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Çam Ağaçlarında Mantar Hastalığının İzlenmesi için Üçgen Yeşillik İndeksi Analizi: İHA Tabanlı Bir Yaklaşım

Year 2024, Volume: 26 Issue: 2, 1 - 15, 23.04.2024

Nizar Polat Abdulkadir Memduhoğlu Yunus Kaya

https://doi.org/10.24011/barofd.1352729

Abstract

Üçgen Yeşillik İndeksi (TGI), insansız hava araçları (İHA) kullanılarak elde edilen yüksek çözünürlüklü hava görüntülerinden türetilen bir bitki örtüsü indeksidir. Görünür spektrumda bitki örtüsünün sağlığını ve dinamiklerini ölçmek için değerli bir araç olarak hizmet eder. TGI, İHA tabanlı görüntülerden elde edilen kırmızı yansıma ve yeşil yansıma dahil olmak üzere temel bileşenleri birleştirir. Kırmızı bileşen klorofil emilimini ve fotosentetik aktiviteyi temsil ederken, yeşil bileşen bitki örtüsü yoğunluğunu ve kanopi yapısını yansıtır. Bu bileşenleri entegre eden TGI, İHA'ları bir veri toplama platformu olarak kullanarak fotosentetik olarak aktif bitki örtüsünün kapsamlı bir ölçümünü sunmaktadır. Bu çalışma, İHA tabanlı görüntülerden elde edilen TGI'nın bitki örtüsü değişikliklerinin izlenmesinde, ekosistem tepkilerinin değerlendirilmesinde ve arazi örtüsü ve biyoçeşitlilikteki değişimlerin izlenmesindeki önemini vurgulamaktadır. Ayrıca, İHA tabanlı hava görüntüleri kullanılarak TGI analizinin uygulanması, mantar hastalıklarından etkilenen bitki örtüsünün doğru bir şekilde tanımlanması ve izlenmesinde umut vaat etmektedir. Bu entegre yaklaşım, yapraklarında gözlemlenen yeşillikteki belirgin değişikliklere dayanarak hastalıklı ağaçların tespit edilmesini sağlar. Çünkü mantar hastalıkları bitkiyi kurutur ve yeşil alanların yok olmasına neden olur. İHA teknolojisinin entegrasyonu, TGI hesaplamasının doğruluğunu ve verimliliğini artırarak bitki örtüsündeki mantar hastalıklarının tespiti bağlamında etkili yönetim ve koruma stratejilerine katkıda bulunur. Bu çalışmada, İHA tabanlı ortofoto kullanılarak TGI üretilmiş ve sağlıklı ve hasta ağaçlar belirlenmiştir. Doğruluk analizine göre, yeşil bitkileri tespit etmek için üretici doğruluğu %99,7 ve kullanıcı doğruluğu %98,5'tir. Mantar hastalığı %98,5 üretici doğruluğu ve %96,5 kullanıcı doğruluğu ile tespit edilebilmiştir. Çalışmanın genel doğruluğu %98,6 olarak hesaplanmıştır.

Keywords

İHA fotogrametrisi görünür bölge bitki örtüsü indeksi Üçgen Yeşillik İndeksi (TGI) mantar hastalığı çam ağacı

