Sandeep Maithani

Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata

Abstract The present study demonstrates the applicability of the Operational Linescan System (OLS) sensor in modelling urban growth at regional level. The nighttime OLS data provides an easy, inexpensive way to map urban areas at a regional scale, requiring a very small volume of data. A cellular automata (CA) model was developed for simulating urban growth in the Indo-Gangetic plain; using OLS data derived maps as input. In the proposed CA model, urban growth was expressed in terms of causative factors like economy, topography, accessibility and urban infrastructure. The model was calibrated and validated based on OLS data of year 2003 and 2008 respectively using spatial metrics measures and subsequently the urban growth was predicted for the year 2020. The model predicted high urban growth in North Western part of the study area, in south eastern part growth would be concentrated around two cities, Kolkata and Howrah. While in the middle portion of the study area, i.e., Jharkhand, Bihar and Eastern Uttar Pradesh, urban growth has been predicted in form of clusters, mostly around the present big cities. These results will not only provide an input to urban planning but can also be utilized in hydrological and ecological modelling which require an estimate of future built up areas especially at regional level.

Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring

Urban areas are the most dynamic region on earth. Their size has been constantly increased during the past and this process will go on in the future. Since there is no standard policy and guidelines for construction of buildings and urban planning, cities tend to have irregular growth. Many cities in the world face the problem of urban sprawl in its suburbs. So issues of urban sprawl need to be settled with the help of technologies such as satellite remote sensing and automated change detection. This paper presents a wavelet based post classification change detection technique that is applied to 1996 and 2004 MSS images of Madurai City, South India to determine the urban growth. The classification stage of the technique uses coilflet wavelet filter to correlate with the MSS land cover images of Madurai city to derive texture feature vector and this feature vector is inputted to a fuzzy-c means classifier, an unsupervised classification procedure. The post classification change detection technique is employed for identifying the newly developed urban fringe of the study area. The error matrix analysis is used to assess the accuracy of the change map. The performance of the presented technique is found superior than that of classical change detection methods such as image differencing, change vector analysis and principal component analysis.

Estimation of urban population in Indo-Gangetic Plains using night-time OLS data

In this study the applicability of a night-time Operational Linescan System (OLS) sensor in urban population estimation has been examined. The study area consisted of the Indian portion of the Indo-Gangetic Plains. Using night-time OLS data, urban areas situated in the study area were mapped and their areal extent was determined. A linear relationship between the natural log of the urban area and the natural log of the corresponding population was established. The model was calibrated for the year 2001 and then validated for the year 1995. Subsequently, the model was modified using ancillary factors such as electricity consumption to reduce the error in population estimation. Thus, this study attempted to explore the applicability of nighttime OLS data in urban population estimation.

An artificial neural network based approach for urban growth zonation in Dehradun city, India

For regulating urban growth, it is imperative to produce urban growth zonation maps, in which future urbanizable areas along with their urban growth potential are delineated. As, these maps provide a rational and scientific basis for taking future decisions regarding the growth of the city. The conventional approach for generating urban growth zonation maps is subjective in nature. To reduce this subjectivity, an artificial neural network (ANN) approach has been proposed for generating urban growth zonation maps. The database required for ANN-based urban growth zonation has been compiled from remote sensing data and other existing maps. GIS is used for handling of this spatial data. A comparison of the ANN- and conventional approach-derived zonation maps was also done. The study demonstrated the potential of ANN for urban growth zonation of an area, which may provide a valuable input to the urban planning authorities for regulating urban growth

Neural networks-based simulation of land cover scenarios in Doon valley, India

Land cover transformation is one of the foremost aspects of human-induced environmental change, having an extensive history dating back to antiquity. The present study aims to simulate the process of land cover change based on different policy-based scenarios so as to provide a basis for sustainable development in Doon valley, India. For this purpose, an artificial neural network-based spatial predictive model was developed for the Doon valley. The predictive model generated future land cover patterns under three policy scenarios, i.e. baseline scenario, compact growth scenario and hierarchical growth scenario (HGS). The simulated land cover patterns mirror where land cover patterns are headed in the valley by year 2021. The result suggests that unabated continuation of the present pattern of land cover transformation will result in a regional imbalance. However, this skewed development can be corrected by altering the current growth trend as revealed in the compact growth and HGSs.

