Detection and mapping of land use land cover with Support vector machines SVM-based change monitoring using Landsat and Sentinel-2 data. The case of Quseir, Red Sea | ||||
Miṣriqiyā | ||||
Volume 2, Issue 2, October 2022, Page 1-30 PDF (1.74 MB) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/misj.2022.273084 | ||||
View on SCiNiTO | ||||
Authors | ||||
Emad Hawash 1; Adel El-Hassanin2; Wafaa Amer3; Alaa EL-Dien EL-Nahry4; Hala Effat5 | ||||
1Natural Resources Dept. Faculty of African Postgraduate Studies (FAPS) Cairo University, Egypt. Red Sea Sustainability Studies Centre (RSSSC) | ||||
2Natural Resources Dept. Faculty of African Postgraduate Studies, Cairo University, Egypt | ||||
3Urban Planning Dept. Faculty of Regional and Urban Planning, Cairo University, Egypt | ||||
4Cont. Training and Teaching Dept. National Authority of Remote Sensing and Space Sciences (NARSS) Ministry of Scientific Research, Egypt | ||||
5Environment and Land-use Dept. National Authority of Remote Sensing and Space Sciences (NARSS) Ministry of Scientific Research, Egypt | ||||
Abstract | ||||
Land use land cover (LULC) mapping and spatial change monitoring are essential to manage the coastal cities and its resources. This study aims to investigate LULC changes of Quseir, a typical small Red Sea coastal city for a long period from 1984 to 2021. Support vector machines (SVMs) classifier and Post classification comparison (PCC) change detection technique were used to analyse two Thematic Mappers (TM), an Enhanced Thematic Mapper plus (ETM+), an Operational Land Imager (OLI) Landsat imagery in addition to a Multi-Spectral Instrument (MSI) Sentinel-2 image cover the study period. Twelve LULC classes have been identified for this study. Accuracy of the classified images and the LULC change were analysed. Along the thirty seven years, Quseir's urban has increased by nine folds and the green area was increased from nil to 6.10 m2 per person. SVM achieved high accuracy classification results for all the studied images of all sensors, while the MSI, 2021, was the highest accuracy. Through data-resampling, combining Landsat and Sentinel-2 satellite datasets for long-term monitoring studies using PCC resulted in more reliable and accurate outputs. Results obtained from this study will fill the gap of rare LULC maps and spatial change information of Quseir during the past four decades. | ||||
Keywords | ||||
Land use land cover change; SVM; PCC; Sentinel-2; Landsat; Quseir Red Sea | ||||
References | ||||
Abdel-Aal, A., M. (1992). New towns and regional development in Egypt. Arts and human sciences magazine, Minia university, 10 (2): 71-135. Abdel-Fattah, M., Kantoush, S. and Sumi, T. (2015). Integrated Management of Flash Flood in Wadi System of Egypt: Disaster Prevention and Water Harvesting. In: Annuals of Disas. Prev. Res. Inst., Kyoto Univ., 58 (B): 485-496. Abdel Ghany, M.K. (2015). Quantitative Morphometric Analysis of Drainage Basins between Qusseir and Abu Dabbab Area, Red Sea Coast, Egypt using GIS and Remote Sensing Techniques, International Journal of Advanced Remote Sensing and GIS, 4(1): 1295-1322. Abd-El Monsef, H., Hassan, M.A.A. and Shata, S. (2017). Using spatial data analysis for delineating existing mangroves stands and siting suitable locations for mangroves plantation. Comput Electron Agric 141: 310–326 Abe, S. (2005). Support Vector Machines for Pattern Classification. 2nd Ed, Springer, 471p. Adler-Golden, S. M., M. W. Matthew, L. S. Bernstein, R. Y. Levine, A. Berk, S. C. Richtsmeier, P. K. Acharya, G. P. Anderson, G. Felde, J. Gardner, M. Hoke, L. S. Jeong, B. Pukall, A. Ratkowski, and H.