References

  • Akca, S. and Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265-2274.
  • Aksoy, H. and Kaptan, S. (2020). Simulation of future forest and land use/cover changes (2019-2039) using the Cellular Automata-Markov Model. Geocarto International, 1-17, DOI: https://doi.org/10.1080/10106049.2020.1778102.
  • Bannari, A., Morin, D., Bonn, F. and Huete, A. R. (1995). A Review of Vegetation Indices. Remote Sensing Reviews, 13(1), 95- 120.
  • Bhupathi, P. and Sevugan, P. (2021). Application of hyperspectral remote sensing technology for plant disease forecasting: An applied review. Annals of the Romanian Society for Cell Biology, 25(6), 4555-4566.
  • Blaga, L., Ilieș, D. C., Wendt, J. A., Rus, I., Zhu, K. and Dávid, L. D. (2023). Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery. Remote Sensing, 15(12), 3168.
  • Brovkina, O., Cienciala, E., Surový, P. and Janata, P. (2018). Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-spatial Information Science, 21(1), 12-20.
  • Chowhan, P. and Chakraborty, A. P. (2022). Remote Sensing Technology—A New Dimension in Detection, Quantification and Tracking of Abiotic and Biotic Stresses. In Plant Stress: Challenges and Management in the New Decade, 445-457.
  • Costanza, K. K., Whitney, T. D., McIntire, C. D., Livingston, W. H. and Gandhi, K. J. (2018). A synthesis of emerging health issues of eastern white pine (Pinus strobus) in eastern North America. Forest Ecology and Management, 423, 3-17.
  • d’Oleire-Oltmanns, S., Marzolff, I., Peter, K. D. and Ries, J. B. (2012). Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco. Remote Sensing, 4(11), 3390-3416.
  • Demir, S. and Başayiğit, L. (2020). Sorunlu Gelişim Gösteren Bitkilerin İnsansız Hava Araçları (İHA) ile Belirlenmesi. Türk Bilim ve Mühendislik Dergisi, 2 (1), 12-22.
  • Demir, S. and Başayiğit, L. (2021). Kısıtlı Sulama Uygulamalarının İHA Multispektral Algılamaya Dayalı Vejetasyon İndekslerine Etkisi. Yuzuncu Yıl University Journal of Agricultural Sciences, 31(3), 629-643.
  • Demirel, M., Kaya, Y. and Polat, N. (2022). Investigation of the effect of UAV flight altitude in map production. In Intercontinental Geoinformation Days, 4, 21-24.
  • Durkaya, B., Kaptan, S. and Durkaya, A. (2020). Socio-economic and cultural sources of conflict between forest villagers and forest; a case study from Black Sea region, Turkey. Crime, Law and Social Change, 74, 155-173.
  • Eitel, J. U. H., Long, D. S., Gessler, P. E. and Smith, A. M. S. (2007). Using in situ measurements to evaluate the new RapidEyeTM satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing. 28, 4183–4190.
  • Gitelson, A. A. (2011). Nondestructive estimation of foliar pigment (chlorophylls, carotenoids, and anthocyanins) contents: Evaluating a semianalytical three-band model. Hyperspectral remote sensing of vegetation, 141.
  • Haboudane, D., Tremblay, N., Miller, J. R. and Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions of Geoscience and Remote Sensing. 46, 423–437.
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  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. and Ferreira, L. G., (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83, 195–213.
  • Hunt, E. R., Daughtry, C. S. T., Eitel, J. U. H. and Long, D. S. (2011). Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal, 103, 1090–1099.
  • Jackson, R. D. and Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine. 11, 185–200.
  • Kaya, Y., Sanli, F. B. and Abdikan, S. (2023). Determination of long-term volume change in lakes by integration of UAV and satellite data: the case of Lake Burdur in Türkiye. Environmental Sci-ence and Pollution Research, 30, 117729–117747.
  • Kaya, Y., Şenol, H. İ., Memduhoğlu, A., Akça, Ş., Ulukavak, M. and Polat, N. (2019). Hacim Hesaplarında İHA Kullanımı: Osmanbey Kampüsü Örneği. Türkiye Fotogrametri Dergisi, 1(1), 7-10.
