文章基本信息

标题：Monitoring Land Use Change by Multi-Temporal Landsat Remote Sensing Imagery
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作者：A. Tayyebi ; M.R. Delavar ; S. Saeedi 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：1037-1042
出版社：Copernicus Publications
摘要：This paper presents a methodology through utilizing remote sensing imagery, GIS-based neuro-fuzzy approach and variety of social and environmental factors for simulating land use change. Two historical Landsat imageries of Tehran Metropolitan Area with twenty year time interval and user-selected socio-economic and environmental variables have been employed in order to simulate land use change. All images were rectified and registered to Universal Transverse Mercator (UTM) WGS 1984 zone 39N. Supervised classification was used to classify the images to different land use categories. Four classes were identified: road, residential area, service centre, administrative area. This work introduces a simulation experiment on urban land use change in which a supervised back propagation neural network has been employed in the parameterization of the simulation model, while GIS is used to model and monitor land use change and perform spatial analysis on the results. This paper adapts land use change model which parameterized for Tehran Metropolitan Area and explores how factors such as road, slope, administrative space, service centre and residential area parameters can influence it. For each cell in the study area, the real change between the two time steps is determined and analyzed compared with the provided variables in order to produce a probability of land use change layer. Parts of two datasets were used to train the neural network while full datasets were utilized to predict land use change modelling. In addition, the impact of training and prediction period on land use is examined. The creation of the GIS based neuro-fuzzy land use change modelling is the major contribution of this paper
关键词：Land Use; Artificial-Intelligence; GIS; Remote Sensing; Change Detection