USING GOOGLE EARTH™ AND GEOGRAPHICAL INFORMATION SYSTEM DATA AS METHOD TO DETECT URBAN SPRAWL AND GREEN SPACES FOR BETTER WELL BEING CASE OF A COASTAL LANDSCAPE

Authors

  • Safa Bel Fekih Boussema University of Sousse, High institute of Agronomic Science of ChottMariem (ISA-CM), Departement of Horticultural Sciences and Landscape, B.P 47.4042 ChottMeriemSousse-Tunisia;, University of Carthage, National Agronomic Institute of Tunis, Lr GREEN TEAM (LR17AGR01), B.P 43, Avenue Charles Nicolle 1082 Tunis Mahrajène-Tunisia https://orcid.org/0000-0001-8811-0578
  • Faiza Khebour Allouche University of Sousse, High institute of Agronomic Science of ChottMariem (ISA-CM), Departement of Horticultural Sciences and Landscape, B.P 47.4042 ChottMeriemSousse-Tunisia; University of Carthage, National Agronomic Institute of Tunis, Lr GREEN TEAM (LR17AGR01), B.P 43, Avenue Charles Nicolle 1082 Tunis Mahrajène-Tunisia
  • Ameni Bekaoui Architect, Student, Research Master Of Urban Planning, Department Of City Planing And Development, National School Of Architecture And Urban Planning, Sidibou Said- Carthage 2026.
  • Yosra Khalifa Master's Degree In Interior Architecture And Space Design At The Higher School Of Design, University Of Lille
  • Houda M’Sadak University of Sousse, High institute of Agronomic Science of ChottMariem (ISA-CM), Departement of Horticultural Sciences and Landscape, B.P 47.4042 ChottMeriemSousse-Tunisia

DOI:

https://doi.org/10.29121/granthaalayah.v8.i9.2020.1524

Keywords:

LULC, Urbanization, Google Earth, Hergla, Green Spaces, Built-Up

Abstract [English]

Coastal landscapes are facing a huge challenge to manage the spatial extension of their built-up area at the expense of the reduction of natural and cultivated areas. This is the case of Hergla city, located in the southern part of Hammamet Gulf, Tunisia. This paper firstly highlights changes of LULC in Hergla city, between 2007 and 2017 using a supervised classification of Landsat images. The evolution of built-up area between 2002 and 2020 is examined expending Google Earth images. Lastly, the geolocalization of green spaces are provided. Then, the superposition of all these analyzes will be used to propose a landscaping for a better human well-being. Finally, this research indicates the importance of analyzing LULC change at multiple scales; it revealed that built-up area has been increased and olive fields reduced from 64 % in 2007 to 30.2 %in 2017. It shows, too, an important urban expansion from 39.9 Ha in 2002 to 48.3 Ha in 2020. However, the green spaces are concentrated in the North and middle part of the city and a proposal for the development of an urban park on the south side will help to balance the spatial distribution of green spaces in this area and ensure better human well-being.

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References

Sudhira, H.S., Ramachandra, T.V., Jagadish, K.S., 2004. Urban sprawl: metrics, dynamics and modelling using GIS. Int. J. Appl. Earth Obs. Geoinformation 5, 29–39.

https://doi.org/10.1016/j.jag.2003.08.002 DOI: https://doi.org/10.1016/j.jag.2003.08.002

Yue, W., Liu, Y., Fan, P., 2013. Measuring urban sprawl and its drivers in large Chinese cities: The case of Hangzhou. Land Use Policy, Themed Issue 1-Guest Editor RomyGreiner Themed Issue 2- GuestEditor DavideViaggi 31, 358–370. https://doi.org/10.1016/j.landusepol.2012.07.018 DOI: https://doi.org/10.1016/j.landusepol.2012.07.018

United Nations, 2014. World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352). Department of Economic and Social Affairs, Population Division.

Haas J. and Ban Y., 2017. Sentinel-1A SAR and Sentinel-2A MSI data fusion for urban ecosystem service mapping, Remote Sensing Applications: Society and Environment. http://dx.doi.org/10.1016/j.rsase.2017.07.006 DOI: https://doi.org/10.1016/j.rsase.2017.07.006

Kontgis, C., Schneider, A., Fox, J., Saksena, S., Spencer, J.H., Castrence, M., 2014. Monitoring periurbanization in the greater Ho Chi Minh City metropolitan area. Appl. Geogr. 53, 377–388.https://doi.org/10.1016/j.apgeog.2014.06.029 DOI: https://doi.org/10.1016/j.apgeog.2014.06.029

Pham, V.C., Pham, T.-T.-H., Tong, T.H.A., Nguyen, T.T.H., Pham, N.H., 2015. The conversion of agricultural land in the peri-urban areas of Hanoi (Vietnam): patterns in space and time. J. LandUse Sci. 10, 224–242. https://doi.org/10.1080/1747423X.2014.884643 DOI: https://doi.org/10.1080/1747423X.2014.884643

