基于随机森林模型的青藏高原冰川预测及分析

doi:10.12118/j.issn.1000-6060.2024.528

Abstract

Abstract:

Glaciers on the Qinghai-Xizang Plateau, China serve as critical indicators for natural resource monitoring and regional climate change analysis. This study investigates glacier dynamics across the plateau by integrating multi-source datasets and developing a robust random forest model (R²=0.72) to generate a 1 km-resolution annual glacier prediction dataset spanning from 2000 to 2020. Key findings include as follows: (1) Spatial distribution patterns: 97.92% of glaciers are located on slopes of 0°-40°, and 99.38% are distributed at elevations of 4000-7000 m. Glacier density is higher on northern slopes than on southern slopes, and western slopes exhibit greater coverage than eastern slopes. (2) Spatiotemporal changes: From 2000 to 2020, glaciers exhibited a clear retreat trend. Spatially, stronger variation signals were observed along the plateau margins, while interior regions showed relatively minor changes. (3) Regional trends: Glaciers in the Himalaya Mountains and Nyainqentanglha Mountains showed significant retreat, while those in the Karakoram Mountains experienced only slight retreat. The Kunlun Mountains exhibited a mixed pattern of slight advancement and retreat.

Key words: glaciers, predict and analysis, random forest model, distribution patterns, spatiotemporal changes, Qinghai-Xizang Plateau

ZHANG Yiming, TANG Yulei, FENG Junbo. Predicting and analysing glaciers in the Qinghai-Xizang Plateau: A random forest model[J].Arid Land Geography, 2025, 48(8): 1342-1352.

