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INVESTIGATION OF THE RELATIONSHIP BETWEEN ALBEDO, LAND SURFACE TEMPERATURE AND NDVI USING LANDSAT-7 AND LANDSAT-8 SATELLITE DATA: A CASE STUDY SAFRANBOLU

Yıl 2023, , 177 - 190, 15.03.2023

Emre Yücer

https://doi.org/10.17780/ksujes.1192591

Öz

Developing remote sensing technologies are effectively used in monitoring the changes in surface parameters in urban areas. Information about urban heat islands is obtained by utilizing the spectral and thermal properties of surfaces at local and global scale. In our study, Safranbolu district of Karabük province, which is on the World Cultural Heritage list, has been chosen as the application area. Landsat-7 satellite image data from 12/08/1999, and Landsat-8 satellite image data from 11/08/2019, were used to calculate the Albedo, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) variables. In the results, it was determined that there were positive relations between LST and albedo, negative relations between LST and NDVI, and negative relations between albedo and NDVI. These relationships were found similarly in both correlation analysis and scatter plots. The main factors affecting the relationship between LST, albedo and NDVI can be listed as the type of material on the surface, the amount of moisture on the surface, vegetation and its density.

Anahtar Kelimeler

Albedo Landsat normalised difference vegetation index Safranbolu land surface temperature