Urban Settlement Pattern and Growth Dynamics in Northwest Himalaya

The urban settlements in Northwest Himalaya (NWH) are experiencing a rapid pace of urbanization. However, this development is rather skewed in nature. The hilly regions of NWH are experiencing a negligible growth in their population, whereas the population pressure is rising in urban settlements at foothills. One of the primary reasons is the lack of economic opportunities and infrastructural facilities. The economic base of these hilly urban settlements, which is mainly based on tourism, was further eroded due to the recent series of natural disasters. A direct outcome has been reduction in the tax base of urban local bodies, due to which they are now struggling to sustain their existing infrastructure leave apart its augmentation.

Understanding Urban Environment in Northwest Himalaya: Role of Geospatial Technology

An urban area is a complex ensemble of interrelated and entangled socio-economic, spatial and environmental processes. The irreversible demographic changes being witnessed in an urban set-up pose threat to the rapidly depleting resources. Therefore, the study of urban footprints and the services with associated infrastructure is a subject that intrigues urban planners. Although planning has more to say about cities, it is equally imperative to explore all perspectives of a holistic regional development keeping in view the social, economic, cultural, environmental and governance issues. Interestingly, with the advent of smart city concept, the conceptualization of six key enablers of the mission, viz. smart governance, smart living, smart people, smart mobility, smart environment and smart economy, finds its complete implementation scope in spatial planning aided with information and communication technology (ICT) solutions. Various pillars of smart planning of an urban set-up ranging from infrastructure to resource management and smart energy to smart environment are achievable with the help of geospatial technologies.

X-band persistent SAR interferometry for surface subsidence detection in Rudrapur City, India

Urban areas due to their dynamic nature often pose serious threats to environment causing overutilization of resources like ground water. The depleting ground water table often causes land subsidence leading to cracks in buildings. This subsidence can be easily mapped using PSInSAR (Persistent Interferometric Synthetic Aperture RADAR). In urban areas, there are many buildings per square kilometer which give permanent scatter and act as good corner reflectors at boundaries right angle to ground and walls. Rudrapur city, which is the headquarters, of Udham Singh Nagar district of Uttarakhand state in India, is also a major industrial hub and attracts skilled and unskilled labour force from the adjoining areas and this is leading to an unprecedented growth of urban sprawl. The city shows sprawling dense urban settlements and huge industrial setups on the outskirts, surrounded by agricultural fields and orchards. Main source of water supply is through bore wells and tube wells. Here, it was found that over the years, ground water has been harnessed for not only household supplies but also for agriculture and industrial purposes which has led to lowering of water table down from around 33.5 m to 45.7 m. This is leading to cracks developing in buildings particularly around the industrial area. The changes over a period of a year from 4th December 2014 till 2nd December 2015, were mapped using PSInSAR technique, with X-band TerraSAR-X datasets.

Calibration of a Multi-criteria Evaluation Based Cellular Automata Model for Indian Cities Having Varied Growth Patterns

The study aims to investigate the efficiency of Cellular Automata (CA) based models for simulation of urban growth in two Indian cities (Dehradun and Saharanpur) having different growth patterns. The transition rules in the CA model were defined using Multi-Criteria Evaluation technique. The model was calibrated by varying two parameters namely the neighbourhood (type and size) and model iterations. The model results were assessed using two measures, i.e., percent correct match and Moran’s Index. It was found that for Dehradun, which had a dispersed growth pattern, Von Neumann neighbourhood of small size produced the highest accuracy, in terms of pattern and location of simulated urban growth. For Saharanpur, which had a compact growth pattern, large neighbourhoods, produced the most optimum results, irrespective of the type of neighbourhood. For both study areas, large number of model iterations failed to increase the accuracy of urban growth assessment.

Simulation of peri-urban growth dynamics using weights of evidence approach

Abstract The study aims to simulate the peri-urban growth dynamics in a growing region of India using Weights of Evidence (WOE) based cellular automata model. The growth process was expressed as a function of four causative variables corresponding to which seven data layers were generated in a Geographic Information Systems environment. The model was calibrated for the period 2000–2005 using Kappa indices and fuzzy set theory based two way comparison method. The Kappa value was 0.7, while the value of Klocation and Khisto were 0.81 and 0.93, respectively. The fuzzy similarity values increased for small to large neighbourhood sizes which showed that the model was able to simulate the contiguous and dense growth. However, for dispersed and isolated growth the model showed less accuracy. The model was validated for period 2005–2010 and revealed a Kappa value of 0.88, while value of Klocation and Khisto were 0.91 and 0.96, respectively.