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Calculation of depot stack volume using UAV technology: A case study of Tekkeköy Forest Depot in Samsun Province

Year 2024, Volume: 5 Issue: 1, 51 - 57, 27.06.2024
https://doi.org/10.59751/agacorman.1465184

Abstract

This study investigates the feasibility of calculating wood volume in a forest depot using Unmanned Aerial Vehicle (UAV) technology. The research was conducted at the Tekkeköy Forest Depot, affiliated with the Samsun Forest Management Directorate of the Amasya Regional Forest Directorate. The volume of 30 beech log stacks in the depot was measured using photogrammetric methods with images collected by the DJI Matrice 300 RTK UAV and Zenmuse P1 camera, processed with Agisoft Metashape software. The log volumes calculated as a result of the UAV flights showed variations in the range of -6.681 m³ to 7.829 m³ and were consistent with the depot volumes. A Paired Sample T-Test was employed to investigate if there was a significant difference between the volume measurements calculated using UAV and software and the actual depot measurements. The analysis results (t = -1.576; p = 0.126) indicated no statistically significant difference between the two methods. These findings suggest that UAV techniques could be a reliable alternative to terrestrial measurements for estimating the volume of log stacks. Considering the limited number of samples in this research, increasing the sample size and diversity in future studies will further rein-force the general applicability and reliability of this method. This study demonstrates the success of using UAVs in log volume estimation and emphasizes the importance of utilizing digital technologies in the forestry sector by offering innovative solutions for estimating the volume of log stacks.

