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当前位置: 首页 > 文章 > 基于多源遥感数据的淮河流域城镇扩张研究 农业机械学报 2016,47 (11) 252-261
Position: Home > Articles > Urban Expansion of Huaihe River Basin Based on Multi-source Remote Sensing Data Transactions of the Chinese Society for Agricultural Machinery 2016,47 (11) 252-261

基于多源遥感数据的淮河流域城镇扩张研究

作  者：
樊勇;朱曦;张圣笛;何宗宜;杨刚
单  位：
爱丁堡大学地球科学与技术学院;宁波大学建筑工程与环境学院;武汉大学资源与环境科学学院;上海市测绘院;信阳师范学院城市与环境科学学院
关键词：
多源遥感数据;城镇扩张;淮河流域;支持向量机
摘  要：
城镇是人类社会发展过程在空间上的重要表现形式,其空间格局与演变是城镇研究的热点问题。淮河流域是中国城镇体系的南北过渡地区,研究这一自然地理单元内城镇扩张过程,视角独特。为客观、快速、准确地重建不同时间序列上淮河流域城镇扩张过程,在DMSP/OLS数据、SPOT-VGT数据、Landsat ETM+数据等多源遥感数据的基础上,提出"DMSP/OLS夜间灯光数据相互校正—NDVI数据重建—流域城镇信息提取—流域城镇扩张分析"的研究思路,并运用该思路分析淮河流域从1998年至2013年16年间的城镇扩张过程。从城市面积、扩张强度、扩张动态度、扩张形态4方面分析了城市扩张规律。研究发现:淮河流域整体与各省扩张基本属于低速扩张型与中速扩张型;淮河流域城镇分布仍较为分散,未形成完整的城市群或城镇体系;这一时期城市扩张时空发展不均衡。
译  名：
Urban Expansion of Huaihe River Basin Based on Multi-source Remote Sensing Data
作  者：
Fan Yong;Zhu Xi;Zhang Shengdi;He Zongyi;Yang Gang;School of Resource and Environmental Sciences,Wuhan University;College of Urban and Environmental Sciences,Xinyang Normal University;Shanghai Municipal Institute of Surveying and Mapping;School of Geo-Sciences,University of Edinburgh;Faculty of Architectural,Civil Engineering and Environment,Ningbo University;
关键词：
multi-source remote sensing data;;urban expansion;;Huaihe River Basin;;support vector machine
摘  要：
In order to grasp the urban expansion process in different temporal sequences objectively,rapidly and accurately,DMSP / OLS data,SPOT-VGT data,Landsat ETM + data,DEM Data and social and economic statistics data were used to rebuild the urban expansion process. Two methods were selected to extract urban areas in Huaihe River Basin( HRB),one was threshold value method,the other was support vector machine( SVM) classification method. The results of two methods were compared with urban areas extracted from Landsat ETM + data and the result of SVM classification method which accuracy above 80% was proved to be more precise both in spatial form and accuracy estimation of urban areas. The urban areas in HRB for the year 1998,2001,2004,2007,2010 and 2013 were extracted using SVM classification method. And there were four features of the extracted urban areas calculated and analyzed to understand the dynamism of urban areas in HRB,including the extended area,urbanization intensive index( UII),extended dynamic degree and spatial pattern. The extended urban areas from2001 to 2013 of HRB approximately showed a linear growth. UIIillustrated that there were still a largenumber of rural areas to be developed in HRB. The extended dynamic degree stated that the urban areas expanded in a medium speed. The spatial pattern and the spatial metric revealed that there was no central city in HRB and the density of cities was not very high.

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