Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Measuring the Intensity of the Surface Urban Heat Islands Using Vegetation and Urban Indices(Case Study: The Cities of Rasht and Langroud)

Document Type : Research Paper

Authors
Arash Karimi Zarchi
Reza Shahhoseini
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
10.22131/sepehr.2019.36614
Abstract
Extended Abstract
Introduction
Heat island phenomenon occurs when the land surface temperature and the air temperature in urban areas are higher than that of the surrounding areas. This temperature difference is shown as the urban heat islands on thermal maps. Information obtained from the urban heat islands can be a useful source in urban planning applications. The availability of reliable information about the urban heat islands plays an important role in predicting and preventing the occurrence of many heating risks in urban areas. One of the common methods of calculating heat islands intensity in urban areas is the use of two temperature sensors installed in the city and around it. Given the limited temperaturemeasuring stations, there is no accurate estimate of the urban heat islands. With the introduction of Remote Sensing technology into the space arena, and with the help of satellite images processing, a precise map can be produced for the land surface temperature, i.e. a precise estimation of the urban heat islands is obtained by calculating the pixels temperature difference at the urban areas and around them. Therefore, one of the important issues in such studies is to detect the urban and non-urban pixels and to separate them from each other.
Materials&Methods
The most important reason for the occurrence of the heat island phenomenon is the change in land use from rural to urban, which is well exhibited in the urban cover index maps.In this paper, in order to measurethe intensity of surface urban heat islands, a method based on generating the urban percentage map was proposed by combining the Land Surface Temperature (LST) map, the Normalized Difference Built-up Index (NDBI) map and the Normalized Difference Vegetation Index (NDVI) map.Considering the relationship between the land surface temperature and the land cover type, it can be said that the relationship between the land surface temperature and the urban percentage map follows a linear function which can be fitted to the land surface temperature graph in terms of land cover type. Finally, the Urban Heat Island Intensity (UHII) map was calculatedfrom the slope of the fitted line.In order to evaluate the strengths and weaknesses of the proposed method, a classification-based method was used to separate the urban and non-urban pixels and to calculate the urban heat island intensity. The proposed method was implemented on the Landsat-7 ETM + satellite data in the city of Rasht and on the Landsat-8 OLI / TIR satellite data in the city of Langroud.
Results&Discussion
The results of the classification-based method indicated a large difference between the maximum and the minimum temperature of the urban areas, which led to a high-temperature changein all land cover typesin the study area. Therefore, the use of the average temperature of each class to calculate the heat island intensity is not a suitable method and the accuracy of the heat islands maps is not high and they cannot be used in applications that require high precision.Although, this problem can be solved by increasing the number of classes, increasing the number of classes requires more training data and a sensor with higher spatial resolution.
By contrast, the results indicated that the proposed method (based on the urban percentage map) had a high accuracy for calculating the urban heat island intensity which was similar for both study areas. Also, fitting a linear function to the values of land surface temperature and the urban percentage map led to decreasing the effect of suspicious pixels (noisy pixels) on the overall accuracy of the estimation of the urban heat island intensity. Meanwhile, the results obtained on two datasets indicated that this method did not require any training data or any other background information about the study area and it can be applied for many satellite images having thermal band with any spatial resolution. However, because of the ineffectiveness of urban cover indicators in desert areas, the heat islands intensity in these regions was underestimated.
Conclusion
In applications that do not require high accuracy in calculating the urban heat island intensity, and there are high spatial resolution satellite imagery and sufficient training data in a region, the use of a classification-based approach seems to be suitable. Since the collection of such data and information is costly, a new method based on the urban percentage map was proposed in this paper by fitting a line to the LST parameter diagram in terms of the NDBI index for measuring the heat island intensity. The results indicated the higher efficiency and accuracy of the proposed method compared to the conventional classification-based methods for calculating the urban heat island intensity.
Keywords
Surface Urban Heat Island Intensity (SUHII)
Land Surface Temperature (LST)
Normalized Difference Built-up Index (NDBI)
Normalized Difference Vegetation Index (NDVI)
1. Ahmed, S. (2018). Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 15-25.
2. Amorim, M. C. D. C. T. (2018). Spatial variability and intensity frequency of surface heat island in a Brazilian city with continental tropical climate through remote sensing. Remote Sensing Applications: Society and Environment, 9, 10-16.
3. Artis, D. A., & Carnahan, W. H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12(4), 313-329.
4. Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heatisland in Birmingham, UK: A comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), 153.
5. Bonafoni, S., Baldinelli, G., & Verducci, P. (2017). Sustainable strategies for smart cities: Analysis of the town development effect on surface urban heat island through remote sensing methodologies. Sustainable Cities and Society, 29, 211-218.
6. Chakraborty, T., & Lee, X. (2019). A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. International Journal of Applied Earth Observation and Geoinformation, 74, 269-280.
7. Chen, W., Zhang, Y., Gao, W., & Zhou, D. (2016). The investigation of urbanization and urban heat island in Beijing based on remote sensing. Procedia-Social and Behavioral Sciences, 216, 141-150.
8. Cheval, S., & Dumitrescu, A. (2015). The summer surface urban heat island of Bucharest (Romania) retrieved from MODIS images. Theoretical and Applied Climatology, 121(3-4), 631-640.
9. de Faria Peres, L., de Lucena, A. J., Rotunno Filho, O. C., & de Almeida França, J. R. (2018). The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 64, 104-116.
