| 摘 要： |
Eco-geographical regionalization involves dividing land into regions by considering both intra-regional consistency and interregional disparity and is based on the pattern of differentiation of eco-geographical elements. Owing to the complexity of the land surface, and the limitation of data and appropriate methods, regions in China have hitherto been mapped manually, meaning that the process of mapping was non-repeatable. To make the regionalization technique repeatable, this study aimed to extract and quantify the expert knowledge of regionalization using an automated method. The rough set method was adopted to extract rules of regionalization based on the existing eco-geographical regionalization map of China, as well as its corresponding meteorological and geological datasets. Then, the rules for regionalization were obtained hierarchically for each natural domain, each temperature zone, and each humidity region. Owing to differences in zonal differentiation, the rule extraction sequence for the eastern monsoon zone and Tibetan Alpine zone was temperature zone first followed by humidity region, with the reverse order being applied for the northwest arid/semi-arid zone. Results show that the extracted indicators were similar to those of the existing (expert-produced) regionalization scheme but more comprehensive. The primary indicator for defining temperature zones was the >= 10 degrees C growing season, and the secondary indicators were the January and July mean temperatures. The primary and secondary indicators for identifying humid regions were aridity index and precipitation, respectively. Eco-geographical regions were mapped over China using these rules and the gridded indicators. Both the temperature zones and humidity regions mapped by the rules show >= 85% consistency with the existing regionalization, which is higher than values for mapping by the commonly used simplified method that uses the classification of one indicator. This study demonstrates that the proposed rough set method can establish eco-geographical regionalization that is quantitative and repeatable and able to dynamically updated. |
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