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Estimating urban vulnerability to flood and heat hazards: A case study in the municipality of Thessaloniki, Greece

Authors

DOI:

https://doi.org/10.18778/1231-1952.29.2.16

Keywords:

urban vulnerability, urban heat island, spectral indices, flood risk, spatial heterogeneity

Abstract

Continuous urban expansion, the conversion of open land to built-up areas and increased energy consumption have diversified the microclimates of cities. These phenomena combined with climate change hazards increase the vulnerability of cities, in a spatially heterogeneous way. Therefore, cities should become more resilient to those threats, by identifying and prioritising highly vulnerable areas. The main purpose of this study is to develop a spatial-based approach to assess the vulnerability of climate-related hazards in the urban environment of Thessaloniki (Greece). In this context, spatial and temporal patterns of land surface temperature were estimated through the calculation of various spectral indices, to conduct an analytical Urban Heat Island vulnerability assessment. Furthermore, the FloodMap-Pro application was used to identify coastal areas that are vulnerable to sea level rise, while historical floods were digitised in order to identify potential urban (flash) flood zones. The most important outcome of this paper is the creation of an integrated spatial vulnerability index, which identifies the urban areas that are prone to all these hazards. The final vulnerability map illustrates how the city of Thessaloniki is exposed to several climate-related hazards and that many areas/neighbourhoods are prone to one or more risk factors.

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References

ALEXANDER, C. (2020), ‘Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)’, International Journal of Applied Earth Observation and Geoinformation, 86, 102013. https://doi.org/10.1016/j.jag.2019.102013
Google Scholar DOI: https://doi.org/10.1016/j.jag.2019.102013

ANDERSON, M. C., NORMAN, J. M., KUSTAS, W. P., HOUBORG, R., STARKS, P. J. and AGAM, N. (2008), ‘A thermal-based remote sensing technique for routine mapping of land-surface carbon, water, and energy fluxes from field to regional scales’, Remote Sensing of Environment, 112, pp. 4227–4241. https://doi.org/10.1016/j.rse.2008.07.009
Google Scholar DOI: https://doi.org/10.1016/j.rse.2008.07.009

ANSELIN, L. (1995), ‘Local indicators of spatial association—LISA’, Geographical Analysis, 27 (2), pp. 93–115.
Google Scholar DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

ANSELIN, L., SYABRI, I. and KHO, Y. (2010), GeoDa: an introduction to spatial data analysis. In Handbook of applied spatial analysis, Berlin, Heidelberg: Springer, pp. 73–89.
Google Scholar DOI: https://doi.org/10.1007/978-3-642-03647-7_5

ARYAL, A., SHAKYA, B., MAHARJAN, M., TALCHABHADEL, R. and THAPA, B. (2021), Evaluation of the Land Surface Temperature using Satellite Images in Kathmandu Valley, 1, pp. 1–10.
Google Scholar DOI: https://doi.org/10.3126/njce.v1i1.43368

ARTIS, D. A. and CARNAHAN, W. H. (1982), ‘Survey of emissivity variability in thermography of urban areas’, Remote Sensing of Environment, 12, pp. 313–329. https://doi.org/10.1016/0034-4257(82)90043-8
Google Scholar DOI: https://doi.org/10.1016/0034-4257(82)90043-8

ASHRAF, M. and NAWAZ, R. (2015), ‘A Comparison of Change Detection Analyses Using Different Band Algebras for Baraila Wetland with Nasa’s Multi-Temporal Landsat Dataset’, JGIS, 07, pp. 1–19. https://doi.org/10.4236/jgis.2015.71001
Google Scholar DOI: https://doi.org/10.4236/jgis.2015.71001

BOSELLO, F. and DE CIAN, E. (2014), ‘Climate change, sea level rise, and coastal disasters. A review of modeling practices’, Energy Economics, 46, pp. 593–605.
Google Scholar DOI: https://doi.org/10.1016/j.eneco.2013.09.002

