Araştırma Makalesi
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NDVI ve LST Faktörlerinin Kentsel Alanlarda Gayrimenkul Değeri Üzerindeki Etkisinin Araştırılması: Ankara, İstanbul, İzmir ve Mersin Örneği

Yıl 2024, Cilt: 5 Sayı: 2, 158 - 171, 26.09.2024
https://doi.org/10.48123/rsgis.1423218

Öz

Bu çalışma, Ankara, İstanbul, İzmir ve Mersin kentlerinin merkezi ilçelerindeki ortalama konut satış fiyatları ile bu kentlerdeki Arazi Yüzey Sıcaklığı (LST) ve Normalleştirilmiş Bitki Örtüsü İndeksi (NDVI) arasındaki ilişkiyi bir regresyon analizi yöntemi ile incelemektedir. Temel amaç, farklı arazi kullanımı ve iklim koşullarına sahip kentler arasındaki NDVI ve LST değerlerindeki farklılıkları gözlemlemek ve bu değişkenlerin gayrimenkul fiyatlarına nasıl katkıda bulunduğunu anlamaktır. Mahallelere ait ortalama konut satış değerleri Endeksa.com sitesinden, NDVI ve LST değerleri ise Landsat 8 uydu görüntülerinden elde edilmiştir. Kentlere ait mahallelerin ortalama satış değeri ile NDVI ve LST arasında anlamlı bir ilişkinin olup olmadığını denetlemek için R2 skoru ve p-değer ölçütleri kullanılarak regresyon analizi gerçekleştirilmiştir. NDVI ve LST'nin İstanbul’un Eyüp, Bahçelievler ve Çekmeköy ilçelerinde konut satış fiyatları üzerinde güçlü etkileri olduğu (R2> 0,7), Ankara'nın Çankaya ve İzmir'in Güzelbahçe ilçesinde ise daha hafif bir etkisi olduğu, Mersin Yenişehir ilçesinde ise diğer ilçelere göre daha yüksek etkisi olduğu tespit edilmiştir. Çalışmanın bulguları, kentlerdeki gayrimenkul piyasasına yeşil alan varlığının ve termal konforun nasıl etki ettiğini mahalle ölçeğinde tespit ederek, kentsel araştırmalara katkı sağlamaktadır.

