地理科学进展 ›› 2005, Vol. 24 ›› Issue (5): 79-87.doi: 10.11820/dlkxjz.2005.05.009

• 地貌演化与土地利用 • 上一篇    下一篇

LUCC驱动力模型研究综述

蔺 卿1,2, 罗格平1, 陈 曦1   

  1. 1. 中国科学院 新疆生态与地理研究所,新疆 乌鲁木齐 830011|
    2. 中国科学院研究生院, 北京 100039
  • 收稿日期:2005-03-01 修回日期:2005-07-01 出版日期:2005-09-25 发布日期:2005-09-25
  • 作者简介:蔺卿(1981- ),男,新疆奇台人,在读硕士研究生,主要从事遥感与地理信息系统应用、土地利用与覆被变化方面的研究,E-mail:lin.qing8@163.com
  • 基金资助:

    西部之光”联合学者项目‘干旱区绿洲LUCC及其对绿洲稳定性影响’与中国科学院知识创新工程重要方向性项目(KZCX3-SW-327-01和KZCX3-SW-326-03)共同支持。

Review of Land-use Model

LIN Qing1,2, LUO Geping1, CHEN Xi1   

  1. 1. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China|
    2. Graduate School of the Chinese Academy of Sciences, Beijing 100039, China
  • Received:2005-03-01 Revised:2005-07-01 Online:2005-09-25 Published:2005-09-25

摘要:

驱动力研究是土地利用变化研究中的核心问题。土地利用变化驱动力模型是分析土地利用变化原因和结果的有力工具,模型通过情景分析可为土地利用规划与决策提供依据。基于不同理论的驱动力研究方法很多,论文选取了几种国内外应用较多的LUCC驱动力模型进行综述,分析了每个模型的优缺点及适用范围,最后得出结论:1) 基于过程的动态模型更适于研究复杂的土地利用系统。2) 基于经验的统计模型能弥补基于过程的动态模型的不足。3) 基于不同学科背景的模型进一步集成将是LUCC驱动力模型未来的发展趋势。

关键词: 模型, 驱动力, 土地利用/土地覆被变化, 土地利用系统

Abstract:

Land use change is one of the main research subjects of global environmental change and sustainable development. Driving force is the core subject of LUCC, including both biophysical and human factors at different temporal and spatial scales. Land-use models are useful for disentangling the complex suite of socio-economic and biophysical forces that influence the rate and spatial pattern of land use change and for estimating the impacts of changes in land use. Furthermore, models can support the exploration of future land use changes under different scenario conditions. Scenario analysis with land use models can support land use planning and policy. Many land use models are available, developed from different disciplinary backgrounds. This paper reviews several current land use change models to identify priority issues for future land use change modelling research. All these models were divided into three classes: empirical and statistical models (e.g., regression model), dynamic (process-based) models (e.g., cellular automata model and system dynamic model) and integrated models (e.g., conversion of land use and its effects, or CLUE model). For each of these models, a review is given and the advantages and disadvantages are discussed. Some suggestions for improving each model have also been put forward. Finally, we get three general conclusions. First, dynamic, process-based simulation models appear to be better suited to predict changes in land-use system than empirical, statistical models. Second, empirical, statistical models can complement dynamic (process-based) simulation models. Third, in the future, an integrated approach to modeling——that is based on multidisciplinary and cross-sectoral combining elements of different modelling techniques——will probably best serve the objective of improving understanding of land-use change processes. If this requirement is satisfied models will better support the analysis of land use dynamics and land use policy formulation.

Key words: driving force, land use and land cover change (LUCC), land use system, model