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GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran)

Yıl 2022, Cilt: 4 Sayı: 2, 87 - 95, 30.12.2022
https://doi.org/10.51489/tuzal.1116553

Öz

Development of remote sensing applications has led to their use in a wide range of environmental studies. One of these aspects is urban studies and especially land surface temperature. In this study, the changes in land surface temperature in the Khorramabad city in Iran were investigated and the causes of land temperature changes were investigated. For this purpose, Landsat satellite images were processed in four periods of 2000, 2007, 2014 and 2021 and were recovered using a single-channel surface temperature algorithm. Temperatures were high in 2000 due to the type of roofs of buildings and the dirt around the city. Decreased in 2007 and 2014 due to the use of roofs that reflect light. In 2021, due to severe manipulations around the city and the destruction of vegetation and change it into built-up bare soil cover caused the temperature to rise again in the suburbs.

Kaynakça

  • Al‐Ghussain, L. (2019). Global warming: review on driving forces and mitigation. Environmental Progress & Sustainable Energy, 38(1), 13-21.
  • Bornstein, R. D. (1968). Observations of the urban heat island effect in New York City. Journal of Applied Meteorology and Climatology, 7(4), 575-582.
  • Brooks, E. B., Wynne, R. H., & Thomas, V. A. (2018). Using window regression to gap-fill Landsat ETM+ post SLC-Off data. Remote Sensing, 10(10), 1502.
  • Cammalleri, C., Anderson, M. C., Ciraolo, G., D'urso, G., Kustas, W. P., La Loggia, G., & Minacapilli, M. (2012). Applications of a remote sensing-based two-source energy balance algorithm for mapping surface fluxes without in situ air temperature observations. Remote Sensing of Environment, 124, 502-515.
  • Chakraborty, T., Hsu, A., Manya, D., & Sheriff, G. (2020). A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 74-88.
  • Collados-Lara, A. J., Fassnacht, S. R., Pardo-Igúzquiza, E., & Pulido-Velazquez, D. (2020). Assessment of high-resolution air temperature fields at rocky mountain national park by combining scarce point measurements with elevation and remote sensing data. Remote Sensing, 13(1), 113.
  • Cristóbal, J., Jiménez-Muñoz, J. C., Prakash, A., Mattar, C., Skoković, D., & Sobrino, J. A. (2018). An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, 10(3), 431.
  • Da Cunha, J. P., & Eames, P. (2016). Thermal energy storage for low and medium temperature applications using phase change materials–a review. Applied energy, 177, 227-238.
  • Feizizadeh, B., & Blaschke, T. (2012, July). Thermal remote sensing for land surface temperature monitoring: Maraqeh County, Iran. In 2012 IEEE International Geoscience and Remote Sensing Symposium (pp. 2217-2220). IEEE.
  • Galve, J. M., Sánchez, J. M., García-Santos, V., González-Piqueras, J., Calera, A., & Villodre, J. (2022). Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison. Remote Sensing, 14(8), 1843.
  • Goldblatt, R., Addas, A., Crull, D., Maghrabi, A., Levin, G. G., & Rubinyi, S. (2021). Remotely sensed derived land surface temperature (LST) as a proxy for air temperature and thermal comfort at a small geographical scale. Land, 10(4), 410.
  • Gui, X., Wang, L., Yao, R., Yu, D., & Li, C. A. (2019). Investigating the urbanization process and its impact on vegetation change and urban heat island in Wuhan, China. Environmental Science and Pollution Research, 26(30), 30808-30825.
  • Halder, B., Bandyopadhyay, J., & Banik, P. (2021). Monitoring the effect of urban development on urban heat island based on remote sensing and geo-spatial approach in Kolkata and adjacent areas, India. Sustainable Cities and Society, 74, 103186.
  • Hashemi Darebadami, S., Darvishi Boloorani, A., AlaviPanah, S. K., Maleki, Mohammad., & Bayat, R. (2019). Investigation of changes in surface urban heat-island (SUHI) in day and night using multi-temporal MODIS sensor data products (Case Study: Tehran metropolitan). Journal of Applied researches in Geographical Sciences, 19(52), 113-128.
  • Hawkes, A. D. (2014). Long-run marginal CO2 emissions factors in national electricity systems. Applied Energy, 125, 197-205.
  • He, B. J. (2019). Towards the next generation of green building for urban heat island mitigation: Zero UHI impact building. Sustainable Cities and Society, 50, 101647.
  • Hoffmann, P., Krueger, O., & Schlünzen, K. H. (2012). A statistical model for the urban heat island and its application to a climate change scenario. International Journal of Climatology, 32(8), 1238-1248.‏
  • Hooker, J., Duveiller, G., & Cescatti, A. (2018). A global dataset of air temperature derived from satellite remote sensing and weather stations. Scientific data, 5(1), 1-11.
  • Howe, P. D., Markowitz, E. M., Lee, T. M., Ko, C. Y., & Leiserowitz, A. (2013). Global perceptions of local temperature change. Nature climate change, 3(4), 352-356.
  • Jiménez‐Muñoz, J. C., & Sobrino, J. A. (2003). A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of geophysical research: atmospheres, 108(D22).
  • 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(10), 1840-1843.
  • Kim, H. H. (1992). Urban heat island. International Journal of Remote Sensing, 13(12), 2319-2336.
