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Yersel Lidar Verisinden 3DFin Yazılımı ile Ağaçların Göğüs Çapının Belirlenmesi

Yıl 2024, Cilt: 20 Sayı: 2, 395 - 407, 28.12.2024
https://doi.org/10.58816/duzceod.1593528

Öz

Göğüs çapı, orman envanterinde ölçülen en yaygın ve en önemli meşcere parametrelerinden birisidir. Orman envanterinde örnek alan içerisindeki tüm ağaçların ölçümü gereklidir. Zor arazi koşullarında envanterin en basit bu işlemi bile zor hale gelmektedir. Bu nedenle gelişen teknolojinin takip edilmesi ve orman envanterine entegrasyonu önemlidir. Yersel lidar tarama ile sağlanan üç boyutlu nokta bulutu verilerinden çeşitli ölçümler yapmak ve göğüs çapı gibi ağaç veya meşcereye ait bazı parametreleri bu verilerden sağlamak mümkün hale gelmektedir. Bu çalışmada Düzce Üniversitesi Yerleşkesi’nde yer alan meşe meşceresinden alınan örnek alan içerisine giren ağaçların çapları önce klasik yöntemle çapölçer vasıtasıyla ölçülmüş ve bulunan değerler daha sonra lidar verisinden hesaplanmış değerlerle karşılaştırılmıştır. Lidar verisinde tek ağaç bazında manuel ölçümler kolayca yapılabilmekle birlikte örnek alan bazındaki çalışmalarda süreci otomatik hale getiren araçlar kullanmak verimliliği artırmaktadır. Bu çalışmada da nokta bulutundan ağaçların göğüs çaplarının belirlenmesi 3DFin yazılımı kullanılarak gerçekleştirilmiştir. Yapılan analizler sonucunda çap ölçer ve lidar ölçümleri arasında %95 güven düzeyinde anlamlı farklar bulunmadığı doğrulanmıştır.

Kaynakça

  • Bazezew, M., Hussin, Y., Kloosterman, E., Ismail, M., Soromessa, T., and Adan, M. (2021). Factual approach for tropical forest parameters measurement and monitoring: future option with a focus on synergetic use of airborne and terrestrial lidar technologies. International Journal of Remote Sensing, 42(9), 3219-3230.
  • Burt, A., Vicari, M., Costa, A., Coughlin, I., Meir, P., Rowland, L., and Disney, M. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science, 8(2). https://doi.org/10.1098/rsos.201458
  • Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., and Kaasalainen, M. (2014). Nondestructive estimates of above‐ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6(2), 198-208.
  • Chang, A., Jung, J., & Kim, Y. (2015). Estimation of forest stand diameter class using airborne lidar and field data. Remote Sensing Letters, 6(6), 419-428.
  • CloudCompare. (2024). CloudCompare (Sürüm V2.13.1) [GPL lisanslı bilgisayar yazılımı]. https://www.cloudcompare.org
  • Dassot, M., Constant, T., & Fournier, M. (2011). The use of terrestrial lidar technology in forest science: application fields, benefits and challenges. Annals of Forest Science, 68(5), 959-974.
  • Delagrange, S., Jauvin, C., & Rochon, P. (2014). Pypetree: a tool for reconstructing tree perennial tissues from point clouds. Sensors, 14(3), 4271-4289.
  • Laino, D., Cabo, C., Prendes, C., Janvier, R., Ordonez, C., Nikonovas, T., Doerr, S., & Santin, C. (2024). 3DFin: A software for automated 3D forest inventories from terrestrial point clouds. Forestry: An International Journal of Forest Research, 97(4), 479–496.
  • Feng, B., Nie, S., Wang, C., Xi, X., Wang, J., Zhou, G. and Wang, H. (2022). Exploring the potential of uav lidar data for trunk point extraction and direct dbh measurement. Remote Sensing, 14(12), 2753.
  • Fitts, L., Russell, M., Domke, G., & Knight, J. (2020). Modeling land use change and forest carbon stock changes in temperate forests in the United States. Carbon Balance Management, 16(20).
  • Hui, Z. (2024). A reliable dbh estimation method using terrestrial lidar points through polar coordinate transformation and progressive outlier removal. Forests, 15(6), 1031.
  • Ige, P., Akinyemi, G., and Smith, A. (2013). Nonlinear growth functions for modeling tree height–diameter relationships forgmelina arborea(roxb.) in south-west nigeria. Forest Science and Technology, 9(1), 20-24.
  • Ivanova, N., Fomin, V., Kusbach, A. (2022). Experience of Forest Ecological Classification in Assessment of Vegetation Dynamics. Sustainability, 14(6), 1-11.
  • Kuo, K., Itakura, K., & Hosoi, F. (2019). Leaf segmentation based on k-means algorithm to obtain leaf angle distribution using terrestrial lidar. Remote Sensing, 11(21), 2536.
  • Özçelik, R., Brooks, J., Diamantopoulou, M., & Wiant, H. (2009). Estimating breast height diameter and volume from stump diameter for three economically important species in Turkey. Scandinavian Journal of Forest Research, 25(1), 32-45.
  • Räty, J., Hietala, A., Breidenbach, J., & Astrup, R. (2023). An analysis of stand-level size distributions of decay-affected norway spruce trees based on harvester data. Annals of Forest Science, 80(1).
  • Ravaglia, J., Fournier, R., Bac, A., Vega, C., Côté, J., Piboule, A., Rémillard, U. (2019). Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data. Forests, 10(7), 599.
  • Straka, T. and Layton, P. (2010). Natural resources management: life cycle assessment and forest certification and sustainability issues. Sustainability, 2(2), 604-623.
  • Vatandaşlar, C., Zeybek, M. (2020). Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey. Turkish Journal of Agriculture and Forestry, 44( 3), 229-242.
  • Wang, D., Takoudjou, S., & Casella, E. (2020). Lewos: a universal leaf‐wood classification method to facilitate the 3d modelling of large tropical trees using terrestrial lidar. Methods in Ecology and Evolution, 11(3), 376-389.
  • Wieser, M., Mandlburger, G., Hollaus, M., Otepka, J., Glira, P., & Pfeifer, N. (2017). A case study of uas borne laser scanning for measurement of tree stem diameter. Remote Sensing, 9(11), 1154.
  • Zhao, K., Garcı́a, M., Liu, S., Guo, Q., Chen, G., Zhang, X., and Meng, X. (2015). Terrestrial lidar remote sensing of forests: maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution. Agricultural and Forest Meteorology, 209-210, 100-113.

