Volume 10, Issue 3 (9-2023)
Journal of Spatial Analysis Environmental Hazards 2023, 10(3): 1-14 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Afrakhteh R, Salman Mahini A, Motagh M, Kamyab H. Modeling thermal changes of urban blocks in relation to landscape structure and configuration in Guilan Province. Journal of Spatial Analysis Environmental Hazards 2023; 10 (3) :1-14
URL: http://jsaeh.khu.ac.ir/article-1-3412-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3412-en.html
1- Environmental Department of Gorgan University of Agricultural Sciences and Natural Resources, Gorgan,Iran , Roshanak_afra@yahoo.com
2- Environmental Department of Gorgan University of Agricultural Sciences and Natural Resources, Gorgan,Iran.
3- GFZ German Research Centre for Geosciences, Department of Geodesy, Section of Remote Sensing and Geoinformatics, Potsdam, Germany.
4- Environmental Department, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan,Iran.
2- Environmental Department of Gorgan University of Agricultural Sciences and Natural Resources, Gorgan,Iran.
3- GFZ German Research Centre for Geosciences, Department of Geodesy, Section of Remote Sensing and Geoinformatics, Potsdam, Germany.
4- Environmental Department, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan,Iran.
Abstract: (3002 Views)
This paper is a discussion of urban heat islands (UHIs), which unique residential areas are characterized by dense central cores surrounded by less dense peripheral lands. UHIs experience higher temperatures due to impermeable surfaces and specific land use patterns. These temperature variations have negative environmental and social impacts, leading to increased energy consumption, air pollution, and public health concerns. It emphasizes the need for simpler approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. The case study of Guilan Plain illustrates the relationship between development patterns and temperature, utilizing techniques like principal component analysis and generalized additive models.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.
Type of Study: Research |
Subject:
Special
Received: 2023/12/22 | Accepted: 2023/09/23 | Published: 2023/09/23
Received: 2023/12/22 | Accepted: 2023/09/23 | Published: 2023/09/23
References
1. Aeinehvand, R., Darvish, A., Baghaei Daemei, A., Barati, S., Jamali, A. and Malekpour Ravasjan, V., 2021. Proposing alternative solutions to enhance natural ventilation rates in residential buildings in the Cfa Climate Zone of Rasht. Sustainability, 13(2):679. [DOI:10.3390/su13020679.]
2. Afrakhteh, R., Asgarian, A., Sakieh, Y. and Soffianian, A., 2016. Evaluating the strategy of integrated urban-rural planning system and analyzing its effects on land surface temperature in a rapidly developing region. Habitat International, 56, pp.147-156. [DOI:10.1016/j.habitatint.2016.05.009.]
3. Alqasemi, A.S., Hereher, M.E., Kaplan, G., Al-Quraishi, A.M.F. and Saibi, H., 2021. Impact of COVID-19 lockdown upon the air quality and surface urban heat island intensity over the United Arab Emirates. Science of the Total Environment, 767, p.144330. [DOI:10.1016/j.scitotenv.2020.144330.]
4. Asgarian, A., Amiri, B.J. and Sakieh, Y., 2015. Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosystems, 18:209-222. [DOI:10.1007/s11252-014-0387-7.]
5. Bozorgi, M., Nejadkoorki, F. and Mousavi, M.B., 2018. Land surface temperature estimating in urbanized landscapes using artificial neural networks. Environmental monitoring and assessment, 190:1-10. [DOI:10.1007/s10661-018-6618-2.]
6. Chen, Z., Zhang, H., Duan, H. and Shi, C., 2021. Improvement of thermal and optical responses of short-term aged thermochromic asphalt binder by warm-mix asphalt technology. Journal of Cleaner Production, 279:123675. [DOI:10.1016/j.jclepro.2020.123675.]
7. Dutta, D., Rahman, A., Paul, S.K. and Kundu, A., 2021. Impervious surface growth and its inter-relationship with vegetation cover and land surface temperature in peri-urban areas of Delhi. Urban Climate, 37, p.100799. [DOI:10.1016/j.uclim.2021.100799.]
8. Guha, S., Govil, H., Dey, A. and Gill, N., 2018. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1):667-678. [DOI:10.1080/22797254.2018.1474494.]
9. Guo, G., Wu, Z., Cao, Z., Chen, Y. and Yang, Z., 2020. A multilevel statistical technique to identify the dominant landscape metrics of greenspace for determining land surface temperature. Sustainable Cities and Society, 61:102263. [DOI:10.1016/j.scs.2020.102263.]