BRUNSELL, N. A. and GILLIES, R. R. (2003), ‘Length Scale Analysis of Surface Energy Fluxes Derived from Remote Sensing’, Journal of Hydrometeorology, 4, pp. 1212–1219. https://doi.org/10.1175/1525-7541(2003)004<1212:LSAOSE>2.0.CO;2
Google Scholar DOI: https://doi.org/10.1175/1525-7541(2003)004<1212:LSAOSE>2.0.CO;2

BUCHHOLZ, S., KOSSMANN, M. and ROOS, M. (2016), ‘INKAS–a guidance tool to assess the impact of adaptation measures against urban heat’, Meteorologische Zeitschrift, 25 (3), pp. 281–289.
Google Scholar DOI: https://doi.org/10.1127/metz/2016/0731

BUYANTUYEV, A. and WU, J. (2010), ‘Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns’, Landscape Ecol, 25, pp. 17–33. https://doi.org/10.1007/s10980-009-9402-4
Google Scholar DOI: https://doi.org/10.1007/s10980-009-9402-4

CARLSON, T. N. and RIPLEY, D. A. (1997), ‘On the relation between NDVI, fractional vegetation cover, and leaf area index’, Remote Sensing of Environment, 62, pp. 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
Google Scholar DOI: https://doi.org/10.1016/S0034-4257(97)00104-1

CHANGNON, S. A., KUNKEL, K. E. and REINKE, B. C. (1996), ‘Impacts and responses to the 1995 heat wave: A call to action’, Bulletin of the American Meteorological Society, 77, pp. 1497–1505.
Google Scholar DOI: https://doi.org/10.1175/1520-0477(1996)077<1497:IARTTH>2.0.CO;2

CHEN, X.-L., ZHAO, H.-M., LI, P.-X. and YIN, Z.-Y. (2006), ‘Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes’, Remote Sensing of Environment, Thermal Remote Sensing of Urban Areas, 104, pp. 133–146. https://doi.org/10.1016/j.rse.2005.11.016
Google Scholar DOI: https://doi.org/10.1016/j.rse.2005.11.016

CHEN, L., LI, M., HUANG, F. and XU, S. (2013), ‘Relationships of LST to NDBI and NDVI in Wuhan City based on Landsat ETM+ image’, [in:] 2013 6th International Congress on Image and Signal Processing (CISP), presented at the 2013 6th International Congress on Image and Signal Processing (CISP), pp. 840–845. https://doi.org/10.1109/CISP.2013.6745282
Google Scholar DOI: https://doi.org/10.1109/CISP.2013.6745282

DEARDORFF, J. W. (1978), ‘Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation’, Journal of Geophysical Research: Oceans, 83, pp. 1889–1903. https://doi.org/10.1029/JC083iC04p01889
Google Scholar DOI: https://doi.org/10.1029/JC083iC04p01889

DEDEKORKUT-HOWES, A., TORABI, E. and HOWES, M. (2020), ‘When the tide gets high: A review of adaptive responses to sea level rise and coastal flooding’, Journal of Environmental Planning and Management, 63 (12), pp. 2102–2143.
Google Scholar DOI: https://doi.org/10.1080/09640568.2019.1708709

DONG, W., LIU, Z., ZHANG, L., TANG, Q., LIAO, H. and LI, X. (2014), ‘Assessing heat health risk for sustainability in Beijing’s urban heat island’, Sustainability, 6, pp. 7334–7357.
Google Scholar DOI: https://doi.org/10.3390/su6107334

DOS SANTOS, A. R., DE OLIVEIRA, F. S., DA SILVA, A. G., GLERIANI, J. M., GONÇALVES, W., MOREIRA, G. L., SILVA, F. G., BRANCO, E. R. F., MOURA, M. M., DA SILVA, R. G. and JUVANHOL, R. S. (2017), ‘Spatial and temporal distribution of urban heat islands’, Science of the Total Environment, 605, pp. 946–956.
Google Scholar DOI: https://doi.org/10.1016/j.scitotenv.2017.05.275

