孙丹峰, 李红, 张凤荣. 基于动态统计规则和景观格局特征的土地利用覆被空间模拟预测[J]. 农业工程学报, 2005, 21(3): 121-125.
    引用本文: 孙丹峰, 李红, 张凤荣. 基于动态统计规则和景观格局特征的土地利用覆被空间模拟预测[J]. 农业工程学报, 2005, 21(3): 121-125.
    Sun Danfeng, Li Hong, Zhang Fengrong. Spatial simulation and prediction of land use and land cover using adaptive stochastic rules and landscape pattern characteristics[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2005, 21(3): 121-125.
    Citation: Sun Danfeng, Li Hong, Zhang Fengrong. Spatial simulation and prediction of land use and land cover using adaptive stochastic rules and landscape pattern characteristics[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2005, 21(3): 121-125.

    基于动态统计规则和景观格局特征的土地利用覆被空间模拟预测

    Spatial simulation and prediction of land use and land cover using adaptive stochastic rules and landscape pattern characteristics

    • 摘要: 土地利用覆被及其变化是一个区域土地资源可持续利用的状态信号。该文建立依据马尔柯夫链模型和最大似然概率原则的统计概率模型,将景观格局特征利用类别共生概率矩阵表达在模型中,其次采用动态统计来考虑不同位置处模型参数的局部化。通过在北京山区初步验证,考虑景观格局特征,模拟结果总精度提高2.4%,Kappa系数提高0.045。随估计参数局部化,模拟精度大幅度提高,总精度提高到90%以上。结果表明:该土地利用覆被模拟模型是可行的,具有所需要基本数据非常简单优点,免除数据收集处理以及关系量化困难等问题。

       

      Abstract: Land use and land cover has become an indicator of regional sustainable development. This paper describes a spatially explicit stochastic model based on Markov chains and maximum likelihood rule, which includes the landscape pattern expressed by class concurrences probability matrix and adaptive local estimation. The experimental results in Beijing mountainous areas show the overall simulation accuracy can be increased by 2.4% and 0.045 in Kappa coefficient if considering the landscape pattern. With increasing local adaptive estimations of model parameters, the simulation accuracy can be increased to above 90%. The model only needs two different time land use and land cover maps to run without the need to describe the complex relationships between biophysical, economic and human factors that affect actual land use and land cover changes.

       

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