Study and prediction of land surface temperature changes of Yazd city: assessing the proximity and changes of land cover

Mansourmoghaddam, Mohammad; Rousta, Iman; Zamani, Mohammadsadegh; Mokhtari, Mohammad Hossein; Karimi Firozjaei, Mohammad; Alavipanah, Seyed Kazem

doi:10.30495/girs.2021.682083

Manuscript ID : GIRS-2102-1897 (R3) Visit : 745 Page: 1 - 27

10.30495/girs.2021.682083

http://dorl.net/dor/20.1001.1.26767082.1400.12.4.1.8

Article Type: Original Research

Study and prediction of land surface temperature changes of Yazd city: assessing the proximity and changes of land cover

Subject Areas : Applications in earth’s climate change

Mohammad Mansourmoghaddam ¹ , Iman Rousta ² , Mohammadsadegh Zamani ³ , Mohammad Hossein Mokhtari ⁴ , Mohammad Karimi Firozjaei ⁵ , Seyed Kazem Alavipanah ⁶

1 - MSc. Student of Remote Sensing and Geographical Information System, Department of Geography, University of Yazd, Iran
2 - Assistant Professor, Department of Geography, University of Yazd, Iran
3 - Assistant Professor, Faculty of Mathematic Sciences, University of Yazd, Iran
4 - Assistant Professor, Department of Management in the Arid Regions, Faculty of Environmental and Desert Studies, University of Yazd, Iran
5 - PhD. Student of Remote Sensing and Geographical Information System, Department of Geography, University of Tehran, Iran
6 - Professor, Department of Geography, University of Tehran, Iran

Received: 2021-02-27 Accepted : 2021-05-25 Published : 2021-12-22

Keywords: Yazd city, Land cover proximity, Prediction of land use, Land surface temperature, Artificial Neural Network, Land use classification,

Abstract :

Background and Objective The expansion of urbanization has increased the scale and intensity of thermal islands in cities. Investigating how cities are affected by these thermal islands plays an important role in the future planning of cities. For this purpose, this study examines and predicts the effect of land cover (LC) changes in the three classes of LC including urban areas, barren lands, and vegetation on land surface temperature (LST) in the city of Yazd during the last 30 years using Landsat 5 and 8 images. This study also examines the effect of the ratio of proximity to the barren land and vegetation classes during this period to examine how the recorded LST is affected by the mentioned ratio.Materials and Methods The LC maps of Yazd city were extracted using a supervised Artificial Neural Network classifier for 1990, 2000, 2010, and 2020. Terrestrial data, google earth, and ground truth maps were used to derive training data. The LST of Yazd was obtained from the thermal band of Landsat 5 and Landsat 8. After that, the LST was classified into six available classes, including 16-20, 21-25, 26-30, 31-35, 36-40, and 41-46°C which has shown that the four last classes play an important role in LST changes in Yazd city during last 30 years. To evaluate the effects of the proximity of barren land and vegetation LC classes on the LST recorded by the sensor, firstly the proximity ratio was calculated in 5×5 kernels for all image pixels. Then the mean of LST was derived based on this ratio of barren and vegetation lands.Results and Discussion The results of this study showed that in Yazd city, from 1990 to 2020, the area of the urban area has grown 91.5 % (33.6 km2) over the last 30 years. Barren and vegetation land, have negative growth in the area over the same period. From 1990 to 2020, barren lands in Yazd experienced a growth -79.4% (21.3 km2), which the sharp growth of urban areas justifies this negative growth in barren lands. Vegetation classes in Yazd from 1990 to 2020, have experienced a growth -68.5% (12.2 km2). The average ground temperature of this city has been constantly increasing during these 30 years. By 2020, the city of Yazd, reaching an average of 38.1°C compared to 29.2°C in 1990, has experienced a 30.4% increase in its average LST. The temperature classes of this city have also moved towards warmer temperature classes in these 30 years. As the main part of the LST area of Yazd, in 1990, in the first place, the class of 26-30 °C with 47 km2 and at the second place the class of 31-35 °C with 26.4 km2 are classified. In 2000, in a reverse trend, the main LST class was 31-35°C with 52.8 km2 as the first place and the 26-30°C class with 20 km2 as the second place. With an increased class, the LST class of 36-40 °C for both 2010 and 2020 with 40.2 and 63 km2 respectively has been recorded as the largest LST class. The LST class of 31-35 °C has been recorded as the second LST class of both years with 33.2 and 9.7 km2, respectively. The difference between these two years is in the growth -70.7% (23.5 km2) of the class area of 31-35°C and the increase of 10.3% (0.8 km2) of the hottest class of the statistical period, 41-46°C, in 2020, compared to 2010. The results of this study also showed that the highest average temperature in all year was recorded for barren lands at 37.3°C. Also, a positive correlation (mean correlation 0.95) was shown between the proximity to barren land cover and the mean LST. However, the sharp upward trend of urban areas in the whole statistical period (91.5% with 33.6 km2) as the second class with the highest average LST after the barren lands with an average of 34.1 °C versus a downward trend of 79.4% (21.3 km2) of barren lands has increased the average LST over a statistical period of 30 years. It is because the decrease of 68.5% (12.2 km2) of vegetation areas as an LC class with the lowest average LST (32.2°C) in the same period, neutralized the effect of decreasing barren lands and intensified the trend of increasing the LST. Meanwhile, a negative correlation (mean correlation -0.97) was established between the ratio of proximity to vegetation and the average LST. The results of forecasting land cover changes in 2030 in the city of Yazd indicate that in a process similar to previous periods, the class of urban areas will increase. This growth will not be significant compared to 2020, with 1.6% (1.1 km2). However, a significant decrease in green areas (vegetation) by -19.6% (1.1 km2) in the same period, along with a slight decrease in barren lands -1.8% (0.1 km2) will cause the earth’s surface to become warmer, and the area of LST classes will be increased by the year. Accordingly, the main area of the LST class in 2030 for the city of Yazd, as in 2020, is forecasted 36-40°C with 58.2 km2 (-7.6% growth compared to 2020). But the dramatic growth of the hottest class of LST over the statistical period (41-46°C) with 166.3% (14.3 km2) growth as the second major class of LST in this year (2030), as well as the negative and dramatic growth of the relatively cooler class 31-35°C with -97.9 % (9.5 km2) in this year indicates the warmer ground surface temperature in 2030.Conclusion The results of this study indicate that in 30 years in Yazd city, the decrease in vegetation in the first place, along with the increase in urban areas in the second place, has caused an increase in LST. Thus, the vegetation class reduces the LST due to its cooling effect considering its water content. In this study, it was shown that by taking all factors into account, the reduction of barren lands will lead to a decrease in LST, and also increasing urban areas with a lower impact factor than barren lands will increase the LST. However, the decrease in the area of green lands (vegetation) in recent years, along with the sharp increase in the area of urban areas has caused an increase in LST. Increasing the proximity to vegetation by creating green areas by increasing the ratio of vegetation in the vicinity of different LC and also reducing the area of barren lands, can be a good solution to deal with the impact of urbanization in recent years on ground surface temperature.