10. Dixon, P. G., & Mote, T. L. (2003). Patterns and causes of Atlanta’s urban heat island–initiated precipitation. Journal of Applied Meteorology, 42(9), 1273-1284.
11. Dou, J., Wang, Y., Bornstein, R., & Miao, S. (2015). Observed spatial characteristics of Beijing urban climate impacts on summer thunderstorms. Journal of Applied Meteorology and Climatology, 54(1), 94-105.
12. Du, H., Wang, D., Wang, Y., Zhao, X., Qin, F., Jiang, H., & Cai, Y. (2016). Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Science of the Total Environment, 571, 461-470.
13. Earl, N., Simmonds, I., & Tapper, N. (2016). Weekly cycles in peak time temperatures and urban heat island intensity. Environmental Research Letters, 11(7), 074003.
14. El-Hattab, M., Amany, S. M., & Lamia, G. E. (2017). Monitoring and assessment of urban heat islands over the Southern region of Cairo Governorate, Egypt. The Egyptian Journal of Remote Sensing and Space Science.
15. Gallo, K., & Tarpley, J. (1996). The comparison of vegetation index and surface temperature composites for urban heat-island analysis. International Journal of Remote Sensing, 17(15), 3071-3076.
16. Jato-Espino, D. (2019). Spatiotemporal statistical analysis of the Urban Heat Island effect in a Mediterranean region. Sustainable Cities and Society, 101427.
17. Johnson, B., Tateishi, R., & Kobayashi, T. (2012). Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers. Remote Sensing, 4(9), 2619-2634.
18. Knapp, S., Kühn, I., Stolle, J., & Klotz, S. (2010). Changes in the functional composition of a Central European urban flora over three centuries. Perspectives in Plant Ecology, Evolution and Systematics, 12(3), 235-244.
19. Langanke, T., Büttner, G., Dufourmont, H., Iasillo, D., Probeck, M., Rosengren, M., . . . Weichselbaum, J. (2013). GIO land (GMES/Copernicus initial operations land) High Resolution Layers (HRLs)–summary of product specifications. European Environment Agency.
20. Li, H., Wolter, M., Wang, X., & Sodoudi, S. (2017). Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM. Theoretical and Applied Climatology, 1-15.
21. Li, H., Zhou, Y., Li, X., Meng, L., Wang, X., Wu, S., & Sodoudi, S. (2018). A new method to quantify surface urban heat island intensity. Science of The Total Environment, 624, 262-272.
22. Li, Y.-y., Zhang, H., & Kainz, W. (2012). Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: Using time-series of Landsat TM/ETM+ data. International Journal of Applied Earth Observation and Geoinformation, 19, 127-138.
23. Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., . . . Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14-37.
24. Mao, K., Qin, Z., Shi, J., & Gong, P. (2005). A practical split window algorithm for retrieving landsurface temperature from MODIS data. International Journal of Remote Sensing, 26(15), 3181-3204.
25. Markham, B. L., & Barker, J. L. (1985). Spectral characterization of the Landsat Thematic Mapper sensors. International Journal of Remote Sensing, 6(5), 697-716.
26. Mathew, A., Khandelwal, S., Kaul, N., & Chauhan, S. (2018). Analyzing the diurnal variations of land surface temperatures for surface urban heat island studies: Is time of observation of remote sensing data important?. Sustainable cities and society, 40, 194-213.
 27. Mills, G. (2008). Luke Howard and the climate of London. Weather, 63(6), 153-157.
28. Oke, T. R. (1976). The distinction between canopy and boundary layer urban heat islands. Atmosphere, 14(4), 268-277.
29. Quanliang, C., Changjian, N., Zhan, L., & Jingxuan, R. (2009). Urban heat island effect research in Chengdu city based on MODIS data. Paper presented at the Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on.
30. Ramanathan, T. T., & Sharma, D. (2017). Multiple Classification Using SVM Based Multi Knowledge Based System. Procedia Computer Science, 115, 307-311.
31. Rizwan, A. M., Dennis, L. Y., & Chunho, L. (2008). A review on the generation, determination and mitigation of Urban Heat Island. Journal of Environmental Sciences, 20(1), 120-128.
32. Rozenstein, O., Qin, Z., Derimian, Y., & Karnieli, A. (2014). Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors, 14(4), 5768-5780.
33. Schneider, K., & Mauser, W. (1996). Processing and accuracy of Landsat Thematic Mapper data for lake surface temperature measurement. International Journal of Remote Sensing, 17(11), 2027-2041.
34. Schott, J. R., & Volchok, W. J. (1985). Thematic Mapper thermal infrared calibration. Photogrammetric Engineering and Remote Sensing, 51, 1351-1357.
35. Stathopoulou, M., & 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(3), 358-368.
36. Stewart, I. D. (2011). A systematic review and scientific critique of methodology in modern urban heat island literature. International Journal of Climatology, 31(2), 200-217.
37. Van de Griend, A., & Owe, M. (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing, 14(6), 1119-1131.
38. Wang, W., Liu, K., Tang, R., & Wang, S. (2019). Remote sensing image-based analysis of the urban heat island effect in Shenzhen, China. Physics and Chemistry of the Earth, Parts A/B/C.
39. Yang, P., Ren, G., & Liu, W. (2013). Spatial and temporal characteristics of Beijing urban heat island intensity. Journal of applied meteorology and climatology, 52(8), 1803-1816.
40. 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.
41. Zhou, B., Rybski, D., & Kropp, J. P. (2017). The role of city size and urban form in the surface urban heat island. Scientific Reports, 7(1), 4791.