References

  • AgiSoft, 2019. Agisoft Metashape. https://www.agisoft.com/
  • Balzter, H., Rowland, C., Saich, P., 2007. Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry. Remote Sensing of Environment 108(3): 224–239. https://doi.org/10.1016/j.rse.2006.11.014
  • Berendt, F., de Miguel-Diez, F., Wallor, E., Blasko, L., Cremer, T., 2021. Comparison of different approaches to estimate bark volume of industrial wood at disc and log scale. Sci. Rep., 11, 15630.
  • Berendt, F., Wolfgramm, F., Cremer, T., 2021. Reliability of photo-optical measurements of log stack gross volume. Silva Fennica 55(3): 10555. https://doi.org/10.14214/sf.10555.
  • Boberg, A., Lilja, J., 2016. Precision vid travmätning av rundvir-ke med en fotoinventeringsteknik applicerat i smarta telefoner. Kandidatarbete n i skogsvetenskap. Swedish University of Agri-cultural Sciences. Uppsala, 33 p
  • Chu, T., Guo, X., 2013. Remote sensing techniques in monito-ring post-fire effects and patterns of forest recovery in boreal forest regions: A review. Remote Sensing, 6(1), 470-520.
  • Cremer, T., Berendt, F., Diez, F. de M., Wolfgramm, F., Blasko, L., 2021. Accuracy of Photo-Optical Measurement of Wood Piles. Environmental Sciences Proceedings 3(1): 90. https://doi.org/10.3390/iecf2020-08192
  • DJI, 2022. Matrice 300. https://www.dji.com (Erişim tarihi: 15 Mart, 2024).
  • Eker, R., Aydın, A., Hübl, J., 2018. Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study. Environmental monitoring and assessment, 190, 1-14.
  • Eker, R., Aydın, A., 2020. The use of Unmanned Aerial Vehicle (UAV) for Tracking Stock Movements in Forest Enterprise De-pots. European Journal of Forest Engineering, 6 (2), 68-77.
  • Eurostat, 2023. Wood products - production and trade. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Wood_products_-_production_and_trade#Roundwood_production( Erişim Tarihi: 15 Mart, 2024)
  • Gollob, C., Ritter, T., Kraßnitzer, R., Tockner, A., Nothdurft, A., 2021. Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology. Remote Sensing 13(16): 3129. https://doi.org/10.3390/rs13163129
  • Hall, R. J., Castilla, G., White, J. C., Cooke, B. J., Skakun, R. S., 2016. Remote sensing of forest pest damage: A review and lessons learned from a Canadian perspective. The Canadian Entomologist, 148(S1), 296-S356.
  • Hopkinson, C., Chasmer, L., Barr, A. G., Kljun, N., Black, T. A., McCaughey, J. H., 2016. Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data. Remote Sensing of Environment, 181, 82-95.
  • Kumar, P., Pandey, P. C., Singh, B. K., Katiyar, S., Mandal, V. P., Rani, M., Patairiya, S., 2016. Estimation of accumulated soil organic carbon stock in tropical forest using geospatial strategy. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 109-123.
  • Löwe, R.; Sedmíková, M.; Natov, P.; Jankovský, M.; Hejcma-nová, P.; Dvořák, J., 2019. Differences in timber volume estima-tes using various algorithms available in the control and informa-tion systems of harvesters. Forests, 10, 388.
  • Lucieer, A., Jong, S. M. D., Turner, D., 2014. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in physical geography, 38(1), 97-116.
  • Magnussen, S., Nord-Larsen, T., Riis-Nielsen, T., 2018. Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012– 2016). Remote Sensing of Environment 211: 146–153. https:// doi.org/10.1016/j.rse.2018.04.015
  • OGM, 2009. Orman Genel Müdürlüğü Üretim Pazarlama Faali-yetleri (Bilanço Sonuçları) Değerlendirmesi. www.ogm.gov.tr (Erişim Tarihi:15 Mart, 2024).
  • OGM, 2024. Orman Genel Müdürlüğü Üretim, Satış ve Stok Faaliyetleri.www.ogm.gov.tr (Erişim Tarihi: 15 Mart, 2024).
  • Pyörälä, J., Saarinen, N., Kankare, V., Coops, N.C., Liang, X., Wang, Y., Holopainen, M., Hyyppä, J., Vastaranta, M., 2019. Variability of wood properties using airborne and terrestrial laser scanning. Remote Sensing of Environment 235: 111474. https://doi.org/10.1016/j.rse.2019.111474
  • Schäfer, E., Heiskanen, J., Heikinheimo, V., Pellikka, P., 2016. Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data. Ecological indicators, 64, 49-58.
  • Shervais, K., (2015). Structure from motion introductory guide. Version Oct, 22, 2015.
  • Steinaker, D. F., Jobbágy, E. G., Martini, J. P., Arroyo, D. N., Pacheco, J. L., Marchesini, V. A., 2016. Vegetation composition and structure changes following roller-chopping deforestation in central Argentina woodlands. Journal of Arid Environments, 133, 19-24.
  • Tang, L., Shao, G., 2015. Drone remote sensing for forestry research and practices. Journal of forestry research, 26, 791-797.
  • Ullah, S., Farooq, M., Shafique, M., Siyab, M. A., Kareem, F., Dees, M., 2016. Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakis-tan. Journal of Mountain Science, 13, 1229-1237.
  • UNECE, 2020. Summary for Policy Markers State of Europe’s Forest. In Proceedings of the Ministerial Conference on the Protection of Forests in Europe, Bratislava, Slovakia, 14–15 April 2020.
  • Xu, D., Wang, H., Xu, W., Luan, Z., Xu, X., 2021. LiDAR App-lications to Estimate Forest Biomass at Individual Tree Scale : Opportunities, Challenges and Future Perspectives. Forests 12(5): 550. https://doi.org/10.3390/f12050550
  • Zhang, J., Hu, J., Lian, J., Fan, Z., Ouyang, X., Ye, W., 2016. Seeing the forest from drones: Testing the potential of lightwe-ight drones as a tool for long-term forest monitoring. Biological Conservation, 198, 60-69.

Depo istif hacminin İHA teknolojisi ile hesaplanması: Samsun ili Tekkeköy Orman Deposu örneği

Year 2024, Volume: 5 Issue: 1, 51 - 57, 27.06.2024
https://doi.org/10.59751/agacorman.1465184