  • Li, K., Chen, Y., & Gao, S. (2022). Uncertainty of city-based urban heat island intensity across 1112 global cities: Background reference and cloud coverage. Remote Sensing of Environment, 271, 112898.
  • 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.
  • Maleki, M., Ahmadi, Z., & Dosti, R. (2019). Kermanshah land surface temperature changes in during 1393-1397 periods. Geography and Human Relationships, 2(3), 309-319.
  • Maleki, M., Tavakoli Sabour, S-M & Javan, F. (2018). Analysis of the Effects of Dam Construction on Vegetation of Peripheral Areas in Different Heights and Slopes. Case: Sulayman Shah and Gushan Dam. Spatial Locational Researches, 2(2), 102-117.
  • Maleki, M., Van Genderen, J. L., Tavakkoli-Sabour, S. M., Saleh, S. S., & Babaee, E. (2020). Land use/cover change in dinevar rural area of West Iran during 2000-2018 and its prediction for 2024 and 2030. Geogr. Tech, 15, 93-105.‏
  • Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 12(3), 131-140.
  • Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., Meili, N., ... & Bou-Zeid, E. (2019). Magnitude of urban heat islands largely explained by climate and population. Nature, 573(7772), 55-60.
  • Mansourmoghaddam, M., Rousta, I., Zamani, M., Mokhtari, M. H., Karimi Firozjaei, M., & Alavipanah, S. K. (2021). Study and prediction of land surface temperature changes of Yazd city: Assessing the proximity and changes of land cover. Journal of RS and GIS for Natural Resources, 12(4), 1-27.
  • Marx, S. M., Weber, E. U., Orlove, B. S., Leiserowitz, A., Krantz, D. H., Roncoli, C., & Phillips, J. (2007). Communication and mental processes: Experiential and analytic processing of uncertain climate information. Global Environmental Change, 17(1), 47-58.
  • Mathew, A., Khandelwal, S., & Kaul, N. (2017). Investigating spatial and seasonal variations of urban heat island effect over Jaipur city and its relationship with vegetation, urbanization and elevation parameters. Sustainable cities and society, 35, 157-177.
  • Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable cities and society, 19, 200-206.
  • Moradipour, F., Moghimi, E., Beglou, M. J., & Yamani, M. (2020). Assessment of urban geomorphological heritage for urban geotourism development in Khorramabad City, Iran. Geoheritage, 12(2), 1-20.
  • Myrup, L. O. (1969). A numerical model of the urban heat island. Journal of Applied Meteorology and Climatology, 8(6), 908-918.
  • Nikpour, Amer., Soleymani, Mohamad & Mohammadyari, Behnaz (2020) Spatial pattern of factors influencing the formation of poverty zones (Case Study: Khorramabad City). Urban Economics, 5(1), 113-126.
  • Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1-24.
  • Pal, S., & Ziaul, S. K. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 125-145.
  • Rajeshwari, A., & Mani, N. D. (2014). Estimation of land surface temperature of Dindigul district using Landsat 8 data. International Journal of Research in Engineering and Technology, 3(5), 122-126.
  • Rizwan, A. M., Dennis, L. Y., & Chunho, L. I. U. (2008). A review on the generation, determination and mitigation of Urban Heat Island. Journal of environmental sciences, 20(1), 120-128.
  • Scott, A. A., Waugh, D. W., & Zaitchik, B. F. (2018). Reduced Urban Heat Island intensity under warmer conditions. Environmental Research Letters, 13(6), 064003.
  • Sekertekin, A. (2019). Validation of physical radiative transfer equation-based land surface temperature using Landsat 8 satellite imagery and SURFRAD in-situ measurements. Journal of Atmospheric and Solar-Terrestrial Physics, 196, 105161.
  • Sekertekin, A., & Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2), 294.
  • Shen, H., Jiang, Y., Li, T., Cheng, Q., Zeng, C., & Zhang, L. (2020). Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data. Remote Sensing of Environment, 240, 111692.
  • Soldatenko, S. A., & Yusupov, R. M. (2019). Optimal control for the process of using artificial sulfate aerosols for mitigating global warming. Atmospheric and Oceanic Optics, 32(1), 55-63.
  • Spence, A., Poortinga, W., Butler, C., & Pidgeon, N. F. (2011). Perceptions of climate change and willingness to save energy related to flood experience. Nature climate change, 1(1), 46-49.
  • Statistics Center of Iran (2016). Population information.
  • Syariz, M. A., Jaelani, L. M., Subehi, L., Pamungkas, A., Koenhardono, E. S., & Sulisetyono, A. (2015). Retrieval of sea surface temperature over Poteran Island water of Indonesia with Landsat 8 TIRS image: A preliminary algorithm. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 87.
  • Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132.
  • Yin, C., Yuan, M., Lu, Y., Huang, Y., & Liu, Y. (2018). Effects of urban form on the urban heat island effect based on spatial regression model. Science of the Total Environment, 634, 696-704. Zare Naghadehi, S., Asadi, M., Maleki, M., Tavakkoli-Sabour, S. M., Van Genderen, J. L., & Saleh, S. S. (2021). Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data. ISPRS International Journal of Geo-Information, 10(8), 513.‏
  • Zhang, H., Qi, Z. F., Ye, X. Y., Cai, Y. B., Ma, W. C., & Chen, M. N. (2013). Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Applied Geography, 44, 121-133.