-H Burke (1999). Atmospheric Correction for Short-wave Spectral Imagery Based on MODTRAN4. JPL Publ., 99(17): 21-29. Afefe, A.A. (2021). Linking territorial and coastal planning: Conservation status and management of mangrove ecosystem at the Egyptian - African Red Sea coast. Aswan University Journal of Environmental Studies, 2(2): 91-114 Ali, S. and Smith, K.A. (2008). Kernel Width Selection for SVM Classification: A Meta-Learning Approach. In: Felici, G. and Vercellis, C. (Eds.), Mathematical Methods for Knowledge Discovery and Data Mining, Information science reference, Hershey, New York, 101-115. Aljenaid, S.S., Kadhem, G.R., AlKhuzaei, M.F. and Alam, J.B. (2022). Detecting and assessing the spatio‑temporal land use land cover changes of Bahrain Island during 1986–2020 using remote sensing and GIS. Earth Systems and Environment, https://doi.org/10.1007/s41748-022-00315-z Alawamy, J.S., Balasundram, S., Hanif, A.H.M. and Teh, C.B.S. (2020). Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017. Sustainability, 12, 4490. DOI: 10.3390/su12114490. Anderson, R., Hardy, E.E., Roach, J.T., & Witmer, R.E. (1976). A land use and land cover classification system for use with remote sensor data. Sioux Falls: US Gov. Printing Office. Al-doski, J., Mansor, S.B. and Shafri, H.Z.M. (2013). Image Classification in Remote Sensing. Journal of Environment and Earth Science, 3, 10. Alganci, U. (2019). Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods. Int. J. Geoinf , 8(3):139. Alnuaim, A.M. and El Naggar, M. H. (2014). Performance of Foundations in Sabkha Soil: Numerical Investigation. Geotech Geol Eng, 32:637–656 Aqeel, A. (2016). Investigation of expansive soils in Obhor Sabkha, Jeddah-Saudi Arabia. Arab J Geosci, 9, 314 Basheer, M.A., El Kafrawy, S.B. and Mekawy, A.A. (2019) Identification of mangrove plant using hyperspectral remote sensing data along the Red Sea, Egypt. Egyptian Journal of Aquatic Biology & Fisheries, 23(1): 27 – 36. Bauer, F., Hadidi, A., Tügel, F. and Hinkelmann, R. (2020). Flash Flood Investigations in El Gouna, Northern Red Sea Governorate, In: A. M. Negm (ed.), Flash Floods in Egypt, Advances in Science, Technology & Innovation. Springer Nature Switzerland AG, 61-81. Cabassi, A. (2012). Kosseir, a phosphate-shipping town. In Piaton, C., Godoli,
CAPMAS, 2021. Population Statistics and Censuses Sector PSCS, Central Agency for Public Mobilization and Statistics, Census - Population (governorates), Imtedad Ramsis, Cairo. Cavur, M., Duzgun, H.S., Kemec, S. and Demirkan, D. C. (2019). Land use and land cover classification of Sentinel 2-a: st Petersburg case study. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.Vol. XLII-1/W2. Chan, J.C., Chan, K., Yeh, A.G. (2001) Detecting the Nature of Change in an Urban Environment: a Comparison of Machine Learning Algorithms. Photogramm Eng Remote Sensing, 67(2):213–225 Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903. Chughtai, A.H., Abbasi, H. and Karas, I.R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sens. Appl.: Soc. Environ. 22:100482 Chuvieco, E. (2020) Fundamentals of Satellite Remote Sensing: An Environmental Approach, 3rd ed., CRC press, 432p. Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices (3rd ed., p. 327). CRC Press. Dewidar, Kh. (2002). Landfill detection in Hurghada, North Red Sea, Egypt, using Thematic Mapper images. Int. J. Remote Sens., 23(5): 939–948. EGSMA, 1991. Basement Rocks of Quseir Quadrangle, Egypt (sheet No. NG-36-NE). Ministry of Petroleum and Mineral Resources, The Egyptian Geological Survey and Mining Authority. Elnaggar, O.M. Temraz, M.G. and Khallaf, M.K. (2020). Detection of flow units of Quseir Formation (Lower Campanian) as a potential reservoir using experimental correlations of capillary pressure derived parameters, Gebel el-Silsila, Egypt. Journal of Petroleum Exploration and Production Technology, 10: 2269–2277 Elnazer, A. A., Salman, S. A. and Asmoay, A. S. (2017). Flash flood hazard affected Ras Gharib city, Red Sea, Egypt: a proposed flash flood channel. Nat. Hazards, 89:1389-1400. EMA (2016). Meteorological data of Qusair and Ras Banas 1986-2016. Egyptian Meteorological Authority, Kobry elQobba, AlWaili, Cairo, 11784. ENVI Geospatial documentation centre. L3Harris Geospatial documentation center. Support Vector Machine. Available online: https://www.harrisgeospatial.com/docs/SupportVectorMachine.html (accessed on 30 Oct., 2022) ESA (2008). Topographic map of Quseir, Egypt, sheet number (NG-36-K3), Ministry of Water Resources and Irrigation, Egyptian Survey Authority. Foody, G. M. and Mathur, A. (2004). Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sens. Environ., 93 (1-2): 107–117. Gaafar, I., El-Shershaby, A., Zeidan, I. and Sayed El-Ahll, L. (2016). Natural radioactivity and radiation hazard assessment of phosphate mining, Quseir-Safaga area, Central Eastern Desert, Egypt. NRIAG Journal of Astronomy and Geophysics, 5(1): 160-172. Green, K., Kempka, D. and Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogramm. Eng. Remote Sens., 60: 331–337 Hawash, E., El-Hassanin, A., Amer, W., El-Nahry, A. and Effat, H. (2021a). Change detection and urban expansion of Port Sudan, Red Sea, using remote sensing and GIS. Environ Monit Assess, 193,723 Hawash, E., El-Hassanin, A., Amer, W., El-Nahry, A. and Effat, H. (2021b). Land use land cover change of Marsa Alam, Red Sea, using remote sensing and GIS. MISJ, 1(2): 148-166. Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. Int J Remote Sens, 23(4): 725–749. Ihlen, V., 2019. Landsat 8 (L8) data users handbook (US geological survey, sioux falls, south Dakota). https://www.usgs.gov/core-science-systems/nli/landsat/landsat-8-data-users-handbook. Accessed 14 Oct. 2021. Ilsever, M. and Ünsalan, C. (2012). Two-Dimensional Change Detection Methods, Remote Sensing Applications. Springer, 72p. Izadi, F., Chamani, A. and Zamani‑Ahmadmahmoodi, R. (2022). How vegetation cover characteristics response to the spread of Prosopis juliflora: a time‑series remote sensing analysis in southern Iran. Environ Monit Assess, 194: 401. Jensen, J. R. (2005). Introductory digital image processing. 3rd ed., Prentice Hall, 526p. Jensen, J. R. (2015). Introductory digital image processing. 4th ed., Pearson Education, 656p. Jumaah, H.J., Ameen, M.H., Mohamed, G.H. and Ajaj, Q.M.(2022). Monitoring and evaluation Al-Razzaza lake changes in Iraq using GIS and remote sensing technology. Egypt. J. Remote. Sens. Space Sci, 25 (1): 313-321 Kamel, M. and Abu El Ella, E.M. (2016). Integration of remote sensing and GIS to manage the sustainable development in the Nile Valley desert fringes of Assiut-Sohag Governorates, Upper Egypt. J Indian Soc Remote Sens, 44(5):759–774 Kamh, S., Ashmawy, M., Kilias, A., and Christaras, B. (2011). Evaluating urban land cover change in the Hurghada area, Egypt, by using GIS and remote sensing. Int J Remote Sens, 33(1): 41-68. Keuchel, J., Naumann, S., Heiler, M. & Siegmund, A. (2003). Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sens. Environ., 86 (4): 530-541. Lambin, E.F., Geist, H.J. and Rindfuss, R.R. (2014). Introduction: Local Processes and Global Impacts, In: Eric F. Lambin, E.F. and Geist, H.J. (Eds), Land-Use and Land-Cover Change: Local Processes and Global Impacts, Springer, 1-8. Lambert, M. J., Traoré, P. G. S., Blaes, X., Baret, P. and Defourny, P. (2018). Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt, Remote Sens. Environ., 216: 647–657. Lillesand, T. M., R. W. Kiefer and J. W. Chipman (2015). Remote Sensing and Image Interpretation, 7th Edition. John Wiley & Sons, Inc., New York, New York, 720 p. Lu, D., Mausel, P., Brondízio, E. and Moran, E. (2004). Change detection techniques. Int J Remote Sens, 25 (12): 2365-2407. Lu, D., and Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens, 28(5): 823–870. Lunetta, R.S. (2000). Remote Sensing Change Detection. 1st Ed. CRC, 350p. Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., Gascon, F. (2017). Sen2Cor for Sentinel-2. 3. Conference of Image and Signal Processing for Remote Sensing. DOI: 10.1117/12.2278218. Mandal, J., Ghosh, N. and Mukhopadhyay, A. (2019). Urban Growth Dynamics and Changing Land-Use Land-Coverof Megacity Kolkata and Its Environs. Mansour, A.M., Nawar, A.H. and Madkour, H.A. (2011). Metal pollution in marine sediments of selected harbours and industrial areas along the Red Sea coast of Egypt. Ann. Naturhist. Mus. Wien, Serie A, 113: 225–244 Maryanti, M. R., Khadijah, H., Uzair, A.M. and Ghazali, M.A. (2016). The urban green space provision using the standards approach: issues and challenges of its implementation in Malaysia, Sustainable Development and Planning VIII, WIT Trans. Ecol. Environ., 210: 369-379. MoHUUC (2014). The National Urban Development Framework in the Arab Republic of Egypt. Report presented by the Ministry of Housing, Utilities and Urban Communities, General Organization for Physical Planning (GOPP), Egypt. Morar, T., Radoslav, R., Spiridon, L. C., and Păcurar, L. (2014). Assessing pedestrian accessibility to green space using GIS. Transylvanian Rev. Adm. Sci., 10(42): 116–139. Nasr, A.M.A. (2015). Geotechnical Characteristics of Stabilized Sabkha Soils from the Egyptian–Libyan Coast. Geotech Geol Eng, 33:893–911 National Ocean Service (without date). Coastal Blue Carbon. National Oceanic and Atmospheric Administration-NOAA. Available at: https://oceanservice.noaa.gov/ecosystems/coastal-blue-carbon/ Accessed at 4 March, 2022. Peiman, R. (2011). Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa Province in Italy. Int J Remote Sens, 32 (15): 4365-4381. Richards, J. A. (2013). Remote Sensing Digital Image Analysis: An Introduction, 5th Ed. Springer Heidelberg New York, London, 494 p. Said, R. (1992). The Geology of Egypt, Elsevier Science Ltd., 1st Edition. Rotterdam, Netherlands, 734 p. Salem, A., Aboud, E., Elsirafy, A. and Ushijima, K. (2005). Structural mapping of Quseir area, northern Red Sea, Egypt, using high-resolution aeromagnetic data, Earth Planets Space, 57: 761–765. Sheded M. G.; Ahmed M. K. and Hammad S. A. (2013). Vegetation Analysis in the Red Sea-Eastern Desert ecotone at the area between Safaga and South Qusseir, Egypt. Helwan Conf. Egypt J Bot. 3rd Int. Conf. 17-18, Helwan Univ. 145-163. Sheykhmousa, M., Kerle, N., Kuffer, M. and Ghaffarian, S. (2019). Post-disaster recovery assessment with machine learning-derived land cover and land use information, Remote Sens., 11, 10, 1174. Singh, A. (1989). Digital change detection techniques using remotely sensed data. Int J Remote Sens, 