Next Article in Journal
Validation of the Suomi NPP VIIRS Ice Surface Temperature Environmental Data Record
Next Article in Special Issue
Scaling of FAPAR from the Field to the Satellite
Previous Article in Journal
Validation of S-NPP VIIRS Sea Surface Temperature Retrieved from NAVO
Previous Article in Special Issue
Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(12), 17246-17257; doi:10.3390/rs71215882

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
*
Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno, Magaly Koch and Prasad S. Thenkabail
Received: 10 October 2015 / Revised: 4 December 2015 / Accepted: 11 December 2015 / Published: 18 December 2015
View Full-Text   |   Download PDF [7678 KB, uploaded 18 December 2015]   |  

Abstract

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top