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Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya

Yıl 2025, Cilt: 14 Sayı: 3, 202 - 217, 26.09.2025
https://doi.org/10.46810/tdfd.1704418

Öz

The relationship between urban green space (UGS) and population is essential for the quality of life in cities. In this study, a method is proposed to determine the relationship between UGS and population in Antalya, Türkiye, and to reveal the change in the short-term, using Sentinel-2 satellite data and object-based image analysis (OBIA). In the study, two different dated Sentinel-2 satellite data were used as the basic data set to analyze the vegetation. The Normalized Difference Vegetation Index (NDVI) was calculated for the threshold value and UGSs were analyzed according to different categories. Then, the Urban Green Space Index (UGSI) was calculated to determine the amount of green space and Per Capita Green Space (PCGS) was also calculated for this purpose. The OBIA general accuracy values of the proposed method are 93% and 94% for 2017 and 2023, respectively. The results showed that PCGS across the study area decreased by 7.33 m² in a short time. It is evaluated that the method proposed in this study, which reveals the short-term change in UGS and PCGS more quickly and at lower cost, can be used effectively in sustainable city management.

Etik Beyan

There is no need for an Ethics Committee Certificate for our study.

Kaynakça

  • Li L, Xin X, Zhao J, Yang A, Wu S, Zhang H, et al. Remote sensing monitoring and assessment of global vegetation status and changes during 2016–2020. Sensors. 2023;23(20):8452.
  • Sun Z, Du W, Jiang H, Weng Q, Guo H, Han Y, et al. Global 10-m impervious surface area mapping: A big earth data based extraction and updating approach. International Journal of Applied Earth Observation and Geoinformation. 2022;109:102800.
  • Chen G, Zhou Y, Voogt JA, Stokes EC. Remote sensing of diverse urban environments: From the single city to multiple cities. Remote Sensing of Environment. 2024;305:114108.
  • ESA [Internet]; 2024 [cited 2024 april 25]. Revisit and Coverage. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/revisit-coverage
  • Oke TR. The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society. 1982;108(455):1–24.
  • De Almeida CR, Teodoro AC, Gonçalves A. Study of the urban heat island (UHI) using remote sensing data/techniques: A systematic review. Environments. 2021;8(10):105.
  • Belal AA, Moghanm FS. Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya governorate, Egypt. The Egyptian Journal of Remote Sensing and Space Science. 2011;14(2):73–9.
  • Nasser IA, Adam E. Urbanisation in Sub-Saharan Cities and the implications for urban agriculture: Evidence-based remote sensing from Niamey, Niger. Urban Science. 2024;8(1):5.
  • Chen W, Huang H, Dong J, Zhang Y, Tian Y, Yang Z. Social functional mapping of urban green space using remote sensing and social sensing data. ISPRS Journal of Photogrammetry and Remote Sensing. 2018;146:436–52.
  • Zhang X, Du S, Wang Q. Integrating bottom-up classification and top-down feedback for improving urban land-cover and functional-zone mapping. Remote Sensing of Environment. 2018;212:231–48.
  • Anteneh MB, Damte DS, Abate SG, Gedefaw AA. Geospatial assessment of urban green space using multi-criteria decision analysis in Debre Markos City, Ethiopia. Environmental Systems Research. 2023;12(1).
  • Pouya S, Aghlmand M. Evaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemic. Environmental Monitoring and Assessment. 2022;194(9).
  • Badiu DL, Iojă CI, Pătroescu M, Breuste J, Artmann M, Niță MR, et al. Is urban green space per capita a valuable target to achieve cities’ sustainability goals? Romania as a case study. Ecological Indicators. 2016;70:53–66.
  • Gül A, Dinç G, Akin T, Koçak Aİ. Kentsel açık ve yeşil alanların mevcut yasal durumu ve uygulamadaki sorunlar. İdealkent. 2020;11:1281–312.
  • Doğu G, Kesim Ü, Sivrikaya Ö. Belediyelerin sporla ilgili işlevleri: Düzce belediyesi örneği. Çağdaş Yerel Yönetimler. 2002;11(1):5–14.
  • Anonymous [Internet]: 2024 [cited 2024 april 25]. Available from: https://csb.gov.tr/turkiyede-kisi-basina-dusen-agaclik-yesil-alan-miktari-12-63-metrekare-oldu-bakanlik-faaliyetleri-39952#:~:text=99%20B%C4%B0N%20485%20HEKTAR%20YE%C5%9E%C4%B0L,bin%20331%20dolar%20olarak%20hesapland%C4%B1
  • Neyns R, Canters F. Mapping of urban vegetation with high-resolution remote sensing: A review. Remote Sensing. 2022;14(4):1031.
  • Sönmez NK, Onur I. Monitoring of land use and land cover changes by using fuzzy supervised classification method: A case study of Antalya, Türkiye. Journal of Food Agriculture & Environment. 2012;10(3-4):963–7.
  • Soyaslan İ. Hepdeniz K. Coğrafi bilgi sistemleri ve uzaktan algılama kullanılarak Burdur İli arazi kullanımının zamansal değişiminin belirlenmesi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2016;7(2):94-101.
  • Deliry SI, Avdan ZY, Avdan U. Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management. Environmental Science and Pollution Research. 2020;28(6):6572–86.
