Introduction

Urbanization or urban growth is a dynamic process involving changes to vast expanses of land cover with the progressive concentration of human population. The process entails a switch from a spread-out pattern of human settlements to compact growth in urban centers. The process of urbanization gained impetus with the Industrial Revolution 200 years ago and accelerated in the 1990s with globalization and the consequent relaxation in the market economy. Rapidly urbanizing landscapes reach a huge population size leading to gradual collapse in urban services; the result is problems with housing, slums, lack of a treated water supply, inadequate infrastructure, higher pollution levels, poor quality of life, and so forth. Urbanization is a product of demographic explosion and poverty induced rural–urban migration. Globalization, liberalization, and privatization are the agents fueling urbanization in most parts of India. However, unplanned urbanization coupled with the lack of a holistic approach is leading to lack of infrastructure and basic amenities. Therefore, proper urban planning with operational, developmental, and restorative strategies is required to ensure the sustainable management of natural resources. The process of urbanization involves migration from rural to urban areas, increased urban population density, increased levels of consumption, corresponding lifestyle changes, and increased energy consumption; these shifts promote an increase in carbon emissions.

Urban growth is the spatial pattern of land development to accommodate anthropogenic demand that influences other land uses (e.g., open spaces, water bodies, etc.). The surface of the earth has been altered considerably by humans over the past 50 years through urbanization. On a global scale, 2.5 billion people were living in urban areas in 1950; the number is expected to be 6 billion by 2030 (Ramachandra, Aithal, and Durgappa 2012). Continuing urban growth raises concerns about thedegradation of theenvironment and its ecological health. Understanding urban growth and change is critical to city planners and resource managers in these rapidly changing environments. Dynamic urban change processes, through which the productive agricultural lands, vegetation, and water bodies are irretrievably lost and transformed at an alarming rate is often referred to as rapid urbanization. Unplanned rapid urbanization changes the structure of the landscape, influencing its functioning quite apart from the lack of basic infrastructure, amenities, enhanced levels of pollution, and changes in local climate and ecology (Aithal and Ramachandra 2016a). This phenomenon is very rapid in India with its urban population growing at around 2.3 percent per annum, and certain cities like Bangalore have a higher population growth at 4.6 percent per annum (Ramachandra, Aithal, and Sowmyashree 2013). Cities in developing countries have grown more compact and more clustered, spreading beyond the boundaries of central cities (Ramachandra, Aithal, and Sowmyashree 2015). This dispersed growth close to the large urban forms having a mixed land use (Aithal and Ramachandra 2016a, 2016b) is known as “urban sprawl.” This leads to the inefficient use of land resources and energy and large-scale encroachment onto agricultural lands, traffic congestion, shortages in urban services and facilities, and major problems of urban poverty. Cities are expanding in all directions, which is resulting in large-scale urban sprawl and changes in urban land use (Ramachandra, Aithal, and Sowmyashree 2015). Large-scale land-use and land-cover changes combined with unplanned urbanization and sprawl has an impact on the environment and the sustainability of natural resources (Ramachandra, Aithal, and Barik 2014; Ramachandra, Aithal, and Sowmyashreeb 2014).