Kaynakça

  • Alkan, Y., & Uslu, C. (2016). Aktif yeşil alanların konut fiyatları üzerine etkisinin araştırılması: Mersin ili Yenişehir ilçesi örneği. İnönü Üniversitesi Sanat ve Tasarım Dergisi, 6(13),1-10.
  • Celik, B., Kaya, S., Alganci, U., & Seker, D. Z. (2019). Assessment of the relationship between land use/cover changes and land surface temperatures: a case study of thermal remote sensing. Fresenius Environmental Bulletin, 28(2), 541-547.
  • Diem, P. K., Nguyen, C. T., Diem, N. K., Diep, N. T. H., Thao, P. T. B., Hong, T. G., & Phan, T. N. (2023). Remote sensing for urban heat island research: Progress, current issues, and perspectives. Remote Sensing Applications: Society and Environment, 33, Article 101081. https://doi.org/10.1016/j.rsase.2023.101081
  • Doğan, Ö. S., & Özdemir, F. (2021). Mersin’de (Akdeniz, Mezitli, Toroslar ve Yenişehir) Yaşayan Suriyeliler: Sosyo-Kültürel Yapı ve Entegrasyon Süreci. Coğrafya Dergisi, 42, 33-47.
  • Endeksa. (2022, Kasım). Evinizin kıymetini bilin. https://www.endeksa.com/tr/ adresinden alınmıştır.
  • Erdem Okumus, D., & Terzi, F. (2021). Evaluating the role of urban fabric on surface urban heat island: The case of Istanbul. Sustainable Cities and Society, 73, Article 103128. https://doi.org/10.1016/j.scs.2021.103128
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.
  • Halder, B., Bandyopadhyay, J., & Banik, P. (2021). Evaluation of the climate change impact on urban heat island based on land surface temperature and geospatial indicators. International Journal of Environmental Research, 15, 819-835.
  • Hidalgo García, D. H., Riza, M., & Díaz, J. A. (2023). Land Surface Temperature Relationship with the Land Use/Land Cover Indices Leading to Thermal Field Variation in the Turkish Republic of Northern Cyprus. Earth Systems and Environment, 7(2), 561-580.
  • Holt, J. R., & Borsuk, M. E. (2020). Using Zillow data to value green space amenities at the neighborhood scale. Urban Forestry & Urban Greening, 56, Article 126794. https://doi.org/10.1016/j.ufug.2020.126794
  • İban, M. C., & Şahin, E. (2022). Monitoring land use and land cover change near a nuclear power plant construction site: Akkuyu case, Turkey. Environmental Monitoring and Assessment, 194(10), Article 724. https://doi.org/10.1007/s10661-022-10437-6
  • Jiao, L., Xu, G., Jin, J., Dong, T., Liu, J., Wu, Y., & Zhang, B. (2017). Remotely sensed urban environmental indices and their economic implications. Habitat International, 67, 22-32.
  • Kalogirou, S. A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1-2), 17-35.
  • Li, W., & Saphores, J. D. (2012). A spatial hedonic analysis of the value of urban land cover in the multifamily housing market in Los Angeles, CA. Urban Studies, 49(12), 2597-2615.
  • Li, W., Saphores, J. D. M., & Gillespie, T. W. (2015). A comparison of the economic benefits of urban green spaces estimated with NDVI and with high-resolution land cover data. Landscape and Urban Planning, 133, 105-117.
  • Liebelt, V., Bartke, S., & Schwarz, N. (2018). Hedonic pricing analysis of the influence of urban green spaces onto residential prices: the case of Leipzig, Germany. European Planning Studies, 26(1), 133-157.
  • Mashhoodi, B. (2021). Environmental justice and surface temperature: Income, ethnic, gender, and age inequalities. Sustainable Cities and Society, 68, Article 102810. https://doi.org/10.1016/j.scs.2021.102810
  • Molenaar, R. E., Heusinkveld, B. G., & Steeneveld, G. J. (2016). Projection of rural and urban human thermal comfort in The Netherlands for 2050. International Journal of Climatology, 36(4), 1708-1723.
  • Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., ... & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316-327.
  • Şekertekin, 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), Article 294. https://doi.org/10.3390/rs12020294
  • Şekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators, 122, Article 107230. https://doi.org/10.1016/j.ecolind.2020.107230
  • Taleghani, M., Tenpierik, M., Kurvers, S., & Van Den Dobbelsteen, A. (2013). A review into thermal comfort in buildings. Renewable and Sustainable Energy Reviews, 26, 201-215.
  • Tan, K. C., Lim, H. S., MatJafri, M. Z., & Abdullah, K. (2012). A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery. Environmental Monitoring and Assessment, 184(6), 3813-3829.
  • Ünal, Ç. (2020) İzmir’in göç analizi (2008-2018). Doğu Coğrafya Dergisi, 25(43), 195-208.
  • Yazar, M., Cetinkaya, I. D., Iban, M. C., & Bilgilioglu, S. S. (2023). The green divide and heat exposure: urban transformation projects in Istanbul. Frontiers in Environmental Science, 11, Article 1265332. https://doi.org/10.3389/fenvs.2023.1265332
  • Zambrano-Monserrate, M. A., Ruano, M. A., Yoong-Parraga, C., & Silva, C. A. (2021). Urban green spaces and housing prices in developing countries: A Two-stage quantile spatial regression analysis. Forest Policy and Economics, 125, Article 102420. https://doi.org/10.1016/j.forpol.2021.102420
  • Zengin, M., Yılmaz, S., & Mutlu, B. E. (2019). Mekansal Termal Konfor Açısından Atatürk Üniversitesi Yerleşkesi Termal Kamera Görüntülerinin Analizi. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 50(3), 239-247.
  • Zorlu, F., & Yoloğlu, A. C. (2022). İstanbul metropoliten alanında nüfus hareketliliğinin dinamikleri. Megaron, 17(2), 221–234. https://doi.org/10.14744/MEGARON.2022.87854

