55
Pertanika Journal of Scholarly Research Reviews
http://www.pjsrr.upm.edu.my/
Measuring and Mapping Urban Growth Patterns
Using Remote Sensing and GIS Techniques
Maher Milad, ABURAS,a Sabrina Ho, ABDULLAH,b* Mohammad Firuz, RAMLI,c Zulfa Hanan, ASH'AARId
abc
Faculty of Environmental Studies, University Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia
*
Abstract – Remote sensing and geographic information system techniques are significant and popular approaches that have been used in recent years to measure and map urban growth patterns. This paper primarily aims to provide a basis for a literature review of urban growth measurement and mapping by using different methods. For this purpose, the general characteristics of measuring and mapping urban growth patterns are described and classified. The strengths and weaknesses of the various methods have been identified from an analysis and discussion of the characteristics of the techniques. Results of reviews confirm that combining quantitative and qualitative techniques, such as Shannon approach and change detection, to measure and map urban growth patterns will improve understanding of the phenomenon of urban growth. Moreover, using social and economic data such as population and income data will improve understanding of the relationships between causes and effects. The integration of social and economic factors with quantitative and qualitative techniques will contribute to a perfect evaluation of urban growth patterns and land use changes, taking technical, social, economic, spatial, and temporal factors into account.
Keywords: Urban growth, urban growth mapping, urban growth measuring, remote sensing, GIS.
Introduction
In recent decades, cities around the world have been facing the issue of urban growth as a result of population and economic growth (Duranton & Puga, 2014). Urban growth gradually leads to the decline of natural and rural lands, and it affects the ecosystem in general (Haas & Ban, 2014). Given this situation, strategies and policies are needed to address and pre-empt this phenomenon before negative effects on the biosphere begin to increase (Zhao, 2011). These strategies and policies may be long-term plans at the local, regional, national, and international levels, such as population and economic strategies and plans (Bengston et al., 2004). However, these strategies and policies may also come in the form of a technology that monitors and controls urban growth phenomenon. Examples of
56
these techniques are remote sensing (RS) and geographic information systems (GIS) (Al-shalabi et al., 2013; Al-sharif & Pradhan, 2013).
Nowadays, city planners and decision makers are taking into account modern techniques such as GIS and RS when they want to create future policies (Liu et al., 2015). GIS and RS are being considered because of several reasons, such as the fact that RS and GIS techniques have spatial and temporal dimensions for monitoring, controlling, analyzing, evaluating, and measuring urban growth patterns and land use changes (Liu et al., 2015; Ramachandra et al., 2013). In addition, these techniques can use quantitative and qualitative methods to identify the causes, impacts, and current and future trends of urban growth patterns (Aithal & Sanna, 2012; Al-shalabi et al., 2013; Shalaby, 2012; Yang, 2010). Socioeconomic and spatial data can be integrated by using RS and GIS techniques to analyze complex patterns of land use changes. Moreover, mathematical equations, such as Shannon entropy equation and landscape metrics, can be used in RS and GIS environments to determine the types of urban patterns (Aithal & Sanna, 2012; Ramachandra et al., 2013; Yang, 2010).
The method of measuring urban growth patterns to evaluate trends and patterns of urban development with the use of RS and GIS techniques is different from that of statistical and mathematical techniques (Bhatta, 2009; Sudhira et al., 2004). The values obtained by using these methods reflect a complex form of urbanization and its characteristics and dimensions over a long period of time (Bhatta, 2009). These values may represent a growth pattern if the values of Shannon entropy approach indicate a compact or dispersed growth (Aithal & Sanna, 2012; Ramachandra et al., 2013; Sudhira et al., 2004). Sometimes, values obtained from urban growth measurement represent several types of growth patterns, such as patchiness, aggregation, clumped-ness, fragmentation, disaggregation, and physical connectedness by using landscape metrics (Ramachandra et al., 2013). In addition, the relationship between urban expansion and different driving forces can be identified (Alsharif & Pradhan, 2014). Generally, urban growth parameters can be used to analyze the spatial sprawl differences of land uses (Ren et al., 2013).
Mapping urban growth patterns and land use changes is one of the significant and simple techniques that are used in urban studies (Hardin et al., 2007). This approach requires a knowledge of image classification and its various algorithms (Lam, 2008), such as Gaussian maximum likelihood method (Ramachandra et al., 2013) and object-based classification (Benz et al., 2004). Change detection between images is considered the important technique that is used to evaluate urban growth patterns and land use changes (Suribabu et al., 2012). Image overlay is one of the simple methods that are used in land use change detection (Hardin et al., 2007). The most important part of change detection technique and image overlay is selecting suitable high-resolution images (Lam, 2008). Results of change detection techniques can be considered as either statistical data, such as change detection tables, or visual data, such as land use maps (Suribabu et al., 2012).
