地球科学进展 ›› 2002, Vol. 17 ›› Issue (5): 748 -753. doi: 10.11867/j.issn.1001-8166.2002.05.0748

新学科·新技术·新发现 上一篇    下一篇

应用集成的遥感识别技术进行土地利用变化分析
陈崇成 1, 汪小钦 1,2, 王钦敏 1, 黄绚 2   
  1. 1.福州大学地球信息科学与技术研究所,福建 福州 350002;2.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
  • 收稿日期:2002-01-04 修回日期:2002-05-23 出版日期:2002-12-20
  • 通讯作者: 陈崇成(1968-),男,福建闽清县人,博士研究生,主要从事资源与环境信息工程、空间信息集成技术、城市与环境遥感等领域研究与开发.E-mail: chencc@fzu.edu.cn E-mail:chencc@fzu.edu.cn
  • 基金资助:

    国家“九五”重点科技攻关项目“县级资源与环境遥感动态监测技术系统示范工程”(编号:96-B02-01-07);福建省重大科技计划“福建省海岸带环境调控及决策支持系统”(编号:闽科计[2000]13)资助.

ANALYSIS ON LAND USE CHANGE BY USING INTEGRATED REMOTE SENSING CHANGE DETECTION TECHNIQUES

CHEN Chong-cheng 1, WANG Xiao-qin 1,2, WANG Qin-min 1, HUANG Xuan 2   

  1. 1. Institute of Geo-information Science and Technology, Fuzhou University, Fuzhou 350002, China; 2. LREIS, Institute of Geographical Science and Resource, CAS, Beijing 100101, China
  • Received:2002-01-04 Revised:2002-05-23 Online:2002-12-20 Published:2002-10-01

以厦门市为研究区域,以1988-1998年为时间跨度,利用Landsat5TM遥感数据开展土地覆盖变化识别中多种遥感数据处理方法的集成应用研究。以后分类比较法的结果为基础,运用改进的差值法定义的"变化"目标进行修正,将两种方法有机集成综合地确定土地覆盖变化。根据变化前与变化后覆盖不同但土地利用方式相同或类似的原则进行合并处理,最后得到厦门市10年间土地利用结构变化各种成因类型及其数量。结果表明,10年间厦门市因城市化引起的土地覆盖变化为590.83km2,变化强度为31.14%,引起厦门市土地利用结构发生变化主要有 8种成因机制类型,面积达351.99km2,变化强度为18.55%。

It is an important aspect for remote sensing applications to detect the changes of urban or regional land cover/ land use and ecological environment using multi-platform images. Integrated remote sensing data processing techniques associated with land cover change detection were explored based on Landsat 5 TM and SPOT Pan in Xiamen City, which time of interest spanned 10 years from 1988 to 1998. In data processing algorithm, a new integrated change detection procedure was characterized, which combined a classical post-classification and an improved band- to -band image differencing approaches. Therein the change transform matrix of land cover educed from post-classification comparison serviced as elementary result, and image differencing was applied to modify it through defining the “changed” objects. Based on the detected land cover changes, the land use change patterns and quantities were depicted and amalgamated in formative mechanism according to consistent rule between pre-changed and post-changed objects. The changed detection statistics indicated that, the changed land cover area and land use area induced by urbanization in Xiamen City were added up to 590.83 km2 and 351.99 km2 respectively during past 10 years from 1988 and 1998. The two change strengths were accounted for 31.14% and 18.55% of the total territory area (including land and in shore) of Xiamen. The change patterns boiled down to 8 main types, such as cultivated land to garden plot and forest, new cultivated land development, construction land engrossing cultivated land.

中图分类号: 

[1]Hame T, Heiler I, Jesus S M.  An unsupervised change detection and recognition system for forestry[J].  International Journal of Remote Sensing, 1998, 19(6): 1 079-1 099.
[2]Ridd M K, Liu J. Comparison of four algorithms for change detection in an urban environment[J]. Remote Sensing of Environment, 1998, 63:95-100.
[3]Mas J F.  Monitoring land-cover change: a comparison of change detection techniques[J]. International Journal of Remote Sensing, 1999, 20(1): 139-152.
[4]Johnson R R, Kasischke E S. Change vector analysis: a technique for the multispectral monitoring of land cover and condition[J]. International Journal of Remote Sensing, 1997, 19(3): 411-426.
[5]Yuan D, Elvidge C. NALC land cover change detection pilot study Washington DC area experiments[J]. Remote Sensing of Environment, 1998, 66:166-178.
[6]Li Xia, Ye Jia'an. Accuracy improvement of land use change detection using principal components analysis: A case study in the pearl river delta[J]. Journal of Remote Sensing, 1997, 1(4): 282-289. [黎夏,叶嘉安. 利用主成分分析改善土地利用变化的遥感监测精度——以珠江三角洲城市用地扩展为例[J]. 遥感学报, 1997, 1(4):282-289.]
[7]Bruzzone L, Serpico S B. Detection of changes in remotely sensed images by the selective use of muli-spectral information[J]. International Journal of Remote Sensing, 1997, 18(18): 3 883-3 888.
[8]Bhattacharya U, Parui S K. An improved backpropagation neural network for detection of road-like features in satellite imagery[J]. International Journal of Remote Sensing, 1997, 18(16):3 379-3 394.
[9]Yuan D, Elividge C. Comparison of relative radiometric normalization techniques [J]. ISPRS Journal of Photogrammetry & Remote Sensing, 1996, 51:117-126.

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