Understanding land-use and land-cover changes is essential to evolving appropriate strategies for sustainable management of natural resources and monitoring environmental changes such as greenhouse gas emissions and urban heat island effects (Ramachandra, Aithal, and Bharath 2013). Spatial data acquired through space-borne sensors since the early 1970s and advancements in geo-informatics have helped in understanding and visualizing landscape dynamics. The collection of remotely sensed data at regular intervals facilitates synoptic analyses of the earth: system function, patterning, and change at local, regional, and global scales over time. Further evaluating the impact of urban growth in this form on the environment and understanding the dynamics of complex urban systems involves modeling and simulation, which require innovative analytical methods and robust techniques. A number of analytical and static urban models have been developed that are based on diverse theories such as urban geometry, size relationship between cities, economic functions, and social patterns with respect to the city. However, these models explain urban expansion and evolving patterns instead of predicting future urban development. In urban modeling, dynamic agent-based modeling as an urban simulation tool has rapidly gained popularity in recent years among urban planners and geographers. Considerable research efforts have developed different dynamic agent-based models (stochastic based on cellular automata, Markov model, etc.) for urban and environmental applications (Aithal, Vinay, and Ramachandra 2014). All these models have some common features, such as the use of transition probabilities in a class transition matrix (Aithal and Ramachandra 2016a). Cellular automata algorithms define the state of the cell based on the previous state of the cells within a neighborhood, using a set of transition rules. Coupled Markov chain (MC) and cellular automata (CA) eliminates the shortcomings of CA and MC respectively. MC quantifies future changes based on past changes, thereby serving as a constraint for CA, which addresses spatial allocation and the location of change. Although CA–Markov gives promising results, it fails to achieve accurate results since the driving forces are not accounted for in this model.

Fuzzy analytical hierarchical process (AHP)-based CA–Markov modeling uses analytical hierarchical process-based modeling to simulate the land-use dynamics based on the agents of change including drivers or constraints, socioeconomic and infrastructural activities, and human actions (Aithal, Vishwanatha, and Ramachandra 2015). Agent-based modeling (ABM) weights/ranks the growth factors and constraints as reflected by the real-world scenarios to develop site suitability maps in order to model the land use; it has emerged as a promising approach in understanding the complex urban processes. This provides ample opportunities and challenges which complement or extend to other approaches. The site suitability maps provide the transitional areas, describing where the particular land use has the probability to change or retain its state. The site suitability maps are combined with the CA–Markov in order to simulate and predict the land-use dynamics.

Cities across the world with increased urbanization and urban populations also account for about two-thirds of the world’s primary energy consumption and about three-quarters of the greenhouse gas (GHG) emissions. Concentration of greenhouse gases in the atmosphere has increased rapidly over the last century due to ever-increasing anthropogenic activity; this has resulted in significant increases in the temperature of the earth causing global warming. Major sources of GHG are transportation (burning of fossil fuel), forests (due to human-induced land-cover changes leading to deforestation), power generation (burning of fossil fuels), agriculture (livestock, farming, rice cultivation, and burning of crop residues), water bodies (wetlands), industry, and urban activities (building, construction, transport, solid and liquid waste). Aggregation of GHG (CO2 and non-CO2 gases), in terms of carbon dioxide equivalent (CO2e), indicate the GHG footprint.

GHG footprint is a measure of the impact of human activities on the environment in terms of the amount of greenhouse gases produced. The study (Ramachandra, Aithal, and Shreejith 2015) calculated the amount of three important greenhouses gases, namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), thereby developing the GHG footprint of the major cities in India. Energy consumption and related GHG emissions in cities largely depend on how the cities grow and operate and on the sources of energy that are used to support these processes. Increased emissions of CO2 and other greenhouse gases are the main contributors to global warming (Ramachandra, Aithal, and Shreejith 2015) with one-third of CO2 emissions resulting from land-use changes. Rapid changes in land use leading to large-scale degradation of natural resources (water bodies, tree cover) alter the carbon sink ability and affect the environment. Land-use change in the form of urbanization is a main factor that determines carbon dynamics and leads to global warming. Studies based on emissions and land use have established a correlation between global warming and enhanced emissions of greenhouse gases and urban heat island effect across the urbanizing regions in the world. Urban heat island (UHI) has been garnering substantial attention in recent decades. Extensive studies have been carried out to explore the impact of past urbanization on the UHI effect in cities with unique urban landscapes and histories – for example, Tokyo, Paris, New York City, Beijing, Houston, Singapore – using numerical models. This emphasizes the need for understanding spatial and temporal patterns of urbanization, land-use changes, greenhouse gas emissions, and urban heat island. This entry analyzes spatiotemporal patterns of urbanization in Bangalore, one of the most rapidly urbanizing cities with environmental sustainability issues..