  • Kim, B. N., Kim, J. H., Ahn, J. Y., Kim, S., Cho, B. K., Kim, Y. H. and Min, J. (2020). A short review of the pinewood nematode, Bursaphelenchus xylophilus. Toxicology and Environmental Health Sciences, 12, 297-304.
  • Kusak, L., Unel, F. B., Alptekin, A., Celik, M. O. and Yakar, M. (2021). Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping. Open Geosciences, 13(1), 1226-1244.
  • Özgüven, M. M. (2018). Hassas Tarım. Akfon kitap kırtasiye, 334s. Ankara. ISBN: 978-605-68762-4-0. Ramoelo, A., Skidmore, A. K., Cho, M. A., Schlerf, M., Mathieu, R. and Heitkönig, I. M. A. (2012). Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation. 19, 151–162.
  • Remondino, F. and El‐Hakim, S. (2006). Image‐based 3D modelling: a review. The Photogrammetric Record. 21(115), 269-291.
  • Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publications, 351(1), 309.
  • Roy, P. S., Ramachandran, R. M., Paul, O., Thakur, P. K., Ravan, S., Behera, M. D., Sarangi, C. and Kanawade, V. P. (2022). Anthropogenic land use and land cover changes—A review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing, 50(8), 1615-1640.
  • Şin, B. and Kadıoğlu, İ. (2019) İnsansız Hava Aracı (İHA) ve görüntü işleme teknikleri kullanılarak yabancı ot tespitinin yapılması. Turkish Journal of Weed Science, 20(2), 211-217
  • Snavely, N., Seitz, S. M. and Szeliski, R. (2008). Modeling the world from internet photo collections. International Journal of Computer Vision. 80, 189-210.
  • Sohl, T. and Sleeter, B. (2012). 15 Role of Remote Sensing for Land-Use and Land-Cover Change Modeling. Remote Sensing of Land Use and Land Cover, 225.
  • Story, M. and Congalton, R. G. (1986) Remote Sensing Brief Accuracy Assessment: A User’s Perspective. Photogrammetric Engineering and Remote Sensing, 523, 397–399
  • Toprak, A. S., Polat, N. and Uysal, M. (2019). 3D modeling of lion tombstones with UAV photogrammetry: a case study in ancient Phrygia (Turkey). Archaeological and Anthropological Sciences. 11(5), 1973-1976.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 8, 127–150.
  • Türkseven, S., Kızmaz, M. Z., Tekin, A. B., Urkan, E. and Serim, A. T. (2016). Tarımda dijital dönüşüm, insansız hava araçlarının kullanılması. Tarım Makinaları Bilim Dergisi, 12 (4), 267-271.
  • Türkseven, S., Tekin, B., Kızmaz, M. Z., Urkan, E. and Serim, A. T. (2018). İnsansız hava araçları ile pamukta yabancı ot florasının tespit edilme olanakları. Türkiye VII. Bitki Koruma Kongresi, 14-17 Kasım 2018, Muğla Türkiye. URL-1: https://www.dji.com/global
  • Uysal, M., Toprak, A. S. and Polat, N. (2013). Photo realistic 3D modeling with UAV: Gedik ahmet pasha mosque in afyonkarahisar. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 659-662.
  • Uysal, M., Toprak, A. S. and Polat, N. (2015). DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill. Measurement, 73, 539-543.
  • Wang, C. Y., Fang, Z. M., Wang, Z., Zhang, D. L., Gu, L. J., Lee, M. R., Liu, L. and Sung, C. K. (2011). Biological control of the pinewood nematode Bursaphelenchus xylophilus by application of the endoparasitic fungus Esteya vermicola. BioControl, 56, 91-100.
  • Wang, L., Li, R., Duan, C., Zhang, C., Meng, X. and Fang, S. (2022) A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19,1–5.
  • Yakar, M. and Doğan, Y. (2018). GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences, 3(2), 50-55.
  • Yakar, M., Ulvi, A., Yiğit, A. Y. and Alptekin, A. (2022). Discontinuity set extraction from 3D point clouds obtained by UAV Photogrammetry in a rockfall site. Survey Review, 1-13.
  • Yiğit, A. Y. (2020). İnsansız hava aracı ile elde edilen veriler yardımıyla yol tespiti, Yüksek lisans tezi, Afyon Kocatepe Universitesi, Fen Bilimleri Enstitüsü, Afyon, Türkiye.
  • Yilmaz, H. M., Yakar, M., Mutluoglu, O., Kavurmaci, M. M. and Yurt, K. (2012). Monitoring of soil erosion in Cappadocia region (Selime-Aksaray-Turkey). Environmental Earth Sciences, 66(1), 75-81.
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Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

