Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Since the appearance on Earth, human has been constantly operating in nature, exploiting its riches, but also adapting it to its own needs. Both developing and developed countries are constantly concerned about the urbanization process. Urbanization, in order to be positive, must be developed correctly. If such a thing does not happen, then this development will negatively affect both the environment and human health. In order to develop adequate strategies and policies for the most sustainable and effective land use management, it is necessary to quantify, monitor, determine the factors that have influenced this change in land use and the spread mapping of the urban environment. In this study, Landsat satellite images were used to determine the spatial-temporal characteristics of the urban sprawl environment in the Municipality of Prishtina for a period of 20 years (2000-2020). To map the land cover for Prishtina from 2000 to 2020, the Supervised maximum likelihood classification was used using the Landsat ETM + and OLI data archives in ArcGIS 10.5 software. Using landscape metrics and detection techniques after the classification of satellite images, enabled and assisted in the evaluation and analysis of trends and patterns of urban sprawl. The determination of the changes and the analysis made revealed that during the period 2000-2020, in Prishtina, there was an increase of the built areas by 16.46 km2 at the expense of the unbuilt areas. That there has been an increase in urban areas was also confirmed by the results of landscape metrics.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
114--125
Opis fizyczny
Bibliogr. 50 poz., rys., tab.
Twórcy
autor
- University of Prishtina, Faculty of Mathematical and Natural Sciences, Department of Geography, Eqrem Çabej str, no 51, 10 000 Prishtina, Kosovo
autor
- University of Prishtina, Faculty of Mathematical and Natural Sciences, Department of Geography, Eqrem Çabej str, no 51, 10 000 Prishtina, Kosovo
Bibliografia
- 1. Aguilera, F., Valenzuela, L.M., Botequilha-Leitão, A. 2011. Landscape metrics in the analysis of urban land use patterns: a case study in a Spanish metropolitan area. Landscape and Urban Planning, 99, 226–238. https://doi.org/10.1016/j.landurbplan.2010.10.004.
- 2. Ahmed, B., Ahmed, R. 2012. Modeling urban land cover growth dynamics using multi-temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 1, 3–31. https://doi.org/10.3390/ijgi1010003.
- 3. Aithal, B., Ramachandra, T. 2013. Measuring urban sprawl in Tier II cities of Karnataka, India. In: Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS). IEEE, 321–329. https://doi.org/10.1109/ghtc-sas.2013.6629939.
- 4. Araya, Y.H., Cabral, P. 2010. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2, 1549–1563. https://doi.org/10.3390/rs2061549.
- 5. Chikowore, T., Willemse, L. 2017. Identifying the changes in the quality of life of Southern African Development Community (SADC) migrants in South Africa from 2001 to 2011. South African Geographical Journal, 99, 86–112. https://doi.org/10.1080/03736245.2016.1208577.
- 6. Cingolani, A. M., Renisonab, D., Zaka, M. R., & Cabido, M. R. 2004. Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify landcover units. Remote Sensing of Environment, 92(1), 84–97. https://doi.org/10.1016/j.rse.2004.05.008.
- 7. Congalton, R. G. 1991. A review of assessing the accuracy of classifcations of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034–4257(91)90048-B.
- 8. Congalton, R. G. 2005. Thematic and positional accuracy assessment of digital remotely sensed data. Proceedings of the seventh annual forest inventory and analysis symposium, 149–154.
- 9. ESRI. 2015. ArcGIS Desktop: Release 10. Environmental Systems Research Institute.
- 10. Fonji, S. F., & Taff, G. N. 2014. Using satellite data to monitor land-use land-cover change in Northeastern Latvia. SpringerPlus, 3(1), 1–15. https://doi.org/10.1186/2193–1801–3-61.
- 11. Foody, G. M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/S0034–4257(01)00295–4.
- 12. Gustafson, E. J. 1998. Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1, 143–156. https://doi.org/10.1007/s100219900011.
- 13. Halimi, M., Sedighifar, Z., & Mohammadi, C. 2017. Analyzing spatiotemporal land use/cover dynamic using remote sensing imagery and GIS techniques case: Kan basin of Iran. GeoJournal, 83(5), 1067–1077. https://doi.org/10.1007/s10708–017–9819–2.
- 14. Hegazy, I. R., & Kaloop, M. R. 2015. Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4, 117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005.