Kaynakça

  • Ahrens, C. D., & Henson, R. (2015). Meteorology Today: An Introduction to Weather, Climate, and the Environment, Eleventy Edition, Cengage Learning, Boston.
  • Akbari, H., Menon, S., & Rosenfeld, A. (2009). Global cooling: increasing world-wide urban albedos to offset CO2. Climatic Change, 94, 275-286. https://doi.org/10.1007/s10584-008-9515-9.
  • Akbari, H., Damon Matthews, H., & Seto, D. (2012). The long-term effect ofincreasing the albedo of urban areas. Environmental Research Letters, 7, 1–10. http://dx.doi.org/10.1088/1748-9326/7/2/024004.
  • Akyürek, Ö., (2020). Termal uzaktan algılama görüntüleri ile yüzey sıcaklıklarının belirlenmesi: Kocaeli örneği. Doğal Afetler ve Çevre Dergisi, 6(2), 377-390. http://dx.doi.org/10.21324/dacd.667594.
  • Anandababu, D., Puruhothaman B. M., & Babu, S.S. (2018). Estimation of land surface temperature using landsat 8 data. International Journal of Advance Research, Ideas And ınnovations ın Technology, 4(2), 177- 186.
  • Anniballe, R., Bonafoni, S., & Pichierri, M. (2014). Spatial and temporal trends of the surface and air heat island over Milan using Modis data. Remote Sensing of Environment.,150, 163-171. https://doi.org/10.1016/j.rse.2014.05.005.
  • Artis, D. A., & Carnahan, W.H. (1982). Survey of Emissivity Variability in Thermography of Urban Areas. Remote Sensing of Environment, 12(4), 313-329. https://doi.org/10.1016/0034-4257(82)90043-8.
  • Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 1–8. https://doi.org/10.1155/2016/1480307.
  • Barsi, J., Schott, J., Hook, S., Raqueno, N., Markham, B., & Radocinski, R. (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607- 11626. https://doi.org/10.3390/rs61111607.
  • Balçık F. B., & Ergene E. M., (2017). Yer yüzey sıcaklığının termal uzaktan algılama verileri ile belirlenmesi: İstanbul örneği. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği 9. Teknik Sempozyumu, ss 21.
  • Bonafoni, S., & Baldinelli, G., & Verducci, P. (2017). Sustainable strategies for smart cities: Analysis of the town development effect on surface urban heat island through remote sensing methodologies. Sustainable Cities and Society, 17(29), 211-218. https://doi.org/10.1016/j.scs.2016.11.005.
  • Bretz, S., Akbari, H., & Rosenfeld, A. (1998). Practical issues for using solar-reflective materials to mitigate urban heat islands. Atmospheric Environment, 32(1), 95-101. https://doi.org/10.1016/S1352-2310(97)00182-9.
  • Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y., (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133-146. https://doi.org/10.1016/j.rse.2005.11.016.
  • Cunha, J., Nóbrega, R., Rufino, I., Erasmi, S., Galvão, C., & Valente, F. (2019). Surface albedo as a proxy for land-cover clearing in seasonally dry forests: evidence from the Brazilian Caatinga. Remote Sensing of Environment, 238. https://doi.org/10.1016/j.rse.2019.111250.
  • Dimoudi, A., Zoras, S., Kantzioura, A., Stogiannou, X., Kosmopoulos, P., & Pallas, C. (2014). Use of cool materials and other bioclimatic interventions in outdoorplaces in order to mitigate the urban heat island in a medium size city in Greece. Sustainable Cities and Society, 13, 89–96. https://doi.org/10.1016/j.scs.2014.04.003.
  • Erener, A., & Sarp G., (2018). Spatiotemporal distribution of ındustrial regions and impact on LST in the case of Kocaeli. FIG Congress Proceedings.
  • Giannini, M.B., Belfiore, O.R., Parenta, C., & Santamaria, R. (2015). Land surface temperature from landsat 5 tm images: comparison of different methods using airborne thermal data. Journal of Engineering Science and Technology Review, 8(3), 83-90.
  • Givoni, B. (1991). Impact of planted areas on urban environmental quality: Areview. Atmospheric Environment, 25, 289–299. https://doi.org/10.1016/0957-1272(91)90001-U.
  • Gupta, R. P. (2003). Remote Sensing Geology (Second Edition), Springer, Verlag.
  • Jeevalakshmi, D., Reddy, S. N., & Manikiam B., (2017). Land surface temperature retrieval from landsat data using emissivity estimation. International Journal of Applied Engineering Research, 12(20), 9679-9687.
  • Landsat. (2022). https://landsat.gsfc.nasa.gov/satellites/.
  • Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A., (2013). Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131, 14-37. https://doi.org/10.1016/j.rse.2012.12.008.
  • Mariano, D. A., Santos, C. A. C., Wardlow, B. D., Anderson, M. C., Schiltmeyer, A. V., Tadesse, T., & Svoboda, M. D. (2018). Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in northeastern Brazil. Remote Sensing of Environment, 213, 129–143. https://doi.org/10.1016/j.rse.2018.04.048.
