Analysis of Urban Growth and Sprawl from Remote Sensing Data Case of Fez, Morocco Accepted Manuscript Original Article/Research Analysis of Urban Growth and Sprawl from Remote Sensing Data Case of Fez[.]
Accepted Manuscript Original Article/Research Analysis of Urban Growth and Sprawl from Remote Sensing Data: Case of Fez, Morocco Abdelkader El Garouani, David J Mulla, Said El Garouani, Joseph Knight PII: DOI: Reference: S2212-6090(16)30066-8 http://dx.doi.org/10.1016/j.ijsbe.2017.02.003 IJSBE 153 To appear in: International Journal of Sustainable Built Environment Received Date: Revised Date: Accepted Date: 21 April 2016 17 January 2017 10 February 2017 Please cite this article as: A El Garouani, D.J Mulla, S El Garouani, J Knight, Analysis of Urban Growth and Sprawl from Remote Sensing Data: Case of Fez, Morocco, International Journal of Sustainable Built Environment (2017), doi: http://dx.doi.org/10.1016/j.ijsbe.2017.02.003 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain Analysis of Urban Growth and Sprawl from Remote Sensing Data: Case of Fez, Morocco a b c Abdelkader EL GAROUANI , David J MULLA , Said EL GAROUANI & Joseph KNIGHT a b c d Faculty of Sciences and Techniques, Sidi Mohamed Ben Abdellah University, Route d’Imouzzer, BP 2202, Fez 30060, Morocco, e-mail: el_garouani@yahoo.fr Department of Soil, Water, and Climate, University of Minnesota, 1991 Upper Buford Circle, 439 Borlaug Hall, Minneapolis, Minnesota, USA, e-mail : mulla003@umn.edu Informatic Department, Faculty of Sciences, Abdelmalek Essaadi University, B.P 2121, Tetouan, 93002, Morocco, e-mail: saidelgarouani@yahoo.fr d Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota, 1991 Upper Buford Circle, 439 Borlaug Hall, Minneapolis, Minnesota, USA, e-mail : jknight@umn.edu Abstract: Fez is the most ancient of the imperial cities of Morocco In Fez the rate of population growth has been spectacular in recent times (484 300 inhabitants in 1982 and 129 768 in 2014) The accelerated rate of population growth has generated a large urban sprawl in all its forms and serious environmental problems In this research, we have analyzed the relationship between urbanization and land use changes and their impact on cityscape in Fez and the importance of the increase in impervious surface areas Satellite imageries and census data have been used to identify different patterns of land use change and growth of the city for the period 1984-2013 Classification and analysis of the satellite imageries were performed using Erdas imagine and ArcGIS Software Urban sprawl in Fez was assessed over 29 years (1984-2013) The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 78% to 87% The maps showed that between 1984 and 2013 the amount of urban or developed land increased by about 121%, while rural cover by agriculture and forest decreased respectively by 11% and 3% Keywords: Urban expansion, classification, GIS, Remote Sensing, Fez, Morocco I- Introduction cannot infiltrate and are primarily associated with transportation and building rooftops (Bauer et al., 2007) Imperviousness increases water runoff, and hence, is a primary determinant of runoff volumes in urbanized areas The impervious surface area provides a measure of land use that is closely correlated with these impacts (Arnold and Gibbons, 1996) It therefore follows that impervious cover information is fundamental to assess flooding risks and flood management in the city Urbanization that is considered as a positive process linked to modernization, industrialization and global integration has economically benefitted only a minority of the urban population (Bhatta, 2010; Sharma, 1985) During the last century, Moroccan society was increasingly urban (Fig 1) The accelerated rate of urbanization in all forms and the population growth in Morocco has been generating serious environmental problems and concern for both the government and interested stakeholders (Lehzam, 2012) Economic development demands sustainable land management Spatial information on land use/land cover types and their change detection in time series are important means for city planning and new development activities (Ewing et al 2002) The present research is undertaken in that The amount of impervious surface in a landscape is an