地理科学进展

地理科学进展 ›› 2007, Vol. 26 ›› Issue (2): 97-105.doi: 10.11820/dlkxjz.2007.02.011

• 水土资源与利用 • 上一篇    下一篇

基于SOFM 网络的云南省土地利用 程度类型划分研究

叶敏婷1,2, 王仰麟1,2, 彭建1,2, 吴健生1,2   

  1. 1. 北京大学环境学院, 北京100871|
    2. 北京大学深圳研究生院城市人居环境科学与技术重点实验室, 深圳518055
  • 收稿日期:2006-08-01 修回日期:2007-01-01 出版日期:2007-03-25 发布日期:2007-03-25
  • 作者简介:叶敏婷, 女, ( 1982- )| 广东深圳人, 硕士研究生, 主要从事景观生态与土地利用的学习与研究。 E- mail: mintingye@gmail.com
  • 基金资助:

    国家自然科学基金重点项目(编号:40635028);国家自然科学基金项目(编号:40471002)。

Classification of Land Use Degr ee in Yunnan Province Based on SOFM Networks

YE Minting1,2, WANG Yanglin1,2, PENG Jian1,2, WU Jiansheng1,2   

  1. 1. College of Environmental Sciences, Peking University, Beijing 100871 China|
    2. The Key Laboratory for Environmental and Urban Sciences, Shenzhen Graduate School, Peking University, Shenzhen, China 518055
  • Received:2006-08-01 Revised:2007-01-01 Online:2007-03-25 Published:2007-03-25

PDF (PC)

赞

被引次数

89 | 13

摘要：

土地利用程度研究是开展土地整理工作和土地可持续利用研究的重要内容之一。本文尝 试以人工神经网络技术作为土地利用程度类型划分工作的理论支撑, 构建了自组织特征映射 SOFM网络, 在分类过程中同时考虑土地利用程度现状情况和影响土地利用程度的社会经济因 素, 最终将云南省土地利用程度分成高土地利用程度- 高人口压力- 高经济压力区等六种类型, 以 为区域土地管理宏观调控提供科学依据。

关键词: SOFM网络, 类型分区, 土地利用程度, 云南省

Abstract:

Study on land use degree is one of the superiority fields of land arrangement and sustainable land use research. Classification of land use degree provides guidelines for utilization and conservation of regional land use as it can indicate regional differentiation regularity and existent problems. A considerable amount of research has been done on land use degree during the last decade. In this paper, Yunnan Province is taken as a case and unsupervised artificial neural network, namely Self- Organizing Feature Mapping (SOFM), is used in land use degree classification. The results indicate that classification of land use degree based on SOFM networks is a promising approach to land use studies. In this paper, Multiple Cropping Index is employed to the land use degree model so as to indicate the quality differences within a specific land use type. More improvements of the model should be brought through by further consideration. As for the data employed as input for training, not only the status quo of land use degree but also the influence factors are included. After the iterative learning phase in the SOFM analysis, six output units representing different classes of land use degree come forth, i.e., High land use degree - high population pressure - high economy pressure region, High land use degree - medium population pressure - medium economy pressure region, Low land use degree - medium population pressure - medium economy pressure region, Low land use degree - low population pressure- low economy pressure region, Medium land use degree- medium population pressurelow economy pressure region, and medium land use degree- low population pressure- low economy pressure region. Accordingly, some advice on utilization and conservation of land use is proposed based on the studying result. From the results obtained so far, it seems that SOFM is superiors over others in many aspects and has been trained to perform complex functions in various fields of application, including land use degree classification. But more improvements should be conducted before further applications.

Key words: classification, land use degree, SOFMmodel, Yunnan Province