  • Ndossi, M. I., & Avdan U., (2016). Açık kaynak kod teknoloji kullanılarak yer yüzey sıcaklığının belirlenmesinde yeni bir eklentinin geliştirilmesi. 6. Uzaktan Algılama-CBS Sempozyumu, ss 1135-1141.
  • Otterman, J. (1974). Baring high-albedo soils by overgrazing: hypothesized desertification mechanism. Science, 186 (4163), 531–533. https://doi.org/10.1126/science.186.4163.531.
  • Oke, T. R. 2002.Boundary Layer Climates. Routledge: New York.
  • Polat, N. (2020). Mardin ilinde uzun yıllar yer yüzey sıcaklığı değişiminin incelenmesi. Türkiye Uzaktan Algılama Dergisi, 2 (1), 10-15.
  • Prata, A. J., Caselles, C. C., Sobrino, J. A., & Ottle, C., (2009), Thermal remote sensing of land surface temperature from satellites: current status and future prospects. Remote Sensing Reviews, 12, 175-224. https://doi.org/10.1080/02757259509532285.
  • Rajasekar, U., & Weng, Q. H. (2009). Spatio-temporal modelling and analysis of urban heat islands by using Landsat TM and ETM plus imagery. International Journal of Remote Sensing, 30(13), 3531–3548. https://doi.org/10.1080/01431160802562289.
  • Roy, S., Pandit, S., Eva, E. E., Bagmar, M. S. H., Papia, M., Banik, L., Dube, T., Rahman, F., & Razi, M.A. (2020), Examining the nexus between land surface temperature and urban growth in chattogram metropolitan area of Bangladesh uaing long term landsat series data. Urban Climate, 2(2020), 1-22. https://doi.org/10.1016/j.uclim.2020.100593.
  • Saco, P. M., Moreno-de las Heras, M., Keesstra, S., Baartman, J., Yetemen, O., & Rodríguez, J. F. (2018). Vegetation and soil degradation in drylands: Nonlinear feedbacks and early warning signals. Current Opinion in Environmental Science & Health, 5, 67–72. https://doi.org/10.1016/j.coesh.2018.06.001.
  • Safranbolu Belediyesi website. (2022). https://www.safranbolu.bel.tr/ Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, 21, 353-66. https://doi.org/10.1080/014311600210876.
  • Stathopoulou, M., Synnefa, A., Caralis, C., Sanamouris, M., Karless, T., & Akbari, H. (2009). A surface heat island study of Athens using high-resolution satellite imagery and measurements of the optical and thermal properties of commonly used building and paving materials. International Journal of Sustainable Energy, 28(1), 59–76. https://doi.org/10.1080/14786450802452753.
  • Shuai, Y., Masek, J. G., Gao, F., Schaaf, C. B., & He, T. (2014). An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sensing of Environment, 152, 467–479. https://doi.org/10.1016/j.rse.2014.07.009.
  • Suehrcke, H., Peterson, E. L., & Selby, N. (2008). Effect of roof solar reflectance onthe building heat gain in a hot climate. Energy and Buildings, 40, 2224–2235. https://doi.org/10.1016/j.enbuild.2008.06.015.
  • Şener, E. (2016). Burdur Gölü Yüzey Sıcaklığı Mevsimsel Değişiminin Landsat 8 Uydu görüntüleri kullanılarak belirlenmesi. Mühendislik Bilimleri ve Tasarım Dergisi, 4(2), 67-73. https://doi.org/10.21923/jesd.31386.
  • Yıldız, A., Bağcı, M., Başaran, C., Çonkar, F. E., & Ayday C., (2017). Landsat 8 uydu verilerinin jeotermal saha araştırmalarında kullanılması: Gazlıgöl (Afyonkarahisar) çalışması. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17, 277-284.
  • Yılmaz, E. (2015). Landsat görüntüleri ile Adana yüzey ısı adası. Coğrafi Bilimler Dergisi, 13(2), 115-138. https://doi.org/10.1501/Cogbil_0000000167.
  • 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. https://doi.org/10.1016/j.rse.2006.09.003.
  • Wang, Y., & Akbari, H. (2016). Analysis of urban heat island phenomenon and mitigation solutions evaluation for Montreal. Sustainable Cities and Society, 26, 438–446. https://doi.org/10.1016/j.scs.2016.04.015.
  • Wang, Z., Erb, A. M., Schaaf, C. B., Sun, Q., Liu, Y., Yang, Y., Shuai, Y., Casey, K. A., & Román, M. O. (2016). Remote sensing of environment early spring post- fi re snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data. Remote Sensing of Environment, 185, 71–83. https://doi.org/10.1016/j.rse.2016.02.059.
  • Zhao, Y., Wang, X., Novillo, C.J., Arrogante-Funes, P., Vázquez-Jiménez, R., & Maestre, F.T. (2018). Albedo estimated from remote sensing correlates with ecosystem multifunctionality in global drylands. J. Arid Environ, 157, 116–123. https://doi.org/10.1016/j.jaridenv.2018.05.010.
  • Zolotokrylin, A. N., Brito-Castillo, L., & Titkova, T. B. (2020). Local climatically-driven changes of albedo and surface temperatures in the Sonoran Desert. Journal of Arid Environments, 178, 104147. https://doi.org/10.1016/j.jaridenv.2020.104147

