Urban Sprawl Monitoring Research Articles

Analysis of Urban Sprawl Using Geographical Information System (GIS) Techniques; A Case Study in Erbil City-Kurdistan of Iraq

Urban sprawl is one of the main threats in the city, and the present study focuses on the discussion and investigation of urban sprawl. Urban sprawl impacts the environment and agricultural fields in Erbil. The city has witnessed a remarkable rapid increase in population and urbanization at the expense of agricultural and pastoral lands, political, economic, and social factors. All these factors led to prevent the city from becoming green, which resulting in some issues like economic, social, and environmental. The most essential application of (GIS) technology is urban sprawl monitoring. It helps to determine the direction of expansion of urban areas, its pattern, and finding the most suitable sites for further urban development.
 (GIS) spatial analysis covered the geo-processing application, which is the classification of maps of Erbil to detect the change detection (LULC) through two periods, 2010 to 2018. It is to discover how much the city has changed and improved over the selected years. The changes and developments that happened over the passing years illustrated in the figures and tables in the results and discussion sections. The purpose of this study is to recommend organized solutions for the unlimited growth of urbanization.
 The results showed the Landsat and Satellite Images of Erbil city due years 2010 and 2018 using ArcGIS Pro. Geo-referencing and digitizing the maps of Erbil gave the spatial data surveying as a result for analyzing the changes and comparing them due to the specified years to see the differences that happened to the city. Furthermore, as a result, the vacant land area decreased by 33.20%, whereas the urban area increased by 15.86% from 2010 to 2018.

Towards an annual urban settlement map in France at 10 m spatial resolution using a method for massive streams of Sentinel-2 data

The size of urban settlements is rapidly increasing worldwide. This sprawl triggers changes in land cover with the consumption of natural areas and affects ecosystems with important ecological, climate, and social transformations. Detecting, mapping, and monitoring the growth and spread of urban areas is therefore important for urban planning, risk analysis, human health, and biodiversity conservation. Satellite images have long been used to map human settlements. The availability of the Sentinel constellation (S2) allows the monitoring of urban sprawl over large areas (e.g., countries) and at high frequency (with possible monthly updates). This massive data stream allows the proposal of new types of urban products at a spatial resolution of 10 meters. In this context, we developed a fully automated and supervised processing chain (URBA-OPT) using open-source libraries and optimized for rapid calculation on high-performance computing clusters. This processing chain has been applied to all of France. The objective is to propose an annual product at 10 m spatial resolution available through the THEIA Data and Services Centre. Our results demonstrate the feasibility and accuracy of producing an annual urban settlement map using this methodology, providing a valuable tool for urban planning and environmental monitoring.

Fully automatic training sample collection for detecting multi-decadal inland/seaward urban sprawl

Accurate and timely monitoring of urban sprawl using remote sensing provides valuable insights for urban management and sustainable development. While existing studies have explored methods for large-scale monitoring of urban sprawl, most of them rely on the collection of manually labeled training samples for supervised classification. Additionally, previous studies generally focused on inland urban sprawl, neglecting the process of urban sprawl towards sea areas, known as seaward urban sprawl, which has profound ecological implications for coastal ecosystems. Here we develop a fully automatic algorithm for detecting urban sprawl, named Fully Automatic Detection of Urban Sprawl (FADUS), using the time-series Landsat imagery without manually collecting training samples or existing urban maps for reference. FADUS automatically generates initial samples through a sequential binary unsupervised clustering technique and then updates them by removing the spectral outliers to obtain a high-quality training set for subsequent supervised classification. A tidal module is included in FADUS to avoid pseudo-urban changes caused by tidal fluctuations so that both inland and seaward urban sprawl can be detected. By applying the algorithm to 75 coastal cities in China, the most rapidly urbanizing region in the world, we uncovered a neglected but dramatic seaward urban sprawl process that has converted sea areas to urban space since 1985. Our results indicate that 9904.36 km2 of coastal wetlands and nearshore seawater areas have been converted to land area, of which 44.83% is currently covered by built-up area. With our method, the fully automatic detection of urban dynamics is now possible for both coastal and inland cities worldwide. Our derived map of seaward urban sprawl in China provides valuable references for coastal land-cover change monitoring, wetland protection and restoration, and integrated coastal management.