Nowadays, techniques to detect land use and cover changes are widely used to evaluate urban growth patterns and its effects on dynamic movement of land use. These techniques primarily aim to determine the changes that are relevant to the problem while ignoring the inconsequential ones
57
(Hardin et al., 2007). Quantitative and qualitative methods and algorithms such as image overlay (Hardin et al., 2007), image differencing (Maktav & Erbek, 2005), decision trees (Wei et al., 2012), post-classification comparison (Madhavan et al., 2001), and neural networks (Pajares et al., 2007) are used to monitor urban changes. This paper provides an overview of the major models and techniques that are used to evaluate and measure urban growth. It also presents land use change detection methods as a significant model in land use studies based on a critical review to study them in detail and determine future development directions.
Measuring Urban Growth Patterns
Several indices have been used to measure urban growth patterns, such as mathematical and statistical indices (Jiang et al., 2007; Sudhira et al., 2004). These indices are integrated with a GIS environment and RS data and techniques to employ them for measuring land use changes spatially and temporally (Punia & Singh, 2012). Many traditional mathematical and statistical approaches are unsuitable for spatial studies such as urban studies for several reasons; for example, with the use of traditional mathematical and statistical approaches, summarizing site information is difficult, and the effects of representation patterns in spatial data are different from those in statistical data (Ren et al., 2013). Hence, mathematical and statistical methods have been integrated and developed with other modern techniques such as GIS and RS techniques to measure spatial dynamics and land use changes (Batty, 2000; Antonio & Scott, 2005). These techniques can be used to better understand urban growth patterns in metropolitan areas if appropriate spatial data are available (Ren et al., 2013).
Generally, various spatial statistical models have been used in the field of land use analysis in urban regions (Antonio & Scott, 2005). Ren et al. (2013) applied Pearson’s chi-square statistics to analyze and evaluate the growth pattern and the overall situation of urban sprawl. Páez and Suzuki (2001) used a dynamic spatial logit model to identify the effect of neighbourhoods on urban patterns and land use changes. Punia & Singh (2012) used Shannon entropy approach (Hn) to identify whether urban growth is compact or dispersed, as well as to identify if urban sprawl exists or not. Aithal & Sanna (2012) used alpha and beta population density metrics as indicators of urbanization and urban growth. Ramachandra et al. (2013) used some landscape metrics to analyze, evaluate, measure, and compute urban growth patterns according to different matrices, such as landscape shape index (LSI) and normalized landscape shape index (NLSI). Suribabu et al. (2012) used statistical change detection of land use to evaluate urban growth patterns.
On the basis of the advantages and disadvantages of the models, as listed in Table 1, some techniques that are used to measure urban growth patterns can be concluded to be stronger than others; one such technique is the Shannon entropy approach, which is used to measure the compactness and dispersion of built-up areas. Moreover, the Shannon entropy approach is used to identify the occurrence of urban growth in certain areas. Furthermore, chi-square method and urban expansion index (UEI) are powerful quantitative techniques that are used to identify the sustainability of urban development and the speed of urban sprawl in an overall area and in each direction and zone. However, these quantitative techniques are insufficient for evaluating and measuring urban growth patterns because
58
they need to be integrated with other visual methods, such as growth maps and land use change detection maps, to obtain a clear understanding of the phenomenon.
Landscape metrics, Shannon entropy, chi-square, UEI, and land use change detection are significant techniques that have been used to measure and map urban growth patterns. Their significance is due to several reasons, such as their use of quantitative and optical measurements. Moreover, these techniques have different methods for measuring urban growth patterns, such as measuring the changes of built-up areas to identify the type and degree of urban expansion (Ramachandra et al., 2013) and mapping historical land use and land covers, to evaluate and monitor urban growth phenomenon for different time periods (Suribabu et al., 2012). However, some measurement techniques such as Shannon approach and density map obtain the same results and are similar in terms of measuring the compactness of built-up areas (Table 1). Other landscape metrics such as NLSI and LSI obtain the same result, that is, they provide a simple measure of class aggregation or clumped-ness. Thorough studies are needed before using these techniques to avoid repetition. Social and economic statistics and methods such as population growth, population density, and household income are important indicators that should be taken into account to evaluate and measure urban growth patterns because they are considered significant causes of urban development.
