基于遥感的中国北方风沙地貌类型分析

doi:10.11821/yj2008010012

地理研究 ›› 2008, Vol. 27 ›› Issue (1): 109-118.doi: 10.11821/yj2008010012

基于遥感的中国北方风沙地貌类型分析

刘海江^1,2, 柴慧霞^1,3, 程维明¹, 钟德才⁴, 周成虎¹

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101;
2. 中国科学院研究生院,北京 100039;
3. 中国科学院新疆生态与地理研究所,乌鲁木齐 830011;
4. 中国科学院寒区旱区环境与工程研究所,兰州 730000

收稿日期:2007-04-19 修回日期:2007-10-28 出版日期:2008-01-25 发布日期:2008-01-25
作者简介:刘海江(1978-),男,内蒙古呼和浩特市人,博士研究生。专业为地图学与地理信息系统。 E-mail:liuhj@lreis.ac.cn
基金资助:
科技部科学数据平台(2005DKA32300-07);中国科学院知识创新工程领域前沿项目

A research of aeolian landform in northern China based on remote sensing imagery

LIU Hai-jiang^1,2, CHAI Hui-xia^1,3, CHENG Wei-ming¹, ZHONG De-cai⁴, ZHOU Cheng-hu¹

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China;
3. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China;
4. Cold and Arid Regions Environmental and Engineering Research Institute, CAS, Lanzhou 730000, China

Received:2007-04-19 Revised:2007-10-28 Online:2008-01-25 Published:2008-01-25
Supported by:
科技部科学数据平台(2005DKA32300-07);中国科学院知识创新工程领域前沿项目

摘要/Abstract

摘要：

风沙地貌在我国有广泛的分布,特别在北方,对区域生态安全、社会经济发展有重大影响。为了了解我国风沙地貌最新状况,通过解译2000~2002年的Landsat7 ETM影像,建立了我国风沙地貌类型数据库。对北方风沙地貌的分析表明:1.我国北方风沙地貌主要分布在新疆、内蒙古、青海、宁夏、甘肃、陕西、吉林、黑龙江8省区,其中以新疆、内蒙古的面积最广、地貌类型最丰富;2.类型多样性上,干旱区沙漠的地貌类型远比半干旱区的沙地丰富,其中有些类型分布普遍,还有些类型仅仅局限于特定区域,是在特定的风力、风向、植被等作用下形成的;3.沙丘动态上,沙漠除古尔班通古特与乌兰布和沙漠以固定、半固定为主外其他都以流动为主,沙地沙丘则以固定、半固定状态占优势。本文从沙丘形态类型角度分析了我国风沙地貌的最新特征,具有定位、定量特点,所建数据库不仅能用于风沙地貌的定期遥感监测,还能对沙丘形态演化进行定位监测研究。

关键词: 风沙地貌, 遥感, 沙漠, 地貌类型

Abstract:

Aeolian landform, mainly distributed in northern China, has important impacts on regional ecological security and socioeconomic development. Although great successes were obtained in aeolian landform research in the past decades, timely monitoring of aeolian landform still need improvement because of its inconstant characteristics.In order to investigate recent situation of sandy deserts in China, an aeolian landform dataset was built by extracting landform information from Landsat 7 ETM imageries which were acquired in 2000. In the present study, we focused on aeolian landform in northern China, which is the main aeolian landform region of China, and analyzed characteristics of different sand dune types quantitatively. The results showed that: 1)Aeolian landform is distributed mainly in eight provinces. They are Xinjiang Uygur Autonomous Region, Inner Mongolia Autonomous Region, Qinghai Province, Ningxia Hui Autonomous Region, Gansu Province, Shaanxi Province, Jilin Province and Heilongjiang Province, among them Xinjiang and Inner Mongolia have the largest area and the richest diversity of aeolian landform. 2)As for the sand dune morphology diversity, sandy deserts in arid regions are much richer than in sandy lands of semi-arid regions. Among all landform types, some types are generally distributed in all aeolian regions, while some morphologic types are only limited in specific areas, resulting from actions of specific wind velocity, wind direction, vegetation coverage. 3)In consideration of dune activity, floating dunes are prevalent in deserts except the Gurbantuggut Desert and the Ulan Buh Desert which are mainly covered by inactive and semi-fixed dunes. In contrast with deserts, sandy lands are dominant by inactive dunes and semi-fixed dunes. This analysis summarized the recent situation of aeolian landform in northern China, and the dataset is important in aeolian landform periodical monitoring and morphologic evolution research.

Key words: aeolian landform, remote sensing, sandy desert, sand dune morphology

刘海江, 柴慧霞, 程维明, 钟德才, 周成虎. 基于遥感的中国北方风沙地貌类型分析[J]. 地理研究, 2008, 27(1): 109-118.

LIU Hai-jiang, CHAI Hui-xia, CHENG Wei-ming, ZHONG De-cai, ZHOU Cheng-hu. A research of aeolian landform in northern China based on remote sensing imagery[J]. GEOGRAPHICAL RESEARCH, 2008, 27(1): 109-118.

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基于遥感的中国北方风沙地貌类型分析

A research of aeolian landform in northern China based on remote sensing imagery

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