FREITAS, S. C., TRIGO, I., MACEDO, J., BARROSO, C., SILVA, R. and PERDIGAO, R. (2013), ‘Land Surface Temperature from multiple geostationary satellites’, International Journal of Remote Sensing, 34, pp. 3051–3068.
Google Scholar DOI: https://doi.org/10.1080/01431161.2012.716925

Flood Map: Elevation Map, Sea Level Rise Map, n.d. URL https://www.floodmap.net/ [accessed on: 19.08.2022].
Google Scholar

GARZILLO, C. and ULRICH, P. (2015), Annex to MS94: Compilation of case study reports A compendium of case study reports from 40 cities in 14 European countries, 94. WWWforEurope Working Paper.
Google Scholar

GEMENETZI, G. (2017), ‘Thessaloniki: The changing geography of the city and the role of spatial planning’, Cities, 64, pp. 88–97. https://doi.org/10.1016/j.cities.2016.10.007
Google Scholar DOI: https://doi.org/10.1016/j.cities.2016.10.007

GISGEOGRAPHY (2019), Landsat 8 Bands and Band Combinations. GIS Geography. URL https://gisgeography.com/landsat-8-bands-combinations/ [accessed on: 21.02.2022].
Google Scholar

GORGANI, S., PANAHI, M. and REZAIE, F. (2013), The Relationship between NDVI and LST in the urban area of Mashhad, Iran.
Google Scholar

HAINES, A., KOVATS, R. S., CAMPBELL-LENDRUM, D. and CORVALAN, C. (2006), ‘Climate change and human health: Impacts, vulnerability and public health’, Public Health, 120, pp. 585–596. https://doi.org/10.1016/j.puhe.2006.01.002
Google Scholar DOI: https://doi.org/10.1016/j.puhe.2006.01.002

IPCC, 2014. CLIMATE CHANGE (2014), ‘Synthesis report’, [in:] Core Writing Team, R. K. Pachauri and L. A. Meyer (eds.), Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC: Geneva, Switzerland, pp. 1–112. https://doi.org/10.1017/CBO9781107415324
Google Scholar DOI: https://doi.org/10.1017/CBO9781107415324

IPCC (2018), Special Report: Global Warming of 1.5 ºC, Incheon: Intergovernmental Panel on Climate Change.
Google Scholar

IPCC (2019), ‘Summary for Policymakers’, [in:] IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, N. Weyer (eds.)]. Forthcoming. https://www.ipcc.ch/srocc/chapter/summary-for-policymakers/ [accessed on: 21.02.2022].
Google Scholar

JAVED, M., KRISHNANAND, S. H., NAGABHUSHAN, P. and CHAUDHURI, B. B. (2016), ‘Visualizing CCITT Group 3 and Group 4 TIFF Documents and Transforming to Run-Length Compressed Format Enabling Direct Processing in Compressed Domain’, Procedia Computer Science, International Conference on Computational Modelling and Security, 85, pp. 213–221. https://doi.org/10.1016/j.procs.2016.05.214
Google Scholar DOI: https://doi.org/10.1016/j.procs.2016.05.214

JONES, H. G. and VAUGHAN, R. A. (2010), Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford, New York: Oxford University Press.
Google Scholar

JU, Y., LINDBERGH, S., HE, Y. and RADKE, J. D. (2019), ‘Climate-related uncertainties in urban exposure to sea level rise and storm surge flooding: a multi-temporal and multi-scenario analysis’, Cities, 92, pp. 230–246.
Google Scholar DOI: https://doi.org/10.1016/j.cities.2019.04.002

KANTZIOURA, A., KOSMOPOULOS, P. and ZORAS, S. (2012), ‘Urban surface temperature and microclimate measurements in Thessaloniki’, Energy and Buildings, 44, pp. 63–72. https://doi.org/10.1016/j.enbuild.2011.10.019
Google Scholar DOI: https://doi.org/10.1016/j.enbuild.2011.10.019