Figures/Tables 15

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Tab. 2

Fig. 4

Fig. 5

Fig. 6

Tab. 3

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

References 23

[1]	周玉杉. 基于多源遥感数据的青藏高原及其周边区域冰川物质平衡变化研究[J]. 地理与地理信息科学, 2019, 35(4): 142.
	[Zhou Yushan. Investigation of glacier mass balance in the Qinghai-Tibet Plateau and its surroundings base on multi-source remote sencing data[J]. Geography and Geo-information Science, 2019, 35(4): 142. ]
[2]	顾云杨. 利用影像大地测量技术监测2000—2020年长江源冰川物质平衡变化[D]. 长沙: 中南大学, 2023.
	[Gu Yunyang. Monitoring the changes of glacier mass balance in the source region of the Yangtze River from 2000 to 2020 with the imaging geodetic method[D]. Changsha: Central South University, 2023. ]
[3]	王宁练, 姚檀栋, 徐柏青, 等. 全球变暖背景下青藏高原及周边地区冰川变化的时空格局与趋势及影响[J]. 中国科学院院刊, 2019, 34(11): 1220-1232.
	[Wang Ninglian, Yao Tandong, Xu Baiqing, et al. Spatiotemporal pattern, trend, and influence of glacier change in Tibetan Plateau and surroundings under global warming[J]. Bulletin of Chinese Academy of Sciences, 2019, 34(11): 1220-1232. ]
[4]	汪赢政, 李佳, 吴立新, 等. 1987—2018年祁连山冰川变化遥感监测及影响因子分析[J]. 冰川冻土, 2020, 42(2): 344-356. doi: 10.7522/j.issn.1000-0240.2019.0080
	[Wang Yingzheng, Li Jia, Wu Lixin, et al. Using remote sensing images to monitor the glacier changes in Qilian Mountains during 1987—2018 and analyzing the impact factors[J]. Journal of Glaciology and Geocryology, 2020, 42(2): 344-356. ] doi: 10.7522/j.issn.1000-0240.2019.0080
[5]	冀琴, 张翠兰, 丁悦凯, 等. 基于多源遥感数据的珠峰自然保护区冰川监测研究[J]. 干旱区地理, 2023, 46(10): 1591-1601. doi: 10.12118/j.issn.1000-6060.2022.624
	[Ji Qin, Zhang Cuilan, Ding Yuekai, et al. Glacier monitoring in Qomolangma Nature Reserve based on multi-source remote sensing data[J]. Arid Land Geography, 2023, 46(10): 1591-1601. ] doi: 10.12118/j.issn.1000-6060.2022.624
[6]	张裕. 1980—2020年唐古拉山冰川变化及其对气候波动的响应[D]. 大连: 辽宁师范大学, 2023.
	[Zhang Yu. Glacier change in Tanggula Mountain and its response to climate fluctuation from 1980 to 2020[D]. Dalian: Liaoning Normal University, 2023. ]
[7]	田梦祺, 段克勤, 石培宏. 基于Google Earth Engine平台的青藏高原冰川变化研究——以普若岗日冰原为例[J]. 地理科学, 2023, 43(6): 943-951. doi: 10.13249/j.cnki.sgs.2023.06.001
	[Tian Mengqi, Duan Keqin, Shi Peihong. Glacier changes on the Qinghai-Tibet Plateau based on Google Earth Engine: A case study of the Purog Kangri glacier[J]. Scientia Geographica Sinica, 2023, 43(6): 943-951. ] doi: 10.13249/j.cnki.sgs.2023.06.001
[8]	张萌. 基于Landsat影像构建新指数提取冰川面积信息的方法研究[D]. 西安: 西北大学, 2020.
	[Zhang Meng. Research on the method of extracting glacier area information based on the new index using Landsat imagery[D]. Xi’an: Northwest University, 2020. ]
[9]	彦立利, 王建. 基于遥感的冰川信息提取方法研究进展[J]. 冰川冻土, 2013, 35(1): 110-118. doi: 10.7522/j.issn.1000-0240.2013.0013
	[Yan Lili, Wang Jian. Study of extracting glacier information from remote sensing[J]. Journal of Glaciology and Geocryology, 2013, 35(1): 110-118. ] doi: 10.7522/j.issn.1000-0240.2013.0013
[10]	张镱锂. 青藏高原边界数据总集[EB/OL]. [2019-06-11]. https://portal.casearth.cn/dataDetail/64b8866cd6067c9150a4a7ad.
	[Zhang Yili. Data sets of Qinghai-Tibet Plateau boundary[EB/OL]. [2019-06-11]. https://portal.casearth.cn/dataDetail/64b8866cd6067c9150a4a7ad. ]
[11]	张镱锂, 李炳元, 郑度. 《论青藏高原范围与面积》一文数据的发表——青藏高原范围界线与面积地理信息系统数据[J]. 地理学报, 2014, 69(增刊1): 65-68.
	[Zhang Yili, Li Bingyuan, Zheng Du. Datasets of the boundary and area of the Tibetan Plateau[J]. Acta Geographica Sinica, 2014, 69(Suppl. 1): 65-68. ]
