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Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development

  • Nasem BadreldinEmail author
  • Assem Abu Hatab
  • Carl-Johan Lagerkvist
Article

Abstract

The Nile Delta of Egypt is increasingly facing sustainability threats, due to a combination of nature- and human-induced changes in land cover and land use. In this paper, an analysis of big time series data from remotely sensed satellite images and the random forests classifier was undertaken to assess the spatial and temporal dynamics of urbanization and cropland in the Nile Delta between 2007 and 2017. Out of thirteen variables, five spectral indices were chosen to build 500 decision trees, with a resulting overall accuracy average of 91.9 ± 1.5%. The results revealed that the urban extent in the Nile Delta has increased, between 2007 and 2017, by 592.4 km2 (1.92%). Particularly, the results indicated that the years 2011 and 2012, which coincided the 2011 political uprising in Egypt, so-called the Arab Spring, were associated with significant land-use changes in the Nile Delta, both in rate and scale. As a result, the cropland area in the region decreased between 2010 and 2011 by 1.63% (502.21 km2). Moreover, the results showed that during the period 2012–2017, the mean annual urbanization rate in the region stood at 60 km2/year. In contrast, croplands decreased during the same period at an average annual rate of 2 km2/year. At the governorates’ level, the results suggested that top agricultural producing governorates in the Nile Delta, such as Elmonoufia, Elkalubia, Elbouhyra, and Elghrbia, witnessed the highest rates of decrease in cropland areas during the period 2012–2017. Over the same period, urban areas increased the most in Elkalubia, Domiate, and Elmonoufia by 1.98%, 1.72%, and 1.34%, respectively. The f indings from this analysis are discussed along with their implications for sustainable land-use and urban planning policies.

Keywords

LULC Urbanization Big data Nile Delta Random forests Sustainable development 

Notes

Acknowledgments

We would like to thank Claire Herbert, the coordinator of the Canadian Watershed Information Network, Scott Watson, and Andre Worms, from the IT Services at the University of Manitoba, for their help in the HPC requests. Moreover, we gratefully acknowledge USGS Earth Resources Observation and Science for the data support. Special thanks go also to Dr. Mohamed Embaby, the head of Central GIS Unit at the National Water Research Center in Cairo, for providing shapefiles of the Egyptian governorates.

Funding information

This study was funded through a research grant (No. 2016-00350) from the Swedish Research Council (FORMAS). The funding agency had no role in the design of the study, data collection, analysis, and interpretation of production of the manuscript.

