remote sensing Article Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China Jinlong Gao 1, Xiaodong Huang 1,*, Xiaofang Ma 1, Qisheng Feng 1, Tiangang Liang 1 and Hongjie Xie 2 1 State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China;
[email protected] (J.G.);
[email protected] (X.M.);
[email protected] (Q.F.);
[email protected] (T.L.) 2 Laboratory for Remote Sensing and Geoinformatics, University of Texas at San Antonio, San Antonio, TX 78249, USA;
[email protected] * Correspondence:
[email protected] Academic Editors: Claudia Notarnicola, Soe Myint and Prasad Thenkabail Received: 15 March 2017; Accepted: 9 May 2017; Published: 12 May 2017 Abstract: 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%.