10. Hou, H. and Estoque, R.C., 2019. Detecting Cooling Effect of Landscape Composition and Configuration: An Urban Heat Island Study on Hangzhou. Abstracts of the ICA, 1:1-2. [DOI:10.1016/j.ufug.2020.126719.]
11. Kuang, W., Liu, Y., Dou, Y., Chi, W., Chen, G., Gao, C., Yang, T., Liu, J. and Zhang, R., 2015. What are hot and what are not in an urban landscape: quantifying and explaining the land surface temperature pattern in Beijing, China. Landscape ecology, 30(2):357-373. [DOI:10.1007/s10980-014-0128-6.]
12. Li, S., Zhao, Z., Miaomiao, X. and Wang, Y., 2010. Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environmental Modelling & Software, 25(12):1789-1800. [DOI:10.1016/j.envsoft.2010.06.011.]
13. Lu, L., Weng, Q., Xiao, D., Guo, H., Li, Q. and Hui, W., 2020. Spatiotemporal variation of surface urban heat islands in relation to land cover composition and configuration: A multi-scale case study of Xi’an, China. Remote Sensing, 12(17):2713. [DOI:10.3390/rs12172713.]
14. Nadoushan, M.A., 2022. Advancing urban planning in arid agricultural-urbanized landscapes of Iran: Spatial modeling evidence from a rapidly developing region. Sustainable Cities and Society, 87:104230. [DOI:10.1016/j.scs.2022.104230.]
15. Osborne, P.E. and Alvares-Sanches, T., 2019. Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes. Computers, Environment and Urban Systems, 76:80-90. [DOI:10.1016/j.compenvurbsys.2019.04.003.]
16. Piracha, A. and Chaudhary, M.T., 2022. Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature. Sustainability 2022, 14, 9234. [DOI:10.3390/su14159234.]
17. Rakoto, P.Y., Deilami, K., Hurley, J., Amati, M. and Sun, Q.C., 2021. Revisiting the cooling effects of urban greening: Planning implications of vegetation types and spatial configuration. Urban Forestry & Urban Greening, 64:127266. [DOI:10.1016/j.ufug.2021.127266.]
18. Roy, R. and Sen, S., 1999. Temporal analysis of Normalised Differential Built up Index and Land Surface Temperature and its link with urbanisation: A case study on Barrackpore sub-division, West Bengal.
19. Shen, C., Hou, H., Zheng, Y., Murayama, Y., Wang, R. and Hu, T., 2022. Prediction of the future urban heat island intensity and distribution based on landscape composition and configuration: A case study in Hangzhou. Sustainable Cities and Society, 83:103992. [DOI:10.1016/j.scs.2022.103992.]
20. Shi, Y., Sun, X., Zhu, X., Li, Y. and Mei, L., 2012. Characterizing growth types and analyzing growth density distribution in response to urban growth patterns in peri-urban areas of Lianyungang City. Landscape and urban planning, 105(4):425-433. [DOI:10.1016/j.landurbplan.2012.01.017.]
21. Siddiqui, A., Kushwaha, G., Nikam, B., Srivastav, S.K., Shelar, A. and Kumar, P., 2021. Analysing the day/night seasonal and annual changes and trends in land surface temperature and surface urban heat island intensity (SUHII) for Indian cities. Sustainable Cities and Society, 75:103374. [DOI:10.1016/j.scs.2021.103374.]
22. Sobrino, J.A., Jiménez-Muñoz, J.C. and Paolini, L., 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, 90(4):434-440. [DOI:10.1016/j.rse.2004.02.003.]
23. Wood, S. and Wood, M.S., 2015. Package ‘mgcv’. R package version, 1(29):729.
24. Yao, L., Li, T., Xu, M. and Xu, Y., 2020. How the landscape features of urban green space impact seasonal land surface temperatures at a city-block-scale: An urban heat island study in Beijing, China. Urban Forestry & Urban Greening, 52:126704. [DOI:10.1016/j.ufug.2020.126704.]
25. Zhang, B., Amani-Beni, M., Shi, Y. and Xie, G., 2018. The summer microclimate of green spaces in Beijing’Olympic park and their effects on human comfort index. Ecol. Sci, 37(5):77-86.
26. Zhao, W., Duan, S.B., Li, A. and Yin, G., 2019. A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote sensing of environment, 221:635-649. [DOI:10.1016/j.rse.2018.12.008.]
27. Zou, M. and Zhang, H., 2021. Cooling strategies for thermal comfort in cities: a review of key methods in landscape design. Environmental Science and Pollution Research, 28(44):62640-62650. [DOI:10.1007/s11356-021-15172-y.]
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
