Investigating the Relationship between Thermal Islands and Green Space Areas and Detecting its Changes (Case Study: Kerman City)

Document Type : Research Paper

Authors

1 Department of Environmental Sciences & Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, Ardakan, Iran

2 Department of Environmental Sciences & Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, P.O. Box184, Ardakan, Iran

3 Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, Ardakan, Iran

Abstract

This study was conducted to investigate the effect of vegetation in the city in the form of green space on Land Surface Temperature (LST) and also to identify the thermal islands of the Kerman city. LST was calculated by inverse method Planck function using Landsat 8 in Google Earth engine. The calculated LST was calculated as the average of two images in the middle months of the four seasons of 2014, 2017 and 2020. Landsat Science Notebook and a split window were used In order to evaluate the efficiency of the method used in calculating the LST. The relationship and the effect of vegetation on the calculated LST for Kerman city have been done using correlation and selection of ring buffer at intervals of 50, 100, 150 and 200 meters. Finally, their seasonal changes were examined using Moran's I autocorrelation index and its position changes were analyzed both as a season-to-season trend and as a general trend. The results showed that the Planck function method and the Landsat Science Notebook method had more accurate results than the Split window method. There is a relationship between the area of the park and its temperature, and the lowest calculated temperatures for green spaces are related to the parks with the largest area. Correlation test analysis showed that in all seasons of the year, LST is inversely related to vegetation density index. Also, the amount and intensity of this negative correlation vary depending on different seasons. The highest negative correlation value of -0.48 was recorded for the summer in 2014. Quantifying the effect of green space on the ambient temperature fluctuation showed that, on average distance 200 meters from vegetated areas, the temperature has increased by 3 degrees resulting from increasing distance from the identified cores as green space; it is clear evidence indicating the effect of green space on the amount of measured temperature. The results of this study showed that the calculation of earth surface temperature provides reliable results in the management of urban space that can be useful in future urban decisions.
Abstract Extended
1-Introduction
Urbanization has many environmental consequences whose various forms have been manifested today. The difference between surface coverage in urban and non-urban areas creates fundamental changes in the nature of the built-up urban area. Urban heat island is an example of unintended climate change affected by the changes on the earth surface and the atmosphere as a result of the urbanization process. In most conducted studies on investigating surface temperature, plants have a very important role in temperature regulation and related environmental equations related. Therefore, monitoring and revealing the role of vegetation in regulating the earth surface temperature can help understand the correct temperature distribution on the earth surface, especially in urban environments and the city of Kerman is no exception. This study was conducted to investigate the effect of present vegetation in the city in the form of green space on earth surface temperature and also to identify the thermal islands of Kerman.
2-Materials and Methods
Land Surface Temperature (LST) was calculated by inverse method of Planck function using Landsat 8 satellite imagery (TIRS Sensor) in Google Earth engine system environment. The calculated LST was calculated as the average of two images in the middle months of the four seasons, spring, summer, autumn and winter of 2014, 2017 and 2020. In order to evaluate the efficiency of the method used in calculating the temperature, the methods of Landsat Science Notebook and a split window were used for a series of images. In addition to calculating the LST, Normalized Difference Vegetation Index (NDVI) was also used to monitor the spatial changes of green space in Kerman. Since it was important to study the trend of vegetation changes in the city of Kerman in the years under study, in order to study its fluctuation using the extended presence points (1) and absence (0) of green space True Skill Statistic (TSS) threshold method was used and plants were identified in the spring from 2014 to 2020. Direction of their changes was also calculated using Directional Distribution analysis in ArcGIS10.4.1. The relationship and effect of vegetation on the calculated LST for the city of Kerman was done using the methods of correlation and selection of ring buffer at intervals of 50, 100, 150 and 200 meters. The thermal island threshold was identified using a raster profile of a 25-kilometer transect from southwest to northeast in QGIS 3.16. After calculating the LST of the earth and determining the range of the beginning of thermal islands, the trend of its changes during the period 2014 to 2020 and also their seasonal changes using Moran's I autocorrelation index were investigated and analysis of changes in their position as a change of season, season and was also examined as a general trend.
3- Results and Discussion
The results of this study showed that the Planck function inverse method and the Landsat Science Notebook method had more accurate results than the Split window method. Accordingly, the results of the average indices of LST and vegetation showed that the calculated land earth surface temperature in the city center is higher than the outskirts of the city and the highest values of the calculated temperature are related to newly added sections to the outskirts of the city. Dense urban structures in the city center have lower temperatures than in the part of the city that has more rupture. On the other hand, there is a relationship between the area of the park and its temperature, and the lowest calculated temperatures for green spaces are related to the parks with the largest area.
Correlation test analysis showed that in all seasons of the year, earth surface temperature is inversely related to vegetation density index. Also, the amount and intensity of this negative correlation varies depending on different seasons. The highest negative correlation value of -0.48 was recorded in the summer of 2013. The detected threshold in the ROC curve was identified as 0.1489 and the values of 96.6% and 94.5% were calculated as sensitivity and specificity for this value, respectively, which indicates the appropriate strength of the threshold in separating vegetation from other structures of the urban environment. Quantifying the effect of green space on the ambient temperature fluctuation showed that the temperature has increased by 3 degrees on average distance of 200 meters from vegetated areas as a result of increasing distance from the identified cores as green space which is clear evidence proving the effect of green space on the amount of temperature has been measured.
The results of the analysis of space changes show that there is a growing trend in the area and number of green spaces in Kerman during the period of this study and the direction of changes in green space was to the west. Moran index analysis also showed that the thermal islands in the city of Kerman have change depending on the season under study and the most stable area has thermal islands in the common areas between zones 1 and 2 in the north of the city.
4- Conclusion
This study highlights the role of vegetation in regulating earth surface temperature in Kerman. The results of this study showed that the calculation of earth surface temperature provides reliable results in the management of urban space which can be useful in future urban decisions.

