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Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index

Yıl 2024, Cilt: 6 Sayı: 2, 52 - 59, 31.12.2024

Şeyma Akça

https://doi.org/10.53093/mephoj.1536466

Öz

This study evaluates the urban green spaces at Harran University's Osmanbey Campus using UAV technology and the Green Leaf Index (GLI). By employing Structure-from-Motion (SfM) photogrammetry, a highly detailed orthophoto of the campus was generated, while the GLI helped to identify and measure the green areas accurately. The analysis revealed that the Total Green Space Area on the campus is 8.8 hectares, within a Total Urban Area of 46.4 hectares. This results in a Green Space Ratio (GSR) of 18.97%. This percentage indicates that nearly 19% of the campus' urban area is covered by green spaces, which represents a moderate yet meaningful level of vegetation that enhances the environmental quality and overall well-being of the campus community. The findings underscore the value of incorporating UAV-based metrics into urban green space assessments and suggest that increasing the GSR to around or above 20% could provide even greater ecological and social benefits.

Anahtar Kelimeler

UAV Photogrammetry Green Space Ratio GLI

Etik Beyan

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Destekleyen Kurum

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Proje Numarası

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Teşekkür

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Kaynakça

  • Kabisch, N., & Haase, D. (2013). Green spaces of European cities revisited for 1990-2006. Landscape and Urban Planning, 110, 113-122.
  • Yılmaz, V., Akar, A., Akar, Ö., Güngör, O., Karslı, F., & Gökalp, E. (2013). İnsansiz hava araci ile üretilen ortofoto haritalarda doğruluk analizi. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği VII. Teknik Sempozyumu (TUFUAB’2013), 23-25 Mayıs 2013.
  • Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E., & Walthall, C. L. (2005). Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status. Precision Agriculture, 6, 359–378.
  • Ritchie, G. L., Rosas-Anderson, P., Schwartz, B. M., & Mako, A. A. (2010). Using RGB and NIR images to classify crop residue in agricultural fields. Computers and Electronics in Agriculture, 70(2), 176-181.
  • Liu, Y., Guo, L., & Huang, J. (2020). UAV-based high-resolution remote sensing for urban green space mapping and evaluation. Remote Sensing, 12(5), 798.
  • Xie, J., & Weng, Q. (2017). Spatiotemporal variations of urban vegetation in Indianapolis with remotely sensed imagery. Remote Sensing, 9(10), 1054.
  • Lahoti, S., Lahoti, A., & Saito, O. (2020). Application of unmanned aerial vehicle (UAV) for urban green space mapping in urbanizing Indian cities. Unmanned Aerial Vehicle: Applications in Agriculture and Environment, 177-188. Liang, H., Li, W., Zhang, Q., Zhu, W., Chen, D., Liu, J., & Shu, T. (2017). Using unmanned aerial vehicle data to assess the three-dimension green quantity of urban green space: A case study in Shanghai, China. Landscape and Urban Planning, 164, 81-90.
  • Yang, D. (2018). Gobi vegetation recognition based on low-altitude photogrammetry images of Uav. In IOP conference series: earth and environmental science (Vol. 186, No. 5, p. 012053). IOP Publishing.
  • Wang, Z., Zheng, Y. C., Li, J. F., Wang, Y. Z., Rong, L. S., Wang, J. X., Wang, Y.Z., Rong, L.S., Wang J.X., & Qi, W. C. (2020). Study on GLI values of Polygonatum odoratum base on multi-temporal of unmanned aerial vehicle remote sensing. Zhongguo Zhong yao za zhi= Zhongguo Zhongyao Zazhi= China Journal of Chinese Materia Medica, 45(23), 5663-5668
  • Cao, X., Liu, Y., Yu, R., Han, D., & Su, B. (2021). A comparison of UAV RGB and multispectral imaging in phenotyping for stay green of wheat population. Remote Sensing, 13(24), 5173.
  • Blancon, J., Dutartre, D., Tixier, M. H., Weiss, M., Comar, A., Praud, S., & Baret, F. (2019). A high-throughput model-assisted method for phenotyping maize green leaf area index dynamics using unmanned aerial vehicle imagery. Frontiers in plant science, 10, 685.
  • Bassine, F. Z., Errami, A., & Khaldoun, M. (2019). Vegetation recognition based on UAV image color index. In 2019 IEEE international conference on environment and electrical engineering and 2019 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe) (pp. 1-4). IEEE.
  • Kalisperakis, I., Stentoumis, C., Grammatikopoulos, L., & Karantzalos, K. (2015). Leaf area index estimation in vineyards from UAV hyperspectral data, 2D image mosaics and 3D canopy surface models. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 299-303.
  • Wang, X., Yan, S., Wang, W., Liubing, Y., Li, M., Yu, Z., Chang, S., & Hou, F. (2023). Monitoring leaf area index of the sown mixture pasture through UAV multispectral image and texture characteristics. Computers and Electronics in Agriculture, 214, 108333.
  • Kaya, E., Demir, N., & Karaca, F. (2021). Photogrammetric Applications in Various Fields. Mersin Photogrammetry Journal, 2(2), 64-75.
  • Dörtbudak, E. B., Akça, Ş., & Polat, N. (2023). Kompleks Binaların 3 Boyutlu Modellenmesinde Eğik ve Nadir hava fotoğrafları kullanımının karşılaştırılması: GAP YENEV Binası Örneği. Türkiye Fotogrametri Dergisi, 5(2), 58-65.
  • Dörtbudak, E. B., Akça, Ş., & Polat, N. (2023). Exploring structural deterioration at historical buildings with UAV photogrammetry. Cultural Heritage and Science, 4(2), 62-68.
  • Akca, S., & Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265-2274.
  • Remondino, F., & El-Hakim, S. (2006). Image-based 3D Modelling: A Review. The Photogrammetric Record, 21(115), 269-291.
  • Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). 'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314.
  • Gruen, A. (2012). Development and Status of Image Matching in Photogrammetry. The Photogrammetric Record, 27(137), 36-57.
  • Polat, N., Memduhoğlu, A., & Akça, Ş. (2022). Determining the change in burnt forest areas with UAV: The example of Osmanbey campus. Advanced UAV, 2(1), 11-16.
  • Dörtbudak, E. B., & Akça, Ş. (2024). Comparing Photogrammetry and Smartphone LIDAR for 3D Documentation: Kızılkoyun Necropolis Case Study. Advanced LiDAR, 4(1), 19-27.
  • Tao, C. Vincent, Yong Hu, and W. Jiang. 14 (2004). Photogrammetric exploitation of IKONOS imagery for mapping applications, International Journal of Remote Sensing, 25, 2833-2853.
  • Tarhan, Ç., (2004). Planlamada Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Disiplinleri Entegrasyonu: Urla ve Balçova Örnekleri, Şehir Plancıları Odası, Planlama Dergisi 3, 106-112.
  • Erden, Ö., (2006). Hava Fotoğrafları ve Uydu Görüntüleri ile Dijital Ortofoto Üretimi ve Kentsel Gelişimin İzlenmesi, Yüksek Lisans Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • European Environment Agency (EEA). (2011). Green infrastructure and territorial cohesion: The concept of green infrastructure and its integration into policies using monitoring systems