Year 2024, Volume: 26 Issue: 2, 1 - 15, 23.04.2024

Nizar Polat Abdulkadir Memduhoğlu Yunus Kaya

https://doi.org/10.24011/barofd.1352729

Abstract

The Triangular Greenness Index (TGI) is a vegetation index derived from high-resolution aerial images acquired using unmanned aerial vehicles (UAVs). It serves as a valuable tool for quantifying vegetation health and dynamics in the visible spectrum. The TGI combines key components, including red reflectance and green reflectance, extracted from UAV-based imagery. The red component represents chlorophyll absorption and photosynthetic activity, while the green component reflects vegetation density and canopy structure. By integrating these components, the TGI offers a comprehensive measure of photosynthetically active vegetation, utilizing UAVs as a data collection platform. This study highlight the importance of the TGI derived from UAV-based imagery in monitoring vegetation changes, assessing ecosystem responses, and tracking variations in land cover and biodiversity. Furthermore, the application of TGI analysis using UAV-based aerial imagery shows promise in accurately identifying and monitoring vegetation affected by fungal diseases. This integrated approach enables the detection of diseased trees based on distinct changes in greenness observed in their foliage. Because fungal diseases dry the plant and cause the green areas to disappear. The integration of UAV technology enhances the accuracy and efficiency of TGI calculation, contributing to effective management and conservation strategies in the context of fungal disease detection in vegetation. In this study, TGI was produced using UAV-based orthophoto and healthy and sick trees were determined. According to the accuracy analysis, producer accuracy for detecting green plants was 99.7% and user accuracy was 98.5%. Fungal disease could be detected with 98.5% producer accuracy and 96.5% user accuracy. The overall accuracy of the study was calculated as 98.6%.

Keywords

UAV Photogrammetry visible region vegetation index Triangular Greenery Index (TGI) fungal disease pine tree

