Estimating urban vulnerability to flood and heat hazards: A case study in the municipality of Thessaloniki, Greece
DOI:
https://doi.org/10.18778/1231-1952.29.2.16Keywords:
urban vulnerability, urban heat island, spectral indices, flood risk, spatial heterogeneityAbstract
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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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 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 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. 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. 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. 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 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 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. 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 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. 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 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 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. 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 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 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 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. 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. 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. 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. 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].
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.
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 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].
GORGANI, S., PANAHI, M. and REZAIE, F. (2013), The Relationship between NDVI and LST in the urban area of Mashhad, Iran.
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 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 DOI: https://doi.org/10.1017/CBO9781107415324
IPCC (2018), Special Report: Global Warming of 1.5 ºC, Incheon: Intergovernmental Panel on Climate Change.
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].
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 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.
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. 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 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 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. 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 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. 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 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. 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. 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. 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. 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.
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. 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 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.
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].
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.
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 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. 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. 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 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 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 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. 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. 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
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. 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]
RANDHI, U. D., SWARAJ, J., KUMAR, K. S. and PATRUDU, T. B. (2021), Sensible heat flux characterization using satellite remote sensing techniques, 6.
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 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 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. 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 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.
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. 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 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 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.
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 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 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. 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 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 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 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].
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].
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].
VOOGT, J. A. and OKE, T. R. (2003), ‘Thermal remote sensing of urban climates’, Remote sensing of environment, 86 (3), pp. 370–384. 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 DOI: https://doi.org/10.1109/36.508406
WWF GREECE, Climate change impacts in Greece in the near future, Athens, September 2009.
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. 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. 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 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. 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. DOI: https://doi.org/10.1016/j.scitotenv.2015.11.168
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