Keywords

References

Abdollahi, A. & Ghasemi, M. (2018). Smart Growth Urban Areas (Case study: Kerman City). Journal of Studies of Human Settlements Planning, 13(4), 1003-1119 (In Persian).
Ahmadi, B., Ghorbani, A., Safarrad, T. & Sobhani, B. (2015). Evaluation of surface temperature in relation to land use/cover using remote sensing data. RS & GIS for Natural Resources, 6(1), 61-77 (In Persian).
Ahmadi, M. & Dadashi Roudbari, A. (2017). The Identification of Urban Thermal Islands based on an Environmental Approach, Case study: Isfahan Province. Journal of Geography and Environmental Planning, 28(3), 1-20 (In Persian).
Ahmadi, M., Ashourloo, D. & Narangifard, M. (2012). Temporal-spatial changes of thermal and land use patterns of Shiraz city using TM & ETM sensor data. Iranian Journal of Remote Sensing & GIS, 4(4), 55-68 (In Persian).
Amiri, R., Alimohamadi, A. & Alavipanah, S. K. (2007). Spatial-temporal variability study of temperature associated with the land use/land cover in the city of Tabriz using thermal and reflection TM and ETM+ Landsat data. Journal of Environmental Studies, 33(43), 107-120.
Asgarzadeh, P., Darvishi Boloorani, A., Bahrami, H. & Hamzeh, S. (2016). Comparison between land surface temperature estimation in single and multi-channel method using LandSat images 8. RS & GIS for Natural Resources, 7(3), 18-29 (In Persian).
Balogun, A. A., Balogun, I. A. & Adeyewa, Z. D. (2010). Comparisons of urban and rural heat stress conditions in a hot–humid tropical city. Global Health Action, 3(1), 5614.
Barati Ghahfarokhi, S., Soltani Kopani, S., Khajeh al-Din, J. & Raygani, B. (2008). Investigation of land use changes in Shahrokh Castle sub-basin using remote sensing technique (period 1975-2002). Journal of Water and Soil Science, 13(47), 349-365 (In Persian).
Borgstrom, S., Lindborg, R. & Elmqvist, T. (2013). Nature conservation for what? Analyses of urban and rural nature reserves in southern Sweden 1909-2006. Landscape and Urban Planning, 117(2013), 66-80.
Davtalab, J., Hafezi, M. & Adib, M. (2015). The effect of green space structure on the average radiant temperature in the microclimate of outdoor space, a case study of hot and dry climate of Sistan. Soffeh, 26(75), 19-41 (In Persian).
Detailed plan of Kerman, Armanshahr consulting engineers, 2011 (In Persian).
Esmaeili, M., Shayesteh, K. & Karami, P. (2020). Effect of Tang Hammam dam on Persian gazelle (Gazella S. Subgutturosa) habitat in no hunting areas of Gharaviz (Kermanshah province). Arid Biome Scientific and Research Journal, 1(10), 136-149 (In Persian).
Faizi Zadeh, B., Dideban, Kh. & Ghulam Nia, Kh. (2015). Estimation of surface temperature using Landsat 8 satellite imagery and separate window algorithm Case study: Mahabad catchment. Scientific-Research Quarterly of Geographical Data (Sepehr), 25(98), 172-181 (In Persian).
Fekrat, H., Asghari Saraskanrood, S. & Alavipanah, K. (2020). Estimation of Ardabil land surface temperature using Landsat images and accuracy assessment of land surface temperature estimation methods with ground truth data. RS & GIS for Natural Resources, 11(14), 114-134 (In Persian).
Foroutan, S. (2019). Monitoring changes in surface parameters in land cover using remote sensing Case study: Langrud and Lahijan cities. Master Thesis in Environment. 124 pages (In Persian).
García-Haro, F. J., Sommer, S. & Kemper, T. (2005). Variable multiple end member spectral mixture analysis (VMESMA). International Journal of Remote Sensing, 26, 2135-2162.
Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X. & Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning, 135, 1-10.