References

  1. Aboel Ghar, M., Shalaby, A., & Tateishi, R. (2004). Agricultural land monitoring in the Egyptian Nile Delta using Landsat data. International Journal of Environmental Studies, 61(6), 651–657.  https://doi.org/10.1080/0020723042000253866.CrossRefGoogle Scholar
  2. Abu Hatab, A., Cavinato, M., Lindemer, A., & Lagerkvist, C. J. (2019). Urban sprawl, food security and agricultural systems in developing countries: a systematic review of the literature. Cities, 94(2019), 129–142.  https://doi.org/10.1016/j.cities.2019.06.001.CrossRefGoogle Scholar
  3. Abutaleb, K., Mohammed, A., & Ahmed, M. (2018). Climate change impacts, vulnerabilities and adaption measures for Egypt’s Nile Delta. Earth Systems and Environment, 2(2018), 183–192.  https://doi.org/10.1007/s41748-018-0047-9.CrossRefGoogle Scholar
  4. Al-Saidi, M., Schellenberg, T., & Roach, E. (2016). Water, energy and food nexus in Egypt - Nexus Country Profile. Nexus Research Focus: TH Köln University of Applied Sciences.Google Scholar
  5. As-syakur, A. R., Adnyana, I.W.S., Arthana, I.W., & Nuarsa, I.W. (2012). Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sensing. 4(10), 2957–2970.  https://doi.org/10.3390/rs4102957.CrossRefGoogle Scholar
  6. Badreldin, N., & Goossens, R. (2013). Monitoring land use/land cover change using multi-temporal Landsat satellite images in an arid environment: a case study of El-Arish, Egypt. Arabian Journal of Geosciences, 7(5), 1671–1681.  https://doi.org/10.1007/s12517-013-0916-3.CrossRefGoogle Scholar
  7. Badreldin, N., Frankl, A., & Goossens, R. (2013). Assessing the spatiotemporal dynamics of vegetation cover as an indicator of desertification in Egypt using multi-temporal MODIS satellite images. Arabian Journal of Geosciences, 7(11), 4461–4475.  https://doi.org/10.1007/s12517-013-1142-8.CrossRefGoogle Scholar
  8. Badreldin, N., Xing, Z., & Goossens, R. (2017). The application of satellite-based model and bi-stable ecosystem balance concept to monitor desertification in arid lands, a case study of Sinai Peninsula. Modeling Earth Systems and Environment, 3(1), 21–37.  https://doi.org/10.1007/s40808-017-0300-5.CrossRefGoogle Scholar
  9. Bajgiran, P. R., Shimizu, Y., Hosoi, F., & Omasa, K. P. (2009). MODIS vegetation and water indices for drought assessment in semi-arid ecosystems of Iran. Journal of Agricultural Meteorology, 65(4), 349–355.  https://doi.org/10.2480/agrmet.65.4.4.CrossRefGoogle Scholar
  10. Baret, F. & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment’, Remote Sensing of Environment, 35(2–3): 161–173.  https://doi.org/10.1016/0034-4257(91)90009-U.CrossRefGoogle Scholar
  11. Bratley, K., & Ghoneim, E. (2018). Modeling urban encroachment on the agricultural land of the eastern Nile Delta using remote sensing and a GIS-based Markov chain model. Land, 7(4), 114–135.  https://doi.org/10.3390/land7040114.CrossRefGoogle Scholar
  12. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.  https://doi.org/10.1023/A:1010933404324.CrossRefGoogle Scholar
  13. Busetto, L., & Ranghetti, L. (2016). MODIStsp: An R package for automatic preprocessing of MODIS Land Products time series’, Computers & Geosciences. Pergamon, 97, 40–48.  https://doi.org/10.1016/J.CAGEO.2016.08.020.CrossRefGoogle Scholar
  14. CAPMAS (Central Agency for Public Mobility and Statistics). (2017). Egypt Census 2017. Cairo: CAPMAS.Google Scholar
  15. CAPMAS (Central Agency for Public Mobility and Statistics). (2018). Egypt in Figues. Cairo: CAPMAS.Google Scholar
  16. Chang, N. B., Han, M., Yao, W., Chen, L. C., & Xu, S. (2010). Change detection of land use and land cover in an urban region with SPOT-5 images and partial Lanczos extreme learning machine. Journal of Applied Remote Sensing, 4(1), 43551.  https://doi.org/10.1117/1.3518096.CrossRefGoogle Scholar
  17. Clark, M. L., Aide, T. M., & Riner, G. (2012). Land change for all municipalities in Latin America and the Caribbean assessed from 250-m MODIS imagery (2001–2010). Remote Sensing of Environment., 126, 84–103.  https://doi.org/10.1016/j.rse.2012.08.013.CrossRefGoogle Scholar
  18. Clos, Joan. (2016). A new urban agenda for the 21st century: The role of urbanisation in sustainable development. In OECD Regional Outlook 2016—Productive Regions for Inclusive Societies. OECD Publishing, Paris.  https://doi.org/10.1787/9789264260245-en.
