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Xiaofang Ma
State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China

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Journal article
Published: 12 May 2017 in Remote Sensing
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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).

ACS Style

Jinlong Gao; Xiaodong Huang; Xiaofang Ma; Qisheng Feng; Tiangang Liang; Hongjie Xie. Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China. Remote Sensing 2017, 9, 475 .

AMA Style

Jinlong Gao, Xiaodong Huang, Xiaofang Ma, Qisheng Feng, Tiangang Liang, Hongjie Xie. Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China. Remote Sensing. 2017; 9 (5):475.

Chicago/Turabian Style

Jinlong Gao; Xiaodong Huang; Xiaofang Ma; Qisheng Feng; Tiangang Liang; Hongjie Xie. 2017. "Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China." Remote Sensing 9, no. 5: 475.

Journal article
Published: 29 May 2015 in Remote Sensing
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With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces.

ACS Style

Jie Deng; Xiaodong Huang; Qisheng Feng; Xiaofang Ma; Tiangang Liang. Toward Improved Daily Cloud-Free Fractional Snow Cover Mapping with Multi-Source Remote Sensing Data in China. Remote Sensing 2015, 7, 6986 -7006.

AMA Style

Jie Deng, Xiaodong Huang, Qisheng Feng, Xiaofang Ma, Tiangang Liang. Toward Improved Daily Cloud-Free Fractional Snow Cover Mapping with Multi-Source Remote Sensing Data in China. Remote Sensing. 2015; 7 (6):6986-7006.

Chicago/Turabian Style

Jie Deng; Xiaodong Huang; Qisheng Feng; Xiaofang Ma; Tiangang Liang. 2015. "Toward Improved Daily Cloud-Free Fractional Snow Cover Mapping with Multi-Source Remote Sensing Data in China." Remote Sensing 7, no. 6: 6986-7006.