Yıl 2020, Cilt 6, Sayı 2, 68 - 77, 30.12.2020

Remzi EKER Abdurrahim AYDIN

https://doi.org/10.33904/ejfe.835793

Öz

The use of Unmanned Aerial Vehicle (UAV) for Tracking Stock Movements in Forest Enterprise Depots

Öz

In forestry applications in Turkey, forest enterprise depots (FEDs) are permanent main places where forest products such as logs and round timbers are stored and presented for sale to the market. The principal functions of FEDs are receiving, classifying, protecting, preparing the forest products for the sales and tracking the stock movements. According to Communiqué No 288 on the Production of Fundamental Forest Products published by General Directorate of Forestry in Turkey, it is obligatory to conduct stocktaking twice a year in FEDs for tracking and controlling the stock movements. The capabilities of using Unmanned Aerial Vehicles (UAVs) in tracking stock movements in FEDs have not been extensively studied yet in the current literature. This study aimed to test UAVs in determining volume of round timber storages inside a FED named "Göl", located in Gölyaka District (Düzce, Turkey). A UAV flight was conducted and volume of round timber storage was calculated from point cloud, and compared to stock records for validation of UAV-based measurements. It was found that UAV-based volume measurements of the stock were quite compatible with available stock records. This study concluded that UAVs could be used in tracking stock movements in FEDs in an effective way.

Anahtar Kelimeler

Forest Enterprise Depots, forest product, stock movement, UAV

Kaynakça

• Aicardi, I., Chiabrando, F., Grasso, N., Lingua, A.M., Noardo, F., Spano, A. 2016. UAV photogrammetry with oblique images: first analysis on data acquisition and processing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic, pp. 835-842.
• Boisvenue, C., Smiley, B.P. White, J.C. Kurz, W.A., Wulder. M.A., 2016. Integration of Landsat Time Series and Field Plots for Forest Productivity Estimates in Decision Support Models. Forest Ecology and Management, 376: 284–297.
• Brodu, N., Lague, D., 2012. 3D Terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology. ISPRS J. Photogramm. Remote Sensing, 68, 121–134.
• Buğday, E., 2018. Capabilities of using UAVs in forest road construction activities. Eur J Forest Eng, 4(2): 56-62.
• Chu, T., Guo, X., 2013. Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review. Remote Sensing, 6 (1): 470–520.
• Clapuyt, F., Vanacker, V., Oost, K.V., 2016. Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms. Geomorphology, 260, 4-15.
• 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, 190:28.
• Eker, R., Bühler, Y., Schlögl, S., Stoffel, A., Aydın, A., 2019. Monitoring of snow cover ablation using very high spatial resolution remote sensing datasets. Remote Sensing, 11, 699.
• Ghulam, A., 2014. Monitoring Tropical Forest Degradation in Betampona Nature Reserve, Madagascar Using Multisource Remote Sensing Data Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (12): 4960–4971.
• Gümüşkaya, İ., 1978. The role of forest depots in marketing and applications in Turkey (original in Turkish). PhD Thesis, İstanbul University, Institute of Natural and Applied Sciences.
• 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, 1–61.
• 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 (2016): 82–95.
• Kamlun, K.U., Arndt, R.B., Phua, M.H., 2016. Monitoring Deforestation in Malaysia between 1985 and 2013: Insight from South-Western Sabah and Its Protected Peat Swamp Area. Land Use Policy, 57 (2016): 418–430.
• Kantay, R., Köse, C., 2009. Forest enterprise depots and storage techniques (original in Turkish). Journal of the Faculty of Forestry Istanbul University, B59(1):75-92.
• Kumar, P., Pandey, P.C., Singh, B.K., Katiyar, S., Mandal, V.P., Rani, M., Tomar, V., Patairiya, S., 2016. Estimation of Accumulated Soil Organic Carbon Stock in Tropical Forest Using Geospatial Strategy. The Egyptian Journal of Remote Sensing and Space Sciences, 19:109–123.
• Lucieer, A., de Jong, S.M., 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.
• Matese, A., 2020. Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”. Forests, 11, 406.
• 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.
• Selmi, E., 2009. Investigations on vertebrata fauna of Düzce-Efteni Lake (original in Turkish). PhD Thesis. Istanbul University, Institute of Natural and Applied Sciences, pp.165.
• Shervais, K., 2015. Structure from Motion, Introductory Guide. Retrieved July 27, 2016, from https://www.unavco.org/education/resources/educational-resources/lesson/field-geodesy/module-materials/sfm-intro-guide.pdf
• Snavely, N, Seitz, SM, Szeliski, R., 2008. Modeling the world from internet photo collections. International Journal of Computer Vision, 80(12): 189–210.
• Srivastava, P.K., Mehta, A., Gupta, M., Singh, S.K., Islam, T., 2015. Assessing Impact of Climate Change on Mundra Mangrove Forest Ecosystem, Gulf of Kutch, Western Coast of India: A Synergistic Evaluation Using Remote Sensing. Theoretical and Applied Climatology 120 (3–4):685–700.
• Steinaker, D.F., Jobb, 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 (2016): 19–24.
• Tang, L., Shao, G., 2015. Drone remote sensing for forestry research and practices. J. For. Res., 26(4):791–797.
• Torresan, C., Corona, P., Scrinzi, G., Vall Marsal, J., 2016. Using Classification Trees to Predict Forest Structure Types from Lidar Data. Annals of Forest Research, 59 (1): 1–18.
• Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S.F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zaldei, A., Wallace, L., 2017. Forestry applications of UAVs in Europe: a review, International Journal of Remote Sensing, 38(8-10): 2427-2447.
• Turk, Y., Boz, F., Aydın, A., Eker, R., 2019a. Evaluation of UAV usage possibility in determining the forest road pavement degradation: preliminary results. 3rd International Engineering Research Symposium (INERS’19), 05-07 September 2019, Düzce Turkey, pp. 630-633.
• Turk, Y., Aydın, A., Eker, R., 2019b. Effectiveness of open-top culverts in forest road deformations: preliminary results from a forest road section, Düzce-Turkey. 2nd International Symposium of Forest Engineering and Technologies, 04-06 September 2019, Tirana-Albenia, pp. 147-152.
• 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 Pakistan. Journal of Mountain Science, 13 (7): 1229–1237.
• Ullman, S., 1979. The interpretation of structure from motion. Proc. R. Soc. London, Ser. B, 203: 405–426, doi:10.1098/rspb.1979.0006.
• Vacca, G., Dessi, A., Sacco, A. 2017. The use of nadir and oblique UAV images for building knowledge. ISPRS Int. J. Geo-Inf., 6:393.
• Yao, H., Qin, R., Chen, X., 2019. Unmanned aerial vehicle for remote sensing applications-a review. Remote Sensing,11:1443.
• Zhang, J., Hu, J., Lian, J., Fan, Z., Ouyang, X., Ye, W., 2016. Seeing the forest from drones: testing the potential of light-weight drones as a tool for long-term forest monitoring. Biological Conservation, 198:60–69.