Abstract

Bu çalışma, İnsansız Hava Aracı (İHA) teknolojisi kullanılarak bir orman deposundaki odun hacminin hesaplanması araştırmaktadır. Çalışma, Amasya Orman Bölge Müdürlüğü Samsun Orman İşletme Müdürlüğü'ne bağlı Tekkeköy Orman Deposu'nda gerçekleştirilmiştir. Depodaki 30 adet kayın tomruk istifi hacmi, DJI Matrice 300 RTK İHA ve Zenmuse P1 kamera ile toplanan görüntüler aracılığıyla Agisoft Metashape yazılımı kullanılarak fotogrametrik yöntemlerle ölçülmüştür. İHA uçuşları sonucunda hesaplanan tomruk hacimlerinde depo kayıtları ile karşılaştırılmış, uygulamada bulunan verilerle depo verileri arasında -6,681 m³ ile +7,829 m³ arasında değişen hacim farkları gözlem-lenmiştir. Eşleştirilmiş Örneklem T-Testi kullanılarak, İHA ve yazılımlar kullanılarak hesaplanan hacim ölçümleri ile gerçek depo ölçümleri arasında fark olup olmadığı araştırılmıştır. Yapılan analiz sonucu (t = -1,576; p = 0,126) iki yöntem arasında istatistiksel olarak anlamlı bir farkın olmadığı ortaya konulmuştur. Bu bulgular, İHA tekniklerinin tomruk istif hacmi tahmininde yersel ölçümlere güvenilir bir alternatif olabileceğini göstermektedir. Araştırmanın sınırlı sayıda örneklemi göz önünde bulundurulduğunda, gelecekteki çalışmalarda örneklem sayısının ve çeşitliliğinin artırılması, bu yöntemin genel uygulanabilirliğini ve güvenilirliğini daha da pekiştirecektir. Bu çalışma, ormancılık sektöründe dijital teknolojilerin kullanımının önemini vurgulamakta ve tomruk istif hacmi tahmininde İHA kullanı-mının başarılı bir yöntem olduğunu ortaya koymaktadır.

Ethical Statement

Çalışmanın tüm süreçlerinin araştırma ve yayın etiğine uygun olduğunu, etik kurallara ve bilimsel atıf gösterme ilkelerine uyduğumu beyan ederim.

Thanks

Bu çalışmanın gerçekleştirilmesine olanak sağlayan Samsun Orman İşletme Müdürlüğü ve Tekkeköy Orman İşletme Şefliği ‘ne, özellikle de İHA uçuşunun gerçekleştirilmesinde yardımcı olan personele teşekkürlerimizi sunarız.