important indicator of environmental quality Impervious surfaces are defined as any surface which water spirit It will analyze the relationship between urban growth and land use changes and their impact on the Fez cityscape This information is an essential tool in decisionmaking and management policy of the city by the local authority and for ensuring sustainable urban growth and development in the study area The period of focus is from 1984 to 2013 Topographical maps, highresolution satellite imageries and other necessary data have been used to detect land use/land cover changes in the study area Merinids, when it replaced Marrakesh as the capital of the kingdom Although the political capital of Morocco was transferred to Rabat in 1912, Fez has retained its status as the country's cultural and spiritual center (Aouni et al 1992) Fez is a religious, touristic and academic center Due to its importance, the historic Medina of Fez was added to the UNESCO World Heritage List in 1981 Over the past 20 years, there were many problems and challenges posed by the rapid growth of Fez just like every other city in Morocco Figure 1: Population growth in urban and rural areas in Morocco (HCP, 2015) In fact, the demand for infrastructure, basic services and housing in expanding urban area in Fez are on the increase Moreover, provision of education, health, transportation, water and sanitation services should be accelerated in urban centers Numerous researchers, including Arvind et al (2006), Lunetta and Balogh (1999), Yuan et al 2005, Zubair (2006) and others have demonstrated the value of multitemporal satellite imagery for classification of land cover The strong development of remote sensing and GIS technology has helped us to study the urban space development III- Methodology Present study is based on spatial remote sensing data as well as non-spatial data available from various sources for different periods Urban development has led to expansion of the cityscape of Fez, leading to changes in land use The study specifically focuses on interpreting the city’s land use change patterns and growth based on satellite and demographic data II- Study Area Fez is located on the northern part of Morocco (Fig 2) The urban community of Fez accounted for 129 768 inhabitants in 2014 and the city has about 30 km² (El Garouani et al., 2011) Founded in 789 by Moulay Idriss 1st and home to the oldest university in the world (Quaraouiyne University built in 857) Fez reached its height in the 13th–14th centuries under the Our approach combined spring and summer images In the summer image, the Fez urban area appears unvegetated and is N Atlantic Ocean Morocco Fez Algeria Mauritania Mali Figure 2: Location of the study area distinguishable from forests and orchards However, the spring image is needed to separate vegetated areas from urban areas with significant amounts of asphalt and concrete and other impervious surfaces that are spectrally similar to bare soil in a summer image The importance of multitemporal imagery was confirmed by determining the transformed divergences for the dataset (Two images in 1984 and two images in 2013) Compared to the single dates, both the average and the minimum separability of classes were increased by the combination of spring and summer images Image processing software has been used for geometric correction of satellite data, supervised classification, accuracy assessment of classification, land use maps (1984 & 2013), change detection, final output maps etc Vegetation Near infrared reflectance Soil This research uses Landsat images to calculate VSW (Vegetation-Soil-Water) index images, that distinguish clearly between Vegetation, Soil and Water elements on the image This VSW index image is the basis for detecting clearly urban areas (My Vo Chi et al., 2009) The VSW index is calculated by the scatter plot of red band versus nearinfrared band, which are common in almost of all types of satellite imagery (Fig 3) That consist to transform or analyze a feature space mathematically to isolate groups of pixels that may be related Each axis represents DNs from one satellite band and plot each pixel in the feature space from an image using its DNs Water Red reflectance Figure 3: Scatterplot of TM4 (Y-axis) and