Application of Remote Sensing and Markov in Investigation and Prediction of Change in Vegetation Cover (Case Study: District 1 of Tehran Municipality)



Land use changes are one of the important consequences of urban spatial extent. Regarding the role of Green space as one of important land use classes in urban ecosystems, the investigations of its changes seem to be crucial. In this study, to estimate the changes of green Space, the NDVI Index was applied to TM and IRS images of 1990 and 2006 and green space maps of District 1 of Tehran municipality by underlining the different greenness levels were generated. The comparison between the areas of greenness classes of green space maps revealed that non-vegetated increased by 1538.28 and poor, moderate and good greenness level green space decreased by 22.59, 15.3 and 0.36 ha respectively. Further more; Post-classification change detection technique was applied to investigate the procedure of changes of the study area's green space based on the conversion of classes. It showed that although the green space area has been increased by 1576.53 ha due to the conversion of non-vegetated to vegetated area, according to the conversion of vegetated area to non-vegetated one by 38.25 ha, it has been decreased by 1538.28, finally. The green space changes were also projected for the next nineteen years when IRAN envisages achieving Vision 1404 using Markov chain analysis. Statistic result shows that maximum probability of green space destruction belongs to poor greenness level green space area by 0.7743. Moreover, the map of applying CA-Markov represents probable spatial distribution of case study's green space in 1404 which is really helpful to urban planning in case of Green space's conservation and developments.