[12]	刘时银, 丁永建, 李晶, 等. 中国西部冰川对近期气候变暖的响应[J]. 第四纪研究, 2006, 26(5): 762-771.
	[Liu Shiyin, Ding Yongjian, Li Jing, et al. Glaciers in response to recent climate warming in western China[J]. Quaternary Sciences, 2006, 26(5): 762-771. ]
[13]	叶庆华, 吴玉伟. 青藏高原2001年冰川数据-TPG2001(V1.0)[EB/OL]. [2018]. https://data.tpdc.ac.cn/zh-hans/data/4fc1e6b1-da41-46ad-a8b5-5f3adc4fde19.
	[Ye Qinghua, Wu Yuwei. Glacier coverage data on the Tibetan Plateau in 2001 (TPG2001, Version1.0)[EB/OL]. [2018]. https://data.tpdc.ac.cn/zh-hans/data/4fc1e6b1-da41-46ad-a8b5-5f3adc4fde19. ]
[14]	叶庆华. 青藏高原2013年冰川数据-TPG2013(V1.0)[EB/OL]. [2018]. https://data.tpdc.ac.cn/zh-hans/data/38d741e0-ec7c-4aa5-8d10-d732a01b8063.
	[Ye Qinghua. Glacier coverage data on the Tibetan Plateau in 2013 (TPG2013, Version1.0)[EB/OL]. [2018]. https://data.tpdc.ac.cn/zh-hans/data/38d741e0-ec7c-4aa5-8d10-d732a01b8063. ]
[15]	叶庆华. 青藏高原2017年冰川数据-TPG2017(V1.0)[EB/OL]. [2019]. https://data.tpdc.ac.cn/zh-hans/data/9777a877-a787-4e43-bf4c-8bee492c75a4.
	[Ye Qinghua. Glacier coverage data on the Tibetan Plateau in 2017 (TPG2017, Version1.0)[EB/OL]. [2019]. https://data.tpdc.ac.cn/zh-hans/data/9777a877-a787-4e43-bf4c-8bee492c75a4. ]
[16]	Ye Q H, Zong J B, Tian L D, et al. Glacier changes on the Tibetan Plateau derived from Landsat imagery: Mid-1970s-2000-2013[J]. Journal of Glaciology, 2017, 63(238): 273-287.
[17]	Svetnik V, Liaw A, Tong C, et al. Random forest: A classification and regression tool for compound classification and QSAR modeling[J]. Journal of Chemical Information and Computer Sciences, 2003, 43(6): 1947-1958. doi: 10.1021/ci034160g pmid: 14632445
[18]	周继华, 来利明, 陈巧娥, 等. 基于机器学习和经验知识的青藏高原多时段植被制图[J]. 科学通报, 2025, 70(1): 134-144.
	[Zhou Jihua, Lai Liming, Chen Qiao’e, et al. Multi temporal vegetation mapping of the Tibetan Plateau via machine learning model simulation and experiential knowledge[J]. Chinese Science Bulletin, 2025, 70(1): 134-144. ]
[19]	Wright M N, Ziegler A. Ranger: A fast implementation of random forests for high dimensional data in C++ and R[J]. Journal of Statistical Software, 2017, 77(1): 1-17.
[20]	丁悦凯, 刘睿, 张翠兰, 等. 喜马拉雅地区叶如藏布流域冰川和冰湖变化遥感监测研究[J]. 干旱区地理, 2022, 45(6): 1870-1880. doi: 10.12118/j.issn.1000-6060.2022.110
	[Ding Yuekai, Liu Rui, Zhang Cuilan, et al. Remote sensing monitoring of glacier and glacial lake changes in Yairu Zangbo Basin, Himalayas[J]. Arid Land Geography, 2022, 45(6): 1870-1880. ] doi: 10.12118/j.issn.1000-6060.2022.110
[21]	刘时银, 姚晓军, 郭万钦, 等. 基于第二次冰川编目的中国冰川现状[J]. 地理学报, 2015, 70(1): 3-16. doi: 10.11821/dlxb201501001
	[Liu Shiyin, Yao Xiaojun, Guo Wanqin, et al. The contemporary glaciers in China based on the Second Chinese Glacier Inventory[J]. Acta Geographica Sinica, 2015, 70(1): 3-16. ] doi: 10.11821/dlxb201501001
[22]	廖美玉, 方秀琴, 蒋心远, 等. 黄河流域近40年土地利用/覆被时空变化特征[J]. 水土保持学报, 2024, 38(2): 165-177.
	[Liao Meiyu, Fang Xiuqin, Jiang Xinyuan, et al. Spatiotemporal characteristics of land use/cover changes in the Yellow River Basin over the past 40 years[J]. Journal of Soil and Water Conservation, 2024, 38(2): 165-177. ]
[23]	叶庆华, 程维明, 赵永利, 等. 青藏高原冰川变化遥感监测研究综述[J]. 地球信息科学学报, 2016, 18(7): 920-930.
	[Ye Qinghua, Cheng Weiming, Zhao Yongli, et al. A review on the research of glacier changes on the Tibetan Plateau by remote sensing technologies[J]. Journal of Geo-information Science, 2016, 18(7): 920-930. ]