TM3 (X-axis) GIS software has been used for the digitization, integration, overlay and presentation of the spatial and non-spatial data of land use change in the city Field surveys were performed throughout the study area using Global Positioning System (GPS) to obtain accurate location point data for each land use class included in the classification scheme Figure presents the methodology used to produce maps of land use change and urban expansion Table presents the classification units of land use identified in the study area Landsat Thematic Mapper (TM) and Operational Land Imager-Thermal Infrared Sensor (OLI_TIRS) data have several advantages for this application: synoptic view, digital, GIS compatible data, availability of data since 1984, and economical costs This paper extends the methods and results of our previous works (El Garouani et al., 2012, 2014 and 2015) and of the present contribution Data used in the research include the multitemporal dataset and topographic maps: Landsat TM and OLI-TIRS images (For 0818-1984, 04-15-1985, 06-15-2013 and 0818-2013) - Google earth images (09-122013) Aerial photographs - Topographic maps Demographics and census data (1982, 1994, 2004 and 2014) Urban development plan of Fez - Field observations, etc Landsat images are described below: The Landsat Thematic Mapper (TM) sensor was carried on Landsat and Landsat 5, and images consist of seven spectral bands with a spatial resolution of 30 meters for Bands to and (Table 2) Figure 4: Flowchart of spatial and temporal changes in urban land cover Table 2: Spectral bands description of Landsat TM Table 1: Land cover classification scheme Level -I Building Agriculture Land Forest Natural land Water bodies Bands Band - Blue Band - Green Band - Red Band - Near Infrared Band - Shortwave Infrared Band - Thermal Band - Shortwave Infrared Level -II Residential and Industrial area Crop Land, Olive trees, Orchard Forest Rangeland River, Lakes, Dam 3.1 Satellite Imagery Wavelength (µm) 0.45-0.52 0.52-0.60 0.63-0.69 0.76-0.90 1.55-1.75 10.40-12.50 2.08-2.35 Landsat Operational Land Imager (OLI) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands to and The ultra-blue Band is useful for coastal and aerosol studies Band is useful for cirrus cloud detection The resolution for Band (panchromatic) is 15 meters (Table 3) To accomplish the objectives of the present study, four available satellite images were obtained from the United States Geological Survey (USGS) databases online resources: It was important to utilize images covering the summer season to ensure that agricultural land surrounding Fez are fully assessed Table 3: Spectral bands description of Landsat OLI for each of the desired classes from the color composite image During the training phase, 40 training sites were selected by onscreen digitization of specific polygons (5 training samples by thematic class) The files obtained were saved and used for the images classification Each training field was assigned a number from to representing land cover classes including: water, urban, industrial area, rangeland, olive trees, orchard, forest and agriculture Bands Wavelength (µm) Band - Ultra Blue (coastal/aerosol) 0.43 - 0.45 Band - Blue 0.45 - 0.51 Band - Green 0.53 - 0.59 Band - Red 0.64 - 0.67 Band - Near Infrared (NIR) 0.85 - 0.88 Band - Shortwave Infrared1 1.57 - 1.65 Band - Shortwave Infrared2 2.11 - 2.29 Band - Panchromatic 0.50 - 0.68 Band - Cirrus 1.36 - 1.38 Before analysis, the images were geometrically corrected During geometric correction, control points are detected on the topographic maps and the satellite images with RMS errors that are estimated below 0.5 pixel After that, the images for 1984 and 2013 were registered on Lambert Conform Conic Projection, datum Merchich, zone I (North Morocco) By using GIS technique (convert vector, overlay and calculate), the urban expansion information (areas, the replacement of land covers to urban area) was assessed over the study periods The areas identified as urban in 2013, but not developed in 1984 had a high greenness value (due to vegetative cover) in the 1984 imagery and thus had low to no impervious surface in the 1984 time period A subset image was created from each Landsat image for subsequent treatment