Remote Sensing and Spatial Overlaying Analysis Based Spatially Sampling for Monitoring Urban Land Change

Abstract

In this paper, the remote sensing classification results of urban land of Jiading district of Shanghai, China in the years of 1995 and 2001 were employed as expatiated case. The theory of sampling with the integration of remote sensing technique and spatial overlaying analysis for monitoring urban land change has been investigated. The dynamic urban land sampling framework (DULSF) is first investigated, in which three parts were built and implemented that: (1) a dynamic changed urban land regionalization (DCULR) in the case area based on the dynamic urban land change degree (ULCD); (2) a dynamic spatial information and sampling framework (DSISF) based on different urban land change rate; and (3) a dynamic changed urban land sampling units (DCULSU). And a crucial metric that dynamic urban land change degrees of sampling unit (DULCDSU) was proposed in the dynamic urban land sampling framework. A dynamic urban land change sampling (ULCS) scheme was then generated while the valid samples were determined, sample distribution strategy was disposed, and accuracy prior and post assessment was carried out. Finally, the dynamic urban land change sampling data is summarized for case area. Some conclusive remarks are revealed that: (1) the theory of dynamic spatial information and sampling framework in this paper is feasible to control the sampling operation, and (2) the proposed spatially sampling scheme is efficient based on the remote sensing and spatial overlaying analysis.