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Manuscript ID : JEST-1905-4677 Visit : 119 Page: 45 - 60

10.22034/jest.2021.44561.4677

Article Type: Original Research

Landscape Metrics as Tool for Investigating the Relationship between Landscape Patterns and Land Surface Temperature in suitable scale(Case Study: Tehran City

Subject Areas : Urban Environment

Fatemeh Effati 1 ( M.Sc., student, Graduate, Environmental Planning, Management and education, Faculty of Environment, University of Tehran. (Corresponding Author) )
Abdolrassoul Salmanmahiny 2 ( Professor, Department of Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. )
Fatemeh SHafie Khorshidi 3 ( M.Sc., student, Graduate Faculty of Humanities Geography Information System & Remote sensing, Tarbiat Modares University )
Saeed Karimi 4 ( Assist. Prof., Graduate Faculty of Environment, University of Tehran. )

Received: 2019-05-05 Accepted : 2020-09-15 Published : 2022-10-23

Keywords: scale, Tehran city, Land surface temperature, Land use/ Land over, Landscape metrics,

Abstract :

Background and Objective: Tehran has experienced extensive population growth in the last decades, leading to a high rate of urban expansion. Land use/land cover (LULC) patterns have noticeably been changed to impervious surfaces that led to the changes in the thermal condition and forming heat islands in this city. So this study wants to evaluate the landscape and the Land surface temperature patterns via using the landscape metrics on a proper scale in Tehran. Material and Methodology:  In this study, a combination of remote sensing, GIS and landscape ecology approach is used to explain the relationship between land use/cover patterns and land surface temperature in Tehran's urban area. We used ETM + Landsat satellite images of February 28, 2013 to create a five class LULC map of the area through Linear Spectral Mixture Analysis and the maximum algorithm methods. Also, Land Surface Temperature map were prepared according to the available methods for thermal band of the sensor and were presented in four zones. Then, the relationship between LST and land use/cover was investigated using 7 landscape metrics (e.g MPS, PAFRAC, COHESION). Findings: We found that impervious surface has the highest percentage of class and mean patch size, cohesion and aggregation, and landscape metrics very well described the LST zone II with impervious surface dominance. Also, the results showed that the 30 m pixel size is good enough for assessing the spatial and ecological characteristics of LULC patterns and their relationships with LST in Tehran Discussion and Conclusion: The results showed the possibility of assessing the relationship between LST and LULC based on the landscape metrics. The findings can be useful for urban planners and environmental managers to decrease urban heat pollution during urban sprawl and development.

References:


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_||_

  1. Bain, D.J and Brush, G.S., 2004. placing the pieces: reconstructing the orginal property mosaic in a warrant and patent watershed. Landscape Ecology, 19(8), 843-856.
    2.
  2. Mahamane, M., Zorrilla-Miras, P., Verweij, P., Ryan, C., Patenaude, G., Grundy, I., & Vollmer, F., Understanding Land Use, Land Cover and Woodland-Based Ecosystem Services Change, Mabalane, Mozambique. Energy and Environment Research, 7(1), 1.
    3.
  3. 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.
    4.
  4. Keshtkar, H., & Voigt,W.,2016. Potential impacts of climate and landscape fragmentation changes on plant distributions: coupling multitemporal satellite imagery with GIS-based cellular automata model. Ecological Informatics, 32, 145–155.
    5.
  5. Zhou W, Qian Y, Li X, Li W, Han L., 2014. Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecology, 29.
    6.
  6. Xiao, R., Ouyang, Z., Zhang, H., Li, W., Schienke, E.W., Wang, X. 2007. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China, Journal of Environmental Sciences 19, 250-256.
    7.
  7. McGarigal, K., 2015. FRAGSTATE help. Amherst: Department of Environmental Conservation University of Massachusetts.
    8.
  8. Botequilha A.L., and Jack Ahern., 2002.applying landscape ecological concepts and metrics in sustainable landscape planning. Landscape and Urban Planning .59:65-93.
    9.
  9. Forman, R.T.T., Godron, M., 1986. Landscape Ecology. Quinn-Woodbine, Inc., United States of America. Gergel, S. E., & Turner, M. G. (Eds.). (2017). Learning landscape ecology: a practical guide to concepts and techniques. Springer.
    10.
  10. Riitters, K. H., O’Neill, R. V., Hunsaker, C. T., Wickham, J. D. , Yankee, D. H. ,. Timmins, S. P., Jones, K. B., and Jackson, B. L., 1995. A factor analysis of landscape pattern and structure metrics, Landscape Ecology. 10: 23–39.
    11.
  11. McGarigal, K., Marks, B.J., 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure, General Technical Report PNW-GTR-351, USDA Forest Service, Pacific Northwest Research Station, Portland.
    12.
  12. Weng, Q., Liu, H., Lu, D., 2007. Assessing the effects of land use and land cover patterns.on thermal conditions using landscape metrics in city of Indianapolis, United States, Urban Ecosystem. 10(2).203–219.
    13.
  13. Li X, Zhou W, Ouyang Z., 2013. Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution? Landscape and Urban Planning, 114:1-8.
    14.
  14. Asgarian, A., Amiri, B. J., & Sakieh, Y., 2015. Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosystem, 18, 209–222.
    15.
  15. Zhang, Y., Odehb, I. O. A., & Ramadanc, E., 2013. Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data. International Journal of Remote Sensing, 34(1), 168–189.
    16.
  16. Madanian, M. Soffianian, A. Soltani Koupai, S. Pourmanafi, S Mehdi Momeni, M., 2018. Analyzing the effects of urban expansion on land surface temperature patterns by landscape metrics: a case study of Isfahan city, Iran. Environ Monit Assess 190:189.
    17.
  17. Xie, M., Wang, Y., Chang, Q., Fu, M., & Ye, M., 2013. Assessment of landscape patterns affecting land surface temperature in different biophysical gradients in Shenzhen, Urban Ecosystem, 16, 871–886.
    18.
  18. Liu, Y., Peng, J., Wang, Y., 2018. Efficiency of landscape metrics characterizing urban land surface temperature.Landscape and Urban Planning, 180, 36-53.
    19.
  19. Hashemi S.M, Alavipanah S.K, Dinarvandi M., 2013. Assessment of Spatial Distribution of Land Surface Temperature in Urban Environment by Remote Sensing Journal of Environmental Studies, 39(1):81-92. (In Persian)
    20.
  20. Statistical Center of Iran., 2016. (In Persian)
    21.
  21. Iran Meteorological Organization., 2016. (In Persian)
    22.
  22. Wu, C & Murray, A. T., 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84, pp. 493-505.
    23.
  23. Tong, X., Liu, T., Singh, V. P., Duan, L., & Long, D., 2016. Development of In Situ Experiments for Evaluation of Anisotropic Reflectance Effect on Spectral Mixture Analysis for Vegetation Cover. IEEE Geoscience and Remote Sensing Letters, 13(5), 636-640.
    24.
  24. Sousa, D., & Small, C., 2017. Global cross-calibration of Landsat spectral mixture models. Remote Sensing of Environment, 192, 139-149.
    25.
  25. Artis, D.A., Carnahan, W.H., 1982. Surv ey of emissivity variability in thermography of urban areas. Remote Sensing of Environment. 12 (4), 313– 329.
    26.
  26. Lu, D and Weng, Q., 2006. Use of impervious surface in urban land use classification. Remote Sens Environ 102(1–2):146–160.
    27.
  27. Jiménez-Muñoz, J. C., Sobrino, J. A., Skoković, D., Mattar, C., & Cristóbal, J., 2014. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, 11, 1840–1843.
    28.
  28. Smith R.M., 1986. Comparing traditional methods for selecting class intervals on choropleth maps. Prof Geog. 38(1):62–67
    29.
  29. Liu, H., and Weng, Q., 2009. Scaling Effect on the Relationship between Landscape Pattern and Land Surface Temperature: A Case Study of Indianapolis, United States, Photogrammetric Engineering & Remote Sensing, 75(3): 291–304.
    30.
  30. McGarigal, K., Cushman, S.A., Neel, M.C., Ene, E., 2002. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps, Computer software program produced by the authors at the University of Massachusetts, Amherst. URL: http://www.umass.edu/landeco/research/fragstats/fragstats.html.
    31.
  31. Sobrino JA, Oltra-Carrió R, Sòria G, Jiménez- Muñoz JC, Franch B, Hidalgo V, Mattar C, Julien Y, Cuenca J, Romaguera M., 2013. Evaluation of the surface urban heat island effect in the city of sensing. International Journal of Remote Madrid by thermal remote Sensing, 34(9-10):3177-3192.
    32.
  32. Su Y-F, Foody GM, Cheng K-S., 2012. Spatial non-stationarity in the relationships between land cover and surface temperature in an urban heat island and its impacts on thermally sensitive populations. Landscape and Urban Planning, 107(2):172-180.
    33.
  33. Yavari, A.R., Sotoudeh, A and Parivar, P., 2007. Urban Environment Quality and Landscape Structure in Arid Mountain Environment. International Journal of Environmental Research.1, 325-340. (In Persian)
    34.

 

 

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