Yıl 2024, Cilt: 6 Sayı: 2, 52 - 59, 31.12.2024

Şeyma Akça

https://doi.org/10.53093/mephoj.1536466

Öz

Proje Numarası

-

Kaynakça

  • Kabisch, N., & Haase, D. (2013). Green spaces of European cities revisited for 1990-2006. Landscape and Urban Planning, 110, 113-122.
  • Yılmaz, V., Akar, A., Akar, Ö., Güngör, O., Karslı, F., & Gökalp, E. (2013). İnsansiz hava araci ile üretilen ortofoto haritalarda doğruluk analizi. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği VII. Teknik Sempozyumu (TUFUAB’2013), 23-25 Mayıs 2013.
  • Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E., & Walthall, C. L. (2005). Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status. Precision Agriculture, 6, 359–378.
  • Ritchie, G. L., Rosas-Anderson, P., Schwartz, B. M., & Mako, A. A. (2010). Using RGB and NIR images to classify crop residue in agricultural fields. Computers and Electronics in Agriculture, 70(2), 176-181.
  • Liu, Y., Guo, L., & Huang, J. (2020). UAV-based high-resolution remote sensing for urban green space mapping and evaluation. Remote Sensing, 12(5), 798.
  • Xie, J., & Weng, Q. (2017). Spatiotemporal variations of urban vegetation in Indianapolis with remotely sensed imagery. Remote Sensing, 9(10), 1054.
  • Lahoti, S., Lahoti, A., & Saito, O. (2020). Application of unmanned aerial vehicle (UAV) for urban green space mapping in urbanizing Indian cities. Unmanned Aerial Vehicle: Applications in Agriculture and Environment, 177-188. Liang, H., Li, W., Zhang, Q., Zhu, W., Chen, D., Liu, J., & Shu, T. (2017). Using unmanned aerial vehicle data to assess the three-dimension green quantity of urban green space: A case study in Shanghai, China. Landscape and Urban Planning, 164, 81-90.
  • Yang, D. (2018). Gobi vegetation recognition based on low-altitude photogrammetry images of Uav. In IOP conference series: earth and environmental science (Vol. 186, No. 5, p. 012053). IOP Publishing.
  • Wang, Z., Zheng, Y. C., Li, J. F., Wang, Y. Z., Rong, L. S., Wang, J. X., Wang, Y.Z., Rong, L.S., Wang J.X., & Qi, W. C. (2020). Study on GLI values of Polygonatum odoratum base on multi-temporal of unmanned aerial vehicle remote sensing. Zhongguo Zhong yao za zhi= Zhongguo Zhongyao Zazhi= China Journal of Chinese Materia Medica, 45(23), 5663-5668
  • Cao, X., Liu, Y., Yu, R., Han, D., & Su, B. (2021). A comparison of UAV RGB and multispectral imaging in phenotyping for stay green of wheat population. Remote Sensing, 13(24), 5173.
  • Blancon, J., Dutartre, D., Tixier, M. H., Weiss, M., Comar, A., Praud, S., & Baret, F. (2019). A high-throughput model-assisted method for phenotyping maize green leaf area index dynamics using unmanned aerial vehicle imagery. Frontiers in plant science, 10, 685.
  • Bassine, F. Z., Errami, A., & Khaldoun, M. (2019). Vegetation recognition based on UAV image color index. In 2019 IEEE international conference on environment and electrical engineering and 2019 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe) (pp. 1-4). IEEE.
  • Kalisperakis, I., Stentoumis, C., Grammatikopoulos, L., & Karantzalos, K. (2015). Leaf area index estimation in vineyards from UAV hyperspectral data, 2D image mosaics and 3D canopy surface models. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 299-303.
  • Wang, X., Yan, S., Wang, W., Liubing, Y., Li, M., Yu, Z., Chang, S., & Hou, F. (2023). Monitoring leaf area index of the sown mixture pasture through UAV multispectral image and texture characteristics. Computers and Electronics in Agriculture, 214, 108333.
  • Kaya, E., Demir, N., & Karaca, F. (2021). Photogrammetric Applications in Various Fields. Mersin Photogrammetry Journal, 2(2), 64-75.