and classification (Fig 5) IV- Results and Discussion 3.2 Image classification 4.1 Land use analysis Image classification is a conventional change detection method The advantage of image classification is the ability to create a series of land cover maps We applied the maximum likelihood supervised classification (MLC) method for time series of Landsat bands and VSW index Urbanization is a major cause of land use changes and land conversions It makes unpredictable and long lasting changes on the landscape An important aspect of change detection is to determine what is actually changing to what i.e which land use class is changing to the other Analyzing the spatial and temporal changes in land use and land cover is one of the effective ways to understand the current environmental status of an area and ongoing changes (Arvind, 2006, Yuan et al 2005 and Zubair, 2006) The land use maps of two points in time, that is, 1984 and 2013 based on automatic classification and visual interpretation respectively depict land use categories changes such as residential, agriculture, industrial, water body, forest, etc (Fig 6).The growth of urban area and accompanying increases in amount of impervious surface area are readily apparent The maximum likelihood algorithm is one of the most widely used in the classification of satellite imagery The method is based on the likelihood that each pixel belongs to a particular class The basic theory assumes that these likelihoods are equal for all classes and that input bands are uniformly distributed The method requires a significant calculation time and is based on a normal distribution of the data in each band in the classification It tends to over-classify signatures with relatively large values in the covariance matrix (Vorovencii and Muntean, 2013) MLC is performed according to the following steps (Richards and Jia, 1993) The method consisted in choosing the training samples 08-18-1984 04-15-1985 08-18-2013 06-15-2013 09-12-2013 Figure 5: False color composites of the Landsat and Google Earth images showing Fez in 1984 and 2013 classification accuracy (Congalton & Green, 1999) Overall accuracy, user’s and producer’s accuracies, and the Kappa statistic were then derived from the error matrices (Table 4) An independent sample of an average of 30 polygons, with about 250 pixels for each selected polygon, was randomly selected from each classification to assess classification accuracies Error matrices as cross-tabulations of the mapped class vs the reference class were used to assess El Gaada dam Dhar Mehrez dam Figure 6: The land cover maps of Fez in 1984 and 2013 Table 4: Error Matrix Analysis of field sites (columns) against Landsat classification (rows) a-1984 Water Urban Industrial area Rangeland Olive trees Orchard Forest Agriculture Total ErrorO Accuracy 130 0 15 0 153 0.15 84.97 Total ErrorO Accuray 284 0 40 18 23 367 0.28 77.38 230 20 0 251 0.09 91.63 99 16 38 0 162 0.39 61.11 0 600 0 14 614 0.02 97.72 0 0 115 118 0.03 97.46 13 13 165 27 223 0.26 73.99 0 0 0 70 58 128 0.45 54.69 0 0 0 220 229 0.04 96.07 14 140 30 0 188 0.26 74.47 0 207 0 58 271 0.24 76.38 20 0 233 46 301 0.33 77.41 68 0 0 75 0.09 90.67 25 162 22 50 274 0.41 59.12 0 0 172 16 189 0.09 91.01 0 0 10 242 255 0.05 94.90 Total 133 239 101 654 153 203 72 323 1878 ErrorC Accuracy 0.02 97.74 0.04 96.23 0.02 98.02 0.08 91.74 0.25 75.16 0.19 81.28 0.03 97.22 0.32 68.11 b-2013 Water Urban Industrial area Rangeland Olive trees Orchard Forest Agriculture Total 311 86 148 276 248 215 194 442 1920 ErrorC 0.10 0.21 0.06 0.33 0.07 0.37 0.11 0.45 Accuray 91.32 79.07 94.59 84.42 93.95 66.05 88.66 54.75 ErrorO = Errors of Omission (expressed as proportions) - ErrorC = Errors of Commission (expressed as proportions) 4.2 Change detection increased approximately 2462 (121%) and olive trees increased 981 (89%) while non-orchard agriculture decreased 4124 (11%) and forest decreased 19 (3%) For the water body surfaces there is an increase of 247% This large increase can be explained by the construction of two dams (El Gaada in the East and Dhar Mehrez in the South) and, on the other hand, by the heavy rainfall in 2013, which allowed the emergence of wetlands in the NW (Table5) Following the classification of imagery from the individual