  • Zaki A, Buchori I, Sejati AW, Liu Y. An object-based image analysis in QGIS for image classification and assessment of coastal spatial planning. The Egyptian Journal of Remote Sensing and Space Science. 2022;25(2):349–59.
  • Yu D, Fang C. Urban remote sensing with spatial big data: A review and renewed perspective of urban studies in recent decades. Remote Sensing. 2023;15(5):1307.
  • Kucharczyk M, Hay GJ, Ghaffarian S, Hugenholtz CH. Geographic object-based image analysis: A Primer and future directions. Remote Sensing. 2020;12(12):2012.
  • Moskal LM, Styers DM, Halabisky M. Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data. Remote Sensing. 2011;3(10):2243–62.
  • Choudhury MAM, Marcheggiani E, Despini F, Costanzini S, Rossi P, Galli A, et al. Urban tree species identification and carbon stock mapping for urban green planning and management. Forests. 2020;11(11):1226.
  • Popa AM, Onose DA, Sandric IC, Dosiadis EA, Petropoulos GP, Gavrilidis AA, et al. Using GEOBIA and vegetation indices to assess small urban green areas in two climatic regions. Remote Sensing. 2022;14(19):4888.
  • Simović I, Dubljević JT, Tošković O, Trkulja MV, Živojinović I. Underlying mechanisms of urban green areas’ influence on residents’ health—A case study from Belgrade, Serbia. Forests. 2023;14(4):765.
  • Ortaçeşme V. Karagüzel O. Atik M. Sayan S. Antalya kentinin aktif yeşil alan varlığı üzerinde bir araştırma. Akdeniz Üniversitesi Ziraat Fakültesi Dergisi. 2000;13(1):11-22.
  • Manavoğlu E. Ortaçeşme V. Konyaaltı kentsel alanında bir yeşil alan sistem önerisi geliştirilmesi. Konyaaltı kentsel alanında bir yeşil alan sistem önerisi geliştirilmesi. 2007;20(2):261-271.
  • Manavoğlu E. Ortaçeşme V. Antalya kenti yeşil alanlarının çok ölçütlü analizi ve planlama stratejilerinin geliştirilmesi. Akdeniz Üniversitesi Ziraat Fakültesi Dergisi. 2015;28(1):11-19.
  • Muratpaşa Belediyesi [Internet]: 2024 [cited 2024 april 20]. Tanıtım. Available from: https://muratpasa-bld.gov.tr/icerik/tanitim
  • TSMS. [Internet]: 2024 [cited 2024 april 21]. Antalya. Available from: https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ANTALYA)
  • ESA [Internet]: 2024 [cited 2024 april 20]. Overview. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/overview
  • ESA [Internet]: 2024 [cited 2024 april 20]. Spatial Resolution. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial 20.04.2024
  • ESA [Internet]: 2024 [cited 2024 april 20]. Level-2A. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a
  • Hashim H. Abd Latif Z. Adnan NA. (2019). Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) Pleiades imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019;42:237-240.
  • Tempa K, Ilunga M, Agarwal A, Tashi N. Utilizing Sentinel-2 satellite imagery for LULC and NDVI change dynamics for Gelephu, Bhutan. Applied Sciences. 2024;14(4):1578.
  • Aryal J, Sitaula C, Aryal S. NDVI threshold-based urban green space mapping from Sentinel-2A at the local governmental area (LGA) level of Victoria, Australia. Land. 2022;11(3):351.
  • Rouse JW, Haas RH, Schell JA, Deering DW. Monitoring vegetation systems in the Great Plains with ERTS. In: Third Earth Resources Technology Satellite-1 Symposium; 1973 Dec 10–14; Washington, DC. Washington (DC): NASA; p. 309–17.
  • Shekhar S, Aryal J. Role of geospatial technology in understanding urban green space of Kalaburagi city for sustainable planning. Urban Forestry & Urban Greening. 2019;46:126450.
  • Johnson B, Jozdani S. Identifying generalizable image segmentation parameters for urban land cover mapping through meta-analysis and regression tree modeling. Remote Sensing. 2018;10(1):73.
  • Kotaridis I, Lazaridou M. Object-based image analysis of different spatial resolution satellite imageries in urban and suburban environment. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2020;XLIII-B3:105–12.
  • Kuang W, Dou Y. Investigating the patterns and dynamics of urban green space in China’s 70 major cities using satellite remote sensing. Remote Sensing. 2020;12(12):1929.
  • Zhu Y, Ling GHT. Spatio-temporal changes and driving forces analysis of urban open spaces in Shanghai between 1980 and 2020: An integrated geospatial approach. Remote Sensing. 2024;16(7):1184.
  • Atasoy M. Monitoring the urban green spaces and landscape fragmentation using remote sensing: a case study in Osmaniye, Turkey. Environmental Monitoring and Assessment. 2018;190(12).
  • Kowe P, Mutanga O, Dube T. Advancements in the remote sensing of landscape pattern of urban green spaces and vegetation fragmentation. International Journal of Remote Sensing. 2021;42(10):3797–832.
  • Dahu BM, Alaboud K, Nowbuth AA, Puckett HM, Scott GJ, Sheets LR. The role of remote sensing and geospatial analysis for understanding COVID-19 population severity: A systematic review. International Journal of Environmental Research and Public Health. 2023;20(5):4298.
  • Olgun R. Orta ölçekli kentler için kentsel yeşil alan sistem önerisi: Niğde kenti örneği. Artium. 2019;7(1):57–69.

