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Spatio-temporal analysis of relationship between artificial light pollution and bird observations in Turkey: a geostatistical approach

Yıl 2024, Sayı: 52, 159 - 178, 25.05.2024
https://doi.org/10.32003/igge.1430386

Öz

Light pollution constitutes an environmental risk arising from the excessive and intense scattering of artificial light sources within a specific geographical area. This environmental pollution has substantial impacts on diverse species. Specifically, it interferes with behaviors such as feeding, migration, and reproduction in avian populations. In recent years, nocturnal light images acquired through remote sensing have been extensively employed for the detection of artificial light pollution. The aim of this research is to investigate the temporal and spatial correlation between artificial light pollution and avian observations in Turkey. Nighttime light images obtained from DMSP and VIIRS satellites between 2000 and 2022, along with the eBird database, the world’s largest citizen science project related to biodiversity, were utilized. In this context, a Space Time Cube data model was created for the temporal and spatial evaluation of observation points. The Emerging Hot Spot and Getis-Ord General G analyses were applied to test the statistical significance of the spatial distribution using the obtained data structure. Consequently, the relationship between the observed changes in pixel-scale nighttime light reflection values and observation points was assessed spatially and temporally. The results indicated a correlation between the numbers of observed avian species and areas with high artificial light changes.

Kaynakça

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  • Baştürk, K., & Aladağ, C. (2009). Maki ve garig topluluklarının Türkiye’deki yayılış alanları ve ekolojik özelliklerinin incelenmesi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 22, 67-80.
  • Burt, C. S., Kelly, J. F., Trankina, G. E., Silva, C. L., Khalighifar, A., Jenkins-Smith, H. C., ve Horton, K. G. (2023). The effects of light pollution on migratory animal behavior. Trends in Ecology ve Evolution, 38(4), 355-368. https://doi.org/10.1016/j.tree.2022.12.006
  • Cabrera-Cruz, S. A., Smolinsky, J. A., & Buler, J. J. (2018). Light pollution is greatest within migration passage areas for nocturnally-migrating birds around the world. Scientific Reports, 8(1), 3261. https://www.nature.com/articles/s41598-018-21577-6
  • Camacho, L. F., Barragán, G., & Espinosa, S. (2021). Local ecological knowledge reveals combined landscape effects of light pollution, habitat loss, and fragmentation on insect populations. Biological Conservation, 262, 109311.https://doi.org/10.1016/j.biocon.2021.109311
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Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal analizi: Jeoistatistiksel bir yaklaşım

Yıl 2024, Sayı: 52, 159 - 178, 25.05.2024
https://doi.org/10.32003/igge.1430386

Öz

Işık kirliliği, belirli bir bölge içerisinde bulunan yapay ışık kaynağının gereğinden fazla ve şiddetli ışık saçması sonucunda oluşan çevresel bir risktir. Bu kirlilik, canlı türleri üzerinde önemli etkilere sahiptir. Örneğin; kuşların, beslenme, göç hareketi ve üreme gibi davranışlarında bozulmalara neden olmaktadır. Yapay ışık kirliliğinin tespit edilebilmesinde son yıllarda uzaktan algılanmış gece ışığı görüntüleri yaygın şekilde kullanılmaktadır. Bu araştırmanın amacı, Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal olarak incelenmesidir. Bu kapsamda, 2000—2022 yılları arasındaki DMSP ve VIIRS uydularından elde edilen gece ışığı görüntüleri ile dünyanın en büyük biyoçeşitlilik ile ilgili vatandaş bilimi projesi olan eBird veri tabanı kullanılmıştır. Buna göre gözlem noktalarının zamansal ve mekânsal değerlendirilmesinde Space Time Cube veri modeli oluşturulmuştur. Elde edilen veri yapısı ile Emerging Hot Spot ve Getis-Ord General G* analizi uygulanarak mekânsal dağılımın istatiksel olarak anlamlılığı test edilmiştir. Sonuç olarak, piksel ölçeğinde elde edilen gece ışığı yansıma değerlerindeki değişim ile gözlem noktaları arasındaki ilişki mekânsal ve zamansal olarak değerlendirilmiştir. Elde edilen sonuçlar gözlemlenen kuş türü sayıları ile yapay ışık değişimin yüksek olduğu alanlar arasında bir ilişki olduğu tespit edilmiştir.

