地理研究 ›› 2007, Vol. 26 ›› Issue (5): 897-906.doi: 10.11821/yj2007050005

• 地表过程研究 • 上一篇    下一篇

喀斯特石漠化小流域景观的空间因子分析——以贵州清镇王家寨小流域为例

周梦维1,2,4, 王世杰1, 李阳兵3   

  1. 1. 中国科学院地球化学研究所环境地球化学国家重点实验室,贵阳 550002;
    2. 中国科学院研究生院,北京 100049;
    3. 贵州师范大学地理与生命科学学院,贵阳 550001;
    4. 中国科学院遥感应用研究所遥感科学国家重点实验室,北京 100101
  • 收稿日期:2006-09-07 修回日期:2007-03-27 出版日期:2007-09-25 发布日期:2007-09-25
  • 作者简介:周梦维(1982-), 女, 湖北监利人, 博士研究生。从事岩溶环境和地理信息系统研究。
  • 基金资助:

    国家重点基础研究发展计划项目(2006CB403201)、中科院科技支黔工程项目、中科院知识创新前沿领域项目和贵州省最高科学成就奖科技匹配项目联合资助。

Spatial factor analysis of karst rocky desertification landscape patterns in Wangjiazhai catchment, Guizhou

ZHOU Meng-wei1,2,4, WANG Shi-jie1, LI Yang-bing3   

  1. 1. State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, CAS, Guiyang 550002, China;
    2. Graduate University of the Chinese Academy of Sciences, Beijing 100049, China;
    3. Department of Resource and Environmental Science, Guizhou Normal University, Guiyang 550002, China;
    4. State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing Applications,CAS,Beijing 100101, China
  • Received:2006-09-07 Revised:2007-03-27 Online:2007-09-25 Published:2007-09-25
  • Supported by:

    国家重点基础研究发展计划项目(2006CB403201)、中科院科技支黔工程项目、中科院知识创新前沿领域项目和贵州省最高科学成就奖科技匹配项目联合资助。

摘要: 以贵州喀斯特地区的王家寨小流域为研究对象,基于多源信息,依托RS和GIS技术获取2005年该流域石漠化景观格局信息,以此为基础进行石漠化景观分布指数、石漠化综合指数、 -s平面分析模型以及三次曲线拟合等分析,旨在从小流域尺度上探讨石漠化景观在坡度、坡向、高程和与村庄距离等空间因子上的分布规律。结果表明:潜在、轻度石漠化景观受坡度影响最显著;其他类型石漠化景观受坡向影响最明显。石漠化程度先随坡度的增大而加重,27°后呈缓解趋势;各坡向中南、东南坡石漠化最严重;随高程增加石漠化加剧;距村庄越远石漠化越严重。初步推断各空间因子对石漠化程度的影响由强至弱的顺序为:坡度、坡向、高程、与村庄的距离。

关键词: 喀斯特石漠化景观, 空间因子, 指数模型, 统计分析, 王家寨小流域, 贵州

Abstract: The impact of spatial factors on karst rocky desertification landscape was studied in Wangjiazhai catchment.Based on the image of SPOT5 in 2005, with the support of the geographic information system (GIS) and remote sensing (RS) software, karst rocky desertification landscape patterns in the studied area was classified as: no, latent, slight, moderate, strong, and extremely strong karst rocky desertification types. Meanwhile, four spatial factors (slope, slope aspect, elevation and distance to the village) were derived from the digital elevation model. Quasi-quantitative analysis was made on the relationships between the above four factors with karst rocky desertification landscape distribution index (RLDI) and karst rocky desertification comprehensive index (RCI), respectively. Results indicate that: (1)The distribution of various rocky desertification landscapes along the spatial gradient is different. For example, the latent and slight rocky desertification is affected by slope aspect more greatly than that on other spatial factors; while the moderate, strong and extremely strong rocky desertification is highly correlated with the slope gradient. (2)The four spatial factors affect rocky desertification in different ways. For example, 27° is a critical angle for rocky desertification intensity which becomes severer firstly, and then becomes slighter around the turning point. As to the effects of slope aspect, due to the aspect of terrane and exogenic forces including solar radiation and precipitation, the severest rocky desertification occurs in regions of south or southeast slope aspect; and less for north, northeast and east cases, while it is slight in other cases. Moreover, with the increase of elevation, the intensity of rocky desertification is in a deteriorative trend. The regulation of rocky desertification along the distance to the village reveals the mode of human activities on rocky desertification process. (3) The fittings between RCI values and four factors, using the cubic curve estimation method, are obviously different in the descending order: slope>slope aspects>elevation>the distance to the village. The order, in a sense, shows the intensities of those spatial factors on RCI values. Though influences of each spatial factor on RCI could be evaluated by using single-factor correlation analysis, it also should notice that those factors affected RCI mutually.

Key words: desertification landscape, spatial factors, index model, statistical analysis, Wangjiazhai catchment, Guizhou