  • Dörtbudak, E. B., Akça, Ş., & Polat, N. (2023). Kompleks Binaların 3 Boyutlu Modellenmesinde Eğik ve Nadir hava fotoğrafları kullanımının karşılaştırılması: GAP YENEV Binası Örneği. Türkiye Fotogrametri Dergisi, 5(2), 58-65.
  • Dörtbudak, E. B., Akça, Ş., & Polat, N. (2023). Exploring structural deterioration at historical buildings with UAV photogrammetry. Cultural Heritage and Science, 4(2), 62-68.
  • Akca, S., & Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265-2274.
  • Remondino, F., & El-Hakim, S. (2006). Image-based 3D Modelling: A Review. The Photogrammetric Record, 21(115), 269-291.
  • Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). 'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314.
  • Gruen, A. (2012). Development and Status of Image Matching in Photogrammetry. The Photogrammetric Record, 27(137), 36-57.
  • Polat, N., Memduhoğlu, A., & Akça, Ş. (2022). Determining the change in burnt forest areas with UAV: The example of Osmanbey campus. Advanced UAV, 2(1), 11-16.
  • Dörtbudak, E. B., & Akça, Ş. (2024). Comparing Photogrammetry and Smartphone LIDAR for 3D Documentation: Kızılkoyun Necropolis Case Study. Advanced LiDAR, 4(1), 19-27.
  • Tao, C. Vincent, Yong Hu, and W. Jiang. 14 (2004). Photogrammetric exploitation of IKONOS imagery for mapping applications, International Journal of Remote Sensing, 25, 2833-2853.
  • Tarhan, Ç., (2004). Planlamada Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Disiplinleri Entegrasyonu: Urla ve Balçova Örnekleri, Şehir Plancıları Odası, Planlama Dergisi 3, 106-112.
  • Erden, Ö., (2006). Hava Fotoğrafları ve Uydu Görüntüleri ile Dijital Ortofoto Üretimi ve Kentsel Gelişimin İzlenmesi, Yüksek Lisans Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • European Environment Agency (EEA). (2011). Green infrastructure and territorial cohesion: The concept of green infrastructure and its integration into policies using monitoring systems
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Şeyma Akça 0000-0002-7888-5078

Proje Numarası -
Erken Görünüm Tarihi 31 Aralık 2024
Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 20 Ağustos 2024
Kabul Tarihi 25 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 2

Kaynak Göster

APA Akça, Ş. (2024). Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index. Mersin Photogrammetry Journal, 6(2), 52-59. https://doi.org/10.53093/mephoj.1536466
AMA Akça Ş. Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index. Mersin Photogrammetry Journal. Aralık 2024;6(2):52-59. doi:10.53093/mephoj.1536466
Chicago Akça, Şeyma. “Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index”. Mersin Photogrammetry Journal 6, sy. 2 (Aralık 2024): 52-59. https://doi.org/10.53093/mephoj.1536466.
EndNote Akça Ş (01 Aralık 2024) Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index. Mersin Photogrammetry Journal 6 2 52–59.
IEEE Ş. Akça, “Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index”, Mersin Photogrammetry Journal, c. 6, sy. 2, ss. 52–59, 2024, doi: 10.53093/mephoj.1536466.
ISNAD Akça, Şeyma. “Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index”. Mersin Photogrammetry Journal 6/2 (Aralık 2024), 52-59. https://doi.org/10.53093/mephoj.1536466.
JAMA Akça Ş. Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index. Mersin Photogrammetry Journal. 2024;6:52–59.
MLA Akça, Şeyma. “Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index”. Mersin Photogrammetry Journal, c. 6, sy. 2, 2024, ss. 52-59, doi:10.53093/mephoj.1536466.
Vancouver Akça Ş. Evaluating Urban Green Spaces Using UAV-Based Green Leaf Index. Mersin Photogrammetry Journal. 2024;6(2):52-9.