Snow cover remote sensing monitoring in the west of Ngari area in northern Tibet from 2013 to 2014

doi:10.6046/gtzyyg.2016.04.28

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Abstract

Based on medium resolution satellite remote sensing(RS) data Landset ETM and OLI from 2013 to 2014, the authors conducted snow cover RS monitoring in the west of Ngari area in northern Tibet. Changing characteristics of snow-covered area over the two years were summed up by utilizing the statistical calculation method. Using the air temperature data, the authors studied in detail the corresponding rule between the snow-covered area changes and the air temperature value changes. Some conclusions have been reached: Every year the maximum period of the snow-covered area is from January to February, about 10 days before or after the beginning of spring. The maximum percentage of the snow-covered area reaches 80.82 percent. The minimum period of the snow-covered area is August, about 10 days before or after the beginning of autumn. The minimum percentage of the snow-covered area is only 0.77 percent. Annually, the decrease from the maximum percentage of the snow-covered area to the minimum percentagelasts for 6-7 months, which is a relatively gradual process. In the second stage, there is a fluctuation percentage of the snow-covered area for about 4-5 months. At last, the increase from the fluctuation percentage to the maximum percentage is a relatively drastic process lasting for 1 month or so.

Keywords NDVI time-series BFAST change monitoring breakpoints

TP79

Issue Date: 20 October 2016

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	LIU Baozhu
	FANG Xiuqin
	HE Qisheng
	RONG Qiyuan

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LIU Baozhu,FANG Xiuqin,HE Qisheng, et al. Snow cover remote sensing monitoring in the west of Ngari area in northern Tibet from 2013 to 2014[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 185-190.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.04.28 OR https://www.gtzyyg.com/EN/Y2016/V28/I4/185

[1] 丁一汇.中国气象灾害大典:综合卷[M].北京:气象出版社,2008:746-824. Ding Y H.Chinese Meteorological Disasters Ceremony,Integrated Volume[M].Beijing:Meteorological Press,2008:746-824.
[2] 刘光轩.中国气象灾害大典:西藏卷[M].北京:气象出版社,2008:148-157. Liu G X.Chinese Meteorological Disasters Ceremony,Xizang Volume[M].Beijing:Meteorological Press,2008:148-157.
[3] 王玮,黄晓东,吕志邦,等.基于MODIS和AMER-E资料的青藏高原牧区雪被制图研究[J]. 草业学报,2013,22(4):227-238. Wang W,Huang X D,Lyu Z B,et al.A study on snow mapping in the Tibetan Plateau based on MODIS and AMSR-E data[J].Acta Prataculturae Sinica,2013,22(4):227-238.
[4] 刘玉洁,郑照军,王丽波.我国西部地区冬季雪盖遥感和变化分析[J].气候与环境研究,2003,8(1):114-123. Liu Y J,Zheng Z J,Wang L B.Remote sensing on snow cover and variation analyzing in west of China[J].Climatic and Environmental Research,2003,8(1):114-123.
[5] 张焜,李晓民,马世斌,等.GF-1图像在中印边境楚鲁松杰村地质灾害调查中的应用[J].国土资源遥感,2016,28(2):139-148.doi:10.6046/gtzyyg.2016.02.22. Zhang K,Li X M,Ma S B,et al.Application of GF-1 image to geological disaster survey in Cosibsumgy village of Sino-India border area[J].Remote Sensing for Land and Resources,2016,28(2):139-148.doi:10.6046/gtzyyg.2016.02.22.
[6] 于惠,张学通,王玮,等.基于AMSR-E数据的青海省雪深遥感监测模型及其精度评价[J].干旱区研究,2011,28(2):255-261. Yu H,Zhang X T,Wang W,et al.Monitoring model and accuracy evaluation of snow depth in Qinghai Province based on AMSR-E data[J].Arid Zone Research,2011,28(2):255-261.
[7] 侯慧姝,杨宏业.MODIS积雪产品及研究应用概述[J].遥感技术与应用,2009,24(2):252-256. Hou H S,Yang H Y.A general introduction to MODIS snow products and its researching application[J].Remote Sensing Technology and Application,2009,24(2):252-256.
[8] 刘玉洁,袁秀卿,张红.用气象卫星资料监测积雪[J].环境遥感,1992,7(1):24-31. Liu Y J,Yuan X Q,Zhang H.Snow cover monitoring using meteorlogical satellite data[J].Remote Sensing of Environment China,1992,7(1):24-31.
[9] 马丽云,李建刚,李帅.基于FY-3/MERSI数据的新疆融雪性洪水灾害监测[J].国土资源遥感,2015,27(4):73-78.doi:10.6046/gtzyyg.2015.04.12. Ma L Y,Li J G,Li S.Snowmelt flood disaster monitoring based on FY-3/MERSI in Xinjiang[J].Remote Sensing for Land and Resources,2015,27(4):73-78.doi:10.6046/gtzyyg.2015.04.12.
[10] 张镱锂,李炳元,郑度.论青藏高原范围与面积[J].地理研究,2002,21(1):1-8. Zhang Y L,Li B Y,Zheng D.A discussion on the boundary and area of the Tibetan Plateau in China[J].Geographical Research,2002,21(1):1-8.
[11] 孙鸿烈.青藏高原的形成演化[M].上海:上海科学技术出版社,1996:1-9. Sun H L.Formation and Evolution of Qinghai-Xizang Plateau[M].Shanghai:Shanghai Science and Technology Publishing House,1996:1-9.

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