Towards the determination of driving factors of varying LST-LCZ relationships – a case study over 25 cities

  • Mehdi Gholami Rostam University of Augsburg
  • Christoph Beck University of Augsburg
Keywords: Land Surface Temperature (LST), Local Climate Zones (LCZs), Wilcoxon rank sum test, Hierarchical cluster analysis,

Abstract


The current study aimed to prove the existence of a significant relation between land surface temperature (LST) and local climate zones (LCZs) and its possibility to be generalized to all cities around the world with different climatic zones and characteristics. The further step in this regard was to find the effective climatic and geographical variables affecting this potential relation. For that, 25 cities all around the world with various climatic conditions were selected based on the availability of appropriate satellite images and level zero data on the World Urban Database and Portal Tool (WUDAPT). After acquiring both LST and LCZ maps the comparison between them was made with the Wilcoxon rank sum test indicating the existence of any meaningful pattern. Then, 8 climatic and geographical variables and all possible combinations thereof were assessed to determine the effective drivers on the LST-LCZ relationship. The results showed that the combination of the latitude, mean and maximum annual temperature affected this connection more than any other considered variables.

References

Alexander, P., & Mills, G. (2014). Local Climate Classification and Dublin’s Urban Heat Island. Atmosphere, 5(4), 755-774. doi:10.3390/atmos5040755

Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 2016, 1-8. doi:10.1155/2016/1480307

Bechtel, B., Alexander, P.J., Beck, C., Böhner, J., Brousse, O., Ching, J., Demuzere, M., Fonte, C., Gál, T., Hidalgo, J., & Hoffmann, P. (2019). Generating WUDAPT Level 0 data: Current status of production and evaluation. Urban Climate, 27, 24-45. doi:10.1016/j.uclim.2018.10.001

Bechtel, B., Alexander, P., Böhner, J., Ching, J., Conrad, O., Feddema, J., Mills, G., See, L., & Stewart, I. (2015). Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities. ISPRS International Journal of Geo-Information, 4(1), 199-219. doi:10.3390/ijgi4010199

Bechtel, B., & Daneke, C. (2012). Classification of Local Climate Zones Based on Multiple Earth Observation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 1191-1202. doi:10.1109/jstars.2012.2189873

Bechtel, B., Demuzere, M., Mills, G., Zhan, W., Sismanidis, P., Small, C., & Voogt, J. (2019). SUHI analysis using Local Climate Zones: A comparison of 50 cities. Urban Climate, 28, doi:10.1016/j.uclim.2019.01.005

Beck, C., Straub, A., Breitner, S., Cyrys, J., Philipp, A., Rathmann, J., Schneider, A., Wolf, K., & Jacobeit, J. (2018). Air temperature characteristics of local climate zones in the Augsburg urban area (Bavaria, southern Germany) under varying synoptic conditions. Urban Climate, 25, 152-166. doi:10.1016/j.uclim.2018.04.007

Brown, M.E., Pinzon, J.E., Didan, K., Morisette, J.T., & Tucker, C.J. (2006). Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors. IEEE Transactions on Geoscience and Remote Sensing, 44(7), 1787-1793. doi:10.1109/tgrs.2005.860205

Cai, M., Ren, C., Xu, Y., Dai, W., & Wang, X.M. (2016). Local Climate Zone Study for Sustainable Megacities Development by Using Improved WUDAPT Methodology: A Case Study in Guangzhou. Procedia Environmental Sciences, 36, 82-89. doi:10.1016/j.proenv.2016.09.017

Cai, M., Ren, C., Xu, Y., Lau, K.K., & Wang, R. (2018). Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology? A case study of Yangtze River Delta, China. Urban Climate, 24, 485-502. doi:10.1016/j.uclim.2017.05.010

Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7), 1991-2007. doi:10.5194/gmd-8-1991-2015

Dobrovolný, P., & Krahula, L. (2015). The spatial variability of air temperature and nocturnal urban heat island intensity in the city of Brno, Czech Republic. Moravian Geographical Reports, 23(3), 8-16. doi:10.1515/mgr-2015-0013

Du, C., Ren, H., Qin, Q., Meng, J., & Zhao, S. (2015). A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data. Remote Sensing, 7(1), 647-665. doi:10.3390/rs70100647

Ellefsen, R. (1991). Mapping and measuring buildings in the canopy boundary layer in ten U.S. cities. Energy and Buildings, 16(3-4), 1025-1049. doi:10.1016/0378-7788(91)90097-m

Fenner, D., Meier, F., Scherer, D., & Polze, A. (2014). Spatial and temporal air temperature variability in Berlin, Germany, during the years 2001–2010. Urban Climate, 10, 308-331. doi:10.1016/j.uclim.2014.02.004

Florio, E.N., Lele, S.R., Chi, C.Y., Sterner, R., & Glass, G.E. (2004). Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: A statistical approach. International Journal of Remote Sensing, 25(15), 2979-2994. doi:10.1080/01431160310001624593

Gál, T., Skarbit, N., & Unger, J. (2016). Urban heat island patterns and their dynamics based on an urban climate measurement network. Hungarian Geographical Bulletin, 65(2), 105-116. doi:10.15201/hungeobull.65.2.2

Geletič, J., Lehnert, M., & Dobrovolný, P. (2016). Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities. Remote Sensing, 8(10), doi:10.3390/rs8100788

Gémes, O., Tobak, Z., & Leeuwen, B.v. (2016). Satellite Based Analysis of Surface Urban Heat Island Intensity. Journal of Environmental Geography, 9(1-2), 23-30. doi:10.1515/jengeo-2016-0004

Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.