Kaynakça

  • Adelabu, S. A., & Olusola, A. O. (2021,July 11-16). Remote sensing of nighttime light: progress, prospects and possibilities in Africa (2013–2021). In 2021 IEEE International Geoscience and Remote Sensing Symposium,Brussels,Belgium, 4484-4487. https://doi.org/10.1109/IGARSS47720.2021.9553473
  • Avcı, M. (2000). Yeryüzünün Zoocoğrafya bölgeleri ve Türkiye’nin yeri. Coğrafya Dergisi, 8, 157-200.
  • Bach, B., Dragicevic, P., Archambault, D., Hurter, C., & Carpendale, S. (2014, Jun 25). A review of temporal data visualizations based on space-time cube operations. In Eurographics Conference on Visualization, Swansea, Wales, United Kingdom,1-19. http://dx.doi.org/10.2312/eurovisstar.20141171
  • Barré, K., Vernet, A., Azam, C., Le Viol, I., Dumont, A., Deana, T., & Kerbiriou, C. (2022). Landscape composition drives the impacts of artificial light at night on insectivorous bats. Environmental Pollution, 292, 118394.https://doi.org/10.1016/j.envpol.2021.118394
  • Baştürk, K., & Aladağ, C. (2009). Maki ve garig topluluklarının Türkiye’deki yayılış alanları ve ekolojik özelliklerinin incelenmesi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 22, 67-80.
  • Burt, C. S., Kelly, J. F., Trankina, G. E., Silva, C. L., Khalighifar, A., Jenkins-Smith, H. C., ve Horton, K. G. (2023). The effects of light pollution on migratory animal behavior. Trends in Ecology ve Evolution, 38(4), 355-368. https://doi.org/10.1016/j.tree.2022.12.006
  • Cabrera-Cruz, S. A., Smolinsky, J. A., & Buler, J. J. (2018). Light pollution is greatest within migration passage areas for nocturnally-migrating birds around the world. Scientific Reports, 8(1), 3261. https://www.nature.com/articles/s41598-018-21577-6
  • Camacho, L. F., Barragán, G., & Espinosa, S. (2021). Local ecological knowledge reveals combined landscape effects of light pollution, habitat loss, and fragmentation on insect populations. Biological Conservation, 262, 109311.https://doi.org/10.1016/j.biocon.2021.109311
  • Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., & Wu, J. (2020). An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data, 13, 889–906. http://dx.doi.org/10.5194/essd-2020-201
  • Çakıcı, A. C. ve Harman, S. (2006). Kuş gözlemciliğinin önemi: Türkiye’de kuş gözlemciliğinin profili. Anatolia: Turizm Araştırmaları Dergisi, 17(2), 161-168.
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  • Fuller, R. M., Devereux, B. J., Gillings, S., Hill, R. A., & Amable, G. S. (2007). Bird distributions relative to remotely sensed habitats in Great Britain: towards a framework for national modelling. Journal of Environmental Management, 84(4), 586-605.https://doi.org/10.1016/j.jenvman.2006.07.001
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  • Gibson, J., Olivia, S., Boe-Gibson, G., & Li, C. (2021). Which night lights data should we use in economics, and where? Journal of Development Economics, 149, 102602. https://doi.org/10.1016/j.jdeveco.2020.102602
  • Goetz, S. J., Steinberg, D., Betts, M. G., Holmes, R. T., Doran, P. J., Dubayah, R., & Hofton, M. (2010). Lidar remote sensing variables predict breeding habitat of a Neotropical migrant bird. Ecology, 91(6), 1569- 1576.https://doi.org/10.1890/09-1670.1
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  • Hölker, F., Wurzbacher, C., Weißenborn, C., Monaghan, M. T., Holzhauer, S. I., & Premke, K. (2015). Microbial diversity and community respiration in freshwater sediments influenced by artificial light at night. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1667), 20140130.http://dx.doi.org/10.1098/rstb.2014.0130
  • Hu, Z., Hu, H., & Huang, Y. (2018). Association between nighttime artificial light pollution and sea turtle nest density along Florida coast: A geospatial study using VIIRS remote sensing data. Environmental Pollution, 239, 30-42.https://doi.org/10.1016/j.envpol.2018.04.021
  • Jiang, W., He, G., Long, T., Wang, C., Ni, Y., & Ma, R. (2017). Assessing light pollution in China based on nighttime light imagery. Remote Sensing, 9(2), 135.https://doi.org/10.3390/rs9020135
  • Jing, X., Shao, X., Cao, C., Fu, X., & Yan, L. (2015). Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for correlating socio-economic variables at the provincial level in China. Remote Sensing, 8(1), 17.https://doi.org/10.3390/rs8010017
