Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 34, Issue 19, Pages 6529-6538Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2013.803631
Keywords
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Funding
- Ministry of Science and Technology of China [2009AA122001]
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Knowledge of snow cover is essential to understanding the global water and energy cycle. Thresholding the normalized difference snow index (NDSI) image is a method frequently used to map snow cover from remotely sensed data. However, the threshold is dependent on the scenario and needs to be determined accordingly. In this study, nine automatic thresholding methods were tested on the NDSI. Comparisons of the automatic thresholding methods, optimal threshold, and support vector machine (SVM) classification show that Otsu's and Nie's methods appear to be the most robust among the nine automatic thresholding methods, achieving comparable accuracies with the latter two approaches. In addition, NDSI from the digital number (DN) can be an efficient substitution for NDSI obtained from atmospherically or topographically corrected data, with similar accuracy.
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