Venables, A.J., 2017. Breaking into tradables: Urban form and urban function in a developing city. J.Urban Econ., Urbanization in Developing Countries: Past and Present 98, 88–97. https://doi.org/10.1016/j.jue.2017.01.002 DOI: https://doi.org/10.1016/j.jue.2017.01.002

Goldblatt, R., Deininger, K., Hanson, G., 2018. Utilizing publicly available satellite data for urban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam, Development Engineering. doi: 10.1016/j.deveng.2018.03.001. DOI: https://doi.org/10.1016/j.deveng.2018.03.001

Gadrani et al., 2018. F assessment of land use/landcover (LULC) change of Tbilisi and surrounding area using remote sensing (RS) and GIS. Annals of Agrarian Science 16 p 163–169. DOI: https://doi.org/10.1016/j.aasci.2018.02.005

CIESIN, 2005. Gridded Population of the World, Version 3 (GPWv3) Data Collection.

Potere, D., Schneider, A., Angel, S., Civco, D.L., 2009. Mapping urban areas on a global scale: which ofthe eight maps now available is more accurate? Int. J. Remote Sens. 30, 6531–6558.https://doi.org/10.1080/01431160903121134 DOI: https://doi.org/10.1080/01431160903121134

Seto, K.C., Fragkias, M., Güneralp, B., Reilly, M.K., 2011. A Meta-Analysis of Global Urban Land Expansion. PLOS ONE 6, e23777. https://doi.org/10.1371/journal.pone.0023777 DOI: https://doi.org/10.1371/journal.pone.0023777

Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., Dech, S., 2012. Monitoring urbanization in mega cities from space. Remote Sens. Environ. 117, 162–176.

https://doi.org/10.1016/j.rse.2011.09.015 DOI: https://doi.org/10.1016/j.rse.2011.09.015

Gaughan, A.E., Stevens, F.R., Linard, C., Jia, P., Tatem, A.J., 2013. High resolution population distribution maps for Southeast Asia in 2010 and 2015. PloS One 8, e55882.

https://doi.org/10.1371/journal.pone.0055882 DOI: https://doi.org/10.1371/journal.pone.0055882

Ban, Y., Jacob, A., Gamba, P., 2015. Space borne SAR data for global urban mapping at 30m resolution using a robust urban extractor. ISPRS J. Photogramm. Remote Sens., Global Land Cover Mappingand Monitoring 103, 28–37. https://doi.org/10.1016/j.isprsjprs.2014.08.004 DOI: https://doi.org/10.1016/j.isprsjprs.2014.08.004

Chen, X., Nordhaus, W., 2015. A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa. Remote Sens. 7, 4937–4947. https://doi.org/10.3390/rs70404937 DOI: https://doi.org/10.3390/rs70404937

Pesaresi, M., EHRLICH Daniele, FERRI Stefano, FLORCZYK Aneta, CARNEIRO FREIRE Sergio Manuel, HALKIA Stamatia, JULEA Andreea Maria, KEMPER Thomas, SOILLE Pierre, SYRRIS Vasileios, 2016. Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Publications Office of the European Union, Ispra (VA), Italy. DOI: https://doi.org/10.1109/IGARSS.2016.7730897

Dhaher N., 2010. Tunisian spatial planning: 50 years of globalization-proof policies. EchoGéo. http://echogeo.revues.org/12055

Chaggar M. and Boubaker M., 2015 The Landscape Biodiversity for Sustainable Urban Development: Case of the City of Hergla. In: Eco-landscape alternatives in the Mediterranean regions. Tunisia, Official Printing Office, Acts of 13th days Horticulture Landscape UR.HPE, 1-3 June 2015, Sousse Tunisia. 452p.

Hamdaoui A., 2015. Scales of landscape analysis of the strategic infrastructures established on the coastalcord of Sousse North. Landscape, Territory and Heritage. Higher Agronomic Institute of Chott Mariem, University of Sousse, Tunisia, 231p.

Chaggar M., and Boubaker M., 2018. “FRAGMENTATION AND DEGRADATION OF THE URBAN LANDSCAPE IN HERGLA, TUNISIA.” International Journal of Engineering Technologies and Management Research, 5(12), 60-77. DOI: https://doi.org/10.29121/ijetmr.v5.i12.2018.329 DOI: https://doi.org/10.29121/ijetmr.v5.i12.2018.329

Agency of Coastal Protection and Development (APAL), 2009. Development of coastal areas Case study: Hergla Beach.

Azabdaftari A., Sunar F, 2016. Soil salinity mapping using multitemporallandsat data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic. DOI: https://doi.org/10.5194/isprs-archives-XLI-B7-3-2016

Gutierrez, M.; Johnson, E., 2010. Temporal Variations of Natural Soil Salinity in an Arid Environment Using Satellite Images.J. S. Am. Earth Sci.30: 46–57. DOI: https://doi.org/10.1016/j.jsames.2010.07.005

Rawat, J.S.; Kumar, M., 2015. Monitoring Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case Study of Hawal bagh Block, District Almora, Uttarakhand, India. Egypt. J. Remote Sens. Space Sci. 18: 77–84. DOI: https://doi.org/10.1016/j.ejrs.2015.02.002

Jensen, J.R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective, 2nd ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA.