References:

Ackerman B. 1985. Temporal march of the Chicago heat island. Journal of Climate and Applied Meteorology, 24(6): 547-554. doi:https://doi.org/10.1175/1520-0450(1985)024<0547:TMOTCH>2.0.CO;2.

Alberti M, Marzluff JM. 2004. Ecological resilience in urban ecosystems: Linking urban patterns to human and ecological functions. Urban Ecosystems, 7(3): 241-265. doi:https://doi.org/10.1023/B:UECO.0000044038.90173.c6.

Amiri R, Weng Q, Alimohammadi A, Alavipanah SK. 2009. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113(12): 2606-2617. doi:https://doi.org/10.1016/j.rse.2009.07.021.

Bischof H, Schneider W, Pinz AJ. 1992. Multispectral classification of Landsat-images using neural networks. IEEE Transactions on Geoscience and Remote Sensing, 30(3): 482-490. doi:https://doi.org/10.1109/36.142926.

Bishop CM. 1995. Neural networks for pattern recognition. Oxford University Press, 1374 AP - Computers - 482 pages.

Borana S, Yadav S. 2017. Prediction of land cover changes of Jodhpur City using cellular automata markov modelling techniques. International Journal of Engineering Science, 17(11): 15402-15406. doi:http://dx.doi.org/10.13140/RG.2.2.10705.38246.

Carlson TN, Arthur ST. 2000. The impact of land use—land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and planetary change, 25(1-2): 49-65. doi:https://doi.org/10.1016/S0921-8181(00)00021-7.

Collobert R, Weston J. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning. pp 160-167. https://doi.org/110.1145/1390156.1390177.

Coseo P, Larsen L. 2014. How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain Urban Heat Islands in Chicago. Landscape and Urban Planning, 125: 117-129. doi:https://doi.org/10.1016/j.landurbplan.2014.02.019.