Houet, T., & Pigeon, G. (2011). Mapping urban climate zones and quantifying climate behaviors: An application on Toulouse urban area (France). Environmental Pollution, 159(8-9), 2180-2192. doi:10.1016/j.envpol.2010.12.027

Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218. doi:10.1007/bf01908075

Imhoff, M.L., Zhang, P., Wolfe, R.E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3), 504-513. doi:10.1016/j.rse.2009.10.008

Jimenez-Munoz, J.C., Sobrino, J.A., Skokovic, D., Mattar, C., & Cristobal, J. (2014). Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters, 11(10), 1840-1843. doi:10.1109/lgrs.2014.2312032

Krayenhoff, E., & Voogt, J. (2016). Daytime Thermal Anisotropy of Urban Neighbourhoods: Morphological Causation. Remote Sensing, 8(2), doi:10.3390/rs8020108

Leconte, F., Bouyer, J., Claverie, R., & Pétrissans, M. (2015). Using Local Climate Zone scheme for UHI assessment: Evaluation of the method using mobile measurements. Building and Environment, 83, 39-49. doi:10.1016/j.buildenv.2014.05.005

Lee, D., & Oh, K. (2016). Classifying Urban Climate Zones Based upon Statistical Analysis of Urban Spatial Characteristics. International Journal of Environmental Science and Development, 7(11), 821-826. doi:10.18178/ijesd.2016.7.11.888

Lehnert, M., Geletič, J., Husák, J., & Vysoudil, M. (2015). Urban field classification by “local climate zones” in a medium-sized Central European city: The case of Olomouc (Czech Republic). Theoretical and Applied Climatology, 122(3-4), 531-541. doi:10.1007/s00704-014-1309-6

Murtagh, F., & Contreras, P. (2011). Methods of Hierarchical Clustering. Computing Research Repository - CORR. 10.1007/978-3-642-04898-2_288.

Mutiibwa, D., Strachan, S., & Albright, T. (2015). Land Surface Temperature and Surface Air Temperature in Complex Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4762-4774. doi:10.1109/jstars.2015.2468594

Oke, T. R., (2004). Initial Guidance to Obtain Representative Meteorological Observations at Urban Sites. World Meteorological Organization. WMO/TD- No. 1250; IOM Report- No. 81

Oyler, J.W., Ballantyne, A., Jencso, K., Sweet, M., & Running, S.W. (2014). Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature. International Journal of Climatology, 35(9), 2258-2279. doi:10.1002/joc.4127

Rajeshwari, A. (2014). Estimation of the land surface temperature of Dindigul district using Landsat 8 data. International Journal of Research in Engineering and Technology, 3(5), 122-126. doi:10.15623/ijret.2014.0305025

Rand, W.M. (1971). Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association, 66(336), 846-850. doi:10.1080/01621459.1971.10482356

Rokach, L., & Maimon, O. (2005). Clustering methods. Data mining and knowledge discovery handbook. US: Springer, pp.321-352. https://doi.org/10.1007/0-387-25465-X_15

Mejbel, S.M., Zakariya, J.O., I. Hassoon, K., & Jameel, A.A. (2018). Land Surface Temperature Retrieval from LANDSAT-8 Thermal Infrared Sensor Data and Validation with Infrared Thermometer Camera. International Journal of Engineering and Technology, 7(4.20), doi:10.14419/ijet.v7i4.20.27402

Skarbit, N., Gal, T., & Unger, J. (2015). Airborne surface temperature differences of the different Local Climate Zones in the urban area of a medium-sized city. 2015 Joint Urban Remote Sensing Event (JURSE), 1-4, Lausanne, Switzerland. Doi: 10.1109/JURSE.2015.7120497

Stewart, I., & Oke, T. (2009a). Newly developed “thermal climate zones” for defining and measuring urban heat island magnitude in the canopy layer. Preprints, Eighth Symposium on Urban Environment. Phoenix, AZ.

Stewart, I., & Oke, T. (2009b). Classifying urban climate field sites by “local climate zones”: the case of Nagano, Japan. Preprints, Seventh International Conference on Urban Climate. Yokohama.

Stewart, I., & Oke, T. (2010). Thermal differentiation of local climate zones using temperature observations from urban and rural field sites. Preprints, Ninth Symposium on Urban Environment. Keystone, CO.

Stewart, I.D., & Oke, T.R. (2012). Local Climate Zones for Urban Temperature Studies. Bulletin of the American Meteorological Society, 93(12), 1879-1900. doi:10.1175/bams-d-11-00019.1

Stewart, I.D., Oke, T.R., & Krayenhoff, E.S. (2013). Evaluation of the ‘local climate zone’ scheme using temperature observations and model simulations. International Journal of Climatology, 34(4), 1062-1080. doi:10.1002/joc.3746

Wang, S., He, L., & Hu, W. (2015). A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data. Remote Sensing, 7(8), 9904-9927. doi:10.3390/rs70809904

Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), doi:10.2307/3001968

Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., & Roy, D.P. (2016). The global Landsat archive: Status, consolidation, and direction. Remote Sensing of Environment, 185, 271-283. doi:10.1016/j.rse.2015.11.032

Xu, Y., Knudby, A., & Ho, H.C. (2014). Estimating daily maximum air temperature from MODIS in British Columbia, Canada. International Journal of Remote Sensing, 35(24), 8108-8121. doi:10.1080/01431161.2014.978957

Published
2020/01/11
Section
Original Research