The Indian city of Pune witnessed rapid growth and deep transformation processes in the last three decades. This paper assesses past developments and recent structures and processes against the concept of urban sustainability. Following an overview of the historical development, the dimensions of sustainability are discussed separately, based on empirical findings. Urban growth puts enormous pressure on Pune’s land and water resources, changing the ecology of the area. The increasing water demand of Pune’s growing population competes with growing energy and water demands. An assessment of future climate change impacts indicates that the storage capacity of the reservoirs is more frequently not met during the rainy season. In addition, extreme dry years can aggravate the effects of land use change on water resources in the future. The city’s growth and especially the large in-migration has also changed Pune’s social fabric significantly. Wealth is distributed unevenly in the city and social disparities can be observed along two fault lines, namely along classes and caste groups. The population development and the increasing socioeconomic polarization are linked to the economic development of the city. Pune’s formal economy has a robust base. However, as in many cities of the Global South, the informal economy is the most relevant source of income for large parts of the population. Pune’s development is challenged by informality, poor infrastructure and inadequate planning and governance. Recently new approaches towards urban renewal and smart city development were launched. These new approaches aim at overcoming blockades in the traditional planning. A special challenge for urban planning is the transformation of urban fringe areas of the city, as this process is currently taking place in an unsustainable manner. The paper concludes that urban development has to become holistic, integrative and participative and should abandon the stereotype vision of the world class city in favor of a sustainable, locally adjusted pathway of development.

Land use and land cover (LULC) classification of a satellite image is one of the prerequisites and plays an indispensable role in many land use inventories and environmental modeling. Many studies viz., forest inventories, hydrology and biodiversity studies, etc., are in demand to account the dynamics of land use and phenology of vegetation. Multi-temporal land use classification accounts the phenology of vegetation and land use dynamics of the study area. In this study, a hybrid classification scheme was developed to prepare a multi-temporal land use classification data set of Sawantwadi taluka of Maharashtra state in India. Parametric classification methods like maximum likelihood and ISODATA clustering methods are combined with the non-parametric decision tree approach to generate the multi-temporal LULC dataset. The accuracy assessment results have shown very promising results with a 93% overall accuracy with a kappa of 0.92

The introduction of the Foreign Direct Investment (FDI) policy in 1991 made India one of the fastest growing economies in the world. This has had a profound effect on India’s urbanization. The rapid urbanization of Indian cities poses a threat to natural and social environments, as expansion of the cities often outpaces the urban planning process. Thus, smart and strategic planning processes that use current and easily available datasets in combination with future urbanization scenarios are needed. To this end, we developed the scenario-based urban growth simulation model (SUSM), which can be used for impact analysis of different planning measures in both spatial and temporal contexts. SUSM uses remote sensing derived inputs, such as land use maps, slope, roads and centres of urban areas along with urban development scenarios. It uses logistic regression for calibration and a constrained stochastic cellular automaton for simulation of urban growth. SUSM is tested in one of the fastest growing urban agglomerations of India: The Pune metropolis, which covers an area of 1642 km². SUSM is calibrated using urban growth maps derived from LANDSAT satellite images from 1992 to 2001. Subsequently, SUSM was used to simulate urban growth of Pune for 2013. A comparison of the SUSM simulation result with the actually measured urban growth derived from a LANDSAT 8 scene from 2013 is used to validate SUSM and to assess the effect of urban plans upon the growth of Pune. Our results show that: (i) SUSM is capable of predicting the location of future urbanization with an accuracy of 79% and a fuzzy kappa index of agreement 0.81; (ii) inclusion of official urban development plans as input for SUSM did not provide a better agreement with the observed growth; (iii) SUSM, parameterized with remote sensing data, can be used effectively to understand urban growth and assess the effects of alternative urban development plans in terms of the spatial expansion of cities.