- 15. Herold, M., Couclelis, H., & Clarke, K. C. 2005. The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems, 29, 369–399. https://doi.org/10.1016/j.compenvurbsys.2003.12.001.
- 16. Jat, M. K., Garg, P. K., & Khare, D. 2008. Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26–43. https://doi.org/10.1016/j.jag.2007.04.002.
- 17. Ji, W., Ma, J., Twibell, R.W., Underhill, K. 2006. Characterizing urban sprawl using multistage remote sensing images and landscape metrics. Computers, Environment and Urban Systems, 30(6), 861–879. https://doi.org/10.1016/j.compenvurbsys.2005.09.002.
- 18. Kamusoko, C., & Aniya, M. 2007. Land use/cover change and landscape fragmentation analysis in the Bindura District, Zimbabwe. Land Degradation & Development, 18(2), 221–233. https://doi.org/10.1002/ldr.761.
- 19. Kamusoko, C., Aniya, M. 2009. Hybrid classification of Landsat data and GIS for land use/cover change analysis of the Bindura district, Zimbabwe. International Journal of Remote Sensing, 30(1), 97–115. https://doi.org/10.1080/01431160802244268.
- 20. KAS (Kosovo Agency of Statistics). (2020). Statistical yearbook of the Republic of Kosovo, Prishtina. https://ask.rks-gov.net/media/5629/vjetari-final2020-per-web.pdf. Accessed 15 January 2021.
- 21. Kityuttachai, K., Tripathi, N.K., Tipdecho, T., & Shrestha, R. 2013. CA-Markov analysis of constrained coastal urban growth modeling: Hua Hin seaside city, Thailand. Sustainability, 5(4), 1480–1500. https://doi.org/10.3390/su5041480.
- 22. Kowe, P., Pedzisai, E., Gumindoga, W., & Rwasoka, D. 2015. An analysis of changes in the urban landscape composition and configuration in the Sancaktepe District of Istanbul Metropolitan City, Turkey using landscape metrics and satellite data. Geocarto International, 30(5), 506–519. https://doi.org/10.10 80/10106049.2014.905638.
- 23. Lee, Y., & Chang, H. 2011. The simulation of land use change by using CA-Markov model: a case study of Tainan City, Taiwan. 19th International Conference on Geoinformatics, 24–26 June, 1–4. https://doi.org/10.1109/geoinformatics.2011.5980819.
- 24. Liu, Y., Hou, S., Kong, X. & Xu, Y. 2011. The analysis on land use change in urban fringe area based on the GIS technology. International Conference on Remote Sensing, Environment and Transportation Engineering, 6444– 6447. https://doi.org/10.1109/rsete.2011.5965832.
- 25. Luck, M., & Wu, J. 2002. A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecology, 17, 327–339. https://doi.org/10.1023/A:1020512723753.
- 26. Magidi, J., & Ahmed, F. 2019. Assessing urban sprawl using remote sensing and landscape metrics: A case study of City of Tshwane, South Africa (1984–2015). The Egyptian Journal of Remote Sensing and Space Science, 22(3), 335–346. https://doi.org/10.1016/j.ejrs.2018.07.003.
- 27. Maktav, D., Erbek, F., & Jürgens, C. 2005. Remote sensing of urban areas. International Journal of Remote Sensing, 26, 655–659. https://doi.org/10.1080/01431160512331316469.
- 28. Mallupattu, P. K., & Sreenivasula Reddy, J. R. 2013. Analysis of land use/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal, 2013, 1–6. https://doi.org/10.1155/2013/268623.
- 29. Mcgarigal, K. & Marks, B. J. 1995. Spatial pattern analysis program for quantifying landscape structure. https://doi.org/10.2737/pnw-gtr-351.
- 30. Mcgarigal, K., Cushman, S., Neel, M. & Ene, E. 2002. FRAGSTATS: spatial pattern analysis program for categorical maps. Department of Agriculture, Forest Service, Pacifica Northwest Research Station.
- 31. McGarigal, K., Tagil, S., & Cushman, S. A. 2009. Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landscape Ecology, 24(3), 433–450. https://doi.org/10.1007/s10980–009–9327-y.
- 32. Moghadam, H. H., & Helbich, M. 2013. Spatiotemporal urbanization processes in the megacity of Mumbai, India: a Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149. https://doi.org/10.1016/j.apgeog.2013.01.009.