KAUFMANN, R. K., ZHOU, L., MYNENI, R. B., TUCKER, C. J., SLAYBACK, D., SHABANOV, N. V. and PINZON, J. (2003), ‘The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data’, Geophysical Research Letters, 30. https://doi.org/10.1029/2003GL018251
Google Scholar DOI: https://doi.org/10.1029/2003GL018251

KAZAK, J. K. (2018), ‘The use of a decision support system for sustainable urbanization and thermal comfort in adaptation to climate change actions – The case of the Wrocław larger urban zone (Poland)’, Sustainability, 10 (4), 1083, pp. 1-15.
Google Scholar DOI: https://doi.org/10.3390/su10041083

KLEEREKOPER, L., van ESCH, M. and SALCEDO, T. B. (2012), ‘How to make a city climate-proof, addressing the urban heat island effect’, Resources, Conservation and Recycling, Climate Proofing Cities, 64, pp. 30–38. https://doi.org/10.1016/j.resconrec.2011.06.004
Google Scholar DOI: https://doi.org/10.1016/j.resconrec.2011.06.004

KIM, H. H. (1992), ‘Urban heat island’, International Journal of Remote Sensing, 13 (12), pp. 319– 336.
Google Scholar DOI: https://doi.org/10.1080/01431169208904271

KING, A. D. and KAROLY, D. J. (2017), ‘Climate extremes in Europe at 1.5 and 2 degrees of global warming. Environ’, Res. Lett., 12, 114031. https://doi.org/10.1088/1748-9326/aa8e2c
Google Scholar DOI: https://doi.org/10.1088/1748-9326/aa8e2c

KOGAN, F. (2001), ‘Operational Space Technology for Global Vegetation Assessment’, Bulletin American Meteorological Society, pp. 1949–1964.
Google Scholar DOI: https://doi.org/10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2

KONOPACKI, S. and AKBARI, H. (2002), Energy savings for heat island reduction strategies in Chicago and Houston (including updates for Baton Rouge, Sacramento, and Salt Lake City), Draft Final Report, LBNL-49638, University of California, Berkeley.
Google Scholar DOI: https://doi.org/10.2172/795970

KUMARI, P., GARG, V., KUMAR, R. and KUMAR, K. (2021), ‘Impact of urban heat island formation on energy consumption in Delhi’, Urban Climate, 36, 100763.
Google Scholar DOI: https://doi.org/10.1016/j.uclim.2020.100763

LALOR, G. C. and ZHANG, C. (2001), ‘Multivariate outlier detection and remediation in geochemical databases’, Science of the total environment, 281 (1–3), pp. 99–109.
Google Scholar DOI: https://doi.org/10.1016/S0048-9697(01)00839-7

LATIF, M. S. (2014), Land Surface Temperature Retrival of Landsat-8 Data Using Split Window Algorithm – A Case Study of Ranchi District.
Google Scholar

LATINOPOULOS, D., MALLIOS, Z. and LATINOPOULOS, P. (2016), ‘Valuing the benefits of an urban park project: A contingent valuation study in Thessaloniki, Greece’, Land Use Policy, 55, pp. 130–141.
Google Scholar DOI: https://doi.org/10.1016/j.landusepol.2016.03.020

LI, B. H. W. (2017), ‘Comparative study on the correlations between NDVI, NDMI and LST’, Advances in Geographical Sciences, 36, pp. 585–596. https://doi.org/10.18306/dlkxjz.2017.05.006
Google Scholar DOI: https://doi.org/10.18306/dlkxjz.2017.05.006

MALIK, M. S., SHUKLA, J. P. and MISHRA, S. (2019), ‘Relationship of LST, NDBI and NDVI using Landsat-8 data in Kandaihimmat Watershed, Hoshangabad, India’, Indian Journal of Geo-Marine Sciences, 48 (01), pp. 25–31.
Google Scholar

MARINO, F. (2017), ‘Top of Atmosphere Reflectance on Sentinel 3’, Earth Starts Beating. https://www.earthstartsbeating.com/2017/04/27/top-of-atmosphere-reflectance-on-sentinel-3/ [accessed on: 21.02.2022].
Google Scholar