Urban Sprawl and Its Multidimensional and Multiscale Measurement

The evaluation and dynamic monitoring of urban sprawl is essential to the sustainable development of cities and therefore attracts enthusiasm from numerous scholars. This study conducted a thorough review of the literature on the multidimensional and multiscale measurement of urban sprawl. Firstly, it provides a definition based on the common characteristics of urban sprawl to contribute to a relatively uniform definition and judging criteria. Secondly, indicators of growth, morphology, density, land use mixture, and accessibility dimensions are sorted out, as well as dimensions that are not widely operationalized but make sense. Thirdly, the review spotlights single-dimensional measures in large-sample comparative studies and booming comparative studies based on multidimensional measures. Furthermore, another focus lies on different spatiotemporal combinations of temporal and spatial scales for the measurement. Overall, there are large gaps in comparative studies on the multidimensional measurement of urban sprawl under multiple spatiotemporal scales. In particular, few micro-scale studies focus on inner-city units, and few measure urban sprawl at multiple scales simultaneously. Finally, the challenges and future of multidimensional and multiscale measurements are discussed: relativity and uncertainty of sprawl criteria; strong dependence on the choice of spatiotemporal scales; comparability of sprawl measurements that remains to be improved; the necessity of long-term international cooperation on the measurement of urban sprawl at the global and regional levels. The article appeals for more multidimensional and multiscale urban sprawl measurement studies based on multi-case comparisons in the future, especially in the developing context.

Spatiotemporal Monitoring of Urban Sprawl in a Coastal City Using GIS-Based Markov Chain and Artificial Neural Network (ANN)

Over the last two decades, globally coastal areas have urbanized rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in towns and cities of the developing world has been characterized by entangled structures and trends exacerbating numerous negative consequences such as pollution, ecological degradation, loss of agricultural land and green areas, and deprived settlements. Substantially, spatial simulation of urban growth and their consequences on coastal areas particularly in Egypt is still very rare. Geospatial modelling coastal urban growth is crucial and has enormous potential for coastal land use transformation and urban sustainability. The key aim of this study was to analyze spatiotemporal changes (2010–2020) and simulate future dynamics (2030 to 2050) of land use/land cover (LULC) in Alexandria Governorate, Egypt. Artificial Neural Network–Multiple Layer Perceptron (ANN-MLP) and Markov Chain techniques were employed within the GIS platform to assess processes of land transitions and predict urban growth trends, patterns and dimensions. The forecasting process was based on three maps of LULC derived from classified Landsat images of 2000, 2010 and 2020. In addition, topographical, demographic, accessibility, proximity factors were generated and developed in the form of raster spatial parameters of urbanization driving forces. The findings revealed that the observed expansion of the built-up area during one decade (2010–2020) was 12,477.51 ha, with a decline in agricultural area (7440.39 ha) and bare land (4904.91 ha). The projected change was forecasted to be 71,544 ha by 2030 and 81,983 ha in 2040 with a total of 35,998 ha increase in the built-up area and residential expansion by 2050. Despite this expected pattern of rapid changes, urban growth will be shaped by the key drivers of proximity to coastline and agricultural land transformation. The analysis indicates that the vertical urban growth will be most likely dominant along the coastal zone due to the lack of vacant lands, whereas the horizontal urban expansion will primarily take place towards the east-northeastern and south-southeastern directions of the city. The present work provides a holistic framework for establishing initial coastal land use plans not only for planners and urban administrators in Alexandria but also for policymakers and coastal municipalities in developing nations.