ALBEDO, YER YÜZEY SICAKLIĞI VE NDVI ARASINDAKİ İLİŞKİNİN LANDSAT-7 VE LANDSAT-8 UYDU VERİLERİ KULLANILARAK İNCELENMESİ: SAFRANBOLU ÖRNEĞİ

Yıl 2023, , 177 - 190, 15.03.2023

Emre Yücer

https://doi.org/10.17780/ksujes.1192591

Öz

Gelişen uzaktan algılama teknolojileri, kentsel alanlarda meydana gelen yüzey parametrelerindeki değişimlerin izlenmesinde etkin şekilde kullanılmaktadır. Yerel ve küresel ölçekte yüzeylerin spektral ve termal özelliklerinden yararlanarak kentsel ısı adaları hakkında bilgiler elde edilmektedir. Çalışmamızda Dünya Kültür Mirası listesinde bulunan Karabük ilinin Safranbolu ilçesi çalışma alanı olarak seçilmiştir. Albedo, Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI) ve Yer Yüzey Sıcaklığı (YYS) değişkenlerinin hesaplanması için 12/08/1999 tarihli Landsat-7 ve 11/08/2019 tarihli Landsat-8 uydu verileri kullanılmıştır. Değişkenler arasındaki ilişkiyi ortaya çıkarmak için korelasyon ve saçılım analizleri uygulanmıştır. Elde edilen sonuçlarda; YYS ve albedo arasında pozitif, YYS ve NDVI arasında negatif, albedo ve NDVI arasında negatif yönlü ilişkinin olduğu tespit edilmiştir. Bu ilişkiler hem korelasyon analizinde hem de saçılım grafiklerinde benzer şekilde çıkmıştır. YYS, albedo ve NDVI arasındaki ilişkiyi etkileyen başlıca etmenler; yüzeydeki malzemenin türü, yüzeydeki nem miktarı, bitki örtüsü ve yoğunluğu şeklinde sıralanabilir.

Anahtar Kelimeler

Albedo Landsat normalize edilmiş fark bitki örtüsü indeksi Safranbolu yer yüzey sıcaklığı