Mapping Urban Growth Patterns
Mapping urban growth patterns commonly includes interpretation of aerial and satellite images. However, more efficient techniques of mapping urban growth patterns should be explored (Grey et al., 2003). Change detection is typically used for this purpose (Suribabu et al., 2012). Nowadays, change detection is commonly used to monitor, measure, and evaluate land use and land cover changes, taking spatial and temporal changes into consideration (Güler et al., 2007). Multi-temporal satellite images that have high spatial resolution are effectively employed to detect urban growth patterns (Yagoub & Kolan, 2006).
Designing the framework of change detection technique
A simple framework of land use change detection technique can be designed by following multiple stages, namely, (i) data collection stage, which requires various data based on data availability, data quality, the needs of the study, and processing requirements (Kilic et al., 2006; Shamsudheen et al., 2005; Suribabu et al., 2012); (ii) data processing stage, which includes different steps to prepare, classify, and produce change detection data (Alkan et al., 2013; Tan et al., 2010); (iii) accuracy assessment stage, which is one of the most important stages for carrying out change detection techniques to assess the previous stages (Güler et al., 2007); (iv) producing different maps and identifying land use change for different time periods in this stage, which are carried out by using change detection techniques (Suribabu et al., 2012). The final output of land use change detection techniques is either statistical data or maps (Figure 2).
59
Table 1: Applications used to evaluate, analyze, and measure urban growth patterns using RS and GIS
techniques Author / Year Place Purpose of the
study
Technique
used Comment
Sudhira et al. (2004)
India Urban sprawl
modeling
Density map
Density map is strongly effective for analyzing urban growth pattern by
using visualization method.
However, this technique is not enough to evaluate urban growth phenomena because it needs to be integrated with quantitative methods to ensure a realistic evaluation. Bhatta et al., (2010a) India Quantifying urban growth Degree-of-goodness
The degree-of-goodness is typically used to indicate to the magnitude of compactness of growth, which is the opposite of growth. This indicator is not commonly used in urban growth studies because other indicators, such as Shannon entropy, provide better results. Li et al., (2010) China Analysis on spatiotemporal dynamics of urban expansion GIS-based buffer gradient analysis
This is an effective approach to identify spatio-temporal changes and their effects on urban land uses. It can be effectively used to understand the regional scale of urbanization and identify the relationship between the major centers and satellite cities. A strong relationship is found between quantitative and qualitative aspects.
Aithal and Sanna (2012)
India Spatial pattern analysis
Landscape metrics
This method is used to appraise and evaluate urban growth patterns. Measuring the gradation of urban patterns from the center to the edges is the main advantage of these metrics. The disadvantages of landscape metrics are that some matrices have the same results, and using them leads to repetition of work. For example, using NLSI and LSI obtains the same result, that is, it provides a simple measure of class aggregation or clumpedness. Ramachandra et al. (2013) India Analysis of spatial patterns of urbanization Shannon Entropy
This is a powerful technique to
measure the compactness and
dispersion of built-up areas. In addition, Shannon entropy is used to identify the occurrence of urban growth in a certain area.
60 Ren et al. (2013) China Spatial expansion and sprawl Person's chi-square
Pearson’s chi-square method is used in urban studies to measure regional development sustainability and planning balance. Pearson’s chi-square is one of the significant quantitative methods that are used to monitor the sustainability of urban development.
Suribabu et al. (2012)
India Land use/cover change detection
Change detection techniques
Change detection is a common method. It has some advantages when used in urban growth studies; for example, it provides a strong visualization of phenomena for different time periods. In addition, it can provide statistical representation to analyze, measure, and identify urban growth in specific areas. Moreover, change detection is easy
to perform and understand.
Furthermore, it can determine differences in land use changes between different time periods. Aithal and
Sanna (2012)
India Spatial pattern analysis
Normalized difference vegetation index (NDVI)
The qualitative side, such as visual illustration, and quantitative side, such as statistical values, have a good relationship, which is useful in evaluating land cover change and the effect of urban development on other types of land categories.
Punia and Singh (2012) India Assessment of urban growth Multivariate statistical technique
This approach is used to identify the relationship between urbanization and its causes.
Ren et al. (2013) China Spatial expansion and sprawl UEI
This is a strong quantitative method that is used to identify the speed of urban development for an overall area or each part of the area. It is an important technique that enables city planners and decision makers to
achieve future sustainable
development based on a realistic evaluation.