Characterizing land transformation and densification using urban sprawl metrics in the South Bengal region of India

The impacts of uncontrolled development may be catastrophic in areas experiencing fast population expansion and climatic variability, so the South Bengal Region, adjacent to Kolkata Municipal Corporation (KMC), is facing similar threats. It is essential to monitor such Land use and land cover changes (LULC) in a rapidly urbanizing country like India for long-term and efficient resource management. Remote Sensing (RS) and Geographic Information Systems (GIS) were used in this study to examine LULC dynamics in the South 24 Parganas district of the South Bengal Region during the past 30 years (1991-2021). Indices for urban sprawl monitoring, including patch size, density and growth, were determined statistically to analyze the urban typologies. It has also derived decadal growth rate and expansion efficiency metrics to assess the rate of changing peri-urban scenarios along the South 24 Parganas district. This research helps evaluate the quantitative aspects of urban expansion observed in eco-sensitive and cyclone-prone regions in the context of developing nations. It also helps to design guidelines based on the issues resulting from unsustainable urban sprawl. This study's findings depict substantial growth in population and built-up densities of immediate peri-urban areas to the core city by 70% at the expense of green and blue spaces.

Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods

Accurate urban boundary data can directly reflect the expansion of urban space, help us accurately grasp the scale and form of urban space, and play a vital role in urban land development and policy-making. However, the lack of reliable multiscale and high-precision urban boundary data products and relevant training datasets has become one of the major factors hindering their application. The purpose of this study is to combine Sentinel-2 remote-sensing images and supplementary geographic data to generate a reliable high-precision urban boundary dataset for Henan Province (called HNUB2018). First, this study puts forward a clear definition of “urban boundary”. Using this concept as its basis, it proposes a set of operable urban boundary delimitation rules and technical processes. Then, based on Sentinel-2 remote-sensing images and supplementary geographic data, the urban boundaries of Henan Province are delimited by a visual interpretation method. Finally, the applicability of the dataset is verified by using a classical semantic segmentation deep learning model. The results show that (1) HNUB2018 has clear and rich detailed features as well as a detailed spatial structure of urban boundaries. The overall accuracy of HNUB2018 is 92.82% and the kappa coefficient reaches 0.8553, which is better than GUB (Henan) in overall accuracy. (2) HNUB2018 is well suited for deep learning, with excellent reliability and scientific validity. The research results of this paper can provide data support for studies of urban sprawl monitoring and territorial spatial planning, and will support the development of reliable datasets for fields such as intelligent mapping of urban boundaries, showing prospects and possibilities for wide application in urban research.

Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach

By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.

Are Cities Truly Dispersed? A Long-Term Analysis of Vertical Profile of Settlements in Athens’ Metropolitan Region

Using nonparametric, exploratory statistics, the spatial structure of the city’s vertical profile was investigated at the municipal scale in Athens’ metropolitan region (Greece), evaluating changes over a sufficiently long time interval (1983–2019) that encompasses different phases of the urban cycle in Greece. Assuming the vertical profile of cities as an honest indicator of urban form, the study was aimed to test the intensity and spatial direction of the (supposed) change in settlement models toward sprawl. Transitioning slowly from a dense to a more dispersed settlement structure, Athens’ dynamics revealed a quite representative model for cities expanding significantly but remaining substantially compact and dense, while responding similarly to different (external) economic stimuli. Nonparametric correlation between the average (vertical) profile of each municipality and the distance from downtown Athens revealed a substantially stable mono-centric structure over time, with small changes over time still responding to factors dependent on the urban gradient. The inherent shift towards “horizontal” urban expansion was relatively modest and characteristic of few periurban contexts. The empirical results of this study can be envisaged as a practical tool of regional planning, allowing continuous monitoring of urban sprawl and land take in complex systems under rapid socioeconomic changes.