Hashemi Dareh Badami, S., Nouraeisafat, A., Karimi, S. & Nazari, S. (2015). Development trend analysis of urban heat island regarding land use/cover changes using time series of landSat images. RS & GIS for Natural Resources, 6(3), 15-28 (In Persian).
Hashemi, S., Kafi, M., Hashemi, M. & Khansefid, M. (2009). Urban green space change process analysis, Case Study: Region Two of Tehran Municipality. Journal of Environmental Sciences, 6(3), 73- 86 (In Persian).
Hereher, M. E. (2017). Effect of land use/cover change on land surface temperatures-The Nile Delta, Egypt. Journal of African Earth Sciences, 126, 75-83.
Isaya Ndossi, M. & Avdan, U. (2016). Application of open source coding technologies in the production of land surface temperature (LST) maps from Landsat: a PyQGIS plugin. Remote sensing, 8(5), 413.
Jiménez-Muñoz, J. C. & Sobrino, J. A. (2009). A single-channel algorithm for land-surface temperature retrieval from ASTER data. IEEE Geoscience and Remote Sensing Letters, 7(1), 176-179.
Joybari Moghadam, Y., Akhundzadeh, M. & Serajian, M. (2015). Introduce a new stand-alone window algorithm to estimate surface temperature from Landsat 8 satellite data. Journal of Geomatics Science and Technology, 5(1), 215-226 (In Persian).
Karami, P. & Mirsanjari, M. (2017). Modeling and Identification of Effective Factors on the Establishment of Ecotourism in Javanrud County by Using Classification Tree. Sustainability, Development & Environment, 4(2), 67-78 (In Persian).
Karami, P., Shayesteh, K. & Esmaeili, M. (2019). Highlighting the importance of vegetation variables on land surface temperature distributed in different land uses/coverings in Javanrood city. Journal of Plant Ecosystem Conservation, 7(15), 323-356 (In Persian).
Karimi Firozjaei, M. & Kiavarz Moghadam, M. (2017). Investigating the relationship between temperature, net radiation flux by biophysical properties and lanuse using LandSat 8 satellite imagery. RS & GIS for Natural Resources, 7(4), 96-79 (In Persian).
Kayet, N., Pathak, K., Chakrabarty, A. & Sahoo, S. (2016). Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Modeling Earth Systems and Environment, 2(3), 1-10.
Kim, Y. H. & Baik, J. J. (2005). Spatial and temporal structure of the urban heat island in Seoul. Journal of Applied Meteorology, 44(5), 591-605.
Mahmoudzadeh, H., Naghdbishi, A. & Momeni, S. (2018). The effect of urban uses in creating thermal islands (Case study of Mashhad). Journal of Geography and environmental hazards, 7(3), 105-119 (In Persian).
Matkan, A., Nohegar, A., Mirbagheri, B. & Torkchin, N. (2014). Assessment relations of land use in heat islands using time series ASTER sensor data (Case study: Bandar Abbas city). RS & GIS for Natural Resources, 5(4), 1-14 (In Persian).
Mokhtari, S., Soltanifar, H. & Yavari, A. (2009). Self-organization in Bozorg Hoveyzeh/Bozorg Al-Hoveyzeh wetland with emphasis on the ecology of the land. Physical Geography Research Quarterly, 41(70), 93-105 (In Persian).
Mousavi Baigi, M., Ashraf, B., Hosseini, A. & Miyan abadi, A. (2012). Investigation of thermal island of Mashhad using satellite images and fractal theory. Journal of Geography and Environmental Hazards, 1(1), 35-48 (In Persian).
Naseri, A. (2015). Investigation and analysis of land use role in the formation of urban thermal islands of Mashhad. Master Thesis, Faculty of Natural Resources and Environment, Birjand, 123 pages (In Persian).
Ningrum, W. (2018). Urban Heat Island towards Urban Climate Urban Heat Island towards Urban Climate. IOP Conference Series, 0-6. https://doi.org/10.1088/1755-1315/118/1/012048