  • Kangal, N. (2023). Kurumsal Kalitenin Ekolojik Ayak İzi Üzerine Etkisinin Ampirik Analizi: E7 Ülkeleri Örneği. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 24(4), 636-645. https://doi.org/10.37880/cumuiibf.1335524
  • Kocaman, G., & Arslan, H. (2023). Ebird verilerinin incelenmesi yolu ile Türkiye’de kuş gözlemciliği üzerine bir değerlendirme. The Journal of Social Sciences, 63(63), 537-552.http://dx.doi.org/10.29228/SOBIDER.68937
  • Kumar, P., Ashawat, M. S., Pandit, V., & Sharma, D. K. (2019). Artificial Light Pollution at Night: A risk for normal circadian rhythm and physiological functions in humans. Current Environmental Engineering, 6(2), 111- 125.https://doi.org/10.2174/2212717806666190619120211
  • LaRoe, J., Holmes, C. M., & Schad, T. (2022). Nightlight Intensity Change Surrounding Nature Reserves: A Case Study in Orbroicher Bruch Nature Reserve, Germany. Remote Sensing, 14(16), 3876. http://dx.doi.org/10.3390/rs14163876
  • Levin, N., Kyba, C. C., Zhang, Q., de Miguel, A. S., Román, M. O., Li, X., & Elvidge, C. D. (2020). Remote sensing of night lights: A review and an outlook for the future. Remote Sensing of Environment, 237, 111443.https://doi.org/10.1016/j.rse.2019.111443
  • Li, X., Zhang, C., Li, W., & Liu, K. (2017). Evaluating the use of DMSP/OLS nighttime light imagery in predicting PM2.5 concentrations in the northeastern United States. Remote Sensing, 9(6), 620. https://doi.org/10.3390/rs9060620
  • Longcore, T., & Rich, C. (2004). Ecological light pollution. Frontiers in Ecology and the Environment, 2(4), 191- 198.https://doi.org/10.1890/1540-9295(2004)002[0191:ELP]2.0.CO;2
  • Mathews, F., Roche, N., Aughney, T., Jones, N., Day, J., Baker, J., & Langton, S. (2015). Barriers and benefits: implications of artificial night-lighting for the distribution of common bats in Britain and Ireland. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1667), 20140124. https://doi.org/10.1098/rstb.2014.0124
  • Meng, L., Zhou, Y., Román, M. O., Stokes, E. C., Wang, Z., Asrar, G. R., & Wang, Y. (2022). Artificial light at night: an underappreciated effect on phenology of deciduo etecody plants. PN etecxus, 1(2), pgac 046. https://doi.org/10.1093/pnasnexus/pgac046
  • Mo, C., Tan, D., Mai, T., Bei, C., Qin, J., Pang, W., & Zhang, Z. (2020). An analysis of spatiotemporal pattern for COIVD‐19 in China based on space‐time cube. Journal of Medical Virology, 92(9), 1587- 1595.https://doi.org/10.1002/jmv.25834
  • Montevecchi, W. A. (2006). Influences of artificial light on marine birds. In Ecological Consequences of Artificial Night Lighting, 94-113 Washington, DC, USA. Island Press.
  • OECD. (1997). Proceeding of the OECD workshop on Non-Regulatory Initiatives for Chemical Risk Management, Organisation for Economic Cooperation and Development, 3-25.
  • Orlowski, J., Harmening, W., & Wagner, H. (2012). Night vision in barn owls: visual acuity and contrast sensitivity under dark adaptation. Journal of Vision, 12(13), 4-4. https://doi.org/10.1167/12.13.4
  • Popelka, S., & Voženílek, V. (2013,March). Specifying of requirements for spatio-temporal data in map by eye- tracking and space-time-cube. In International Conference on Graphic and Image Processing, Arizona, USA, 8768, 974-978.http://dx.doi.org/10.1117/12.2011438
  • Porter, P. S., Rao, S. T., & Hogrefe, C. (2002). Linear trend analysis: a comparison of methods. Atmospheric Environment, 36(27), 4420-4421.
  • Purwanto, P., Utaya, S., Handoyo, B., Bachri, S., Astuti, I. S., Utomo, K. S. B., & Aldianto, Y. E. (2021). Spatiotemporal analysis of COVID-19 spread with emerging hotspot analysis and space–time cube models in East Java, Indonesia. ISPRS International Journal of Geo-Information, 10(3), 133. https://doi.org/10.3390/ijgi10030133 Rich, C., & Longcore, T. (2013). Ecological Consequences of Artificial Night Lighting, Washington, DC, USA, Island Press. Sader, S. A., Powell, G. V., & Rappole, J. H. (1991). Migratory bird habitat monitoring through remote sensing. International Journal of Remote Sensing, 12(3), 363-372. Salmon, M. (2006). Protecting sea turtles from artificial night lighting at Florida’s oceanic beaches. In C. Rich & T. Longcore (Eds.), Ecological Consequences of Artificial Night Lighting, 141-168, Washington, DC, USA. Island Press.