years, a multi-date postclassification comparison change detection algorithm was used to determine changes in land cover in the interval; 1984–2013 According to the results from 1984 to 2013, the urban change was large, urban areas increased from 2041 (1984) to 4503 (2013) (Table 5) Error matrices were used to assess classification accuracy and are summarized in Table The overall accuracies for 1984 and 2013 were, respectively, 87% and 78.5% User’s (Field) and producer’s (Landsat classification) accuracies of individual classes were consistently high, ranging from 55% to 98% The relationship between population growth and growth in urban land area as determined from the Landsat-derived change maps was also examined Development patterns of Fez reflect the distribution of population and households because residential land uses take over all the land that is developed (HCP, 2015) The average annual growth in urban area determined from the Landsat change detection was 4.2 % from 1984 to 2013 This compares to an annual population growth Classification maps were generated for two years (Fig 6) and the individual class area and change statistics are summarized in Table From 1984 to 2013, urban area rate of approximately 3.9 % from 1982 to 2014 So the growth in urban area is relatively higher than the population growth rate Population and urban expansion data were also tabulated (Table 6) An urban sprawl index (USI) was calculated as the ratio of urban expansion to population increase (OECD, 2013) The urban sprawl index measures the growth in urban area over time adjusted for the growth in population When the population changes, the index measures the increase in the urban area over time relative to a benchmark where the built-up area would have increased in line with population The index is equal to zero when both population and urban area are stable over time It is larger than zero when the growth of the urban area is greater than the growth of population, i.e the density of the metropolitan area has decreased associated with omission and commission errors in the Landsat classifications change map Registration errors and edge effects can also cause apparent errors in the determination of change vs no-change This comparative approach has demonstrated how landscape changes can be derived from satellite imagery in the urban spatial structure Interpretation of Fez’s growth over a period of 29 years allows a deeper understanding of growth mechanisms, underlying drivers of urban expansion, and their effects on local livelihoods According to our observations, urban sprawl has a negative impact on infrastructure and the sustainability of Fez The information on land use change reveals both the desirable and undesirable changes and classes that are “relatively” stable overtime This information serves as a vital tool in decisions making and policy formulation by the local authority For example, due to urban expansion, Fez lacks vegetation cover Needless to say that vegetation and open green spaces (parks) are the most important parameters of quality of urban environment assessment Hence, a vigorous focus needs to be given to grow more trees and also develop green belts that can reduce a city’s ecological footprint and carbon emissions significantly A suitable strategy to reclaim industrial wastelands is also required In Fez, the USI = 4.2 / 3.9 = 1.08 It is slightly higher than zero, so the urban area is slightly higher than the population growth This index provides a way to assess the degree of sprawl for each region To further evaluate the results of land cover conversions, a matrix of land cover changes from 1984 to 2013 was created (Table 7) In the table, unchanged pixels are located along the major diagonal of the matrix These results indicate that increases in urban areas mainly came from conversion of agricultural, rangeland and orchard land to urban uses during the period, 1984–2013 Of the 462 of total growth in urban land use, 22 65 was converted from agricultural land, 82 from orchard and 70 from rangeland We note that 70 of rangeland was converted to urban between 1984 and 2013, while at the same time, of urban was converted to rangeland These changes may be classification errors Classification errors may also