Sentinel-2 Uydu Verileri ve Nesne Tabanlı Görüntü Analizi Kullanarak Kentsel Yeşil Alan-Nüfus İlişkisinin Mekansal Değerlendirilmesi: Antalya Örneği

Yıl 2025, Cilt: 14 Sayı: 3, 202 - 217, 26.09.2025
https://doi.org/10.46810/tdfd.1704418

Öz

Kentsel yeşil alan (KYA) ile nüfus arasındaki ilişki, kentlerdeki yaşam kalitesi için önemlidir. Bu çalışmada, Türkiye, Antalya'da KYA ile nüfus arasındaki ilişkiyi belirlemek ve kısa dönemdeki değişimi ortaya koymak için Sentinel-2 uydu verileri ve nesne tabanlı görüntü analizi (OBIA) kullanılarak bir yöntem önerilmiştir. Çalışmada bitki örtüsünü analiz etmek için iki farklı tarihli Sentinel-2 uydu görüntüsü temel veri seti olarak kullanılmıştır. Eşik değeri için Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI) hesaplanmış ve KYA’lar farklı kategorilere göre analiz edilmiştir. Ardından, yeşil alan miktarını belirlemek için Kentsel Yeşil Alan İndeksi (KYAI) ve yeşil alanı belirlemek için Kişi Başına Yeşil Alan (KBYA) hesaplanmıştır. Önerilen yöntemin OBIA genel doğruluk değerleri 2017 ve 2023 yılları için sırasıyla %93 ve %94'tür. Sonuçlar, çalışma alanı genelinde PCGS’nin kısa vadede 7.33 m² azaldığını göstermiştir. KYA ve KBYA’da kısa vadeli değişimi daha hızlı ve daha düşük maliyetle ortaya koyan bu çalışmada önerilen yöntemin sürdürülebilir kent yönetiminde etkin bir şekilde kullanılabileceği değerlendirilmektedir.