References

  • Akca, S. and Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265-2274.
  • Aksoy, H. and Kaptan, S. (2020). Simulation of future forest and land use/cover changes (2019-2039) using the Cellular Automata-Markov Model. Geocarto International, 1-17, DOI: https://doi.org/10.1080/10106049.2020.1778102.
  • Bannari, A., Morin, D., Bonn, F. and Huete, A. R. (1995). A Review of Vegetation Indices. Remote Sensing Reviews, 13(1), 95- 120.
  • Bhupathi, P. and Sevugan, P. (2021). Application of hyperspectral remote sensing technology for plant disease forecasting: An applied review. Annals of the Romanian Society for Cell Biology, 25(6), 4555-4566.
  • Blaga, L., Ilieș, D. C., Wendt, J. A., Rus, I., Zhu, K. and Dávid, L. D. (2023). Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery. Remote Sensing, 15(12), 3168.
  • Brovkina, O., Cienciala, E., Surový, P. and Janata, P. (2018). Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-spatial Information Science, 21(1), 12-20.
  • Chowhan, P. and Chakraborty, A. P. (2022). Remote Sensing Technology—A New Dimension in Detection, Quantification and Tracking of Abiotic and Biotic Stresses. In Plant Stress: Challenges and Management in the New Decade, 445-457.
  • Costanza, K. K., Whitney, T. D., McIntire, C. D., Livingston, W. H. and Gandhi, K. J. (2018). A synthesis of emerging health issues of eastern white pine (Pinus strobus) in eastern North America. Forest Ecology and Management, 423, 3-17.
  • d’Oleire-Oltmanns, S., Marzolff, I., Peter, K. D. and Ries, J. B. (2012). Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco. Remote Sensing, 4(11), 3390-3416.
  • Demir, S. and Başayiğit, L. (2020). Sorunlu Gelişim Gösteren Bitkilerin İnsansız Hava Araçları (İHA) ile Belirlenmesi. Türk Bilim ve Mühendislik Dergisi, 2 (1), 12-22.
  • Demir, S. and Başayiğit, L. (2021). Kısıtlı Sulama Uygulamalarının İHA Multispektral Algılamaya Dayalı Vejetasyon İndekslerine Etkisi. Yuzuncu Yıl University Journal of Agricultural Sciences, 31(3), 629-643.
  • Demirel, M., Kaya, Y. and Polat, N. (2022). Investigation of the effect of UAV flight altitude in map production. In Intercontinental Geoinformation Days, 4, 21-24.
  • Durkaya, B., Kaptan, S. and Durkaya, A. (2020). Socio-economic and cultural sources of conflict between forest villagers and forest; a case study from Black Sea region, Turkey. Crime, Law and Social Change, 74, 155-173.
  • Eitel, J. U. H., Long, D. S., Gessler, P. E. and Smith, A. M. S. (2007). Using in situ measurements to evaluate the new RapidEyeTM satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing. 28, 4183–4190.
  • Gitelson, A. A. (2011). Nondestructive estimation of foliar pigment (chlorophylls, carotenoids, and anthocyanins) contents: Evaluating a semianalytical three-band model. Hyperspectral remote sensing of vegetation, 141.
  • Haboudane, D., Tremblay, N., Miller, J. R. and Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions of Geoscience and Remote Sensing. 46, 423–437.
  • Harris Geospatial. (2016). Raster Color Slices. https://www.harrisgeospatial.com/docs/ColorSlices.html Accessed 24 June 2023.
  • Harris Geospatial. (2023). Calculate Confusion Matrices. https://www.l3harrisgeospatial.com/docs/CalculatingConfusionMatrices.html, Accessed 24 June 2023.
  • Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V. and Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENS and Sentinel-2 bands. Remote Sensing of Environment. 115, 2141–2151.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. and Ferreira, L. G., (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83, 195–213.
  • Hunt, E. R., Daughtry, C. S. T., Eitel, J. U. H. and Long, D. S. (2011). Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal, 103, 1090–1099.
  • Jackson, R. D. and Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine. 11, 185–200.
  • Kaya, Y., Sanli, F. B. and Abdikan, S. (2023). Determination of long-term volume change in lakes by integration of UAV and satellite data: the case of Lake Burdur in Türkiye. Environmental Sci-ence and Pollution Research, 30, 117729–117747.
  • Kaya, Y., Şenol, H. İ., Memduhoğlu, A., Akça, Ş., Ulukavak, M. and Polat, N. (2019). Hacim Hesaplarında İHA Kullanımı: Osmanbey Kampüsü Örneği. Türkiye Fotogrametri Dergisi, 1(1), 7-10.