Assessment of urban sprawl using landscape metrics and Shannon’s entropy model approach in town level of Barrackpore sub-divisional region, India

A city has expanded from the core to peripheral areas through the growth of urbanization process and based on criteria, such as economic development, social, political forces as well as morphological characteristics. Due to increases in the population and anthropogenic activities in a city, the spatial boundary has also extended to provide accommodation along with the fringe areas that lead to fragmentation of urban morphology and influence the local ecology. From the historical past to present conditions and future predictions of urban growth would be able to visualize by space-borne remote sensing techniques to plan the appropriate infrastructure of the city. The present study analyzed the spatio-temporal land-use patterns in the town level in the Barrackpore sub-division area of West Bengal, which has become very compact urban areas in the eastern zone of the country. The spatial assessment of the urban growth pattern of 16 municipalities has been analyzed using remotely sensed data for the year of 1972, 2001 and 2016 with the spatial landscape metrics approach and Shannon’s entropy model. The overall scenario of land use changes reveals that the non-urban areas (vegetation, agriculture, wetland and water bodies) have been decreasing, on the other hand, built-up areas are increasing during the decades. Spatial landscape metrics indicate the nature of the fragmentation of the urban landscape, compactness of the towns and provided the intensity about the sprawl characteristics. The results show that the whole urban landscapes of every town are aggregating into a large patch in the recent years (2016) as compared to the past years. The fragmentation of landscape into small patches happened from 1972 to 2001 on a large scale that indicates sprawl; the conversion of small patches to large single urban patches can be seen from 2001 to 2016 pointed out the maximally aggregation of town which would affect the ecological environment. Shannon’s entropy model was applied to understand the level of urban growth at every corner of municipalities by the zoning of each town. The value of Shannon’s model confirms the dispersed random urbanization in the outskirt of the towns. The spatio-temporal urban sprawl monitoring through these two methods would help the towns’ administration, planners to manage and take better planning to build sustainability for livelihood.

Multi-level monitoring of three-dimensional building changes for megacities: Trajectory, morphology, and landscape

Three-dimensional (3D) building change detection is important for megacities for updating geo-databases, urban sprawl monitoring, disaster assessments and energy budgets. However, few studies examine how to execute the transition of multi-level change monitoring of urban buildings from 2D to 3D. This study presents a new automated Object–Grid–City Block 3D building change detection (OGB) approach that entails the application of multi-temporal aerial Light Detection and Ranging (LiDAR) point clouds. First, building labels at various phases were performed using a graph cuts algorithm to assist with 3D change detection of buildings. Then, we introduced a bi-threshold model to consider and capture trajectories of building change broken down into categories from 1 to 5 and obtain a complete 3D change detection map. In order to reveal the vertical building landscape changes, a set of 3D building landscape metrics was developed for block level change monitoring. Upon examination, the results for the northern part of Brooklyn, New York, USA were confirmed to be robust and refined; the completeness, correctness, and quality values for trajectories 1–5 were 92–95%, 93–97%, 89–95%, respectively. More importantly, the OGB approach can not only effectively monitor intensity, direction (increase or decrease), and spatial pattern changes in 2D and 3D morphological parameters of buildings at the grid level, but can also reflect vertical changes in building structures, and reveal horizontal fragmentations and aggregations of buildings at the block level.

Assessment of Urban Sprawl Using Geospatial Techniques in Awka Town, Anambra State, Nigeria

Cities and towns in developing countries are experiencing an unplanned and uncontrolled development known as urban sprawl. This research examines urban sprawl in Awka metropolis, between 1986 and 2016. Landsat 5 Thematic Mapper (TM) of 1986, Landsat Enhanced Thematic Mapper plus (ETM+) data of the year 2000 and Enhanced Thematic Mapper plus (ETM+) 2016 were used for this study. Geographic Information System was employed for the analysis of the data. Result showed that Awka metropolis witnessed a remarkable growth between 1986 and 2016 from mere 3444.48 Km2 in 1986 to about 11,452.46 Km2 in 2016. This growth contributed to the sharp decline in farmland from 25,500.51 km2 (19.13%) in 1986 to 17,848.44 km2 (13.60%) in 2000 and further declined to 10,037.07 km2 (10.01%) in 2016. Bare surfaces witnessed an increase over the years of this study. This increase is as a result of clearing of natural vegetation for urban development, thereby exposing the land to direct contact with rainfall, leading to gully erosion in the area. Light vegetation also witnessed a sharp decreased from (38.53%) in 1986 to (8.53%) in 2000. It further decreased (8.53%) to (4.62%) in 2016. Therefore, increase in population led to the increase in urban sprawl in the study area. The study recommends that there is a need for a regular monitoring of urban sprawl and development in the study area by the state government especially with the aid of geospatial techniques for better decision making.