61
Figure 2: Framework of land use change detection technique
Data Collection
Data that are used for detecting urban growth patterns and land use changes by using RS and GIS techniques are almost similar in most land use and urban growth studies (Table 2). The difference in such data is generally about the quality of data, such as the resolution of satellite images (Kidane et al., 2012; Shamsudheen et al., 2005; Tan et al., 2010). Data are one of the important issues in land use change detection applications; for example, satellite images are considered significant data because they are used to generate land use and land cover maps (Fan et al., 2008; Kilic et al., 2006). Topographic maps are used to generate basic layers, such as boundaries and roads, as well as to enhance satellite images and for georeferencing purposes (Suribabu et al., 2012). Field data that are obtained by GPS are used to take samples of different types of land uses (truth points) for two reasons, namely, (i) image correction and (ii) image classification (Alkan et al., 2013; Dewan & Yamaguchi, 2009; Kilic et al., 2006). Furthermore, socioeconomic data, such as population data are used to identify the relationship between land use changes for different times (Amuti & Xinguo, 2012; Dafalla et al., 2014).
Several satellite images have been used in urban studies to evaluate patterns of urban growth; these images include Landsat MSS, TM, ETM+ 30M resolution, Spot 10-5-2.5-1.5 M, Aster satellite imagery 15 M, IRS-1B, IRS-P6 30 M, QuickBird 0.65 M, and IKONOS 0.82 M. A high-resolution image ensures increased accuracy of land use classification results (Afifi et al., 2013; Alexander et al., 2012; Rahman et al., 2012; Raja et al., 2013; Rosli et al., 2014; Suribabu et al., 2012). Some previous studies have used land use maps that were generated either from satellite images or aerial photos to evaluate urban growth (Dewan & Corner, 2014). Other types of data, such as topographic maps and digital elevation model, are used as supporting data to extract other data, such as basic layers, roads, slope, and elevation layer, which can be used in analyses such as land suitability analysis and land use
Differences Map Quantitative Values Producing change detection map Yes Accuracy Assessment Data Processing Data Collection No Final output
62
prediction (Sabet et al., 2011). Previous urban studies did not rely mainly on topographic maps because of some considerations, such as data availability, accuracy of classification, and a disproportionate classification type with urban application.
Table 2: The significant data used in the land use change detection studies
Author/Year Place Type of Data Topographic and cadastral Map Satellite Images Aerial Photo` Land use maps GPS data socio-economic data Shamsudheen et al. (2005) India
Yagoub and Kolan (2006)
UAE
Kilic et al. (2006) Turkey
Güler et al. (2007) Turkey
Zheng, Heath, and Ducey (2008)
North America
Fan et al. (2008) China
Dewan and Yamaguchi (2009)
India
Tan et al. (2010) Malaysia
Zhou, Li, and Chen (2011)
China
Suribabu et al. (2012)
India
Amuti and Xinguo (2012)
China
Kidane et al. (2012) Ethiopia
Matinfar, Panah, Zand, and Khodaei (2013)
Iran
Alkan et al. (2013) Turkey
Vorovencii (2014) Romania
Dafalla et al. (2014) Sudan
Data Processing
Generally, data processing centers on correcting satellite images and other data, such as aerial photo and topographic maps, which can be used to produce realistic land use maps (Afifi et al., 2013; Al-sharif & Pradhan, 2013; Ramachandra et al., 2013). Data can be prepared by using a number of methods to achieve the perform of land use change detection (Amuti & Xinguo, 2012; Matinfar et al., 2013; Suribabu et al., 2012; Zheng et al., 2008); these methods include processing satellite images through geometric correction by using ground control point technique (Afifi et al., 2013) and image registration by using georeferenced maps (Farooq & Ahmad, 2008). Atmospheric and radiometric correction are the most important steps for processing satellite images. Several techniques such as Chavez’s COST model, which is based on dark object subtraction method, have been used to solve
63
the issue of haze and dust in the atmosphere (Chavez, 1996; Dafalla et al., 2014; Vorovencii, 2014). Histogram equalization was used for spectral correction and image enhancement, as well as to increase the volume of visible information for images (Suribabu et al., 2012).
Other data processing steps center on preparing satellite images for land use classification (Dewan & Yamaguchi, 2009; Fan et al., 2008; Kidane et al., 2012; Kilic et al., 2006). Creating a color composite is the first step in this part of data processing and is performed to visualize heterogeneous patches in the urban landscape. (Afifi et al., 2013; Ramachandra et al., 2013). Table 3 describes the significant land use classification techniques that have been utilized in urban studies and their accuracy based on kappa index.
Table 3: Overview of significant image classification techniques and their accuracy
Author(s)/year Method used Software used Description and comments Accuracy
Aithal and Sanna (2012) Ramachandra et al. (2013) Alsharif and Pradhan (2014) Supervised classification maximum likelihood algorithm Open source program GRASS, ARC/INFO GIS software package version 10
This is one of the significant supervised classification methods. It uses signatures from training sites, which include overall land use categories. It should use FCC to visualize heterogeneous patches in land use by using green, red, and NIR bands. 0.89 0.90 0.86 Lackner and Conway (2008) Tehrany, Pradhan, and Jebuv (2014) Object-based (or object-oriented) aCognition 4.0 software and ENVI.