Panda, S. & Jain, M. K. (2017). Effects of Green Space Spatial Distribution on Land Surface Temperature: Implications for Land Cover Change as Environmental Indices. International Journal of Earth Sciences and Engineering, 10(2), 180-184.
Parks and Green Space Organization of Kerman. Statistics and information on managed areas (2020). 1 page (In Persian).
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.
Sadeghinia, A., Alijani, B., Ziaian Firoozabadi, P. & Khaledi, Sh. (2013). Application of spatial autocorrelation technique in the analysis of thermal islands in Tehran. Scientific Journals Management System, 13(30), 67-90 (In Persian).
Shahmohamadi, P., Sodoudi, S. & Cubasch, U. (2013). Mitigation of urban heat island effects as an environmental phenomena in Tehran metropolitan area: the way to move forward sustainability. Caspian Journal of Applied Sciences Research, 2(9), 126-135.
Shakiba, A., Ziaian Firoozabadi, P., Ashourloo, D. & Namdari, S. (2009). Analysis of land use relationship and land cover and thermal islands of Tehran, using ETM+data. Iranian Journal of Remote Sensing & GIS, 1(1), 39-56 (In Persian).
Soltaninejad, H., Khalili, S., Shahi, Z. & Razavian, M. (2019). Modifications in Green Spaces of Kerman, Using Landsat Images Time Series (2000-2018). Journal of GIS & RS Application in Planning, 10(1), 72-84 (In Persian).
Sultana, S. & Satyanarayana, A. N. V. (2020). Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000-2018 over a sub-tropical Indian City. Sustainable Cities and Society, 52, 101846.
Sun, D. & Kafatos, M. (2007). Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America. Geophysical Research Letters, 34(24).
Valizadeh K., Ghulam Nia, Kh., Einali, G. & Mousavi, M. (2017). Estimation of surface temperature and extraction of thermal islands using separate window algorithm and multivariate regression. Journal of Research and Urban Planning, 8(30), 35-50 (In Persian).
Veysi, Sh., Naseri, A., Hamzeh, S. & Moradi, P. (2016). Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images. RS & GIS for Natural Resources, 7(1), 27-40 (In Persian).
Weng, Q., Lu, D. & Liang, B. (2006). Urban surface biophysical descriptors and land surface temperature variations. Photogrammetric Engineering & Remote Sensing, 72(11), 1275- 1286.
Weng, Q., Lu, D. & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sensing of Environment, 89(4), 467-483.
Xiao, X. D., Dong, L., Yan, H., Yang, N. & Xiong, Y. (2018). The influence of the spatial characteristics of urban green space on the urban heat island effect in Suzhou Industrial Park. Sustainable Cities and Society, 40(2018), 428-439.
Yuan, F. & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of environment, 106(3), 375-386.
Yue, Y., Wang, K., Bing, Z., Chen, Z., Jiao, Q., Bo, L. & Chen, H. (2009). Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in Karst region, Southwest China. Environ. Monit. Assess, 160(1-4), 157-168.
Zangi abadi, A., Mahin, N. & Kamali Baghrahi, A. (2015). Analysis of the process and manner of physical development of Kerman city from its inception until now. Journal of Urban Social Geography, 2(4), 23-42 (In Persian).
Zhao, Z. Q., He, B. J., Li, L. G., Wang, H. B. & Darko, A. (2017). Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China. Energy and Buildings, 155, 282-295.
Zolfaghari, F., Azarnivand, H., Khosravi, H., Zehtabian, Gh. & Khaliqi Sigaroodi, Sh. (2018). Effect of vegetation on microclimate creation in ecosystems of arid regions (Case study: Sistan plain). Journal of Range and Watershed Management, 71(4), 901-914 (In Persian).

Files

Share

How to cite

Statistics