MARTINS, J. P. (1999), The Hourly Land Surface Temperature from the Copernicus Global Land Service – Part 1: the updated algorithm with inclusion of vegetation dynamics and of Indian Ocean Data Coverage mission.
Google Scholar

MATSA, M. and MUPEPI, O. (2022), ‘Flood risk and damage analysis in urban areas of Zimbabwe. A case of 2020/21 rain season floods in the city of Gweru’, International Journal of Disaster Risk Reduction, 67, 102638. https://doi.org/10.1016/j.ijdrr.2021.102638
Google Scholar DOI: https://doi.org/10.1016/j.ijdrr.2021.102638

MEMON, R. A., LEUNG, D. Y. C. and LIU, C. H. (2009), ‘An investigation of urban heat island intensity (UHII) as an indicator of urban heating’, Atmospheric Research, 94, pp. 491–500.
Google Scholar DOI: https://doi.org/10.1016/j.atmosres.2009.07.006

MORAN, P. A. P. (1950), ‘Notes on continuous stochastic phenomena’, Biometrika, 37 (1–2), pp. 17–23.
Google Scholar DOI: https://doi.org/10.1093/biomet/37.1-2.17

MUSHORE, T. D., MUTANGA, O. and ODINDI, J. (2022), ‘Estimating urban LST using multiple remotely sensed spectral indices and elevation retrievals’, Sustainable Cities and Society, 78, 103623. https://doi.org/10.1016/j.scs.2021.103623
Google Scholar DOI: https://doi.org/10.1016/j.scs.2021.103623

NEMANI, R., PIERCE, L., RUNNING, S. and GOWARD, S. (1993). ‘Developing Satellite-derived Estimates of Surface Moisture Status’, Journal of Applied Meteorology and Climatology, 32, pp. 548–557. https://doi.org/10.1175/1520-0450(1993)032<0548:DSDEOS>2.0.CO;2
Google Scholar DOI: https://doi.org/10.1175/1520-0450(1993)032<0548:DSDEOS>2.0.CO;2

OKE, T. R. (1982), ‘The energetic basis of the urban heat island’, Quarterly Journal of the Royal Meteorological Society, 108, pp. 1–24. https://doi.org/10.1002/qj.49710845502
Google Scholar DOI: https://doi.org/10.1002/qj.49710845502

PITIDIS, V., TAPETE, D., COAFFEE, J., KAPETAS, L. and PORTO DE ALBUQUERQUE, J. (2018), ‘Understanding the implementation challenges of urban resilience policies: Investigating the influence of urban geological risk in Thessaloniki, Greece’, Sustainability, 10 (10), 3573.
Google Scholar DOI: https://doi.org/10.3390/su10103573

PURVIS, M. J., BATES, P. D. and HAYES, C. M. (2008), ‘A probabilistic methodology to estimate future coastal flood risk due to sea level rise’, Coastal Engineering, 55 (12), pp. 1062–1073.
Google Scholar DOI: https://doi.org/10.1016/j.coastaleng.2008.04.008

QGIS DEVELOPMENT TEAM (2019), QGIS Geographic Information System (3.6). Open Source Geospatial Foundation Project. https://www.qgis.org
Google Scholar

QIAO, Z., TIAN, G., ZHANG, L., and XU, X. (2014), ‘Influences of urban expansion on urban heat island in Beijing during 1989–2010’, Advances in Meteorology, 2014, pp. 1–11.
Google Scholar DOI: https://doi.org/10.1155/2014/187169

RAI, R. (2019), Assessment of LST Variation in Kathmandu, Nepal. ArcGIS StoryMaps. https://storymaps.arcgis.com/stories/a70d27a801bf4972a005e03cb004e068 [accessed on: 21.02.2022]
Google Scholar

RANDHI, U. D., SWARAJ, J., KUMAR, K. S. and PATRUDU, T. B. (2021), Sensible heat flux characterization using satellite remote sensing techniques, 6.
Google Scholar