Khorramabad Şehrindeki (Luristan-İran) Arazi Yüzey Sıcaklığı Değişimlerinin CBS Tabanlı Değerlendirilmesi

Yıl 2022, Cilt: 4 Sayı: 2, 87 - 95, 30.12.2022
https://doi.org/10.51489/tuzal.1116553

Öz

Uzaktan algılama uygulamalarının gelişmesi, çeşitli çevresel çalışmalarda kullanılmasına yol açmıştır. Bu çalışmalardan biri de kentsel çalışmalar ve özellikle arazi yüzey sıcaklığıdır. Bu çalışmada İran'ın Khorramabad şehrinde arazi yüzey sıcaklığındaki değişimler incelenmiş ve arazi sıcaklık değişimlerinin sebepleri araştırılmıştır. Bu amaçla Landsat uydu görüntüleri 2000, 2007, 2014 ve 2021 olmak üzere dört periyotta işlenmiş ve tek kanallı yüzey sıcaklığı algoritması kullanılarak iyileştirilmiştir. 2000 yılında binalardaki çatı tipi şehrin çevresindeki kirlilik nedeniyle sıcaklıklar yüksekti. 2007 ve 2014 yılları arasında ışığı yansıtan çatıların kullanılması nedeniyle bu sıcaklıklar azalmıştır. 2021 yılında, şehrin etrafındaki şiddetli manipülasyonlar ve bitki örtüsünün yok edilmesi ve çıplak toprak örtüsüne dönüştürülmesi banliyölerde sıcaklığın yeniden yükselmesine neden olmuştur.