  19. Darwish, K. H., Safaa, M., Momou, A., & Saleh, S. A. (2013). Egypt: land degradation issues with special reference to the impact of climate change. In Combating Desertification in Asia, Africa and the Middle East (pp. 113–136). Dordrecht: Springer.  https://doi.org/10.1007/978-94-007-6652-5_6.CrossRefGoogle Scholar
  20. Didan, K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid V006. NASA EOSDIS LP DAAC.  https://doi.org/10.5067/MODIS/MOD13Q1.006.
  21. Didan, K., et al. (2015). MODIS Vegetation Index User’s Guide (MOD13 Series).  https://doi.org/10.5067/MODIS/MOD13Q1.006.
  22. EEAA (Egyptian Environmental Affairs Agency). (2010). Egypt second national communication under the United Nations Framework Convention on Climate change. Cairo: EEAA.Google Scholar
  23. El Banna, M. M., & Frihy, O. E. (2009). Human-induced changes in the geomorphology of the northeastern coast of the Nile Delta, Egypt. Geomorphology, 107(1–2), 72–78.  https://doi.org/10.1016/j.geomorph.2007.06.025.CrossRefGoogle Scholar
  24. El Bastawesy, M., Cherif, O. H., & Sultan, M. (2017). The geomorphological evidences of subsidence in the Nile Delta: analysis of high resolution topographic DEM and multi-temporal satellite images. Journal of African Earth Sciences, 136, 252–261.  https://doi.org/10.1016/J.JAFREARSCI.2016.10.013.CrossRefGoogle Scholar
  25. El-Hefnawi, A. I. (2005). Protecting agricultural land from urbanization or managing the conflict between informal urban growth while meeting the demands of the communities: lessons learnt from the Egyptian policy reforms. In World Bank urban research symposium, 4–6 April, 2005. Brasilia: Brasil.Google Scholar
  26. El-Marsafawy, S., Swelam, A., & Ghanem, A. (2018). Evolution of crop water productivity in the Nile Delta over three decades (1985–2015). Water, 10(9), 1168.  https://doi.org/10.3390/w10091168.CrossRefGoogle Scholar
  27. Foody, G. M. (2010). Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sensing of Environment, 114(10), 2271–2285.  https://doi.org/10.1016/j.rse.2010.05.003.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. Francesch-Huidobro, M., Dabrowski, M., Tai, Y., Chan, F., & Stead, D. (2017). Governance challenges of flood-prone Delta cities: integrating flood risk management and climate change in spatial planning. Progress in Planning, 114, 1–27.  https://doi.org/10.1016/J.PROGRESS.2015.11.001.CrossRefGoogle Scholar
  30. Frenkel, A., & Orenstein, D. E. (2012). Can urban growth management work in an era of political and economic change?’Journal of the. American Planning Association, 78(1), 16–33.  https://doi.org/10.1080/01944363.2011.643533.CrossRefGoogle Scholar
  31. Giosan, L., Syvitski, J., Constantinescu, S., & Day, J. (2014). Climate change: protect the world’s Deltas. Nature, 516(7529), 31–33.  https://doi.org/10.1038/516031a.CrossRefGoogle Scholar
  32. Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300.  https://doi.org/10.1016/J.PATREC.2005.08.011.CrossRefGoogle Scholar
  33. GTZ (the German Technical Cooperation Agency). (2009). Cairo’s informal areas between urban challenges and hidden potentials. Cairo: GTZ.Google Scholar
  34. Hardisky, M., Klemas, V. & Smart, M. (1983) ‘The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of spartina alterniflora canopies’, Photogrammetric Engineering & Remote Sensing, 49: 77–83Google Scholar
  35. Hassan, G. F. (2012). Regeneration as an approach for the development of informal settlements in Cairo metropolitan. Alexandria Engineering Journal, 51(3), 229–239.  https://doi.org/10.1016/J.AEJ.2012.02.003.CrossRefGoogle Scholar
  36. He, J., Liu, Y., Yu, Y., Tang, W., Xiang, W., & Liu, D. (2013). A counterfactual scenario simulation approach for assessing the impact of farmland preservation policies on urban sprawl and food security in a major grain-producing area of China. Applied Geography, 37(1), 127–138.  https://doi.org/10.1016/j.apgeog.2012.11.005.CrossRefGoogle Scholar
  37. Hereher, M. E. (2010). Vulnerability of the Nile Delta to sea level rise: an assessment using remote sensing. Geomatics, Natural Hazards and Risk, 1(4), 315–321.  https://doi.org/10.1080/19475705.2010.516912.CrossRefGoogle Scholar