References

  • AgiSoft, 2019. Agisoft Metashape. https://www.agisoft.com/
  • Balzter, H., Rowland, C., Saich, P., 2007. Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry. Remote Sensing of Environment 108(3): 224–239. https://doi.org/10.1016/j.rse.2006.11.014
  • Berendt, F., de Miguel-Diez, F., Wallor, E., Blasko, L., Cremer, T., 2021. Comparison of different approaches to estimate bark volume of industrial wood at disc and log scale. Sci. Rep., 11, 15630.
  • Berendt, F., Wolfgramm, F., Cremer, T., 2021. Reliability of photo-optical measurements of log stack gross volume. Silva Fennica 55(3): 10555. https://doi.org/10.14214/sf.10555.
  • Boberg, A., Lilja, J., 2016. Precision vid travmätning av rundvir-ke med en fotoinventeringsteknik applicerat i smarta telefoner. Kandidatarbete n i skogsvetenskap. Swedish University of Agri-cultural Sciences. Uppsala, 33 p
  • Chu, T., Guo, X., 2013. Remote sensing techniques in monito-ring post-fire effects and patterns of forest recovery in boreal forest regions: A review. Remote Sensing, 6(1), 470-520.
  • Cremer, T., Berendt, F., Diez, F. de M., Wolfgramm, F., Blasko, L., 2021. Accuracy of Photo-Optical Measurement of Wood Piles. Environmental Sciences Proceedings 3(1): 90. https://doi.org/10.3390/iecf2020-08192
  • DJI, 2022. Matrice 300. https://www.dji.com (Erişim tarihi: 15 Mart, 2024).
  • Eker, R., Aydın, A., Hübl, J., 2018. Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study. Environmental monitoring and assessment, 190, 1-14.
  • Eker, R., Aydın, A., 2020. The use of Unmanned Aerial Vehicle (UAV) for Tracking Stock Movements in Forest Enterprise De-pots. European Journal of Forest Engineering, 6 (2), 68-77.
  • Eurostat, 2023. Wood products - production and trade. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Wood_products_-_production_and_trade#Roundwood_production( Erişim Tarihi: 15 Mart, 2024)
  • Gollob, C., Ritter, T., Kraßnitzer, R., Tockner, A., Nothdurft, A., 2021. Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology. Remote Sensing 13(16): 3129. https://doi.org/10.3390/rs13163129
  • Hall, R. J., Castilla, G., White, J. C., Cooke, B. J., Skakun, R. S., 2016. Remote sensing of forest pest damage: A review and lessons learned from a Canadian perspective. The Canadian Entomologist, 148(S1), 296-S356.
  • Hopkinson, C., Chasmer, L., Barr, A. G., Kljun, N., Black, T. A., McCaughey, J. H., 2016. Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data. Remote Sensing of Environment, 181, 82-95.
  • Kumar, P., Pandey, P. C., Singh, B. K., Katiyar, S., Mandal, V. P., Rani, M., Patairiya, S., 2016. Estimation of accumulated soil organic carbon stock in tropical forest using geospatial strategy. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 109-123.
  • Löwe, R.; Sedmíková, M.; Natov, P.; Jankovský, M.; Hejcma-nová, P.; Dvořák, J., 2019. Differences in timber volume estima-tes using various algorithms available in the control and informa-tion systems of harvesters. Forests, 10, 388.
  • Lucieer, A., Jong, S. M. D., Turner, D., 2014. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in physical geography, 38(1), 97-116.
  • Magnussen, S., Nord-Larsen, T., Riis-Nielsen, T., 2018. Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012– 2016). Remote Sensing of Environment 211: 146–153. https:// doi.org/10.1016/j.rse.2018.04.015
  • OGM, 2009. Orman Genel Müdürlüğü Üretim Pazarlama Faali-yetleri (Bilanço Sonuçları) Değerlendirmesi. www.ogm.gov.tr (Erişim Tarihi:15 Mart, 2024).
  • OGM, 2024. Orman Genel Müdürlüğü Üretim, Satış ve Stok Faaliyetleri.www.ogm.gov.tr (Erişim Tarihi: 15 Mart, 2024).
  • Pyörälä, J., Saarinen, N., Kankare, V., Coops, N.C., Liang, X., Wang, Y., Holopainen, M., Hyyppä, J., Vastaranta, M., 2019. Variability of wood properties using airborne and terrestrial laser scanning. Remote Sensing of Environment 235: 111474. https://doi.org/10.1016/j.rse.2019.111474
  • Schäfer, E., Heiskanen, J., Heikinheimo, V., Pellikka, P., 2016. Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data. Ecological indicators, 64, 49-58.
  • Shervais, K., (2015). Structure from motion introductory guide. Version Oct, 22, 2015.
  • Steinaker, D. F., Jobbágy, E. G., Martini, J. P., Arroyo, D. N., Pacheco, J. L., Marchesini, V. A., 2016. Vegetation composition and structure changes following roller-chopping deforestation in central Argentina woodlands. Journal of Arid Environments, 133, 19-24.
  • Tang, L., Shao, G., 2015. Drone remote sensing for forestry research and practices. Journal of forestry research, 26, 791-797.
  • Ullah, S., Farooq, M., Shafique, M., Siyab, M. A., Kareem, F., Dees, M., 2016. Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakis-tan. Journal of Mountain Science, 13, 1229-1237.
  • UNECE, 2020. Summary for Policy Markers State of Europe’s Forest. In Proceedings of the Ministerial Conference on the Protection of Forests in Europe, Bratislava, Slovakia, 14–15 April 2020.
  • Xu, D., Wang, H., Xu, W., Luan, Z., Xu, X., 2021. LiDAR App-lications to Estimate Forest Biomass at Individual Tree Scale : Opportunities, Challenges and Future Perspectives. Forests 12(5): 550. https://doi.org/10.3390/f12050550
  • Zhang, J., Hu, J., Lian, J., Fan, Z., Ouyang, X., Ye, W., 2016. Seeing the forest from drones: Testing the potential of lightwe-ight drones as a tool for long-term forest monitoring. Biological Conservation, 198, 60-69.
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Forest Biometrics
Journal Section Research Articles
Authors

Turan Sönmez 0000-0001-7041-1479

Emir Talha Karahan 0009-0004-1521-2468

Furkan Emre Aksakal 0009-0001-6465-3153

Burhan Gencal 0000-0001-7185-5725

Early Pub Date June 26, 2024
Publication Date June 27, 2024
Submission Date April 5, 2024
Acceptance Date June 4, 2024
Published in Issue Year 2024 Volume: 5 Issue: 1

Cite

APA Sönmez, T., Karahan, E. T., Aksakal, F. E., Gencal, B. (2024). Depo istif hacminin İHA teknolojisi ile hesaplanması: Samsun ili Tekkeköy Orman Deposu örneği. Ağaç Ve Orman, 5(1), 51-57. https://doi.org/10.59751/agacorman.1465184