Investigating the Impact of NDVI and LST Factors on Real Estate Values in Urban Areas: Ankara, Istanbul, Izmir and Mersin Cases

Yıl 2024, Cilt: 5 Sayı: 2, 158 - 171, 26.09.2024
https://doi.org/10.48123/rsgis.1423218

Öz

This study examines the relationship between average housing prices in central districts of Ankara, Istanbul, Izmir, and Mersin and Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) in these cities through a regression analysis method. The primary aim is to observe differences in NDVI and LST values among cities with different land uses and climatic conditions and understand how these variables contribute to real estate prices. Average housing sales values for neighborhoods were obtained from Endeksa.com website, while NDVI and LST values were derived from Landsat 8 satellite images. Regression analysis was performed using R2 scores and p-values as criteria to examine whether there is a significant relationship between average neighborhood sales values and NDVI and LST. The study found that NDVI and LST have a strong impact on housing prices in Istanbul's Eyüp, Bahçelievler, and Çekmeköy districts (R2 > 0.7), a milder effect in Ankara's Çankaya and Izmir's Güzelbahçe districts, and a higher impact in Mersin's Yenişehir district compared to other districts. The findings of the study contribute to urban research by identifying how the presence of green spaces and thermal comfort affects real estate market at the neighborhood level in cities.