Urban Sprawl Monitoring with the help of Remote Sensing and GIS Techniques

Urban Sprawl Monitoring with the help of Remote Sensing and GIS Techniques

A Novel Technique Based on Deep Learning and a Synthetic Target Database for Classification of Urban Areas in PolSAR Data

The classification of urban areas in polarimetric synthetic aperture radar (PolSAR) data is a challenging task. Moreover, urban structures oriented away from the radar line of sight pose an additional complexity in the classification process. The characterization of such areas is important for disaster relief and urban sprawl monitoring applications. In this paper, a novel technique based on deep learning is proposed, which leverages a synthetic target database for data augmentation. The PolSAR dataset is rotated by uniform steps and collated to form a reference database. A stacked autoencoder network is used to transform the information in the augmented dataset into a compact representation. This significantly improves the generalization capabilities of the network. Finally, the classification is performed by a multilayer perceptron network. The modular architecture allows for easy optimization of the hyperparameters. The synthetic target database is created and the classification performance is evaluated on an L-band airborne UAVSAR dataset and L-band space-borne ALOS-2 dataset acquired over San Francisco, USA. The proposed technique shows an overall accuracy of $91.3{\%}$ . An improvement over state-of-the-art techniques is achieved, especially in urban areas rotated away from the radar line of sight.

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

Monitoring urban growth and measuring urban sprawl is essential for improving urban planning and development. In this paper, we introduce a supervised approach for the delineation of urban areas using commonly available topographic data and commercial GIS software. The method uses a supervised parameter optimization approach along with buffer-based quality measuring method. The approach was developed, tested and evaluated in terms of possible usage in monitoring built-up areas in spatial science at a very fine-grained level. Results show that built-up area boundaries can be delineated automatically with higher quality compared to the settlement boundaries actually used. The approach has been applied to 166 settlement bodies in Germany. The study shows a very efficient way of extracting settlement boundaries from topographic data and maps and contributes to the quantification and monitoring of urban sprawl. Moreover, the findings from this study can potentially guide policy makers and urban planners from other countries.

AN IMPROVED SNAKE MODEL FOR REFINEMENT OF LIDAR-DERIVED BUILDING ROOF CONTOURS USING AERIAL IMAGES

Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas) has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.

AN IMPROVED SNAKE MODEL FOR REFINEMENT OF LIDAR-DERIVED BUILDING ROOF CONTOURS USING AERIAL IMAGES

Abstract. Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas) has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.

BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES

Abstract. There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES

There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

Spatial modeling of urban sprawl around Greater Bhubaneswar city, India

In developing countries, where urbanization rates are high, urban sprawl is a significant contributor of the land use change. However, characterizing sprawl has become a contentious issue with numerous arguments both for and against the phenomenon. Meanwhile, effective metrics to characterize sprawl in India are required to characterize this. We have attempted to capture urban sprawl over the landscape and hence adopt landscape metrics, entropy and principal component analysis for characterizing sprawling process. The measurement and monitoring of land-use changes in these areas are crucial to government officials and city planners who urgently need updated information for planning and management purposes. This paper examines the use of landscape metrics and entropy in the measurement and monitoring of urban sprawl by the integration of remote sensing and GIS techniques. The advantages of the entropy method are its simplicity and easy integration with GIS. The measurement of entropy is devised based on locational factors-distances from central business district and reveal spatial patterns of urban sprawl. The entropy space can be conveniently used to differentiate various kinds of urban growth patterns. The application of the method in the Bhubaneswar Metropolitan Area, one of the fastest growing and planned cities in India, has demonstrated that it is very useful and effective for the monitoring of urban sprawl. It provides a useful tool for the quantitative measurement that is much needed for rapidly growing regions in identifying the spatial dynamics, variations and changes of urban sprawl patterns.