  38. Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.  https://doi.org/10.1016/0034-4257(88)90106-X CrossRefGoogle Scholar
  39. Ihaka, R., & Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314.  https://doi.org/10.1080/10618600.1996.10474713.CrossRefGoogle Scholar
  40. Jin, Y., Liu, X., Chen, Y., & Liang, X. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong. International Journal of Remote Sensing, 39(23), 8703–8723.  https://doi.org/10.1080/01431161.2018.1490976.CrossRefGoogle Scholar
  41. Kleemann, J., Baysal, G., Bulley, H. N., & Fürst, C. (2017). Assessing driving forces of land use and land cover change by a mixed-method approach in North-Eastern Ghana, West Africa. Journal of environmental management, 196, 411–442.  https://doi.org/10.1016/j.jenvman.2017.01.053.CrossRefGoogle Scholar
  42. Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263.  https://doi.org/10.1127/0941-2948/2006/0130.CrossRefGoogle Scholar
  43. Li, L., Vrieling, A., Skidmore, A., Wang, T., Muñoz, A. R., & Turak, E. (2015). Evaluation of MODIS spectral indices for monitoring hydrological dynamics of a small, seasonally-flooded wetland in Southern Spain. Wetlands, 35(5), 851–864.  https://doi.org/10.1007/s13157-015-0676-9.CrossRefGoogle Scholar
  44. Liaw, A. & Wiener, M. (2018). Breiman and Cutler’s random forests for classification and regression’, p. 29. Available at: https://cran.r-project.org/web/packages/randomForest/index.html (Accessed: 5 December 2018).
  45. Liu, P., Di, L., Du, Q., & Wang, L. (2018). Remote sensing big data: theory, methods and applications. Remote Sensing, 10(5), 711.  https://doi.org/10.3390/rs10050711.CrossRefGoogle Scholar
  46. MALR (Ministry of Agriculture and Land Reclaimation). (2009). Sustinable agricultural decelopment strategy towards 2030. Cairo: MALR.Google Scholar
  47. Nitze, I., Barrett, B., & Cawkwell, F. (2015). Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series. International Journal of Applied Earth Observation and Geoinformation, 34, 136–146.  https://doi.org/10.1016/J.JAG.2014.08.001.CrossRefGoogle Scholar
  48. Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222.  https://doi.org/10.1080/01431160412331269698.CrossRefGoogle Scholar
  49. Peel, M. C., & Finlayson, B. L. (2007). & Mcmahon, T.A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11, 1633–1644.  https://doi.org/10.5194/hess-11-1633-2007.CrossRefGoogle Scholar
  50. Pontius, R. G., & Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429.  https://doi.org/10.1080/01431161.2011.552923.CrossRefGoogle Scholar
  51. Radwan, T. M., Blackburn, G. A., Whyatt, J. D., & Atkinson, P. M. (2019). Dramatic loss of agricultural land due to urban expansion threatens food security in the Nile Delta, Egypt. Remote Sensing, 11, 332.  https://doi.org/10.3390/rs11030332.CrossRefGoogle Scholar
  52. Ramadan, R. (2015). Food security and its measurement in Egypt. In CIHEAM Watch Letter 32. Zaragoza: CIHEAM.Google Scholar
  53. Roberts, D. A., Smith, M. O. & Adams, J. B. (1993). Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data. Remote Sensing of Environment, 44(2–3), 255–269.  https://doi.org/10.1016/0034-4257(93)90020-X.CrossRefGoogle Scholar
  54. Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing., 67, 93–104.  https://doi.org/10.1016/J.ISPRSJPRS.2011.11.002.CrossRefGoogle Scholar
  55. Schinasi, L. H., Benmarhnia, T., & De Roos, A. J. (2018). Modification of the association between high ambient temperature and health by urban microclimate indicators: a systematic review and meta-analysis. Environmental Research, 161, 168–180.  https://doi.org/10.1016/J.ENVRES.2017.11.004.CrossRefGoogle Scholar
  56. Schößer, B., Helming, K., & Wiggering, H. (2010). Assessing land use change impacts - a comparison of the sensor land use function approach with other frameworks. Journal of Land Use Science, 5(2), 159–178.  https://doi.org/10.1080/1747423X.2010.485727.CrossRefGoogle Scholar
  57. Sonnino, R. (2016). The new geography of food security: exploring the potential of urban food strategies. Geographical Journal, 182(2), 190–200.  https://doi.org/10.1111/geoj.12129.CrossRefGoogle Scholar