Objects are generated as a square shape in traditional LULC classification methods. Object-based image classification generates objects in various shapes and scale.. Objects in this type of image classification are meaningful because they are classified based on texture, context, and geometry.
0.77 0.91
Alrababah and Alhamad (2006)
Liu and Weng (2013) Spruce et al., (2014) Unsupervised classification ERDAS IMAGINE
This method is usually used when no sampling sites are present. This method is useful for computing land use and land cover categories. However, it is not commonly used in LULC studies. Some types of unsupervised classification exist, such as K-means and ISODATA. The idea of unsupervised classification is that pixels are grouped into specific types of LULC based on the reflectance properties of pixels.
0.78 0.83 0.89
64 Taskin Kavzoglu
and Mather (1999)
Pal and Mather (2003) Kavzoglu and Mather (2003) Artificial neural networks SNNS v4.0 package Matlab
This method is different from conventional classification methods, thereby paving the way for intelligent machines. It consists of the following: (i) one input layer; (ii) at least one hidden layer; (iii) and one output layer. The availability and the required experience are the significant disadvantages of this method.
0.83 0.82 Brown de Colstoun et al. (2000) de Colstoun et al. (2003)
Pal and Mather (2003)
Decision trees
C4.5 decision tree software,
ENVI
A modern land use classification technique. The simplicity and hierarchical structure of decision trees can be of benefit for both experienced and inexperienced users to interpret, test algorithms, and analyze. Despite its flexibility, the use of decision trees with high spatial resolution data has not been fully discovered. However, this method improved the accuracy of land use classification compared with other conventional methods.
0.80 0.83
Accuracy assessment is the significant stage to evaluate preprocessing and processing stages, as well as to validate image classification (Amuti & Xinguo, 2012; Güler et al., 2007; Matinfar et al., 2013; Suribabu et al., 2012; Tan et al., 2010). Kappa index is one of the significant methods that are used to measure the classification accuracy in urban growth and land use change detection studies (Alkan et al., 2013; Güler et al., 2007; Kilic et al., 2006). According to the Anderson scheme, the acceptable kappa index value is more than 0.85 (Anderson, 1976). Object-based (or object-oriented) method has a higher kappa index of 0.91 based on Table 3. This result means that this method is the most accurate method in classifying land use because it generates objects with various shapes and scales.
Change Detection Techniques
Change detection techniques for evaluating urban growth and land use changes are commonly implemented by using satellite images or land use maps that have the same spatial resolution with the use of GIS and RS tools and software (Alkan et al., 2013; Kilic et al., 2006; Suribabu et al., 2012; Tan et al., 2010). Several urban change detection techniques exist. However, researchers have not yet agreed on which method is the best for land use change detection because of errors of environmental and sensor factors, as well as image resolution (Liu et al., 2004). Several studies detecting urban change . Most of these studies relied on kappa index and overall accuracy to identify which method is more accurate for urban change detection (Liu et al., 2004; Wei et al., 2012). Generally, satellite image quality and data processing method can be concluded to significantly affect urban change detection and ensure a low error rate.
65
Post-classification comparison is one of the common methods that is used for detecting land use changes. The advantages of this method is to highlight changes of land use categories in terms of increases and decreases and positively or negatively by using a change matrix (Madhavan et al.,2001). The findings of Liu et al. (2004) were used to compare the Image Ratioing, Image Regression, and PCA methods. The results of this study confirms that the PCA method has the highest rate of accuracy. This study also confirms that the Image Ratioing method is unsuitable for change detection purposes. The concept of a neural network model based Fuzzy is that each pixel in the image is considered as a node and each node represents a fuzzy value which represents the value of land use change. The simplicity of this method is that the level of change in land use is confined between +1 and -1 (i.e. the result either positive or negative). +1 the maximum of land use change and -1 there is no land use change (Pajares et al. 2007). Moreover, image differencing commonly used in urban growth and land use studies. This algorithm like image overlay method needs identical bands from two satellite images for a different time period at least. This method suitable to know the changes for each land use negatively or positively (Maktav & Erbek 2005). Image overlay method needs to high images resolution such as SPOT, IKONOS, and Quickbird satellite images. The advantages of this method are easy to use, and the output of this method can be both visual and statistical data. In addition, this method is easy to understand by those non-specialists and useful for evaluating land use movement and urban growth patterns (Suribabu et al.,2012). In addition, Decision Trees method is considered more flexible than conventional classification techniques such as a maximum likelihood method. This method is suitable for studies of land use change that used accurate classification methods. This method depends on spectral characteristics (Wei et al., 2012).
