Detecting of land use change with remote sensing technique (Case study: Shahriar province)

The loss of spatial balance and inharmonious physics development across regions، especially in the metropolitans has made monitoring changes in land use and its direction in harmony with reducing the harmful effects in the long and short run became the main issues of policymakers and policy planners’ concerns. The aim of the study was to detect changing in Shahriar''s land use via three methods of support vector machine (SVM)، neural network classifier (NNC) and maximum likelihood classifier (MLC) during (1987-2009) using LandSat digital images. The results showed that during a 22 years period، the urban development increased and agricultural land declined. The decline in agricultural land has been 37، 26 and 25 percent based on SVM، NNC and MLC methods، respectively. Furthermore، the result indicated that urban land has been increased 57 and 41 percent via SVM and NNC، while agriculture land has been decreased four percent based on MLC، which demonstrates the weaknesses in accounting estimates of this method.