  • Senzaki, M., Barber, J. R., Phillips, J. N., Carter, N. H., Cooper, C. B., Ditmer, M. A., & Francis, C. D. (2020). Sensory pollutants alter bird phenology and fitness across a continent. Nature, 587(7835), 605-609. https://doi.org/10.1038/s41586-020-2903-7
  • Shi, K., Huang, C., Yu, B., Yin, B., Huang, Y., & Wu, J. (2014). Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sensing Letters, 5(4), 358- 366.https://doi.org/10.1080/2150704X.2014.905728
  • Song, Y., & Miller, H. J. (2012). Exploring traffic flow databases using space-time plots and data cubes. Transportation, 39, 215-234. http://dx.doi.org/10.1007/s11116-011-9343-z
  • Starek, M. J., Mitasova, H., Wegmann, K. W., & Lyons, N. (2013). Space-time cube representation of stream bank evolution mapped by terrestrial laser scanning. IEEE Geoscience and Remote Sensing Letters, 10(6), 1369-1373.https://doi.org/10.1109/LGRS.2013.2241730
  • Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., & Kelling, S. (2009). eBird: a citizen-based bird observation network in the biological sciences. Biological Conservation, 142, 2282-2292. https://doi.org/10.1016/j.biocon.2009.05.006
  • Sun, W., Zhang, X., Wang, N., & Cen, Y. (2017). Estimating population density using DMSP-OLS night-time imagery and land cover data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6), 2674-2684.http://dx.doi.org/10.1109/JSTARS.2017.2703878
  • Topuz, E. (2009). Endüstriyel tehlikeli maddeler için çevresel risk değerlendirme yaklaşımı, (Doktora Tezi), İstanbul Teknik Üniversitesi.
  • Turan, L., & Arıkan, K. (2011). Hatay ve risk altındaki göçmen kuşlar. Hacettepe Üniversitesi Çevre Eğitimi, Kuş Araştırma ve Halkalama Merkezi.
  • Ünal, O., & Gökçeoğlu, M. (2003). Akdeniz üniversitesi kampus florası (Antalya-Türkiye). Akdeniz University Journal of the Faculty of Agriculture, 16(2), 143-154.
  • Visser, M. E., Both, C., & Lambrechts, M. M. (2004). Global climate change leads to mistimed avian reproduction. Advances in Ecological Research, 35, 89-110. https://doi.org/10.1016/S0065-2504(04)35005-1
  • Wang, S., Li, W., Zhou, Y., Yan, F., Wang, F., & Liu, W. (2015). Space–time evolution of historical drought hazards in eastern China. Natural Hazards, 77, 2027-2047.DOI: 10.1007/s11069-015-1685-y
  • Weisshaupt, N., Leskinen, M., Moisseev, D. N., & Koistinen, J. (2022). Anthropogenic illumination as guiding light for nocturnal bird migrants identified by remote sensing. Remote Sensing, 14(7), 1616.https://doi.org/10.3390/rs14071616
  • Winkler, D. W., Dunn, P. O., & McCulloch, C. E. (2002). Predicting the effects of climate change on avian life- history traits. Proceedings of the National Academy of Sciences, 99(21), 13595- 13599.https://doi.org/10.1073/pnas.212251999
  • Xi, W., Du, S., Wang, Y. C., & Zhang, X. (2019). A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and Change detection. Remote Sensing of Environment, 231, 111212.http://dx.doi.org/10.1016/j.rse.2019.111212
  • Yeşilyurt, E. B., Latif, K., & Akaydın, G. (2008). Hacıkadın Vadisi Florası Üzerine Bir Araştırma Ankara/Türkiye. Biyolojik Çeşitlilik ve Koruma, 1(2), 25-52.
  • Yıldırım, V., Yurdakul, E., Karaağaç, G. A., Koçer, M., & Uyguçgil, H. (2023). Eskişehir Kent Merkezindeki Trafik Kazalarının Zamana Bağlı Konumsal Analizi. Turkish Journal of Remote Sensing and GIS, 4(1), 17- 32.https://doi.org/10.48123/rsgis.1167844
  • Zheng, Q., Weng, Q., & Wang, K. (2019). Developing a new cross-sensor calibration model for DMSP-OLS and Suomi-NPP VIIRS night-light imageries. ISPRS Journal of Photogrammetry and Remote Sensing, 153, 36- 47.https://doi.org/10.1016/j.isprsjprs.2019.04.019
  • Zhuo, L., Zheng, J., Zhang, X., Li, J., & Liu, L. (2015). An improved method of night-time light saturation reduction based on EVI. International Journal of Remote Sensing, 36(16), 4114-4130.https://doi.org/10.1080/01431161.2015.1073861
Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Coğrafi Bilgi Sistemleri, Çevre Sorunları , Uzaktan Algılama
Bölüm ARAŞTIRMA MAKALESİ
Yazarlar

