cover is an important process in catchment management and hazard mitigation. However, generating snow maps using survey techniques is often expensive, tedious and time consuming. Within the South African context, field surveys are therefore not ideal for the often highly dynamic snow covers. As an alternative, thematic cover–types based on remotely sensed data-sets are becoming popular. In this study we hypothesise that the reduced dimensionality using Principal Components Analysis (PCA) in concert Normalized Difference Snow Index (NDSI) is valuable for improving the accuracy of snow cover maps. Using the recently launched 11 spectral band Landsat 8 dataset, we propose a new technique that combines the principal component imager generated using PCA with commonly used NDSI, referred to as Normalised Difference Principal Component Snow Index (NDPCSI) to improve snow mapping accuracy. Results show that both NDPCSI and NDSI with high classification accuracies of 84.9% and 76.8% respectively, were effective in mapping snow. Results from the study also indicate that NDSI was sensitive to water bodies found on lower grounds within the study area while the PCA was able to de-correlate snow from water bodies and shadows. Although the NDSI and NDPCSI produced comparable results, the NDPCSI was capable of mapping snow from other related land covers with better accuracy. The superiority of the NDPCSI can particularly be attributed to the ability of principal component analysis to de-correlate snow from water bodies and shadows. The accuracy of both techniques was evaluated using a higher spatial resolution Landsat 8 panchromatic band and Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired on the same day. The findings suggest that NDPCSI is a viable alternative in mapping snow especially in heterogeneous landscape that includes water bodies.

south african journal of geomatics

A comparison of Normalised Difference Snow Index (NDSI) and Normalised Difference Principal Component Snow Index (NDPCSI) techniques in distinguishing snow from related land cover types