RIZWAN, A. M., DENNIS, L. Y. C. and LIU, C. (2008), ‘A review on the generation, determination and mitigation of Urban Heat Island’, Journal of Environmental Sciences, 20, pp. 120–128. https://doi.org/10.1016/S1001-0742(08)60019-4
Google Scholar DOI: https://doi.org/10.1016/S1001-0742(08)60019-4

ROY, D. P., KOVALSKYY, V., ZHANG, H. K., VERMOTE, E. F., YAN, L., KUMAR, S. S. and EGOROV, A. (2016), ‘Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity’, Remote Sensing of Environment, Landsat 8 Science Results, 185, pp. 57–70. https://doi.org/10.1016/j.rse.2015.12.024
Google Scholar DOI: https://doi.org/10.1016/j.rse.2015.12.024

SANTAMOURIS, M., CARTALIS, C., SYNNEFA, A. and KOLOKOTSA, D. (2015), ‘On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings-a review’, Energy and Buildings, 98, pp. 119–124.
Google Scholar DOI: https://doi.org/10.1016/j.enbuild.2014.09.052

SEDAGHAT, A. and SHARIF, M. (2022), ‘Mitigation of the impacts of heat islands on energy consumption in buildings: A case study of the city of Tehran, Iran’, Sustainable Cities and Society, 76, 103435. https://doi.org/10.1016/j.scs.2021.103435
Google Scholar DOI: https://doi.org/10.1016/j.scs.2021.103435

SHAH, S., SHRESTHA, R., TIMILSINA, P. and THAPA, M. (2018), Satellite Imagery Based Observation of Land Surface Temperature of Kathmandu Valley 7, 8.
Google Scholar

SHARMA, A., WASKO, C. and LETTENMAIER, D. P. (2018), ‘If precipitation extremes are increasing, why aren’t floods?’, Water Resources Research, 54, pp. 8545– 8551.
Google Scholar DOI: https://doi.org/10.1029/2018WR023749

SMITH, T. M., REYNOLDS, R. W., PETERSON, T. C. and LAWRIMORE, J. (2008), ‘Improvements to NOAA’s Historical Merged Land–Ocean Surface Temperature Analysis (1880–2006)’, Journal of Climate, 21, pp. 2283–2296. https://doi.org/10.1175/2007JCLI2100.1
Google Scholar DOI: https://doi.org/10.1175/2007JCLI2100.1

SOBRINO, J. A., JIMÉNEZ-MUÑOZ, J. C. and PAOLINI, L. (2004), ‘Land surface temperature retrieval from LANDSAT TM 5’, Remote Sensing of Environment, 90, pp. 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Google Scholar DOI: https://doi.org/10.1016/j.rse.2004.02.003

STAMOU, A., MANIKA, S. and PATIAS, P. (2013), ‘Estimation of land surface temperature and urban patterns relationship for urban heat island studies’, International Conference on Changing Cities: Spatial, morphological, formal & socio-economic dimensions, 18 to 21 June 2013, Skiathos island, pp. 2007–2013.
Google Scholar

STATHOPOULOU, M., CARTALIS, C. and KERAMITSOGLOU, I. (2004), ‘Mapping micro-urban heat islands using NOAA/AVHRR images and CORINE Land Cover: an application to coastal cities of Greece’, International Journal of Remote Sensing, 25, pp. 2301–2316. https://doi.org/10.1080/01431160310001618725
Google Scholar DOI: https://doi.org/10.1080/01431160310001618725

STATHOPOULOU, M. and CARTALIS, C. (2007), ‘Daytime urban heat islands from Landsat ETM+ and Corine land cover data: An application to major cities in Greece’, Solar Energy, 81, pp. 358–368. https://doi.org/10.1016/j.solener.2006.06.014
Google Scholar DOI: https://doi.org/10.1016/j.solener.2006.06.014

SU, W., ZHANG, Y., YANG, Y. and YE, G. (2014), ‘Examining the impact of greenspace patterns on land surface temperature by coupling LiDAR data with a CFD model’, Sustainability, 6 (10), pp. 6799–6814.
Google Scholar DOI: https://doi.org/10.3390/su6106799