Hüseyin Can Öngül 0000-0003-1383-3442

Şevki Danacıoğlu 0000-0003-1118-352X

Yayımlanma Tarihi 25 Mayıs 2024
Gönderilme Tarihi 1 Şubat 2024
Kabul Tarihi 29 Mart 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 52

Kaynak Göster

APA Öngül, H. C., & Danacıoğlu, Ş. (2024). Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal analizi: Jeoistatistiksel bir yaklaşım. Lnternational Journal of Geography and Geography Education(52), 159-178. https://doi.org/10.32003/igge.1430386
AMA Öngül HC, Danacıoğlu Ş. Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal analizi: Jeoistatistiksel bir yaklaşım. IGGE. Mayıs 2024;(52):159-178. doi:10.32003/igge.1430386
Chicago Öngül, Hüseyin Can, ve Şevki Danacıoğlu. “Türkiye’de Yapay ışık kirliliği Ile Kuş gözlemleri arasındaki ilişkinin Zamansal Ve mekânsal Analizi: Jeoistatistiksel Bir yaklaşım”. Lnternational Journal of Geography and Geography Education, sy. 52 (Mayıs 2024): 159-78. https://doi.org/10.32003/igge.1430386.
EndNote Öngül HC, Danacıoğlu Ş (01 Mayıs 2024) Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal analizi: Jeoistatistiksel bir yaklaşım. lnternational Journal of Geography and Geography Education 52 159–178.
IEEE H. C. Öngül ve Ş. Danacıoğlu, “Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal analizi: Jeoistatistiksel bir yaklaşım”, IGGE, sy. 52, ss. 159–178, Mayıs 2024, doi: 10.32003/igge.1430386.
ISNAD Öngül, Hüseyin Can - Danacıoğlu, Şevki. “Türkiye’de Yapay ışık kirliliği Ile Kuş gözlemleri arasındaki ilişkinin Zamansal Ve mekânsal Analizi: Jeoistatistiksel Bir yaklaşım”. lnternational Journal of Geography and Geography Education 52 (Mayıs 2024), 159-178. https://doi.org/10.32003/igge.1430386.
JAMA Öngül HC, Danacıoğlu Ş. Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal analizi: Jeoistatistiksel bir yaklaşım. IGGE. 2024;:159–178.
MLA Öngül, Hüseyin Can ve Şevki Danacıoğlu. “Türkiye’de Yapay ışık kirliliği Ile Kuş gözlemleri arasındaki ilişkinin Zamansal Ve mekânsal Analizi: Jeoistatistiksel Bir yaklaşım”. Lnternational Journal of Geography and Geography Education, sy. 52, 2024, ss. 159-78, doi:10.32003/igge.1430386.
Vancouver Öngül HC, Danacıoğlu Ş. Türkiye’de yapay ışık kirliliği ile kuş gözlemleri arasındaki ilişkinin zamansal ve mekânsal analizi: Jeoistatistiksel bir yaklaşım. IGGE. 2024(52):159-78.