  58. Souza, C. M., Roberts, D. A. & Cochrane, M. A. (2005). Combining spectral and spatial information to map canopy damage from selective logging and forest fires. Remote Sensing of Environment, 98(2–3), 329–343.  https://doi.org/10.1016/J.RSE.2005.07.013.CrossRefGoogle Scholar
  59. Suthaharan, S. (2014). Big data classification: problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70–73.  https://doi.org/10.1145/2627534.2627557.CrossRefGoogle Scholar
  60. Tellioglu, I., & Konandreas, P. (2017). Agricultural policies, trade and sustainable development in Egypt. Rome: FAO.Google Scholar
  61. Thomlinson, J., Bolstad, P., & Cohen, W. (1999). Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map products. Remote Sensing of Environment, 70(1), 16–28.CrossRefGoogle Scholar
  62. Tornos, L., Huesca, M., Dominguez, J. A., Moyano, M. C., Cicuendez, V., Recuero, L., & Palacios-Orueta, A. (2015). Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 110–124.  https://doi.org/10.1016/J.ISPRSJPRS.2014.12.006.CrossRefGoogle Scholar
  63. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.  https://doi.org/10.1016/0034-4257(79)90013-0.CrossRefGoogle Scholar
  64. UNDESA United Nations Department of Economic and Social Affairs. (2018). World urbanization prospects: the 2018 revision. New York: UNDESA.Google Scholar
  65. UNDP (United Nations Development Programme). (2009). Adaptation in Egypt through integrated coastal zone management. UNDP: UNDP Climate Change Adaptation. Cairo.Google Scholar
  66. UNFCC (United Nations Framework Convention on Climate Change). (2015). Nationally determined contribution- Egypt. Available at http://www4.unfccc.int/ndcregistry/Pages/Search.aspx?k=Egypt (Accessed on October 1st, 2019).
  67. USAID. (2010). Egypt-land tenure and propery rights profile. USAID: USAID country profile property rights and resource governance. Cairo.Google Scholar
  68. Visvizi, A., Lytras, M. D., Damiani, E., & Mathkour, H. (2018). Policy making for smart cities: innovation and social inclusive economic growth for sustainability. Journal of Science and Technology Policy Management, 9(2), 126–133.  https://doi.org/10.1108/JSTPM-07-2018-079.CrossRefGoogle Scholar
  69. Wan, Z. (2013). Collection-6 MODIS land surface temperature products users’ guide.  https://doi.org/10.5067/MODIS/MYD11A2.006.CrossRefGoogle Scholar
  70. WDI (World Development Indicators). (2018). Urban population in Egypt. World Bank: Washington D.C.Google Scholar
  71. WFP (World Food Program). (2013). Food security and nutritional status in Egypt worsening amidst economic challenges. Cairo: WFP.Google Scholar
  72. World Bank. (2007). Egypt- Analysis of housing supply mechanisms. World Bank: Washington D.C.Google Scholar
  73. Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53.  https://doi.org/10.1080/17538947.2016.1239771.CrossRefGoogle Scholar
  74. Yehia, M. (2013). Green urbanism: a vision for sustainable urban renewal in Alexandria. In: Fenech A. et al. (eds): Global climate change, biodiversity and sustainabilty: challenges and opportunities (p. 460). University of Prince Edward Island.Google Scholar
  75. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.  https://doi.org/10.1080/01431160304987.CrossRefGoogle Scholar
  76. Zhai, Y., Qu, Z., & Hao, L. (2018). Land cover classification using integrated spectral, temporal and spatial features derived from remotely sensed images. Remote Sensing, 10(3), 383.  https://doi.org/10.3390/rs10030383.CrossRefGoogle Scholar
  77. Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171.  https://doi.org/10.1016/J.RSE.2014.01.011.CrossRefGoogle Scholar
  78. Zikopoulos, P., & Eaton, C. (2011). Understanding big data: analytics for enterprise class hadoop and streaming data. New York: McGraw-Hill Osborne Media.Google Scholar

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Authors and Affiliations

  1. 1.Department of Soil ScienceUniversity of ManitobaWinnipegCanada
  2. 2.Department of EconomicsThe Swedish University of Agricultural SciencesUppsalaSweden
  3. 3.Department of Economics and Rural DevelopmentArish UniversityArishEgypt

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