Kaynakça

  • Al‐Ghussain, L. (2019). Global warming: review on driving forces and mitigation. Environmental Progress & Sustainable Energy, 38(1), 13-21.
  • Bornstein, R. D. (1968). Observations of the urban heat island effect in New York City. Journal of Applied Meteorology and Climatology, 7(4), 575-582.
  • Brooks, E. B., Wynne, R. H., & Thomas, V. A. (2018). Using window regression to gap-fill Landsat ETM+ post SLC-Off data. Remote Sensing, 10(10), 1502.
  • Cammalleri, C., Anderson, M. C., Ciraolo, G., D'urso, G., Kustas, W. P., La Loggia, G., & Minacapilli, M. (2012). Applications of a remote sensing-based two-source energy balance algorithm for mapping surface fluxes without in situ air temperature observations. Remote Sensing of Environment, 124, 502-515.
  • Chakraborty, T., Hsu, A., Manya, D., & Sheriff, G. (2020). A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 74-88.
  • Collados-Lara, A. J., Fassnacht, S. R., Pardo-Igúzquiza, E., & Pulido-Velazquez, D. (2020). Assessment of high-resolution air temperature fields at rocky mountain national park by combining scarce point measurements with elevation and remote sensing data. Remote Sensing, 13(1), 113.
  • Cristóbal, J., Jiménez-Muñoz, J. C., Prakash, A., Mattar, C., Skoković, D., & Sobrino, J. A. (2018). An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band. Remote Sensing, 10(3), 431.
  • Da Cunha, J. P., & Eames, P. (2016). Thermal energy storage for low and medium temperature applications using phase change materials–a review. Applied energy, 177, 227-238.
  • Feizizadeh, B., & Blaschke, T. (2012, July). Thermal remote sensing for land surface temperature monitoring: Maraqeh County, Iran. In 2012 IEEE International Geoscience and Remote Sensing Symposium (pp. 2217-2220). IEEE.
  • Galve, J. M., Sánchez, J. M., García-Santos, V., González-Piqueras, J., Calera, A., & Villodre, J. (2022). Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison. Remote Sensing, 14(8), 1843.
  • Goldblatt, R., Addas, A., Crull, D., Maghrabi, A., Levin, G. G., & Rubinyi, S. (2021). Remotely sensed derived land surface temperature (LST) as a proxy for air temperature and thermal comfort at a small geographical scale. Land, 10(4), 410.
  • Gui, X., Wang, L., Yao, R., Yu, D., & Li, C. A. (2019). Investigating the urbanization process and its impact on vegetation change and urban heat island in Wuhan, China. Environmental Science and Pollution Research, 26(30), 30808-30825.
  • Halder, B., Bandyopadhyay, J., & Banik, P. (2021). Monitoring the effect of urban development on urban heat island based on remote sensing and geo-spatial approach in Kolkata and adjacent areas, India. Sustainable Cities and Society, 74, 103186.
  • Hashemi Darebadami, S., Darvishi Boloorani, A., AlaviPanah, S. K., Maleki, Mohammad., & Bayat, R. (2019). Investigation of changes in surface urban heat-island (SUHI) in day and night using multi-temporal MODIS sensor data products (Case Study: Tehran metropolitan). Journal of Applied researches in Geographical Sciences, 19(52), 113-128.
  • Hawkes, A. D. (2014). Long-run marginal CO2 emissions factors in national electricity systems. Applied Energy, 125, 197-205.
  • He, B. J. (2019). Towards the next generation of green building for urban heat island mitigation: Zero UHI impact building. Sustainable Cities and Society, 50, 101647.
  • Hoffmann, P., Krueger, O., & Schlünzen, K. H. (2012). A statistical model for the urban heat island and its application to a climate change scenario. International Journal of Climatology, 32(8), 1238-1248.‏
  • Hooker, J., Duveiller, G., & Cescatti, A. (2018). A global dataset of air temperature derived from satellite remote sensing and weather stations. Scientific data, 5(1), 1-11.
  • Howe, P. D., Markowitz, E. M., Lee, T. M., Ko, C. Y., & Leiserowitz, A. (2013). Global perceptions of local temperature change. Nature climate change, 3(4), 352-356.
  • Jiménez‐Muñoz, J. C., & Sobrino, J. A. (2003). A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of geophysical research: atmospheres, 108(D22).
  • 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(10), 1840-1843.
  • Kim, H. H. (1992). Urban heat island. International Journal of Remote Sensing, 13(12), 2319-2336.
  • Li, K., Chen, Y., & Gao, S. (2022). Uncertainty of city-based urban heat island intensity across 1112 global cities: Background reference and cloud coverage. Remote Sensing of Environment, 271, 112898.
  • 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.
  • Maleki, M., Ahmadi, Z., & Dosti, R. (2019). Kermanshah land surface temperature changes in during 1393-1397 periods. Geography and Human Relationships, 2(3), 309-319.