cause other unusual changes For example, 288 of agricultural land changed to rangeland and 42 of rangeland changed to agricultural land These changes are most likely On the other hand, as the city grows in size and population, harmony among the spatial, social and environmental aspects of a city and between its inhabitants becomes of paramount importance Urban development should be guided by a sustainable planning and management vision that promotes interconnected green space, a multi-modal transportation system, and mixed-use development Diverse public and private partnerships should be used to create sustainable and livable communities that protect historic, cultural, and environmental resources In addition, policymakers, regulators and developers should support sustainable site planning and construction techniques that reduce pollution and create a balance between built and natural systems So, what innovative approaches can be taken up to achieve this goal? This work will help to find answers to this question In study area, some strategies should be adopted by the local authorities and stakeholders including: - Ensuring environmental sustainability - Good governance and enhanced urban development - Planning for flood mitigation In this work, we try to produce the necessary data and information for adaptation and implementation of these strategies in Fez context - Provision of adequate and affordable housing for all Table 5: Land use change between 1984 and 2013 (hectares) Land use Water Urban Industrial area Rangeland Olive trees Orchard Forest Agriculture 08-18-1984 98 041 116 023 100 211 641 38 761 08-18-2013 Change (2013-1984) 241 462 51 117 981 291 -19 -4 124 339 503 167 140 081 502 622 34 637 Percent Change % 247 121 43 89 24 -3 -11 Table 6: Comparison of urban area estimates from Landsat classifications and the demographics and census data 982 994 004 2014 2014-1982 Percent Change (%) Annual growth (%) 494 300 770 200 951 871 112 072 617 772 125 3.9 Year Population Year 1984 2013 2013-1984 Percent Change (%) Annual growth (%) Urbanisation (ha) 041 503 462 121 4.2 Table 7: Matrix of land cover and changes (ha) from 1984 to 2013 Industrial Rangeland area Olive trees Orchard 119 120 339 70 47 82 10 265 503 115 32 167 826 288 140 Olive trees 49 016 23 985 081 Orchard 24 966 506 502 Forest 0 616 622 Agriculture 0 42 22 34 563 34 637 1984 Total 98 041 116 023 100 211 641 38 761 46 990 Water Urban Water 95 0 Urban 027 Industrial area Rangeland 10 Forest Agriculture 2013 Total REFERENCES Aouni L.M., Baatti M., Lazrak et A Drocourt D (1992) - Sauvegarde de la Ville Fès Rapport sur le patrimoine historique 129 p V- Conclusion Through this research, the urban expansion of the Fez study area over different periods using multi-temporal satellite images (Landsat - Google Earth) was achieved The classification and VSW Index was able to delineate soil, water, vegetation and urban clearly The main direction of urban expansion in Fez is expansion and increased construction on the West and South of the city The consistent and high quality impervious surface data provided the Landsat classifications is critical to developing new flood management strategies for protection as well as for rehabilitation Information from remote sensing data can play a significant role in quantifying and understanding the nature of changes in land cover and where they are occurring Such information is essential to planning for urban growth and development Arnold, C.L., and Gibbons, C.J., (1996) Impervious surface coverage: the emergence of a key environmental indicator, Journal of the American Planning Association, 62(2), pp 243258, Arvind C Pandy and M S Nathawat (2006) Land Use Land Cover Mapping Through Digital Image Processing of Satellite Data – A case study from Panchkula, Ambala and Yamunanagar Districts, Haryana State, India Bhatta B., (2010) - Analysis of Urban Growth and Sprawl from Remote Sensing Data Advances in Geographic Information Science, DOI 10.1007/978-3-642-05299-6_2 Bauer Marvin E., Brian C Loeffelholz and Bruce Wilson (2007) - Estimating and Mapping Impervious Surface Area by Regression Analysis of Landsat Imagery Preprint of chapter in Remote Sensing of Impervious Surfaces, 2007 Edited by Qihao Weng CRC Press, Boca