Kaynakça

  • Alkan, Y., & Uslu, C. (2016). Aktif yeşil alanların konut fiyatları üzerine etkisinin araştırılması: Mersin ili Yenişehir ilçesi örneği. İnönü Üniversitesi Sanat ve Tasarım Dergisi, 6(13),1-10.
  • Celik, B., Kaya, S., Alganci, U., & Seker, D. Z. (2019). Assessment of the relationship between land use/cover changes and land surface temperatures: a case study of thermal remote sensing. Fresenius Environmental Bulletin, 28(2), 541-547.
  • Diem, P. K., Nguyen, C. T., Diem, N. K., Diep, N. T. H., Thao, P. T. B., Hong, T. G., & Phan, T. N. (2023). Remote sensing for urban heat island research: Progress, current issues, and perspectives. Remote Sensing Applications: Society and Environment, 33, Article 101081. https://doi.org/10.1016/j.rsase.2023.101081
  • Doğan, Ö. S., & Özdemir, F. (2021). Mersin’de (Akdeniz, Mezitli, Toroslar ve Yenişehir) Yaşayan Suriyeliler: Sosyo-Kültürel Yapı ve Entegrasyon Süreci. Coğrafya Dergisi, 42, 33-47.
  • Endeksa. (2022, Kasım). Evinizin kıymetini bilin. https://www.endeksa.com/tr/ adresinden alınmıştır.
  • Erdem Okumus, D., & Terzi, F. (2021). Evaluating the role of urban fabric on surface urban heat island: The case of Istanbul. Sustainable Cities and Society, 73, Article 103128. https://doi.org/10.1016/j.scs.2021.103128
  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.
  • Halder, B., Bandyopadhyay, J., & Banik, P. (2021). Evaluation of the climate change impact on urban heat island based on land surface temperature and geospatial indicators. International Journal of Environmental Research, 15, 819-835.
  • Hidalgo García, D. H., Riza, M., & Díaz, J. A. (2023). Land Surface Temperature Relationship with the Land Use/Land Cover Indices Leading to Thermal Field Variation in the Turkish Republic of Northern Cyprus. Earth Systems and Environment, 7(2), 561-580.
  • Holt, J. R., & Borsuk, M. E. (2020). Using Zillow data to value green space amenities at the neighborhood scale. Urban Forestry & Urban Greening, 56, Article 126794. https://doi.org/10.1016/j.ufug.2020.126794
  • İban, M. C., & Şahin, E. (2022). Monitoring land use and land cover change near a nuclear power plant construction site: Akkuyu case, Turkey. Environmental Monitoring and Assessment, 194(10), Article 724. https://doi.org/10.1007/s10661-022-10437-6
  • Jiao, L., Xu, G., Jin, J., Dong, T., Liu, J., Wu, Y., & Zhang, B. (2017). Remotely sensed urban environmental indices and their economic implications. Habitat International, 67, 22-32.
  • Kalogirou, S. A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1-2), 17-35.
  • Li, W., & Saphores, J. D. (2012). A spatial hedonic analysis of the value of urban land cover in the multifamily housing market in Los Angeles, CA. Urban Studies, 49(12), 2597-2615.
  • Li, W., Saphores, J. D. M., & Gillespie, T. W. (2015). A comparison of the economic benefits of urban green spaces estimated with NDVI and with high-resolution land cover data. Landscape and Urban Planning, 133, 105-117.
  • Liebelt, V., Bartke, S., & Schwarz, N. (2018). Hedonic pricing analysis of the influence of urban green spaces onto residential prices: the case of Leipzig, Germany. European Planning Studies, 26(1), 133-157.
  • Mashhoodi, B. (2021). Environmental justice and surface temperature: Income, ethnic, gender, and age inequalities. Sustainable Cities and Society, 68, Article 102810. https://doi.org/10.1016/j.scs.2021.102810
  • Molenaar, R. E., Heusinkveld, B. G., & Steeneveld, G. J. (2016). Projection of rural and urban human thermal comfort in The Netherlands for 2050. International Journal of Climatology, 36(4), 1708-1723.
  • Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., ... & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316-327.
  • Şekertekin, 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), Article 294. https://doi.org/10.3390/rs12020294
  • Şekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators, 122, Article 107230. https://doi.org/10.1016/j.ecolind.2020.107230
  • Taleghani, M., Tenpierik, M., Kurvers, S., & Van Den Dobbelsteen, A. (2013). A review into thermal comfort in buildings. Renewable and Sustainable Energy Reviews, 26, 201-215.
  • Tan, K. C., Lim, H. S., MatJafri, M. Z., & Abdullah, K. (2012). A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery. Environmental Monitoring and Assessment, 184(6), 3813-3829.
  • Ünal, Ç. (2020) İzmir’in göç analizi (2008-2018). Doğu Coğrafya Dergisi, 25(43), 195-208.
  • Yazar, M., Cetinkaya, I. D., Iban, M. C., & Bilgilioglu, S. S. (2023). The green divide and heat exposure: urban transformation projects in Istanbul. Frontiers in Environmental Science, 11, Article 1265332. https://doi.org/10.3389/fenvs.2023.1265332
  • Zambrano-Monserrate, M. A., Ruano, M. A., Yoong-Parraga, C., & Silva, C. A. (2021). Urban green spaces and housing prices in developing countries: A Two-stage quantile spatial regression analysis. Forest Policy and Economics, 125, Article 102420. https://doi.org/10.1016/j.forpol.2021.102420
  • Zengin, M., Yılmaz, S., & Mutlu, B. E. (2019). Mekansal Termal Konfor Açısından Atatürk Üniversitesi Yerleşkesi Termal Kamera Görüntülerinin Analizi. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 50(3), 239-247.
  • Zorlu, F., & Yoloğlu, A. C. (2022). İstanbul metropoliten alanında nüfus hareketliliğinin dinamikleri. Megaron, 17(2), 221–234. https://doi.org/10.14744/MEGARON.2022.87854
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme, Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Selin Uyar 0009-0005-4807-0993

Muzaffer Can İban 0000-0002-3341-1338

Erken Görünüm Tarihi 24 Eylül 2024
Yayımlanma Tarihi 26 Eylül 2024
Gönderilme Tarihi 21 Ocak 2024
Kabul Tarihi 6 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 5 Sayı: 2

Kaynak Göster

APA Uyar, S., & İban, M. C. (2024). NDVI ve LST Faktörlerinin Kentsel Alanlarda Gayrimenkul Değeri Üzerindeki Etkisinin Araştırılması: Ankara, İstanbul, İzmir ve Mersin Örneği. Türk Uzaktan Algılama Ve CBS Dergisi, 5(2), 158-171. https://doi.org/10.48123/rsgis.1423218

Creative Commons License
Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.