Urban green spaces play a substantial role in alleviation of multiple environmental challenges caused by intensified urbanization. However, urban forests themselves are prone to degradation from unsustainable human activities. The forests of Delhi Ridge provide myriad of essential ecosystem services to the citizens of Delhi, though it is under immense anthropogenic pressure. This study is carried out for identifying the overall land use/land cover dynamics in the four different parts of Delhi Ridge in light of rampant urbanization in the area. Variations were estimated for the period of three decades (1989-2020) with the intervening gap of 5 years and Landsat data has been analyzed for this period. All the four parts of Delhi Ridge exhibit overall increase in the green cover with maximum percentage change in area in moderately dense forest class has been observed in Central Ridge (494.31%). On the contrary, there is also an increase in built-up area at the boundaries and also within the Ridge forest depicting severe human interference with maximum percentage change in built-up observed in Southern Ridge (339.28%). The present study provides baseline information on the spatiotemporal changes observed in the Delhi Ridge forest and also highlights the impact of urbanization. Such findings hold substantial implications in framing rational urban policies for ensuring long-term conservation of the fragile habitat of Delhi Ridge forest.

In the recent decades, cities have been expanding at a great pace which changes the landscape rapidly as a result of inflow of people from rural areas and economic progression. Therefore, understanding spatiotemporal dynamics of human induced land use land cover changes has become an important issue to deal with the challenges for making sustainable cities. This study aims to determine the rate of landscape transformations along with its causes and consequences as well as predicting urban growth pattern in Delhi and its environs. Landsat satellite images of 1989, 2000, 2010 and 2020 were used to determine the changes in land use land cover using supervised maximum likelihood classification. Subsequently, Land Change Modeler (LCM) module of TerrSet software was used to generate future urban growth for the year 2030 based on 2010 and 2020 dataset. Validation was carried out by overlaying the actual and simulated 2020 maps. The change detection results showed that urban and open areas increased by 13.44% and 2.40%, respectively, with a substantial decrease in crop land (10.88%) from 1989 to 2020 and forest area increased by 3.48% in 2020 due to restoration programmes. Furthermore, the simulated output of 2030 predicted an increase of 24.30% in urban area and kappa coefficient 0.96. Thus, knowledge of the present and predicted changes will help decision-makers and planners during the process of formulating new sustainable policies, master plans and economic strategies for rapidly growing cities with urban blue-green infrastructures.