数据	来源	分辨率
冰川分布数据	国家青藏高原科学数据中心	1 km
NDVI数据	MODIS传感器	1 km
SRTM高程数据	美国太空总署、美国国防部国家测绘局	30 m
产水模数数据	中国科学院资源环境科学数据中心	1 km
气温格点数据	国家气象信息中心	0.5°
降水格点数据	国家气象信息中心	0.5°
土地利用数据	国家基础地理信息中心	30 m
土地利用数据	欧洲航天局	300 m
GDP数据	中国科学院地理科学与资源研究所	1 km
人口密度数据	社会经济数据和应用中心	1 km

坡度/(°)	占比/%		坡向/(°)	占比/%		海拔/m	占比/%
坡度/(°)	2017年	2020年	坡向/(°)	2017年	2020年	海拔/m	2017年	2020年
0~10	23.68	23.68	0.0~22.5	8.90	8.90	0~1000	0.00	0.00
10~20	36.46	36.46	22.5~67.5	15.28	15.27	1000~2000	0.00	0.00
20~30	27.61	27.61	67.5~112.5	13.38	13.38	2000~3000	0.11	0.01
30~40	10.17	10.17	112.5~157.5	12.79	12.82	3000~4000	3.33	0.56
40~50	1.92	1.92	157.5~202.5	13.29	13.29	4000~5000	26.39	18.29
50~60	0.16	0.16	202.5~247.5	9.84	9.85	5000~6000	49.79	69.99
60~70	0.00	0.00	247.5~292.5	8.70	8.66	6000~7000	19.79	11.10
70~80	0.00	0.00	292.5~337.5	10.78	10.73	7000~8000	0.56	0.05
80~90	0.00	0.00	337.5~360.0	7.04	7.10	8000~9000	0.03	0.00

M-K检验数据	Z值	P值
青藏高原全区冰川数据	-3.3519	<0.01
喜马拉雅山冰川数据	-2.3252	<0.05
喀喇昆仑山冰川数据	-1.1777	>0.05
昆仑山冰川数据	-0.0906	>0.05
念青唐古拉山冰川数据	-2.9291	<0.01

Predicting and analysing glaciers in the Qinghai-Xizang Plateau: A random forest model

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 23

Related Articles 10

Metrics

Comments

Recommended 0

[1]	LU Han, ZENG Yongnian, WANG Pancheng. Spatiotemporal variation of actual evapotranspiration and its influencing factors in the northeast Qinghai-Xizang Plateau [J]. Arid Land Geography, 2025, 48(5): 753-764.
[2]	MA Xiaomin, ZHANG Zhibin, GUO Qianqian, ZHAO Xuewei, ZHANG Ning. Spatial and temporal evolution and driving factors of population in Lanzhou City from 2000 to 2020 [J]. Arid Land Geography, 2025, 48(1): 168-178.
[3]	MU Jianxin, LI Zhongqin, WANG Puyu, LIANG Pengbin, WANG Yanqiang, BAI Changbin, WANG Fanglong. Glaciers in Saur Mountains: Current situation and evolutionary process [J]. Arid Land Geography, 2024, 47(8): 1277-1291.
[4]	CHE Yanjun, ZHANG Mingjun, CHEN Yaning, ZHU Chenggang, LIU Yuting. Changes in water volume of Ayakkum Lake in the eastern Kunlun Mountains and its replenishment relationship in the last 30 years [J]. Arid Land Geography, 2024, 47(7): 1116-1126.
[5]	CHEN Lihua, CHE Yanjun, CAO Yun, ZHANG Mingjun, GU Lailei, WU Jiakang, LYU Weiwei. Responses of glacier and lake to local climate change in the Jingyu Lake Basin, east Kunlun Mountains [J]. Arid Land Geography, 2024, 47(10): 1640-1650.
[6]	JI Qin, ZHANG Cuilan, DING Yuekai, CAO Xiangqin, LIANG Wenli. Glacier monitoring in Qomolangma Nature Reserve based on multi-source remote sensing data [J]. Arid Land Geography, 2023, 46(10): 1591-1601.
[7]	PAN Qun,SHI Haiyang,ZHANG Wenqiang,LUO Geping,CHEN Chunbo. Spatiotemporal distribution of rat population density in grassland on the north slope of Tianshan Mountains based on Cubist model [J]. Arid Land Geography, 2022, 45(4): 1200-1211.
[8]	JIANG Li,WEI Tianxing,LI Yiran,WEI Anqi. Effects of topographical factors on tree species distribution of shelter forest in Loess hilly region of northern Shaanxi [J]. Arid Land Geography, 2021, 44(6): 1763-1771.
[9]	WANG Jing, YANG Taibao, JI Qin, QIN Yan, HU Fansheng. Change of the modern glaciers in the eastern Himalaya near China and Bhutan border area from 1990 to 2015 [J]. Arid Land Geography, 2019, 42(3): 542-550.
[10]	MU Jian-xin, LI Zhong-qin, ZHANG Hui, XU Chun-hai. Mass balance variation of continental glacier and temperate glacier and their response to climate change in western China: Taking Urumqi Glacier No.1 and Parlung No.94 Glacier as examples [J]. 干旱区地理, 2019, 42(1): 20-29.