In India, for last two decades, increasing pace of urban trends has made it necessary to identify the causative factors leading to drastic changes in natural landscapes. Thus, in order to find an appropriate solution to minimize the damages to flora and fauna inhabiting the affected natural landscapes, trend analysis on the transition factors need to be evaluated. The swelling population density and outspreading urban sprawls, majorly in metropolitan municipalities, leads to intensifying demand of natural resources like water, energy, land surface thereby further results to deforestation, desertification and rigorous loss in agricultural lands. These land parcel variations mutually contribute in changing global environment and near-surface temperatures (Osgouei et al., 2019). Urbanization and changing life patterns during last three decades necessitated urban planners to refurbish an effective methodology and estimate the spatial extent of urbanization. Irregular shapes/sizes of urban features triggers hinderance to evaluate precisely the urban extent and its causative factors. In India, the evolving urbanized area resemble the fallow farmland because of their equivalent reflectance values (Long et al., 2009; Webster, 2001). Consequently, densely time stacked image analysis solves the aforementioned delinquent by categorizing urban and fallow farmland features appropriately. With saturated development in urban core, pre-urbanized segments located far away (ranges can be estimated using threshold proximity analysis) from city premises experiences the urban sprawl. VHR (1-4m) imagery is used to keenly introspect the urban changes occurring far away from peripheries of the densely populated areas (Ban et al., 2010; Del Frate et al., 2007).
Accuracy in urban area identification and modeling is of substantial interest to the municipal authorities for applications on urban planning such as resource allocation, management and distribution, facility provision and promotional policies (Jat et al., 2008). Non-parametric techniques like machine learning classification, decision tree algorithms and knowledge-based classifiers are used extensively to classify Landsat Imagery (Osgouei et al., 2019). Analysis and prediction modelling of impervious area using classification techniques consume high computational power and time. An alternative technique to demarcate the urban areas is by point sampling in addition to a supervised, unsupervised or knowledge based systematic learning technique (Bradley, 1997; Lu and Weng, 2005; Mundia and Aniya, 2005; Reddy et al., 2019; Stuckens et al., 2000; Vogelmann et al., 1998). The basic radiometric, geometric and gap filling (particularly for Landsat 7 images) corrections are not required for densely stacked images. The major factors influencing image classification techniques are: algorithms to be used to classify images, and what dataset is used (multi-spectral or multi-temporal or multi-fusor). Typically, the accuracy to classify satellite images for 3 major classes (vegetation, water and urban) is higher (over 85%) but while identifying more number of features, it becomes difficult and time consuming (Herold et al., 2003; Jia et al., 2014; Li et al., 2014; Lu et al., 2005). Out of various machine learning algorithms, support vector machines (SVM) and maximum likelihood classification (MLC) have been found as the most effective techniques (Osgouei et al., 2019). Various image enhancement procedures like principal component analysis (PCA), independent component analysis (ICA) or multiple marginal fisher analysis are used to eradicate the dependency of bands and correlation between them. The enhanced image can further be used for cataloguing in order to simplify and improve the classification processes for multi-spectral images. Landsat archive and open source datasets streamlines the evaluation of urban areas through remote sensing environment. With seasonal variations, the fallow farmland changes from barren to shallow vegetation and vice-versa whereas the built-up area remains the identical for all the seasons (Schneider, 2012). The availability of dense time stacked images in this archive facilitates the periodic (seasonal or yearly) analysis of images to clearly differentiate variations in minute features (similar features in case of fallow farmland and new built-up area).
Even though the urbanized areas are characterized, the question to isolate the reasons for increasing urban sprawl still remains unanswered. The evaluation of numerous factors contributing to increase in urban sprawl plays a vital role for discrete planning authorities towards computing development strategies intended for laid-back resource allocation and guaranteed forthcoming supplies of natural resources (Jat et al., 2008). Cihlar 2000; Weng 2001; Wang et al. 2003; Sudhira et al. 2004; Alsharif & Pradhan 2014; Abdullahi et al. 2015; Alsharif et al. 2015; Al-sharif & Pradhan 2016; Amini Parsa et al. 2016, addressed the primary causative factors responsible for increasing spatial extent of urbanization. The factors are broadly categorized as: geomorphological (elevation, slope), demographical (zoning status, Euclidian distance to national, state and local highways, railway stations, hefty communities, industrial, commercial or residential complexes), economic (employment rate, richness index), social and cultural (spiritual, tourism, large gathering areas, historic structures) (Mustafa et al., 2018). In India, marketing strategies and religious parameters show complex association with built-up expansion by reason of asymmetrical commercialization and conviction. Traditional methods to evaluate these factors includes manual mapping that necessitates employment, time and huge investment.
Remote sensing environment along with regression operation provides competent practice which is not time consuming and offers more accuracy for long-term outcomes (Haack and Rafter, 2006; Sudhira et al., 2004; Yang and Liu, 2005). Regression analysis of the causative factors and increasing urban spread using remote sensing is looked-for dynamically in this study. In this study, an effort is undertaken to inspect spatial and temporal applications of GIS and Remote Sensing to classify built-up area in Vellore city and adjoining areas. Population density and Euclidean distance from varied features were considered as the causative factors for urban sprawl. For this purpose, cloud free time series Landsat images from 2002 to 2020 were obtained from USGS database for Landsat and Sentinel images. Remote sensing and GIS techniques were used to obtain data for land parcel occupied by impervious extent. Initially, the unsupervised classification algorithms (either ISO or K-means) were implemented for each Landsat imagery after enhancing them through PCA or ICA to endure handler precision just before selecting distinctive pixel distribution. Training data in the order of 10 times the number of bands (10n) were carefully chosen by means of image to image comparison technique (comparing classified image with historical archive of Google Earth images), and ground truth data obtained from toposheets and survey maps. Further, MLC or SVM approaches intended for supervised classification are applied to classify the subsequent enhanced images and urban area are validated through Shannon entropy or patchiness change matrix taking place on landscape levels. The statistical analysis such as MLR and Trend analysis for identification of relationship between urban spread and causative factors was performed.