النمو الحضري دون تعويض للمساحات الخضراء: إطار لدمج الأدلة الجغرافية المكانية متعددة المصادر من أجل التخطيط المعماري المستجيب للمناخ في بنغازي، ليبيا
DOI:
https://doi.org/10.65405/41md9s79الكلمات المفتاحية:
Benghazi; geospatial evidence fusion; Sentinel-2; Copernicus EMS; urban green space; climate-responsive architecture; spatial planningالملخص
Rapid urban change in data-scarce cities is difficult to translate into defensible architectural planning because geospatial products often differ in extent, resolution, class definition, and date. This study develops a provenance-aware multi-source evidence-fusion framework for Benghazi using Copernicus EMSN080 urban-sprawl products (2017–2020) and an independent Sentinel-2 NDVI land-cover assessment (2017–2021). Copernicus shows artificial surfaces increasing from 162.253 to 168.569 km², a gain of 6.316 km² (3.89%) and a CAGR of 1.281%. The Sentinel-2 study reports urban land increasing from 57.4227 to 59.0490 km² while green space declined from 12.1338 to 8.1908 km². Re-analysis shows the green-to-urban ratio falling from 0.211 to 0.139 (−34.36%). The combined evidence supports disturbed-land infill, protection and reconnection of residual green infrastructure, lake-edge safeguards, and annual geospatial monitoring.
التنزيلات
المراجع
Beele, E., Aerts, R., Reyniers, M., & Somers, B. (2024). Spatial configuration of green space matters: Associations between urban land cover and air temperature. Landscape and Urban Planning, 249, 105121. https://doi.org/10.1016/j.landurbplan.2024.105121
Ben Haj Ncir, N., Rejeb, A., & Claramunt, C. (2026). Urbanization and thermal vulnerability: Spatio-temporal assessment in a Mediterranean city. Sustainable Cities and Society, 142, 107311. https://doi.org/10.1016/j.scs.2026.107311
Bufarwa, M. T. (2022). Assessing the spatio-temporal changes in urban green spaces of Benghazi using remote sensing technique (NDVI). Libyan Journal of Basic Sciences, 19(5), 68–79.
Copernicus. (2020, November 12). The Copernicus Emergency Management Service analyses the evolution of the city of Benghazi three years after benchmark study. https://mapping.emergency.copernicus.eu/news/the-copernicus-emergency-management-service-analyses-the-evolution-of-the-city-of-benghazi-three-years-after-benchmark-study/
Copernicus Data Space Ecosystem. (2026). Sentinel-2. https://dataspace.copernicus.eu/data-collections/copernicus-sentinel-missions/sentinel-2
Copernicus Emergency Management Service. (2020). EMSN080: Land use mapping and population dynamics analysis in Benghazi, Libya. Risk and Recovery Mapping. https://mapping.emergency.copernicus.eu/activations/EMSN080/
Copernicus Land Monitoring Service. (2020). Urban Atlas land cover/land use and street tree layer 2012 and 2018: Product user manual.
Eliades, N.-G. H., Kefalas, G., & Lorilla, R. S. (2025). An integrated approach for enhancing the planning perspective of peri-urban and urban green spaces: The case of the Limassol Metropolitan Area in Cyprus. Cities, 167, 106332. https://doi.org/10.1016/j.cities.2025.106332
Eneche, P. S. U., Atun, F., Zeng, Y., & Pfeffer, K. (2024). Robust drivers of urban land surface temperature dynamics across diverse landscape characters: An augmented systematic literature review. Ecological Indicators, 163, 112056. https://doi.org/10.1016/j.ecolind.2024.112056
European Environment Agency. (n.d.). Urban Atlas. https://www.eea.europa.eu/en/analysis/maps-and-charts/urban-atlas
Hegazy, T. A.-M., Abd El Galil, M., Abou Samra, R. M., Mohamed, M. T., & Al Saleh, S. S. M. (2022). Detecting NDVI & LST in the municipalities of Al Jabal Al Akhdar Region – Libya using remote sensing and GIS. Scientific Journal for Damietta Faculty of Science, 12(1), 63–70.
Joint Research Centre, European Commission. (2020). Land use mapping and population dynamics analysis in Benghazi, Libya (2020-07-24) [Data set]. data.europa.eu.
Kalantar, B., Al-Najjar, H. A. H., Rizeei, H. M., Amazeeq, M. S. A. B., Idrees, M. O., Halin, A. A., & Mansor, S. (2019). Urban planning using a geospatial approach: A case study of Libya. In Sustainability in Urban Planning and Design. IntechOpen. https://doi.org/10.5772/intechopen.86355
Koresh, M. A., & Abzabez, A. A. (2026). The Future of the Libyan-European Geoeconomic Partnership in the Era of Global Energy Transition: A Conceptual Framework of Complex Interdependence. Shihab Journal of Humanities, 2(2), 16-21.
Khan, M. S., & Li, Y. (2024). Comparative study and effects of urban green scape on the land surface temperature of a large metropolis and green city. Heliyon, 10(3), e24912. https://doi.org/10.1016/j.heliyon.2024.e24912
Albaraesi, M. J. S., Ali, M. A. M. A., Dalla, L. O. B., EL-sseid, M. A. M., Medeni, T. D., Medeni, İ. T., & Alnnale, T. (2025). Random construction in the city of Al-Bayda during the period 2011-2022 and its irregular expansion and its impact on the urban landscape. مجلة العلوم الشاملة, 10(ملحق 38), 2590-2614.
Li, H., Zhao, Y., Wang, C., Ürge-Vorsatz, D., Carmeliet, J., & Bardhan, R. (2024). Cooling efficacy of trees across cities is determined by background climate, urban morphology, and tree trait. Communications Earth & Environment. https://doi.org/10.1038/s43247-024-01908-4
Li, Y., et al. (2024). Green spaces provide substantial but unequal urban cooling globally. Nature Communications, 15, 7108. https://doi.org/10.1038/s41467-024-51355-0
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879–1900. https://doi.org/10.1175/BAMS-D-11-00019.1
Wan, B., Shen, N., Zhang, H., & Sheng, Q. (2026). Analyzing the impact of urban morphology on urban land surface temperature from the perspective of spatial configuration and explainable machine learning: A case study of seven cities. Sustainable Cities and Society, 138, 107165. https://doi.org/10.1016/j.scs.2026.107165
Wang, Z., Zhou, R., Rui, J., & Yu, Y. (2025). Revealing the impact of urban spatial morphology on land surface temperature in plain and plateau cities using explainable machine learning. Sustainable Cities and Society, 118, 106046. https://doi.org/10.1016/j.scs.2024.106046
