URBAN GROWTH MAPPING AND RELATED LAND DYNAMICS USING GEOMATIC TECHNIQUES

Abstract

As population increases and national economies continue to move away from the agriculture-based system, cities grow and spread. The urban sprawl often infringes upon agricultural and forest land. An unplanned urban sprawl is emerging as a major threat to urban development, especially in developing countries. In recent decades, the ever-increasing population and urbanization have led to urban sprawl. The dynamic nature of urbanization creates ambiguities in measurements of urbanization growth and its extent. Understanding of urban growth and landscape characterization through historical and current land uses provides the data necessary for the evaluation of environmental impacts on land-use change. Decision-makers and urban planners need authenticated and accurate data for making dynamic decisions. Remote Sensing and Geographical Information System (GIS) are such tools, which help the planners to create and manage data accurately. Remote sensing is all about capturing the feature information from a distance in different bands of the electromagnetic spectrum. Remote sensing data enables the identification, delineation and mapping of land use and land cover classes, which is important for global monitoring studies, resource management and planning strategies. GIS is used as an analysis tool as a means of specifying logical and mathematical relationships among map layers to get new derivative map layers. There are a number of environmental, socio-economic, political and historical factors that impact land use and land cover. These factors further interact dynamically resulting in diverse sequences and trajectories of landscape changes depending upon the specific context in which they arise. Developing a relationship between land-use changes and factors responsible for urbanization is one of the primary goals of the urban research agenda. Future urban expansion and managing urban services require detailed information about ongoing growth processes and development patterns. Hence this thesis analyzes urban parameters such as expansion, density, growth pattern, land transformations in Lucknow, the capital city of the state Uttar Pradesh of India and its relationship with the thermal profile. The research is carried out in multiple phases. The first phase comprises of modeling of urban growth trajectories and the spatiotemporal pattern for extracting the expansion phenomena of urbanization. Then, the critical analysis of the spatial-temporal morphological characteristic of the urban landscape is performed for analyzing the dispersion phenomena with respect to influencing factors contributing to the growth pattern. The study emphasizes understanding the urban sprawl of the region as a pattern and process. The trajectory model is found to be successful in extracting and identifying different types of expansions contributing to a city development, where ribbon and edge expansion is found prominent in the city. To capture the density dynamics of the city, an exhaustive urban land density model is developed. The study revealed the Inverse S-shape pattern of density existed in the city where the majority of density lies under the medium density category. Further, a critical analysis of rural-urban transitions and transformations is performed. This phase analyzes the spatiotemporal dynamics of the urbanization process at an urban, suburban, sub-rural and rural level. A shifting trend of rural to sub-urban and finally to urban is observed from the study. The next phase is carried out for analyzing the impact of landscape patterns and dynamics on land surface temperature. The main focus of the study is to provide effective methodologies for quantitative and qualitative analysis of urban morphology on the UHI effect concerning LULC changes. The study revealed the critical zones and environmental health index for the city. Moreover the comparative analysis of urban growth models is carried out. In which, three different urban growth models and their adequacy has been analyzed, to predict urban expansion for the year 2025. The result depicts the increased urbanization, mainly on the prime agricultural land. Finally, an algorithm is proposed for the extraction of urban land information from Unmanned Aerial Vehicle (UAV) data. This analysis primarily analyzes the potential land available within the city for future development. The algorithm provided acceptable accuracies in extracting the vacant urban land.