Conclusion
It can be concluded that the modern trend of measuring and mapping urban growth patterns have taken into account the integration of quantitative and spatio-temporal techniques. This kind of integration can be prepared, implemented, and developed in a GIS and RS environment. The spatial, temporal, and dynamic dimensions of urban growth processes can be determined using GIS and RS data and via methods such as satellite imagery and land use as well as via the cover change detection technique. At the same time, the shape, size, speed, patterns, trends, and sustainability of urban growth can be extracted using quantitative techniques such as the Shannon Entropy approach, Landscape Analysis, and the Urban Expansion Intensity Index. Therefore, this study fully recommends the integration between quantitative and spatio-temporal techniques to promote a better understanding of urban growth operations. This a novel approach that can be used to help and support planners and decision makers to create sustainable future plans for urban development.
Acknowledgements
The study presented here is the part of research project funded by Universiti Putra Malaysia.
References
Afifi, A. A., Elsemary, M., & Wahab, M. (2013). Urban Sprawl of greater Cairo and its Impact on the Agricultural Land Using Remote Sensing and Digital Soil Map. Journal of Applied Sciences Research, 9(8), 5159-5167.
66
Aithal, B. H., & Sanna, D. D. (2012). Insights to urban dynamics through landscape spatial pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 18, 329-343.
Al-shalabi, M., Pradhan, B., Billa, L., Mansor, S., & Althuwaynee, O. F. (2013). Manifestation of remote sensing data in modeling urban sprawl using the SLEUTH model and brute force calibration: a case study of Sana’a city, Yemen. Journal of the Indian Society of Remote Sensing, 41(2), 405-416.
Al-sharif, A. A., & Pradhan, B. (2013). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 1-11.
Al-sharif, A. A., Pradhan, B., Shafri, H. Z. M., & Mansor, S. (2014). Quantitative analysis of urban sprawl in Tripoli using Pearson's Chi-Square statistics and urban expansion intensity index. Paper presented at the IOP Conference Series: Earth and Environmental Science.
Alexander, K. W., Benjamin, M., & Grephas, O. P. (2012). Urban landuse suitability assessment using geoinformation techniques for Kisumu municipality in Kenya. Int. J. Research and Reviews in Applied Sciences, 13.
Alkan, M., Oruc, M., Yildirim, Y., Seker, D., & Jacobsen, K. (2013). Monitoring Spatial and Temporal Land Use/Cover Changes; a Case Study in Western Black Sea Region of Turkey. Journal of the Indian Society of Remote Sensing, 41(3), 587-596.
Alrababah, M., & Alhamad, M. (2006). Land use/cover classification of arid and semi‐arid Mediterranean landscapes using Landsat ETM. International Journal of Remote Sensing, 27(13), 2703-2718.
Alsharif, A. A., & Pradhan, B. (2014). Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149-163.
Amuti, T., & Xinguo, L. (2012). Land Cover Change Detection in Oasis of Hotan River Basin in Northwestern China Future Control and Automation (pp. 45-51): Springer.
Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964): US Government Printing Office.
Batty, M. (2000). GeoComputation using cellular automata. Paper presented at the Geocomputation.
Bengston, D. N., Fletcher, J. O., & Nelson, K. C. (2004). Public policies for managing urban growth and protecting open space: policy instruments and lessons learned in the United States. Landscape and urban planning, 69(2), 271-286.
Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of
photogrammetry and remote sensing, 58(3), 239-258.
Bhatta, B. (2009). Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. International Journal of Remote Sensing, 30(18), 4733-4746.
Bhatta, B., Saraswati, S., & Bandyopadhyay, D. (2010a). Quantifying the degree-of-freedom, degree-of-sprawl, and degree-of-goodness of urban growth from remote sensing data. Applied Geography, 30(1), 96-111. Bhatta, B., Saraswati, S., & Bandyopadhyay, D. (2010b). Urban sprawl measurement from remote sensing data.
Applied Geography, 30(4), 731-740.
Brown de Colstoun, E., Yang, W., DeFries, R., Hansen, M., & Townshend, J. (2000). Surface type visible/infrared imager radiometer suite algorithm theoretical basis document, version 3.0: Available from World Wide Web site) Available at: http://npoesslib. ipo. noaa. gov/atbdÀviirs. htm.
Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing, 62(9), 1025-1035.
67
Dafalla, M. S., Abdel-Rahman, E. M., Siddig, K. H., Ibrahim, I. S., & Csaplovics, E. (2014). Land Use and Land Cover Changes in Northern Kordofan State of Sudan: A Remotely Sensed Data Analysis Nile River Basin (pp. 269-283): Springer.
de Colstoun, E. C. B., Story, M. H., Thompson, C., Commisso, K., Smith, T. G., & Irons, J. R. (2003). National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier. Remote sensing of Environment, 85(3), 316-327.