SUN, D. and KAFATOS, M. (2007), ‘Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America’, Geophysical Research Letters, 34. https://doi.org/10.1029/2007GL031485
Google Scholar DOI: https://doi.org/10.1029/2007GL031485

TUCKER, C. J. (1979), ‘Red and photographic infrared linear combinations for monitoring vegetation’, Remote Sensing of Environment, 8, pp. 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
Google Scholar DOI: https://doi.org/10.1016/0034-4257(79)90013-0

TUCKER, C. J., PINZON, J. E., BROWN, M. E., SLAYBACK, D. A., PAK, E. W., MAHONEY, R., VERMOTE, E. F. and EL SALEOUS, N. (2005), ‘An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data’, International Journal of Remote Sensing, 26, pp. 4485–4498. https://doi.org/10.1080/01431160500168686
Google Scholar DOI: https://doi.org/10.1080/01431160500168686

USGS (2022a), Using the USGS Landsat Level-1 Data Product, U.S. Geological Survey, n.d. https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product [accessed on: 18.02.2022].
Google Scholar

USGS (2022b), Normalized Difference Moisture Index, U.S. Geological Survey, n.d. https://www.usgs.gov/landsat-missions/normalized-difference-moisture-index [accessed on: 01.03.2022].
Google Scholar

USGS (2022c), What are the band designations for the Landsat satellites?, U.S. Geological Survey, n.d. https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites [accessed on: 01.03.2022].
Google Scholar

VOOGT, J. A. and OKE, T. R. (2003), ‘Thermal remote sensing of urban climates’, Remote sensing of environment, 86 (3), pp. 370–384.
Google Scholar DOI: https://doi.org/10.1016/S0034-4257(03)00079-8

WAN, Z., DOZIER, J. and DOZIER, J. (1996), ‘A generalized split-window algorithm for retrieving land-surface temperature from space’, IEEE Transactions on Geoscience and Remote Sensing, 34, pp. 892–905. https://doi.org/10.1109/36.508406
Google Scholar DOI: https://doi.org/10.1109/36.508406

WWF GREECE, Climate change impacts in Greece in the near future, Athens, September 2009.
Google Scholar

XIA, J., FALCONER, R. A., LIN, B. and TAN, G. (2011), ‘Modelling flash flood risk in urban areas’, [in:] Proceedings of the Institution of Civil Engineers-Water Management, 164 (6), pp. 267–282, Thomas Telford Ltd.
Google Scholar DOI: https://doi.org/10.1680/wama.2011.164.6.267

YIANNAKOU, A. and SALATA, K. D. (2017), ‘Adaptation to climate change through spatial planning in compact urban areas: a case study in the city of Thessaloniki’, Sustainability, 9 (2), p. 271.
Google Scholar DOI: https://doi.org/10.3390/su9020271

ZHA, Y., GAO, J. and NI, S. (2003), ‘Use of normalized difference built-up index in automatically mapping urban areas from TM imagery’, International Journal of Remote Sensing, 24, pp. 583–594. https://doi.org/10.1080/01431160304987
Google Scholar DOI: https://doi.org/10.1080/01431160304987

ZHANG, C., LUO, L., XU, W. and LEDWITH, V. (2008), ‘Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland’, Science of the Total Environment, 398 (1–3), pp. 212–221.
Google Scholar DOI: https://doi.org/10.1016/j.scitotenv.2008.03.011

ZHOU, D., ZHANG, L., HAO, L. SUN, G., LIU, Y. and ZHU, C. (2016), ‘Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China’, Science of the Total Environment, 544, pp. 617–626.
Google Scholar DOI: https://doi.org/10.1016/j.scitotenv.2015.11.168

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2023-03-07

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Lazaridis, V., & Latinopoulos, D. (2023). Estimating urban vulnerability to flood and heat hazards: A case study in the municipality of Thessaloniki, Greece. European Spatial Research and Policy, 29(2), 309–340. https://doi.org/10.18778/1231-1952.29.2.16

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