  • Kim, B. N., Kim, J. H., Ahn, J. Y., Kim, S., Cho, B. K., Kim, Y. H. and Min, J. (2020). A short review of the pinewood nematode, Bursaphelenchus xylophilus. Toxicology and Environmental Health Sciences, 12, 297-304.
  • Kusak, L., Unel, F. B., Alptekin, A., Celik, M. O. and Yakar, M. (2021). Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping. Open Geosciences, 13(1), 1226-1244.
  • Özgüven, M. M. (2018). Hassas Tarım. Akfon kitap kırtasiye, 334s. Ankara. ISBN: 978-605-68762-4-0. Ramoelo, A., Skidmore, A. K., Cho, M. A., Schlerf, M., Mathieu, R. and Heitkönig, I. M. A. (2012). Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. International Journal of Applied Earth Observation and Geoinformation. 19, 151–162.
  • Remondino, F. and El‐Hakim, S. (2006). Image‐based 3D modelling: a review. The Photogrammetric Record. 21(115), 269-291.
  • Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publications, 351(1), 309.
  • Roy, P. S., Ramachandran, R. M., Paul, O., Thakur, P. K., Ravan, S., Behera, M. D., Sarangi, C. and Kanawade, V. P. (2022). Anthropogenic land use and land cover changes—A review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing, 50(8), 1615-1640.
  • Şin, B. and Kadıoğlu, İ. (2019) İnsansız Hava Aracı (İHA) ve görüntü işleme teknikleri kullanılarak yabancı ot tespitinin yapılması. Turkish Journal of Weed Science, 20(2), 211-217
  • Snavely, N., Seitz, S. M. and Szeliski, R. (2008). Modeling the world from internet photo collections. International Journal of Computer Vision. 80, 189-210.
  • Sohl, T. and Sleeter, B. (2012). 15 Role of Remote Sensing for Land-Use and Land-Cover Change Modeling. Remote Sensing of Land Use and Land Cover, 225.
  • Story, M. and Congalton, R. G. (1986) Remote Sensing Brief Accuracy Assessment: A User’s Perspective. Photogrammetric Engineering and Remote Sensing, 523, 397–399
  • Toprak, A. S., Polat, N. and Uysal, M. (2019). 3D modeling of lion tombstones with UAV photogrammetry: a case study in ancient Phrygia (Turkey). Archaeological and Anthropological Sciences. 11(5), 1973-1976.
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 8, 127–150.
  • Türkseven, S., Kızmaz, M. Z., Tekin, A. B., Urkan, E. and Serim, A. T. (2016). Tarımda dijital dönüşüm, insansız hava araçlarının kullanılması. Tarım Makinaları Bilim Dergisi, 12 (4), 267-271.
  • Türkseven, S., Tekin, B., Kızmaz, M. Z., Urkan, E. and Serim, A. T. (2018). İnsansız hava araçları ile pamukta yabancı ot florasının tespit edilme olanakları. Türkiye VII. Bitki Koruma Kongresi, 14-17 Kasım 2018, Muğla Türkiye. URL-1: https://www.dji.com/global
  • Uysal, M., Toprak, A. S. and Polat, N. (2013). Photo realistic 3D modeling with UAV: Gedik ahmet pasha mosque in afyonkarahisar. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 659-662.
  • Uysal, M., Toprak, A. S. and Polat, N. (2015). DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill. Measurement, 73, 539-543.
  • Wang, C. Y., Fang, Z. M., Wang, Z., Zhang, D. L., Gu, L. J., Lee, M. R., Liu, L. and Sung, C. K. (2011). Biological control of the pinewood nematode Bursaphelenchus xylophilus by application of the endoparasitic fungus Esteya vermicola. BioControl, 56, 91-100.
  • Wang, L., Li, R., Duan, C., Zhang, C., Meng, X. and Fang, S. (2022) A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19,1–5.
  • Yakar, M. and Doğan, Y. (2018). GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences, 3(2), 50-55.
  • Yakar, M., Ulvi, A., Yiğit, A. Y. and Alptekin, A. (2022). Discontinuity set extraction from 3D point clouds obtained by UAV Photogrammetry in a rockfall site. Survey Review, 1-13.
  • Yiğit, A. Y. (2020). İnsansız hava aracı ile elde edilen veriler yardımıyla yol tespiti, Yüksek lisans tezi, Afyon Kocatepe Universitesi, Fen Bilimleri Enstitüsü, Afyon, Türkiye.
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There are 48 citations in total.

Details

Primary Language English
Subjects Environmental Management (Other)
Journal Section Research Articles
Authors

Nizar Polat 0000-0002-6061-7796

Abdulkadir Memduhoğlu 0000-0002-9072-869X

Yunus Kaya 0000-0003-2319-4998

Early Pub Date March 29, 2024
Publication Date April 23, 2024
Published in Issue Year 2024 Volume: 26 Issue: 2

Cite

APA Polat, N., Memduhoğlu, A., & Kaya, Y. (2024). Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach. Bartın Orman Fakültesi Dergisi, 26(2), 1-15. https://doi.org/10.24011/barofd.1352729
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