Next Article in Journal
Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions
Next Article in Special Issue
Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring
Previous Article in Journal
Optimizing the Processing of UAV-Based Thermal Imagery
Previous Article in Special Issue
Evaluating Consistency of Snow Water Equivalent Retrievals from Passive Microwave Sensors over the North Central U. S.: SSM/I vs. SSMIS and AMSR-E vs. AMSR2
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 475;

Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
Laboratory for Remote Sensing and Geoinformatics, University of Texas at San Antonio, San Antonio, TX 78249, USA
Author to whom correspondence should be addressed.
Academic Editors: Claudia Notarnicola, Soe Myint and Prasad Thenkabail
Received: 15 March 2017 / Revised: 4 May 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
(This article belongs to the Special Issue Snow Remote Sensing)
Full-Text   |   PDF [7031 KB, uploaded 12 May 2017]   |  


It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 °C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot). View Full-Text
Keywords: snow disaster; risk assessment; early warning; artificial neural network; pastoral area snow disaster; risk assessment; early warning; artificial neural network; pastoral area

Graphical abstract

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).

Share & Cite This Article

MDPI and ACS Style

Gao, J.; Huang, X.; Ma, X.; Feng, Q.; Liang, T.; Xie, H. Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China. Remote Sens. 2017, 9, 475.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



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