Deng, J., Wang, K., Deng, Y., & Qi, G. (2008). PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data. International Journal of Remote Sensing, 29(16), 4823-4838.
Dewan, A. M., & Corner, R. J. (2014). Spatiotemporal Analysis of Urban Growth, Sprawl and Structure Dhaka Megacity (pp. 99-121): Springer.
Dewan, A. M., & Yamaguchi, Y. (2009). Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environmental monitoring and assessment, 150(1-4), 237-249.
Duranton, G., & Puga, D. (2014). Chapter 5-the Growth of Cities. A. Philippe and ND Steven, Handbook of Economic Growth. Elsevier, 781-853.
Fan, F., Wang, Y., & Wang, Z. (2008). Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images.
Environmental monitoring and assessment, 137(1-3), 127-147.
Farooq, S., & Ahmad, S. (2008). Urban sprawl development around Aligarh city: a study aided by satellite remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 36(1), 77-88.
Grey, W., Luckman, A., & Holland, D. (2003). Mapping urban change in the UK using satellite radar interferometry. Remote sensing of Environment, 87(1), 16-22.
Güler, M., Yomralıoğlu, T., & Reis, S. (2007). Using landsat data to determine land use/land cover changes in Samsun, Turkey. Environmental monitoring and assessment, 127(1-3), 155-167.
Haas, J., & Ban, Y. (2014). Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze, River Delta and the Pearl River Delta. International Journal of Applied Earth Observation and Geoinformation, 30, 42-55.
Hardin, P. J., Jackson, M. W., & Otterstrom, S. M. (2007). Mapping, measuring, and modeling urban growth Geo-spatial technologies in urban environments (pp. 141-176): Springer.
Jiang, F., Liu, S., Yuan, H., & Zhang, Q. (2007). Measuring urban sprawl in Beijing with geo-spatial indices. Journal of Geographical Sciences, 17(4), 469-478.
Kavzoglu, T., & Mather, P. (2003). The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24(23), 4907-4938.
Kavzoglu, T., & Mather, P. M. (1999). Pruning artificial neural networks: an example using land cover classification of multi-sensor images. International Journal of Remote Sensing, 20(14), 2787-2803. Kidane, Y., Stahlmann, R., & Beierkuhnlein, C. (2012). Vegetation dynamics, and land use and land cover
change in the Bale Mountains, Ethiopia. Environmental monitoring and assessment, 184(12), 7473-7489.
Kilic, S., Evrendilek, F., Berberoglu, S., & Demirkesen, A. (2006). Environmental monitoring of land-use and land-cover changes in a Mediterranean region of Turkey. Environmental monitoring and assessment, 114(1-3), 157-168.
Lackner, M., & Conway, T. M. (2008). Determining land-use information from land cover through an object-oriented classification of IKONOS imagery. Canadian Journal of Remote Sensing, 34(2), 77-92. Lam, N. S.-N. (2008). Methodologies for mapping land cover/land use and its change Advances in land remote
68
Li, X., Zhang, L., & Liang, C. (2010). A GIS-based buffer gradient analysis on spatiotemporal dynamics of urban expansion in Shanghai and its major satellite cities. Procedia Environmental Sciences, 2, 1139-1156.
Liu, F., Shi, L., Zhang, Z., & Zhao, X. (2015). Evaluating Urban Expansion of Beijing during 1973-2013, by Using GIS and Remote Sensing Geo-Informatics in Resource Management and Sustainable Ecosystem (pp. 635-642): Springer.
Liu, H., & Weng, Q. (2013). Landscape metrics for analysing urbanization-induced land use and land cover changes. Geocarto International, 28(7), 582-593.
Liu, Y., Nishiyama, S., & Yano, T. (2004). Analysis of four change detection algorithms in bi-temporal space with a case study. International Journal of Remote Sensing, 25(11), 2121-2139.
Madhavan, B. B., Kubo, S., Kurisaki, N., & Sivakumar, T. (2001). Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing. International Journal of Remote Sensing, 22(5), 789-806.
Maktav, D., & Erbek, F. (2005). Analysis of urban growth using multi‐temporal satellite data in Istanbul, Turkey. International Journal of Remote Sensing, 26(4), 797-810.
Matinfar, H. R., Panah, S. K. A., Zand, F., & Khodaei, K. (2013). Detection of soil salinity changes and mapping land cover types based upon remotely sensed data. Arabian Journal of Geosciences, 6(3), 913-919.