Deakin M, Allwinkle S. 2007. Urban regeneration and sustainable communities: The role of networks, innovation, and creativity in building successful partnerships. Journal of Urban Technology, 14(1): 77-91. doi:https://doi.org/10.1080/10630730701260118.

Dos Santos AR, de Oliveira FS, da Silva AG, Gleriani JM, Gonçalves W, Moreira GL, Silva FG, Branco ERF, Moura MM, da Silva RG. 2017. Spatial and temporal distribution of urban heat islands. Science of the Total Environment, 605: 946-956. doi:https://doi.org/10.1016/j.scitotenv.2017.05.275.

Exelis visual information solutions. 2015. ENVI 53 help.

Grimmond C. 2006. Progress in measuring and observing the urban atmosphere. Theoretical and Applied Climatology, 84(1): 3-22. doi:https://doi.org/10.1007/s00704-005-0140-5.

Hou H, Ding F, Li Q. 2018. Remote sensing analysis of changes of urban thermal environment of Fuzhou city in China in the past 20 years. Journal of Geo-information Science, 20(3): 385-395.

Jensen JR. 2005. Introductory digital image processinga remote sensing perspective. vol 621.3678 J4/2005. 526 p.

Jiang J, Tian G. 2010. Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environmental Sciences, 2: 571-575. doi:https://doi.org/10.1016/j.proenv.2010.10.062.

Jianping L, Bai Z, Feng G. 2005. RS-and-GIS-supported forecast of grassland degradation in southwest Songnen plain by Markov model. Geo-spatial Information Science, 8(2): 104-109. doi:https://doi.org/10.1007/BF02826848.

Kavzoglu T, Colkesen I. 2009. A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5): 352-359. doi:https://doi.org/10.1016/j.jag.2009.06.002.

LANDSAT 8 data users handbook. 2016. Using the USGS Landsat8 product, US Department of the Interior-US Geological Survey–NASA.

Li C, Wang J, Wang L, Hu L, Gong P. 2014. Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery. Remote Sensing, 6(2): 964-983. doi:https://doi.org/10.3390/rs6020964.

Li X, Zhou Y, Asrar GR, Imhoff M, Li X. 2017. The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Science of the Total Environment, 605: 426-435. doi:https://doi.org/10.1016/j.scitotenv.2017.06.229.

Liu G, Chen S, Gu J. 2019. Urban renewal simulation with spatial, economic and policy dynamics: The rent-gap theory-based model and the case study of Chongqing. Land Use Policy, 86: 238-252. doi:https://doi.org/10.1016/j.landusepol.2019.04.038.

Logsdon MG, Bell EJ, Westerlund FV. 1996. Probability mapping of land use change: A GIS interface for visualizing transition probabilities. Computers, Environment and Urban Systems, 20(6): 389-398. doi:https://doi.org/10.1016/S0198-9715(97)00004-5.

Muller MR, Middleton J. 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2): 151-157. doi: https://doi.org/10.1007/BF00124382.

Qiao Z, Liu L, Qin Y, Xu X, Wang B, Liu Z. 2020. The impact of urban renewal on land surface temperature changes: a case study in the main city of Guangzhou, China. Remote Sensing, 12(5): 794. doi:https://doi.org/10.3390/rs12050794.

Rousta I, Sarif MO, Gupta RD, Olafsson H, Ranagalage M, Murayama Y, Zhang H, Mushore TD. 2018. Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: A case study of Metropolitan City Tehran (1988–2018). Sustainability, 10(12): 4433. doi:https://doi.org/10.3390/su10124433.

Story M, Congalton RG. 1986. Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing, 52(3): 397-399.

Thompson WD, Walter SD. 1988. A reappraisal of the kappa coefficient. Journal of Clinical Epidemiology, 41(10): 949-958. doi:https://doi.org/10.1016/0895-4356(88)90031-5.

USGS. 2014. OLI and TIRS Calibration Notices. Landsat 8 Reprocessing to Begin February 3, 2014.

Wang R, Derdouri A, Murayama Y. 2018. Spatiotemporal simulation of future land use/cover change scenarios in the Tokyo metropolitan area. Sustainability, 10(6): 2056. doi:https://doi.org/10.3390/su10062056.

XIU L-n, Xiang-nan L. 2011. Current status and future direction of the study on artificial neural network classification processing in remote sensing. Remote Sensing Technology and Application, 18(5): 339-345.

Ziaul S, Pal S. 2016. Image based surface temperature extraction and trend detection in an urban area of West Bengal, India. Journal of Environmental Geography, 9(3-4): 13-25. doi:http://dx.doi.org/10.1515/jengeo-2016-0008.

_||_