Raton, FL General patterns and trends of land use change in Fez were evaluated by classifying the amount of land area that was converted from agricultural, forest and rangeland use to urban use (impervious area) during the period from 1984 to 2013; comparing the results of Landsat-derived statistics to estimates from other inventories; quantitatively assessing the accuracy of change detection maps; and analyzing the major urban land use change patterns in relation to population growth Congalton, R G., & Green, K (1999) - Assessing the accuracy of remotely sensed data: Principles and practices Boca Rotan, Florida’Lewis Publishers, pp 43–64 El Garouani A., Alobeid A & El Garouani S (2015) - A model from aerial/terrestrial imagery and topographic maps: 3D Modelling of Fez GIM-INTERNATIONAL, The Global Magazine for Geomatics, Vol 29, January, 2015 El Garouani A., Alobeid A & El Garouani S (2014) - Digital surface model based on aerial image stereo pairs for 3D building International Journal of Sustainable Built Environment (ELSEVIER, ScienceDirect), N°3, pp 119–126 Acknowledgment: The authors wish to thank the Sidi Mohamed Ibn Abdellah University (Fez, Morocco) and the Fulbright Foreign Scholarship Program for the funding of this research conducted at the University of Minnesota, Minneapolis, Minnesota, USA and at the Faculty of Science and Techniques (Fez, Morocco) The authors also thank Mr I Laacouri for his support El Garouani A., Barry R A., El Garouani S., & Lahrach A (2012) - Geospatial database template for urban management in Fez (Morocco) Journal of Geographic Information System, Vol 4, N° 4, 335 – 340 pp El Garouani A., Barry R A & Lahrach A (2011) GIS for City Planning, Cas of Fez (Morocco) GTC 2011, Geomatics Technologies in the City, First International Geomatics, Symposium, 10 – 13 May 2011, Jeddah, Saudi Arabia 11 Ewing R., Pendall R & Chen D (2002) Measuring sprawl and its impact Smart Growth America, Volume 1, 55 p OECD (2013) – Urbanisation and urban forms, in OECD regions at a Glance 2013, OECD Publishing, Paris, DOI: http://dx.doi.org./10.1787/reg_glance2013-7-en HCP (Haut Commissariat au Plan, Maroc) (2015) Recensement général de la population et de l'habitat au Maroc http://www.hcp.ma Richards, J A & Jia X., (1993) Remote Sensing Digital Image Analysis: An Introduction Springer, (Second Edition) Lehzam A (2012) - Urban development in Morocco: Challenges and Perspectives International Scientific Meeting "Future Challenges of the new urban world: What model of development for the Moroccan city? », IRES, Rabat, October 1-2, 2012 Sharma K D., (1985) - Urban development in the Metropolitan Shadow: A Case Study from Haryana Inter-India Publication, New Delhi, India Yuan F., Sawaya K E., Loeffelholz B C & Bauer M E (2005) - Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing Remote Sensing of Environment 98, pp 317 – 328 Lunetta, R S., & Balogh, M (1999) Application of multi-temporal Landsat TM imagery for wetland identification Photogrammetric Engineering and Remote Sensing, 65, 1303– 1310 Vorovencii I., & Muntean M.D., (2013) Evaluation of supervised classification algorithms for Landsat TM images RevCAD 14/2013 My Vo Chi, Lan Pham Thi & Son Tong Si (2009) Monitoring urban space expansion using Remote sensing data in Ha Long city, Quang Ninh province in VietNam 7th FIG Regional Conference Spatial Data Serving People: Land Governance and the Environment – Building the Capacity Hanoi, Vietnam, 19-22 October 2009 Zubair A O., (2006) - Change detection in land use and land cover using remote sensing data and GIS: A case study of Ilorin and its environs in Kwara State www.geospatialworld.net/uploads/thesis 12 .. .Analysis of Urban Growth and Sprawl from Remote Sensing Data: Case of Fez, Morocco a b c Abdelkader EL GAROUANI , David J MULLA , Said EL GAROUANI & Joseph KNIGHT a b c d Faculty of Sciences... came from conversion of agricultural, rangeland and orchard land to urban uses during the period, 1984–2013 Of the 462 of total growth in urban land use, 22 65 was converted from agricultural land,... Processing of Satellite Data – A case study from Panchkula, Ambala and Yamunanagar Districts, Haryana State, India Bhatta B., (2010) - Analysis of Urban Growth and Sprawl from Remote Sensing Data