Remote Sensing Survey of Existing Glaciers in Qinghai-Tibet Plateau

doi:10.6046/gtzyyg.2010.s1.11

Abstract
References
Related Articles
Metrics

Download: PDF(639 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

An investigation of existing glaciers in Qinghai-Tibet Plateau based on remote sensing technology has revealed

the areas of existing glaciers in different ridges. The decreased area of existing glaciers in Qinghai-Tibet Plateau from

the middle of the 1960’s nearly to the 2000’s was also found out. Different kinds of remote sensing data suitable for the

investigation were chosen according to the characteristics of glaciers distribution. Terrain correction of the remote

sensing data is necessary to reduce the influence of topography on the investigation precision. Much seasonal snow existent

on the high mountains must be eliminated. It is difficult to eliminate seasonal snow in most areas because it is so similar

to the existing glacier. Many ways are summarized to eliminate the seasonal snow. Till the year of 2000, the area of

existing glacier in Qinghai-Tibet Plateau was 46 887.23 km2, suggesting a shrinkage of nearly 3 941.68 km2. About 131.4 km2

of existing glaciers was reduced per year from the middle of 1960’s approximately to the 2000’s in Qinghai-Tibetan

Plateau.

Keywords Data fusion Dempster-Shafer theory of evidence Fussy Kohonen neural network remote sensing classification

:
	TP 79

Issue Date: 13 November 2010

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	LIU Chun-ping
	LIU Wei-qiang
	KONG Ling
	XIA De-shen

Cite this article:

LIU Chun-ping,LIU Wei-qiang,KONG Ling, et al. Remote Sensing Survey of Existing Glaciers in Qinghai-Tibet Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(s1): 45-48.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.s1.11 OR https://www.gtzyyg.com/EN/Y2010/V22/Is1/45

［1］施雅风.青藏高原晚新生代隆升与环境变化［M］.广州：广东科技出版社，1998.

［2］安瑞珍.中国冰川目录——Ⅵ昆仑山区（喀拉米兰河—克里雅河内流区）［M］.兰州：中国科学院兰州冰川冻土研究所，1994.

［3］焦克勤.中国冰川目录——Ⅶ青藏高原内陆水系（多格错仁湖和茶卡湖流域区）［M］.北京：科学出版社，1988.

［4］焦克勤.中国冰川目录——Ⅶ青藏高原内陆水系（扎日南木错湖流域区）［M］.北京：科学出版社，1988.

［5］焦克勤.中国冰川目录——Ⅶ青藏高原内陆水系（色林错流域区）［M］.北京：科学出版社，1992.

［6］刘潮海.中国冰川目录——Ⅳ帕米尔山区（喀什噶尔河等流域）［M］.北京：世界文化出版社，2001.

［7］罗祥瑞.中国冰川目录——Ⅳ帕米尔山区［M］.北京：科学出版社，1988.

［8］米德生.中国冰川目录——Ⅺ恒河水系、Ⅻ印度河水系［M］.西安：西安地图出版社，2002.

［9］蒲健辰.中国冰川目录——Ⅷ长江水系［M］.北京：世界文化出版社，1994.

［10］蒲健辰.中国冰川目录——Ⅸ澜沧江流域、Ⅹ怒江流域［M］.西安：西安地图出版社，2001.

［11］王宗太.中国冰川目录——Ⅰ祁连山区［M］.兰州：中国科学院兰州冰川冻土研究所，1981.

［12］杨惠安.中国冰川目录——Ⅵ昆仑山区（米兰河—车尔臣河内流区）［M］.兰州：中国科学院兰州冰川冻土研究所，1994.

［13］杨惠安.中国冰川目录——Ⅵ昆仑山区（和田河流域区）［M］.北京：科学出版社，1988.

［14］杨惠安.中国冰川目录——Ⅶ青藏高原内陆水系（阿雅格库木库里湖和可可西里湖流域区）［M］.北京：科学出版社，1988.

［15］杨惠安.中国冰川目录——Ⅴ喀喇昆仑山区（叶尔羌河流域）［M］.北京：科学出版社，1989.

［16］张惠兴.中国冰川目录——Ⅶ青藏高原内陆水系（班公湖流域区）［M］.北京：科学出版社，1988.

［17］方洪宾，赵福岳，张振德.青藏高原现代生态地质环境遥感调查与演变研究［M］.北京：地质出版社，2009.

［18］李吉均，苏珍.横断山冰川环境［M］.北京：科学出版社，1996.

［19］鲁安新.青藏高原各拉丹冬地区冰川变化的遥感监测［J］.冰川冻土，2002，24（5）：559-562.

［20］刘时银.黄河上游阿尼玛卿山区冰川波动与气候变化［J］.冰川冻土，2002，24（6）：701-707.

［21］蒲健辰,等.可可西里马兰山的冰川的近期变化［J］.冰川冻土，2001，23（2）：189-192.

［22］苏珍,等.青藏高原冰川对气候变化的响应及趋势预测［J］. 武汉：地球科学进展，1999， 14（6）：607-612.

［23］苏珍,等.喀喇昆仑山——昆仑山地区冰川与环境［M］.北京：科学出版社，1998.

[1]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3]	SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[4]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[5]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[6]	ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[7]	HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[8]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[9]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[10]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[11]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[12]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[13]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[14]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.

Viewed

Full text

Abstract

Cited

Shared

Discussed