Etik Beyan

Çalışmamız için Etik Kurul Belgesine İhtiyaç Yoktur

Kaynakça

  • Li L, Xin X, Zhao J, Yang A, Wu S, Zhang H, et al. Remote sensing monitoring and assessment of global vegetation status and changes during 2016–2020. Sensors. 2023;23(20):8452.
  • Sun Z, Du W, Jiang H, Weng Q, Guo H, Han Y, et al. Global 10-m impervious surface area mapping: A big earth data based extraction and updating approach. International Journal of Applied Earth Observation and Geoinformation. 2022;109:102800.
  • Chen G, Zhou Y, Voogt JA, Stokes EC. Remote sensing of diverse urban environments: From the single city to multiple cities. Remote Sensing of Environment. 2024;305:114108.
  • ESA [Internet]; 2024 [cited 2024 april 25]. Revisit and Coverage. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/revisit-coverage
  • Oke TR. The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society. 1982;108(455):1–24.
  • De Almeida CR, Teodoro AC, Gonçalves A. Study of the urban heat island (UHI) using remote sensing data/techniques: A systematic review. Environments. 2021;8(10):105.
  • Belal AA, Moghanm FS. Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya governorate, Egypt. The Egyptian Journal of Remote Sensing and Space Science. 2011;14(2):73–9.
  • Nasser IA, Adam E. Urbanisation in Sub-Saharan Cities and the implications for urban agriculture: Evidence-based remote sensing from Niamey, Niger. Urban Science. 2024;8(1):5.
  • Chen W, Huang H, Dong J, Zhang Y, Tian Y, Yang Z. Social functional mapping of urban green space using remote sensing and social sensing data. ISPRS Journal of Photogrammetry and Remote Sensing. 2018;146:436–52.
  • Zhang X, Du S, Wang Q. Integrating bottom-up classification and top-down feedback for improving urban land-cover and functional-zone mapping. Remote Sensing of Environment. 2018;212:231–48.
  • Anteneh MB, Damte DS, Abate SG, Gedefaw AA. Geospatial assessment of urban green space using multi-criteria decision analysis in Debre Markos City, Ethiopia. Environmental Systems Research. 2023;12(1).
  • Pouya S, Aghlmand M. Evaluation of urban green space per capita with new remote sensing and geographic information system techniques and the importance of urban green space during the COVID-19 pandemic. Environmental Monitoring and Assessment. 2022;194(9).
  • Badiu DL, Iojă CI, Pătroescu M, Breuste J, Artmann M, Niță MR, et al. Is urban green space per capita a valuable target to achieve cities’ sustainability goals? Romania as a case study. Ecological Indicators. 2016;70:53–66.
  • Gül A, Dinç G, Akin T, Koçak Aİ. Kentsel açık ve yeşil alanların mevcut yasal durumu ve uygulamadaki sorunlar. İdealkent. 2020;11:1281–312.
  • Doğu G, Kesim Ü, Sivrikaya Ö. Belediyelerin sporla ilgili işlevleri: Düzce belediyesi örneği. Çağdaş Yerel Yönetimler. 2002;11(1):5–14.
  • Anonymous [Internet]: 2024 [cited 2024 april 25]. Available from: https://csb.gov.tr/turkiyede-kisi-basina-dusen-agaclik-yesil-alan-miktari-12-63-metrekare-oldu-bakanlik-faaliyetleri-39952#:~:text=99%20B%C4%B0N%20485%20HEKTAR%20YE%C5%9E%C4%B0L,bin%20331%20dolar%20olarak%20hesapland%C4%B1
  • Neyns R, Canters F. Mapping of urban vegetation with high-resolution remote sensing: A review. Remote Sensing. 2022;14(4):1031.
  • Sönmez NK, Onur I. Monitoring of land use and land cover changes by using fuzzy supervised classification method: A case study of Antalya, Türkiye. Journal of Food Agriculture & Environment. 2012;10(3-4):963–7.
  • Soyaslan İ. Hepdeniz K. Coğrafi bilgi sistemleri ve uzaktan algılama kullanılarak Burdur İli arazi kullanımının zamansal değişiminin belirlenmesi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2016;7(2):94-101.
  • Deliry SI, Avdan ZY, Avdan U. Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management. Environmental Science and Pollution Research. 2020;28(6):6572–86.
  • Zaki A, Buchori I, Sejati AW, Liu Y. An object-based image analysis in QGIS for image classification and assessment of coastal spatial planning. The Egyptian Journal of Remote Sensing and Space Science. 2022;25(2):349–59.
  • Yu D, Fang C. Urban remote sensing with spatial big data: A review and renewed perspective of urban studies in recent decades. Remote Sensing. 2023;15(5):1307.
  • Kucharczyk M, Hay GJ, Ghaffarian S, Hugenholtz CH. Geographic object-based image analysis: A Primer and future directions. Remote Sensing. 2020;12(12):2012.
  • Moskal LM, Styers DM, Halabisky M. Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data. Remote Sensing. 2011;3(10):2243–62.