Campbell, J.B.; Wynne, R.H., 2011. Introduction to Remote Sensing, 5th ed.; Guilford Press: New York, NY, USA.

Mubako S., Belhaj O., Heyman J., Hargrove W. and Reyes C., 2018. Monitoring of Land Use/Land-Cover Changes in the Arid Transboundary Middle Rio Grande Basin Using Remote Sensing. Remote Sens. 10: 2005. doi:10.3390/rs10122005. DOI: https://doi.org/10.3390/rs10122005

National Institutes of Statistics, 2014. Census of the population.

Agency of Coastal Protection and Development (APAL), 2018. Coastal Technical Report of Hergla.

National Institutes of Statistics, 2020. Estimate of the population to 1 July 2020.

Carr S., Francis M., Rivlin L.G. and Stone A.M., 1992. Public Space Environment and Behavior, 415p.

Panduro T.E and Veie K.L., 2013. Classification and valuation of urban green spaces – A hedonic house price valuation. konomiskeRåds, 32p. DOI: https://doi.org/10.1016/j.landurbplan.2013.08.009

Cvejić R., Eler K., Pintar M., Železnikar Š., Haase D., Kabisch N. and Strohbach M., 2015. A typology of urban green spaces, ecosystem services provisioning services and demands. Green Surge, 68p.

Khalifa Y., 2018. Spatial analysis and landscaping of a leisure space next to a wetland: case of sebkhaHalq El Mingel. Professional End of Studies Project. High institute of Agronomic Science of ChottMariem-Tunisia.

Yin, Z.-Y., Stewart, D.J., Bullard, S., MacLachlan, J.T., 2005. Changes in urban built-up surface and population distribution patterns during 1986–1999: A case study of Cairo, Egypt. Comput. Environ. Urban Syst., Remote Sensing for Urban Analysis 29, 595–616.

https://doi.org/10.1016/j.compenvurbsys.2005.01.008 DOI: https://doi.org/10.1016/j.compenvurbsys.2005.01.008

Bagan, H., Yamagata, Y., 2015. Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data. GI Science Remote Sens.52, 765–780. https://doi.org/10.1080/15481603.2015.1072400 DOI: https://doi.org/10.1080/15481603.2015.1072400

Bousemma, et al., 2018. State of the art of greenway concept application in Tunisian green policy: A case study of an urban landscape in Sousse city. IJEGEO 5(1):36-50. DOI: https://doi.org/10.30897/ijegeo.353818

Malarvizhi et al, 2016. Use of High-Resolution Google Earth Satellite Imagery in Landuse Map Preparation for Urban Related Applications. Procedia Tchnology 24 1835-1842. DOI: https://doi.org/10.1016/j.protcy.2016.05.231

Patel, N.N.; Angiuli, E.; Gamba, P.; Gaughan, A.; Lisini, G.; Stevens, F.R.; Tatem, A.J.; Trianni, G., 2015. Multitemporal settlement and population mapping from Landsat using Google Earth Engine. Int. J.Appl. Earth Obs. Geoinf. 35, 199–208 DOI: https://doi.org/10.1016/j.jag.2014.09.005

Trianni, G.; Lisini, G.; Angiuli, E.; Moreno, E.A.; Dondi, P.; Gaggia, A.; Gamba, P., 2015. Scaling up to national/regional urban extent mapping using Landsat data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 3710–3719. DOI: https://doi.org/10.1109/JSTARS.2015.2398032

Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.;Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; Kommareddy, 2013. A High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853. DOI: https://doi.org/10.1126/science.1244693

Goldblatt, R., You, W., Hanson, G., Khandelwal, A.K., 2016. Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine. Remote Sens. 8,634. https://doi.org/10.3390/rs8080634 DOI: https://doi.org/10.3390/rs8080634

Ohri, A., Poonam, 2012. Urabn sprawl mapping and landuse change detection using Remote Sensing and GIS. International Journal of Remote Sensing and GIS 1 (1), 12-25.

Jacobson, A., Dhanota, J., Godfrey, J., Jacobson, H., Rossman, Z., Stanish, A., Walker, H., Riggio, J., 2015. Anoval approach to mapping land conversion using Google Earth with an application to East Africa. Environmental Modeling & Software 72, 1-9. DOI: https://doi.org/10.1016/j.envsoft.2015.06.011

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Published

2020-10-07

How to Cite

Boussema, S. B. F., Allouche, F. K., Bekaoui, A., Khalifa, Y., & M’Sadak, H. (2020). USING GOOGLE EARTH™ AND GEOGRAPHICAL INFORMATION SYSTEM DATA AS METHOD TO DETECT URBAN SPRAWL AND GREEN SPACES FOR BETTER WELL BEING CASE OF A COASTAL LANDSCAPE. International Journal of Research -GRANTHAALAYAH, 8(9), 266–276. https://doi.org/10.29121/granthaalayah.v8.i9.2020.1524