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Open AccessArticle

An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data

1
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2
Cold & Arid Region Environmental & Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
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Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno, Magaly Koch and Prasad S. Thenkabail
Remote Sens. 2015, 7(12), 17246-17257; https://doi.org/10.3390/rs71215882
Received: 10 October 2015 / Revised: 4 December 2015 / Accepted: 11 December 2015 / Published: 18 December 2015
The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones on Qilian Mountain in the Upper Heihe River Basin (UHRB) were chosen as example regions. By analyzing the spectral signature of snow-covered and snow-free evergreen coniferous forests with Landsat Operational Land Imager (OLI) data, a novel spectral band ratio using near-infrared (NIR) and shortwave infrared (SWIR) bands, defined as (ρnir − ρswir)/(ρnir + ρswir), is proposed. Our research shows that this band ratio, named the normalized difference forest snow index (NDFSI), can be used to effectively distinguish snow-covered evergreen coniferous forests from snow-free evergreen coniferous forests in UHRB. View Full-Text
Keywords: remote sensing; snow identification; forest; OLI remote sensing; snow identification; forest; OLI
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Wang, X.-Y.; Wang, J.; Jiang, Z.-Y.; Li, H.-Y.; Hao, X.-H. An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data. Remote Sens. 2015, 7, 17246-17257.

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