10: 989–1003. Singh, S.K., Kanga, S., Meraj, G., Farooq, M. and Sudhanshu (2021). Geographic Information Science for Land Resource Management, 1st ed., John Wiley & Sons, Inc. York, New York, 406 p. Steffen, W.L. (2004). Global Change and the Earth System: A Planet Under Pressure. Springer, 336p. Stow, D. A., Tinney, L. R., & Estes, J. E. (1980). Deriving land use/land cover change statistics from Landsat: A study of prime agricultural land. Proceedings of the 14th International Symposium on Remote Sensing of Environment held in Ann Arbor in 1980 (Ann Arbor, Michigan: Environmental Research Institute of Michigan), 1227–1237. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Bray, M. and Tanvir Islam (2012). Selection of classification techniques for land use/land cover change investigation. Adv. Space Res. 50: 1250–1265 Turner II, B.L., Moss, R.H. and Skole., D. L. (1993). Relating Land Use and Global Land-Cover Change. International Geosphere-Biosphere Program, Report No. 24/HDP, Report No. 5, Stockholm., Sweden. USGS (2019a). USGS EROS Archive - Sentinel-2 - Comparison of Sentinel-2 and Landsat. Earth Resources Observation and Science (EROS) Centre. Available online: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2-comparison-sentinel-2-and-landsat#:~:text=The%20main%20visible%20and%20near,spatial%20resolution%20of%2060%20meters. Accessed on: Oct., 28, 2022. USGS (2019b). Landsat 8 (L8) Data Users Handbook. LSDS-1574 Version 5.0, USGS. Available online: https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf. Accessed on: Oct., 28, 2022. USGS (2019c). USGS EROS Archive - Sentinel-2, USGS. Available online: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2. Accessed on: Oct., 28, 2022. Vanderstraete, T., Goossens, R., & Ghabour, T. K. (2006). The use of multi-temporal Landsat images for the change detection of the coastal zone near Hurghada Egypt. Int. J. Remote Sens., 27(17): 3645–3655. Vapnik., V. (1995). The Nature of Statistical Learning Theory. Springer, New York, 188 p. Verburg, P. H., Neumann, K. and Nol, L.(2011). Challenges in using land use and land cover data for global change studies. Glob. Change Biol., 17: 974–989. Walker, B. and Steffen, W. (1999). The nature of global change, In: Walker, B., Steffen, W., Canadell, J. and Ingram, J. (Eds.), The Terrestrial Biosphere and Global Change: Implications for natural and managed, ecosystems, International Geosphere-Biosphere Programme Book Series, 1–18 Youssef, A. M., Pradhan, B., Gaber, A. F. D. and Buchroithner, M. F. (2009).Geomorphological hazard analysis along the Egyptian Red Sea coast between Safaga and Quseir. Nat. Hazards Earth Syst. Sci., 9: 751–766. Yousif, M. and Sracek, O. (2016). Integration of geological investigations with multi-GIS data layers for water resources assessment in arid regions: El Ambagi Basin, Eastern Desert, Egypt, Environ. Earth Sci., 75: 684. Yuan, D., Elvidge, C.D. and Lunetta, R.S. (1998) Survey of Multispectral Methods for Land Cover Change Analysis. In: Lunetta, R.S. and Elvidge, C.D., Eds., Remote Sensing Change Detection: Environmental Monitoring. Methods and Application, Taylor and Francis Ltd., 21-39. Zahran M. and Willis A. (2009). The vegetation of Egypt. 2nd ed., Springer, 456 p Zhang, R., Tang, X., You, S., Duan, K., Xiang, H. and Luo, H. (2020). A novel feature-level fusion framework using optical and SAR remote sensing images for land use/land cover (LULC) classification in cloudy mountainous area. Appl. Sci., 10, 8: 2928.
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