  • Maleki, M., Tavakoli Sabour, S-M & Javan, F. (2018). Analysis of the Effects of Dam Construction on Vegetation of Peripheral Areas in Different Heights and Slopes. Case: Sulayman Shah and Gushan Dam. Spatial Locational Researches, 2(2), 102-117.
  • Maleki, M., Van Genderen, J. L., Tavakkoli-Sabour, S. M., Saleh, S. S., & Babaee, E. (2020). Land use/cover change in dinevar rural area of West Iran during 2000-2018 and its prediction for 2024 and 2030. Geogr. Tech, 15, 93-105.‏
  • Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 12(3), 131-140.
  • Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., Meili, N., ... & Bou-Zeid, E. (2019). Magnitude of urban heat islands largely explained by climate and population. Nature, 573(7772), 55-60.
  • Mansourmoghaddam, M., Rousta, I., Zamani, M., Mokhtari, M. H., Karimi Firozjaei, M., & Alavipanah, S. K. (2021). Study and prediction of land surface temperature changes of Yazd city: Assessing the proximity and changes of land cover. Journal of RS and GIS for Natural Resources, 12(4), 1-27.
  • Marx, S. M., Weber, E. U., Orlove, B. S., Leiserowitz, A., Krantz, D. H., Roncoli, C., & Phillips, J. (2007). Communication and mental processes: Experiential and analytic processing of uncertain climate information. Global Environmental Change, 17(1), 47-58.
  • Mathew, A., Khandelwal, S., & Kaul, N. (2017). Investigating spatial and seasonal variations of urban heat island effect over Jaipur city and its relationship with vegetation, urbanization and elevation parameters. Sustainable cities and society, 35, 157-177.
  • Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable cities and society, 19, 200-206.
  • Moradipour, F., Moghimi, E., Beglou, M. J., & Yamani, M. (2020). Assessment of urban geomorphological heritage for urban geotourism development in Khorramabad City, Iran. Geoheritage, 12(2), 1-20.
  • Myrup, L. O. (1969). A numerical model of the urban heat island. Journal of Applied Meteorology and Climatology, 8(6), 908-918.
  • Nikpour, Amer., Soleymani, Mohamad & Mohammadyari, Behnaz (2020) Spatial pattern of factors influencing the formation of poverty zones (Case Study: Khorramabad City). Urban Economics, 5(1), 113-126.
  • Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1-24.
  • Pal, S., & Ziaul, S. K. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 125-145.
  • Rajeshwari, A., & Mani, N. D. (2014). Estimation of land surface temperature of Dindigul district using Landsat 8 data. International Journal of Research in Engineering and Technology, 3(5), 122-126.
  • Rizwan, A. M., Dennis, L. Y., & Chunho, L. I. U. (2008). A review on the generation, determination and mitigation of Urban Heat Island. Journal of environmental sciences, 20(1), 120-128.
  • Scott, A. A., Waugh, D. W., & Zaitchik, B. F. (2018). Reduced Urban Heat Island intensity under warmer conditions. Environmental Research Letters, 13(6), 064003.
  • Sekertekin, A. (2019). Validation of physical radiative transfer equation-based land surface temperature using Landsat 8 satellite imagery and SURFRAD in-situ measurements. Journal of Atmospheric and Solar-Terrestrial Physics, 196, 105161.
  • Sekertekin, A., & Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2), 294.
  • Shen, H., Jiang, Y., Li, T., Cheng, Q., Zeng, C., & Zhang, L. (2020). Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data. Remote Sensing of Environment, 240, 111692.
  • Soldatenko, S. A., & Yusupov, R. M. (2019). Optimal control for the process of using artificial sulfate aerosols for mitigating global warming. Atmospheric and Oceanic Optics, 32(1), 55-63.
  • Spence, A., Poortinga, W., Butler, C., & Pidgeon, N. F. (2011). Perceptions of climate change and willingness to save energy related to flood experience. Nature climate change, 1(1), 46-49.
  • Statistics Center of Iran (2016). Population information.
  • Syariz, M. A., Jaelani, L. M., Subehi, L., Pamungkas, A., Koenhardono, E. S., & Sulisetyono, A. (2015). Retrieval of sea surface temperature over Poteran Island water of Indonesia with Landsat 8 TIRS image: A preliminary algorithm. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 87.
  • Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132.
  • Yin, C., Yuan, M., Lu, Y., Huang, Y., & Liu, Y. (2018). Effects of urban form on the urban heat island effect based on spatial regression model. Science of the Total Environment, 634, 696-704. Zare Naghadehi, S., Asadi, M., Maleki, M., Tavakkoli-Sabour, S. M., Van Genderen, J. L., & Saleh, S. S. (2021). Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data. ISPRS International Journal of Geo-Information, 10(8), 513.‏
  • Zhang, H., Qi, Z. F., Ye, X. Y., Cai, Y. B., Ma, W. C., & Chen, M. N. (2013). Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Applied Geography, 44, 121-133.
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Mohammad Hassan Khamesi-maybodi 0000-0001-8267-3628

