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首页> 外文期刊>Advances in Meteorology >Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China
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Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China

机译：基于多源空间数据挖掘方法的土地覆盖制图气候模拟-以华北农牧交错带为例

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摘要

The land use and land cover change (LUCC) is one of the prime driving forces of climate change. Most attention has been paid to the influence of accuracy of the land cover data in numerous climate simulation projects. The accuracy of the temporal land use data from Chinese Academy of Sciences (CAS) is higher than 90%, but the high-precision land cover data is absent. We overlaid land cover maps from different sources, and the grids with consistent classification were selected as the sample grids. By comparing the results obtained with different decision tree classifiers with the WEKA toolkit for data mining, it was found that the C4.5 algorithm was more suitable for converting land use data of CAS classification to land cover data of IGBP classification. We reset the decision rules with Net Primary Productivity (NPP) and Normalized Difference Vegetation Index (NDVI) as the indicators. The accuracy of the reclassified land cover data was proven to reach 83.14% through comparing with the Terrestrial Ecosystem Monitoring Sites and high resolution images. Therefore, it is feasible to produce the temporal land cover data with this method, which can be used as the parameters of dynamical downscaling in the regional climate simulation.
机译：土地利用和土地覆被变化（LUCC）是气候变化的主要驱动力之一。在许多气候模拟项目中，人们最关注的是土地覆盖数据准确性的影响。来自中国科学院（CAS）的临时土地利用数据的准确性高于90％，但缺少高精度的土地覆盖数据。我们覆盖了不同来源的土地覆盖图，并选择了分类一致的网格作为样本网格。通过将使用不同决策树分类器的结果与WEKA工具包进行数据挖掘相比较，发现C4.5算法更适合将CAS分类的土地利用数据转换为IGBP分类的土地覆盖数据。我们以净初级生产力（NPP）和归一化植被指数（NDVI）为指标来重置决策规则。与陆地生态系统监测点和高分辨率图像相比，重新分类的土地覆盖数据的准确性达到了83.14％。因此，用这种方法产生时空的土地覆盖数据是可行的，可以作为区域气候模拟中动态降尺度的参数。

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