Páez, A., & Suzuki, J. (2001). Transportation impacts on land use change: an assessment considering neighborhood effects. Journal of the Eastern Asia Society for Transportation Studies, 4(6), 47-59. Pajares, G., Sánchez-Beato, A., Cruz, J. M., & Ruz, J. J. (2007). A Neural Network Model for Image Change
Detection Based on Fuzzy Cognitive Maps Pattern Recognition and Image Analysis (pp. 595-602): Springer.
Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote sensing of Environment, 86(4), 554-565.
Punia, M., & Singh, L. (2012). Entropy approach for assessment of urban growth: a case study of Jaipur, India. Journal of the Indian Society of Remote Sensing, 40(2), 231-244.
Rahman, A., Kumar, S., Fazal, S., & Siddiqui, M. A. (2012). Assessment of land use/land cover change in the North-West District of Delhi using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 40(4), 689-697.
Raja, R. A., Anand, V., Kumar, A. S., Maithani, S., & Kumar, V. A. (2013). Wavelet based post classification change detection technique for urban growth monitoring. Journal of the Indian Society of Remote Sensing, 41(1), 35-43.
Ramachandra, T., Bharath, H., & Sowmyashree, M. (2013). Analysis Of Spatial Patterns Of Urbanisation Using Geoinformatics And Spatial Metrics. Theoretical and Empirical Researches in Urban Management, 8(4), 5-24.
Ren, P., Gan, S., Yuan, X., Zong, H., & Xie, X. (2013). Spatial Expansion and Sprawl Quantitative Analysis of Mountain City Built-Up Area Geo-Informatics in Resource Management and Sustainable Ecosystem (pp. 166-176): Springer.
Rosli, A., Reba, M., Roslan, N., & Room, M. (2014). Sustainable Urban Forestry Potential Based Quantitative And Qualitative Measurement Using Geospatial Technique. Paper presented at the IOP Conference Series: Earth and Environmental Science.
Sabet Sarvestani, M., Ibrahim, A. L., & Kanaroglou, P. (2011). Three decades of urban growth in the city of Shiraz, Iran: A remote sensing and geographic information systems application. Cities, 28(4), 320-329.
69
Shalaby, A. (2012). Assessment of Urban Sprawl Impact on the Agricultural Land in the Nile Delta of Egypt Using Remote Sensing and Digital Soil Map'. International Journal of Environment and Sciences, 1(4), 253-262.
Shamsudheen, M., Dasog, G., & Tejaswini, N. (2005). Land use/land cover mapping in the coastal area of North Karnataka using remote sensing data. Journal of the Indian Society of Remote Sensing, 33(2), 253-257. Spruce, J. P., Smoot, J. C., Ellis, J. T., Hilbert, K., & Swann, R. (2014). Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products, using Landsat data and C-CAP products. Geocarto International, 29(5), 470-485.
Sudhira, H., Ramachandra, T., & Jagadish, K. (2004). Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29-39.
Suribabu, C., Bhaskar, J., & Neelakantan, T. (2012). Land use/cover change detection of Tiruchirapalli City, India, using integrated remote sensing and GIS tools. Journal of the Indian Society of Remote Sensing, 40(4), 699-708.
Tan, K. C., San Lim, H., MatJafri, M. Z., & Abdullah, K. (2010). Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60(7), 1509-1521.
Tehrany, M. S., Pradhan, B., & Jebuv, M. N. (2014). A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International, 29(4), 351-369.
Vorovencii, I. (2014). Assessment of some remote sensing techniques used to detect land use/land cover changes in South-East Transilvania, Romania. Environmental monitoring and assessment, 186(5), 2685-2699.
Wei, W., Yunjia, W., Qing, W., Dajun, L., & Zhijie, W. (2012). Application of Decision Tree in Land Use Classification. Paper presented at the Proceedings of the 2011, International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19–20, 2011, Melbourne, Australia. Yagoub, M., & Kolan, G. R. (2006). Monitoring coastal zone land use and land cover changes of Abu Dhabi
using remote sensing. Journal of the Indian Society of Remote Sensing, 34(1), 57-68.
Yang, X. (2010). Integration of Remote Sensing with GIS for Urban Growth Characterization Geospatial
Analysis and Modelling of Urban Structure and Dynamics (pp. 223-250): Springer.
Zhao, P. (2011). Managing urban growth in a transforming China: Evidence from Beijing. land use policy, 28(1), 96-109.
Zheng, D., Heath, L. S., & Ducey, M. J. (2008). Satellite detection of land-use change and effects on regional forest aboveground biomass estimates. Environmental monitoring and assessment, 144(1-3), 67-79. Zhou, Q., Li, B., & Chen, Y. (2011). Remote sensing change detection and process analysis of long-term land
