An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Liang, Ting; Huang, Xiaodong; Wu, Cai Xia; Liu, Xing Yuan; Li, Wen Long; Zheng, Guo; Jian, Ren

doi:10.1016/j.rse.2007.06.001

Cited by 148 publications

(100 citation statements)

References 18 publications

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“…The MODIS data are processed in three steps: (1) mosaicking the five individual tiles into one map and re-projecting it to geographic latitude/longitude projection for each period by using MRT, (2) extracting the data of Tibet Autonomous Region from the merged five tiles' data using GIS, and (3) extracting the snow and cloud information for all periods in GIS. In order to compare with the snow observations at the ground stations, two further steps were taken: (4) The MODIS snow parameter for each location of the 37 stations is extracted by using 'The Maximum Neighborhood Analysis' tool in GIS [42,43]. The procedure is to extract the values of the station pixel and its eight surrounding neighbor pixels from the MOD10A2 data.…”

Section: Modis Eight-day Snow Productsmentioning

confidence: 99%

Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015

2017

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Abstract:The seasonal snow cover of the Tibetan Plateau exerts a profound environmental influence both regionally and globally. Daily observations of snow depth at 37 meteorological stations in Tibet and MODIS eight-day snow products (MOD10A2) during the period 2001-2015 are analyzed with respect to the frequency and spatial distribution of snow cover for each season and for various altitude ranges. The results show that the average snow cover percentage was 16%. Snow cover frequency was less than 21% for 70% of the Tibetan area, while it was more than 40% in eastern Tibet and in the Himalayas. We also estimated the variations in the starting times of snow accumulation and ablation. During the 15 years, both datasets revealed a significant trend of earlier onset of ablation, but no evident trend for the start of accumulation. The two datasets differed slightly with respect to the seasonal variation of snow cover. MODIS data showed more snow in winter than in other seasons, but the ground data showed most snow in early spring. For the station locations, the correlation between ground and MODIS snow cover percentage (number of snow-covered stations/number of cloud-free stations) is 0.77. Combining the advantages of remote sensing data and ground observation data is the best way to investigate snow in Tibet.

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Section: Modis Eight-day Snow Productsmentioning

confidence: 99%

Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015

2017

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“…Simulation exper-iments using climate models indicated that, with continuing global warming, the snow cover in China would show more variations in space and time than ever before (Shi et al, 2011;Ji and Kang, 2013). Spatiotemporal variations of snow cover are also manifested as snowstorms or blizzards, particularly excessive snowfall over a short time duration (Bolsenga and Norton, 1992;Liang et al, 2008;Gao, 2009;Wang et al, 2013;Llasat et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Variability in snow cover phenology in China from 1952 to 2010

Cao

Xie

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Daily snow observation data from 672 stations in China, particularly the 296 stations with over 10 mean snow cover days (SCDs) in a year during the period of 1952-2010, are used in this study. We first examine spatiotemporal variations and trends of SCDs, snow cover onset date (SCOD), and snow cover end date (SCED). We then investigate the relationships of SCDs with number of days with temperature below 0 • C (TBZD), mean air temperature (MAT), and Arctic Oscillation (AO) index. The results indicate that years with a positive anomaly of SCDs for the entire country include 1955, 1957, 1964, and 2010, and years with a negative anomaly of SCDs include 1953SCDs include , 1965SCDs include , 1999SCDs include , 2002SCDs include , and 2009. The reduced TBZD and increased MAT are the main reasons for the overall late SCOD and early SCED since 1952. This explains why only 12 % of the stations show significant shortening of SCDs, while 75 % of the stations show no significant change in the SCDs trends. Our analyses indicate that the distribution pattern and trends of SCDs in China are very complex and are not controlled by any single climate variable examined (i.e. TBZD, MAT, or AO), but a combination of multiple variables. It is found that the AO has the maximum impact on the shortening trends of SCDs in the Shandong peninsula, Changbai Mountains, Xiaoxingganling, and north Xinjiang, while the combined TBZD and MAT have the maximum impact on the shortening trends of SCDs in the Loess Plateau, Tibetan Plateau, and Northeast Plain.

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“…Many studies have been conducted on snow cover area monitoring (Gutzler and Rosen, 1992;Hall et al, 2001;Liang et al, 2008b;Wang and Xie, 2009;Gao et al, 2010;Paudel et al, 2011), snow depth (SD) simulation (Stowe et al, 1991;Chang et al, 1996;Frei et al, 2012;Yu et al, 2012), snow disaster risk assessment (Romanov et al, 2002), loss evaluation post-disaster (i.e., the research for the pastoral areas after snow disasters have occurred and have resulted in losses of livestock) (Nakamura and Shindo, 2001), snow disaster and avalanche mapping, as well as their relations to climate change (Jones and Jamieson, 2001;Hendrikx et al, 2005;Bocchiola et al, 2008;Delparte et al, 2008;Hirashima et al, 2008;Lato et al, 2012). In 2006, the Chinese government issued a national standard for grading snow disasters in pastoral area (GB/T 20482-2006) (General Administration of Quality Supervision of China, 2006).…”

Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

Nat. Hazards Earth Syst. Sci.

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This study develops a model for early warning of snow-caused livestock disasters on a county basis and proposes a method of qualitative risk assessment of snow disasters at 500 m resolution for pastoral areas on the Tibetan Plateau (TP). Data used for the model development include remote sensing data, statistical data of weather, livestock, and social economy, and 45 typical snow disaster cases from 2000 to 2010. The principal component analysis (PCA) approach is used to choose 7 crucial factors that contribute over 85% of information for early warning snow disasters on the TP. They are mean annual probability of snow disaster, number of snow-covered days, livestock stocking rate, continual days of mean daily temperature below −10 °C, grassland burial index, rate of snow-covered grassland, and per livestock gross domestic product. The chosen 411 cases from 2008 to 2010 are used to validate the prediction results from the developed early warning model, with an overall accuracy of 85.64% in predicting snow disasters and no disasters. This suggests that the early warning approach developed in the study has operational potential for predicting snow disasters on the TP

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An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Cited by 148 publications

References 18 publications

Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015

Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015

Variability in snow cover phenology in China from 1952 to 2010

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

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