- 33. Morefield, P., Fann, N., Grambsch, A., Raich, W., & Weaver, C. 2018. Heat-Related Health Impacts under Scenarios of Climate and Population Change. International Journal of Environmental Research and Public Health, 15(11), 2438. http://dx.doi.org/10.3390/ijerph15112438.
- 34. Mubea, K., Ngigi, T., & Mundia, C. 2011. Assessing Application of Markov Chain Analysis in Nakuru. Applied Geoinformatics for Society and Environment, 182–188.
- 35. Municipality of Prishtina. 2013. Municipal Development Plan of Prishtina 2012–2022. https://prishtinaonline.com/uploads/prishtina_pzhk_2012–2022_shqip%20(1).pdf. Accessed 18 February 2021.
- 36. Petropoulos, G. P., Kalivas, D. P., Georgopoulou, I. A., & Srivastava, P. K. 2015. Urban vegetation cover extraction from hyperspectral imagery and geographic information system spatial analysis techniques: case of Athens, Greece. Journal of Applied Remote Sensing, 9(1), 1–17. https://doi.org/10.1117/1.JRS.9.096088.
- 37. Pili, S., Grigoriadis, E., Carlucci, M., Clemente, M., & Salvati, L. 2017. Towards sustainable growth? A multi-criteria assessment of (changing) urban forms. Ecological Indicators, 76, 71–80. https://doi.org/10.1016/j.ecolind.2017.01.008.
- 38. Qin, Z., Karnieli, A., Berliner, P. 2001. A monowindow algorithm for retrieving land surface temperature from Landsat TM data and its application to the IsraelEgypt border region. International Journal of Remote Sensing, 22(18), 3719–3746. https://doi.org/10.1080/01431160010006971.
- 39. Roberts, D. A., Keller, M., & Soares, J. V. 2003. Studies of land-cover, land-use, and biophysical properties of vegetation in the Large Scale Biosphere Atmosphere experiment in Amazônia. Remote Sensing of Environment, 87(4), 377–388. https://doi.org/10.1016/j.rse.2003.08.012.
- 40. Seto, K. C., & Fragkias, M. 2005. Quantifying spatiotemporal patterns of urban landuse change in four cities of China with time series landscape metrics. Landscape Ecology, 20(7), 871–888. https://doi.org/10.1007/s10980–005–5238–8.
- 41. Sudhira, H. S., & Ramachandra, T. 2007. Characterising urban sprawl from remote sensing data and using landscape metrics. 10th International conference on computers in urban planning and urban management, 1–12.
- 42. Sudhira, H. S., Ramachandra, T. & Jagadish, K. 2003a. Urban sprawl pattern recognition and modeling using GIS. Map India Conference, 28–31.
- 43. Sudhira, H. S., Ramachandra, T., Jagadish, K. 2003b. Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29–39. https://doi.org/10.1016/j.jag.2003.08.002.
- 44. Sudhira, H. S., Ramachandra, T., Raj, K.S., & Jagadish, K. 2003c. Urban growth analysis using spatial and temporal data. Journal of the Indian Society of Remote Sensing, 31, 299–311. https://doi.org/10.1007/BF03007350.
- 45. Unhabitat, 2016. Urbanisation and Development: Emerging Futures. World Cities Report 2016: United Nations Human Settlements Programme.
- 46. USGS .2019. Using the USGS Landsat 8 Product. Retrieved from https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf.
- 47. Vaz, E. 2014. Managing urban coastal areas through landscape metrics: an assessment of Mumbai’s mangrove system. Ocean & Coastal Management, 98, 27–37. https://doi.org/10.1016/j.ocecoaman.2014.05.020.
- 48. Vaz, E., Taubenböck, H., Kotha, M., & Arsanjani, J. J. 2017. Urban change in Goa, India. Habitat International, 68, 24–29. https://doi.org/10.1016/j.habitatint.2017.07.010.
- 49. Verma, P., Raghubanshi, A., Srivastava, P. K., & Raghubanshi, A. S. 2020. Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Modeling Earth Systems and Environment, 6(2), 1045–1059. https://doi.org/10.1007/s40808–020–00740-x.
- 50. Yu, X. J., & Ng, C. N. 2007. Spatial and temporal dynamics of urban sprawl along two urban–rural transects: a case study of Guangzhou, China. Landscape and Urban Planning, 79(1), 96–109. https://doi.org/10.1016/j.landurbplan.2006.03.008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9b590430-e252-47bc-a633-f998f668feca