近35年苏南典型地区的城镇扩展动态研究——以1966～2001年常熟市为例

利用常熟市不同时期 (1 966、1 984、1 992、1 999、2 0 0 0、2 0 0 1年 )、不同类型卫星遥感影像 (Corona、TM、SPOT、ETM )进行多波段合成 ,图像增强处理与精校正 ,建立不同影像城镇用地解译标志 ,通过人机交互判读 ,获得不同时期的各乡镇城镇用地矢量图的方法。统计出 35年来常熟市城镇建设用地扩大近 6 .4倍。城镇扩展空间上受到地貌条件和城市经济水平以及政策决策影响 ,城镇多数沿河流或交通线方向扩展 ,各城镇间扩展数量和相对速率有明显差异 ,且存在一定地理分布规律。结合社会经济的统计资料 ,分析发现城镇扩展与不断增加的城镇人口、GDP极显著相关 ,各乡镇单位城镇面积创造的GDP有较大的差异。

DYNAMIC EXPANDING OF TYPICAL CITY IN THE SOUTH JIANGSU PROVINCE IN RECENT 35 YEARS A CASE STUDY OF CHANGSHU CITY FROM 1966 TO 2001

In this paper, 6 phrases of satellite images include Corona?TM?SPOT?ETM data which represent the information of Changshu of 1966,1984,1992,1999,2000,2001 respectively were dealt with multi band composition, image enhancement, precision emendation, interpretation index establishment, computer assistant visual interpretation. Vector maps of expanding towns of Changshu City from 1966 to 2001 are made by interpreting images above. The statistical result shows that the area of city is 6.4 times bigger in 2001 than in 1966. From 1984 to 2001, it becomes bigger and bigger of average area of town of every person for 24 towns. In expending space, city expending is affected by geography, physiognomy condition, economic level and policy decision making. Extending directions of most of towns are accordant with the main rivers and major roads. There is significant difference in amount and relative rate in the extending among 24 towns, and there is geographic disciplinarian in the distribution of towns at different expending levels. Most of towns that have large expanding rate have convenient communication, but towns that have low expanding rates are far from major roads and rivers. According to many years' statistics of city expanding and historical social and economic data, it is found that city expanding is related to rural urban migration, GDP and original dimension evidently, while is not to total population, total families, total industry production and total employee. Through the statistic, we found that there is significant difference in the values of GDP of per urban area among 24 towns.