Kaynakça

  • Ahrens, C. D., & Henson, R. (2015). Meteorology Today: An Introduction to Weather, Climate, and the Environment, Eleventy Edition, Cengage Learning, Boston.
  • Akbari, H., Menon, S., & Rosenfeld, A. (2009). Global cooling: increasing world-wide urban albedos to offset CO2. Climatic Change, 94, 275-286. https://doi.org/10.1007/s10584-008-9515-9.
  • Akbari, H., Damon Matthews, H., & Seto, D. (2012). The long-term effect ofincreasing the albedo of urban areas. Environmental Research Letters, 7, 1–10. http://dx.doi.org/10.1088/1748-9326/7/2/024004.
  • Akyürek, Ö., (2020). Termal uzaktan algılama görüntüleri ile yüzey sıcaklıklarının belirlenmesi: Kocaeli örneği. Doğal Afetler ve Çevre Dergisi, 6(2), 377-390. http://dx.doi.org/10.21324/dacd.667594.
  • Anandababu, D., Puruhothaman B. M., & Babu, S.S. (2018). Estimation of land surface temperature using landsat 8 data. International Journal of Advance Research, Ideas And ınnovations ın Technology, 4(2), 177- 186.
  • Anniballe, R., Bonafoni, S., & Pichierri, M. (2014). Spatial and temporal trends of the surface and air heat island over Milan using Modis data. Remote Sensing of Environment.,150, 163-171. https://doi.org/10.1016/j.rse.2014.05.005.
  • Artis, D. A., & Carnahan, W.H. (1982). Survey of Emissivity Variability in Thermography of Urban Areas. Remote Sensing of Environment, 12(4), 313-329. https://doi.org/10.1016/0034-4257(82)90043-8.
  • Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 1–8. https://doi.org/10.1155/2016/1480307.
  • Barsi, J., Schott, J., Hook, S., Raqueno, N., Markham, B., & Radocinski, R. (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607- 11626. https://doi.org/10.3390/rs61111607.
  • Balçık F. B., & Ergene E. M., (2017). Yer yüzey sıcaklığının termal uzaktan algılama verileri ile belirlenmesi: İstanbul örneği. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği 9. Teknik Sempozyumu, ss 21.
  • Bonafoni, S., & Baldinelli, G., & Verducci, P. (2017). Sustainable strategies for smart cities: Analysis of the town development effect on surface urban heat island through remote sensing methodologies. Sustainable Cities and Society, 17(29), 211-218. https://doi.org/10.1016/j.scs.2016.11.005.
  • Bretz, S., Akbari, H., & Rosenfeld, A. (1998). Practical issues for using solar-reflective materials to mitigate urban heat islands. Atmospheric Environment, 32(1), 95-101. https://doi.org/10.1016/S1352-2310(97)00182-9.
  • Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y., (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133-146. https://doi.org/10.1016/j.rse.2005.11.016.
  • Cunha, J., Nóbrega, R., Rufino, I., Erasmi, S., Galvão, C., & Valente, F. (2019). Surface albedo as a proxy for land-cover clearing in seasonally dry forests: evidence from the Brazilian Caatinga. Remote Sensing of Environment, 238. https://doi.org/10.1016/j.rse.2019.111250.
  • Dimoudi, A., Zoras, S., Kantzioura, A., Stogiannou, X., Kosmopoulos, P., & Pallas, C. (2014). Use of cool materials and other bioclimatic interventions in outdoorplaces in order to mitigate the urban heat island in a medium size city in Greece. Sustainable Cities and Society, 13, 89–96. https://doi.org/10.1016/j.scs.2014.04.003.
  • Erener, A., & Sarp G., (2018). Spatiotemporal distribution of ındustrial regions and impact on LST in the case of Kocaeli. FIG Congress Proceedings.
  • Giannini, M.B., Belfiore, O.R., Parenta, C., & Santamaria, R. (2015). Land surface temperature from landsat 5 tm images: comparison of different methods using airborne thermal data. Journal of Engineering Science and Technology Review, 8(3), 83-90.
  • Givoni, B. (1991). Impact of planted areas on urban environmental quality: Areview. Atmospheric Environment, 25, 289–299. https://doi.org/10.1016/0957-1272(91)90001-U.
  • Gupta, R. P. (2003). Remote Sensing Geology (Second Edition), Springer, Verlag.
  • Jeevalakshmi, D., Reddy, S. N., & Manikiam B., (2017). Land surface temperature retrieval from landsat data using emissivity estimation. International Journal of Applied Engineering Research, 12(20), 9679-9687.
  • Landsat. (2022). https://landsat.gsfc.nasa.gov/satellites/.
  • Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A., (2013). Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131, 14-37. https://doi.org/10.1016/j.rse.2012.12.008.
  • Mariano, D. A., Santos, C. A. C., Wardlow, B. D., Anderson, M. C., Schiltmeyer, A. V., Tadesse, T., & Svoboda, M. D. (2018). Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in northeastern Brazil. Remote Sensing of Environment, 213, 129–143. https://doi.org/10.1016/j.rse.2018.04.048.
  • Ndossi, M. I., & Avdan U., (2016). Açık kaynak kod teknoloji kullanılarak yer yüzey sıcaklığının belirlenmesinde yeni bir eklentinin geliştirilmesi. 6. Uzaktan Algılama-CBS Sempozyumu, ss 1135-1141.
