Assessment Land Changes in the Area of Kahrizak Waste Disposal Center by Using Spatial Information System and Remote Sensing Methods - Hatamzadeh , Vahid; Vahidi , Sara; Nouri , Paniz; Karbalaei , Niloofar (Novel Perspectives of Geography, Environment and Earth Sciences Vol. 9, 2023)
For more than 50 years, Kahrizak waste center has been a neighbor of Tehran metropolis. With the development of the Tehran city and the increase in waste production, it's destructive effects on the environment. Land use units that are always changing under the influence of natural events, human actions, social and economic issues, especially around big cities like Tehran is obvious. In these changes, various lands are converted into built-up lands, whether residential, commercial, industrial, or road network, and in some cases, they are left as barren lands. In this research, by using landsat8 satellite images and remote sensing technique, land use changes of Kahrizak waste center have been investigated in thematic maps which is more available for users, Five vegetation indices NDVI, EVI, SAVI, LAI and VCI and six supervised classification methods maximum likelihood, parallel networks, minimum distance from the mean, neural network, Mahalanobis distance and support vector machines with four circular, linear, polynomial kernels and radial with the same training and test data on the image of 2022 were investigated by using two parameters of overall accuracy and Kappa coefficient. For this research 4classes was selected soil, water, building (urban area) and agricultural lands, the results showed that the maximum likelihood classification method is the best method with overall accuracy of 90.99% and Kappa coefficient of 0.85 and the high similarity of the generated user map classes to the original satellite image. Then, a user map was generated from all the images from 2011 to 2022 using the maximum likelihood classification method. After calculating the area of the classes, the results showed that the area of the building class increased by 71% and the agricultural land class decreased by 80%.