Yayımlanma Tarihi 30 Aralık 2022
Kabul Tarihi 11 Kasım 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 4 Sayı: 2

Kaynak Göster

APA Khamesi-maybodi, M. H. (2022). GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran). Türkiye Uzaktan Algılama Dergisi, 4(2), 87-95. https://doi.org/10.51489/tuzal.1116553
AMA Khamesi-maybodi MH. GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran). TUZAL. Aralık 2022;4(2):87-95. doi:10.51489/tuzal.1116553
Chicago Khamesi-maybodi, Mohammad Hassan. “GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran)”. Türkiye Uzaktan Algılama Dergisi 4, sy. 2 (Aralık 2022): 87-95. https://doi.org/10.51489/tuzal.1116553.
EndNote Khamesi-maybodi MH (01 Aralık 2022) GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran). Türkiye Uzaktan Algılama Dergisi 4 2 87–95.
IEEE M. H. Khamesi-maybodi, “GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran)”, TUZAL, c. 4, sy. 2, ss. 87–95, 2022, doi: 10.51489/tuzal.1116553.
ISNAD Khamesi-maybodi, Mohammad Hassan. “GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran)”. Türkiye Uzaktan Algılama Dergisi 4/2 (Aralık 2022), 87-95. https://doi.org/10.51489/tuzal.1116553.
JAMA Khamesi-maybodi MH. GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran). TUZAL. 2022;4:87–95.
MLA Khamesi-maybodi, Mohammad Hassan. “GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran)”. Türkiye Uzaktan Algılama Dergisi, c. 4, sy. 2, 2022, ss. 87-95, doi:10.51489/tuzal.1116553.
Vancouver Khamesi-maybodi MH. GIS-Based Assessment of Land Surface Temperature Changes Over Khorramabad City (Lorestan, Iran). TUZAL. 2022;4(2):87-95.