  • Otterman, J. (1974). Baring high-albedo soils by overgrazing: hypothesized desertification mechanism. Science, 186 (4163), 531–533. https://doi.org/10.1126/science.186.4163.531.
  • Oke, T. R. 2002.Boundary Layer Climates. Routledge: New York.
  • Polat, N. (2020). Mardin ilinde uzun yıllar yer yüzey sıcaklığı değişiminin incelenmesi. Türkiye Uzaktan Algılama Dergisi, 2 (1), 10-15.
  • Prata, A. J., Caselles, C. C., Sobrino, J. A., & Ottle, C., (2009), Thermal remote sensing of land surface temperature from satellites: current status and future prospects. Remote Sensing Reviews, 12, 175-224. https://doi.org/10.1080/02757259509532285.
  • Rajasekar, U., & Weng, Q. H. (2009). Spatio-temporal modelling and analysis of urban heat islands by using Landsat TM and ETM plus imagery. International Journal of Remote Sensing, 30(13), 3531–3548. https://doi.org/10.1080/01431160802562289.
  • Roy, S., Pandit, S., Eva, E. E., Bagmar, M. S. H., Papia, M., Banik, L., Dube, T., Rahman, F., & Razi, M.A. (2020), Examining the nexus between land surface temperature and urban growth in chattogram metropolitan area of Bangladesh uaing long term landsat series data. Urban Climate, 2(2020), 1-22. https://doi.org/10.1016/j.uclim.2020.100593.
  • Saco, P. M., Moreno-de las Heras, M., Keesstra, S., Baartman, J., Yetemen, O., & Rodríguez, J. F. (2018). Vegetation and soil degradation in drylands: Nonlinear feedbacks and early warning signals. Current Opinion in Environmental Science & Health, 5, 67–72. https://doi.org/10.1016/j.coesh.2018.06.001.
  • Safranbolu Belediyesi website. (2022). https://www.safranbolu.bel.tr/ Sobrino, J. A., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of Remote Sensing, 21, 353-66. https://doi.org/10.1080/014311600210876.
  • Stathopoulou, M., Synnefa, A., Caralis, C., Sanamouris, M., Karless, T., & Akbari, H. (2009). A surface heat island study of Athens using high-resolution satellite imagery and measurements of the optical and thermal properties of commonly used building and paving materials. International Journal of Sustainable Energy, 28(1), 59–76. https://doi.org/10.1080/14786450802452753.
  • Shuai, Y., Masek, J. G., Gao, F., Schaaf, C. B., & He, T. (2014). An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sensing of Environment, 152, 467–479. https://doi.org/10.1016/j.rse.2014.07.009.
  • Suehrcke, H., Peterson, E. L., & Selby, N. (2008). Effect of roof solar reflectance onthe building heat gain in a hot climate. Energy and Buildings, 40, 2224–2235. https://doi.org/10.1016/j.enbuild.2008.06.015.
  • Şener, E. (2016). Burdur Gölü Yüzey Sıcaklığı Mevsimsel Değişiminin Landsat 8 Uydu görüntüleri kullanılarak belirlenmesi. Mühendislik Bilimleri ve Tasarım Dergisi, 4(2), 67-73. https://doi.org/10.21923/jesd.31386.
  • Yıldız, A., Bağcı, M., Başaran, C., Çonkar, F. E., & Ayday C., (2017). Landsat 8 uydu verilerinin jeotermal saha araştırmalarında kullanılması: Gazlıgöl (Afyonkarahisar) çalışması. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17, 277-284.
  • Yılmaz, E. (2015). Landsat görüntüleri ile Adana yüzey ısı adası. Coğrafi Bilimler Dergisi, 13(2), 115-138. https://doi.org/10.1501/Cogbil_0000000167.
  • 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. https://doi.org/10.1016/j.rse.2006.09.003.
  • Wang, Y., & Akbari, H. (2016). Analysis of urban heat island phenomenon and mitigation solutions evaluation for Montreal. Sustainable Cities and Society, 26, 438–446. https://doi.org/10.1016/j.scs.2016.04.015.
  • Wang, Z., Erb, A. M., Schaaf, C. B., Sun, Q., Liu, Y., Yang, Y., Shuai, Y., Casey, K. A., & Román, M. O. (2016). Remote sensing of environment early spring post- fi re snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data. Remote Sensing of Environment, 185, 71–83. https://doi.org/10.1016/j.rse.2016.02.059.
  • Zhao, Y., Wang, X., Novillo, C.J., Arrogante-Funes, P., Vázquez-Jiménez, R., & Maestre, F.T. (2018). Albedo estimated from remote sensing correlates with ecosystem multifunctionality in global drylands. J. Arid Environ, 157, 116–123. https://doi.org/10.1016/j.jaridenv.2018.05.010.
  • Zolotokrylin, A. N., Brito-Castillo, L., & Titkova, T. B. (2020). Local climatically-driven changes of albedo and surface temperatures in the Sonoran Desert. Journal of Arid Environments, 178, 104147. https://doi.org/10.1016/j.jaridenv.2020.104147
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)
Bölüm Jeoloji Mühendisliği
Yazarlar

Emre Yücer 0000-0003-0417-9338

Yayımlanma Tarihi 15 Mart 2023
Gönderilme Tarihi 21 Ekim 2022
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Yücer, E. (2023). ALBEDO, YER YÜZEY SICAKLIĞI VE NDVI ARASINDAKİ İLİŞKİNİN LANDSAT-7 VE LANDSAT-8 UYDU VERİLERİ KULLANILARAK İNCELENMESİ: SAFRANBOLU ÖRNEĞİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(1), 177-190. https://doi.org/10.17780/ksujes.1192591