  • Choudhury MAM, Marcheggiani E, Despini F, Costanzini S, Rossi P, Galli A, et al. Urban tree species identification and carbon stock mapping for urban green planning and management. Forests. 2020;11(11):1226.
  • Popa AM, Onose DA, Sandric IC, Dosiadis EA, Petropoulos GP, Gavrilidis AA, et al. Using GEOBIA and vegetation indices to assess small urban green areas in two climatic regions. Remote Sensing. 2022;14(19):4888.
  • Simović I, Dubljević JT, Tošković O, Trkulja MV, Živojinović I. Underlying mechanisms of urban green areas’ influence on residents’ health—A case study from Belgrade, Serbia. Forests. 2023;14(4):765.
  • Ortaçeşme V. Karagüzel O. Atik M. Sayan S. Antalya kentinin aktif yeşil alan varlığı üzerinde bir araştırma. Akdeniz Üniversitesi Ziraat Fakültesi Dergisi. 2000;13(1):11-22.
  • Manavoğlu E. Ortaçeşme V. Konyaaltı kentsel alanında bir yeşil alan sistem önerisi geliştirilmesi. Konyaaltı kentsel alanında bir yeşil alan sistem önerisi geliştirilmesi. 2007;20(2):261-271.
  • Manavoğlu E. Ortaçeşme V. Antalya kenti yeşil alanlarının çok ölçütlü analizi ve planlama stratejilerinin geliştirilmesi. Akdeniz Üniversitesi Ziraat Fakültesi Dergisi. 2015;28(1):11-19.
  • Muratpaşa Belediyesi [Internet]: 2024 [cited 2024 april 20]. Tanıtım. Available from: https://muratpasa-bld.gov.tr/icerik/tanitim
  • TSMS. [Internet]: 2024 [cited 2024 april 21]. Antalya. Available from: https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ANTALYA)
  • ESA [Internet]: 2024 [cited 2024 april 20]. Overview. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/overview
  • ESA [Internet]: 2024 [cited 2024 april 20]. Spatial Resolution. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial 20.04.2024
  • ESA [Internet]: 2024 [cited 2024 april 20]. Level-2A. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a
  • Hashim H. Abd Latif Z. Adnan NA. (2019). Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) Pleiades imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019;42:237-240.
  • Tempa K, Ilunga M, Agarwal A, Tashi N. Utilizing Sentinel-2 satellite imagery for LULC and NDVI change dynamics for Gelephu, Bhutan. Applied Sciences. 2024;14(4):1578.
  • Aryal J, Sitaula C, Aryal S. NDVI threshold-based urban green space mapping from Sentinel-2A at the local governmental area (LGA) level of Victoria, Australia. Land. 2022;11(3):351.
  • Rouse JW, Haas RH, Schell JA, Deering DW. Monitoring vegetation systems in the Great Plains with ERTS. In: Third Earth Resources Technology Satellite-1 Symposium; 1973 Dec 10–14; Washington, DC. Washington (DC): NASA; p. 309–17.
  • Shekhar S, Aryal J. Role of geospatial technology in understanding urban green space of Kalaburagi city for sustainable planning. Urban Forestry & Urban Greening. 2019;46:126450.
  • Johnson B, Jozdani S. Identifying generalizable image segmentation parameters for urban land cover mapping through meta-analysis and regression tree modeling. Remote Sensing. 2018;10(1):73.
  • Kotaridis I, Lazaridou M. Object-based image analysis of different spatial resolution satellite imageries in urban and suburban environment. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2020;XLIII-B3:105–12.
  • Kuang W, Dou Y. Investigating the patterns and dynamics of urban green space in China’s 70 major cities using satellite remote sensing. Remote Sensing. 2020;12(12):1929.
  • Zhu Y, Ling GHT. Spatio-temporal changes and driving forces analysis of urban open spaces in Shanghai between 1980 and 2020: An integrated geospatial approach. Remote Sensing. 2024;16(7):1184.
  • Atasoy M. Monitoring the urban green spaces and landscape fragmentation using remote sensing: a case study in Osmaniye, Turkey. Environmental Monitoring and Assessment. 2018;190(12).
  • Kowe P, Mutanga O, Dube T. Advancements in the remote sensing of landscape pattern of urban green spaces and vegetation fragmentation. International Journal of Remote Sensing. 2021;42(10):3797–832.
  • Dahu BM, Alaboud K, Nowbuth AA, Puckett HM, Scott GJ, Sheets LR. The role of remote sensing and geospatial analysis for understanding COVID-19 population severity: A systematic review. International Journal of Environmental Research and Public Health. 2023;20(5):4298.
  • Olgun R. Orta ölçekli kentler için kentsel yeşil alan sistem önerisi: Niğde kenti örneği. Artium. 2019;7(1):57–69.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ziraat Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Mesut Çoşlu 0000-0003-3952-6563

Yayımlanma Tarihi 26 Eylül 2025
Gönderilme Tarihi 26 Mayıs 2025
Kabul Tarihi 14 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 14 Sayı: 3

Kaynak Göster

APA Çoşlu, M. (2025). Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya. Türk Doğa ve Fen Dergisi, 14(3), 202-217. https://doi.org/10.46810/tdfd.1704418
AMA Çoşlu M. Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya. TDFD. Eylül 2025;14(3):202-217. doi:10.46810/tdfd.1704418
Chicago Çoşlu, Mesut. “Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya”. Türk Doğa ve Fen Dergisi 14, sy. 3 (Eylül 2025): 202-17. https://doi.org/10.46810/tdfd.1704418.
EndNote Çoşlu M (01 Eylül 2025) Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya. Türk Doğa ve Fen Dergisi 14 3 202–217.
IEEE M. Çoşlu, “Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya”, TDFD, c. 14, sy. 3, ss. 202–217, 2025, doi: 10.46810/tdfd.1704418.
ISNAD Çoşlu, Mesut. “Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya”. Türk Doğa ve Fen Dergisi 14/3 (Eylül2025), 202-217. https://doi.org/10.46810/tdfd.1704418.
JAMA Çoşlu M. Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya. TDFD. 2025;14:202–217.
MLA Çoşlu, Mesut. “Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya”. Türk Doğa ve Fen Dergisi, c. 14, sy. 3, 2025, ss. 202-17, doi:10.46810/tdfd.1704418.
Vancouver Çoşlu M. Spatial Evaluation of Urban Green Space-Population Relationship Using Sentinel-2 Satellite Data and Object-Based Image Analysis: A Case Study Antalya. TDFD. 2025;14(3):202-17.