Determination of Diameter at Breast Height of Trees from Terrestrial Lidar Data with 3DFin Software

Yıl 2024, Cilt: 20 Sayı: 2, 395 - 407, 28.12.2024
https://doi.org/10.58816/duzceod.1593528

Öz

Diameter at breast height is one of the most common and important stand parameters measured in forest inventory. Forest inventory requires the measurement of all trees within the sample area. In difficult terrain conditions, even this simple process of inventory becomes difficult. Therefore, it is important to follow the developing technology and integrate it into forest inventory. It is becoming possible to make various measurements from 3D point cloud data provided by terrestrial lidar scanning and to provide some parameters of the tree or stand such as diameter at breast height from these data. In this study, tree diameters within a sample area taken from an oak stand were calculated from the lidar data and compared with the values measured with a conventional compass. While manual measurements can be easily made on a single tree basis in lidar data, using tools that automate the process in sample area-based studies increases efficiency. For this reason, the diameter at breast height of the trees was measured from the point cloud using 3DFin software. As a result of the analysis, it was found that there were no significant differences between compass and lidar measurements at 95% confidence level.

Kaynakça

  • Bazezew, M., Hussin, Y., Kloosterman, E., Ismail, M., Soromessa, T., and Adan, M. (2021). Factual approach for tropical forest parameters measurement and monitoring: future option with a focus on synergetic use of airborne and terrestrial lidar technologies. International Journal of Remote Sensing, 42(9), 3219-3230.
  • Burt, A., Vicari, M., Costa, A., Coughlin, I., Meir, P., Rowland, L., and Disney, M. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science, 8(2). https://doi.org/10.1098/rsos.201458
  • Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., and Kaasalainen, M. (2014). Nondestructive estimates of above‐ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6(2), 198-208.
  • Chang, A., Jung, J., & Kim, Y. (2015). Estimation of forest stand diameter class using airborne lidar and field data. Remote Sensing Letters, 6(6), 419-428.
  • CloudCompare. (2024). CloudCompare (Sürüm V2.13.1) [GPL lisanslı bilgisayar yazılımı]. https://www.cloudcompare.org
  • Dassot, M., Constant, T., & Fournier, M. (2011). The use of terrestrial lidar technology in forest science: application fields, benefits and challenges. Annals of Forest Science, 68(5), 959-974.
  • Delagrange, S., Jauvin, C., & Rochon, P. (2014). Pypetree: a tool for reconstructing tree perennial tissues from point clouds. Sensors, 14(3), 4271-4289.
  • Laino, D., Cabo, C., Prendes, C., Janvier, R., Ordonez, C., Nikonovas, T., Doerr, S., & Santin, C. (2024). 3DFin: A software for automated 3D forest inventories from terrestrial point clouds. Forestry: An International Journal of Forest Research, 97(4), 479–496.
  • Feng, B., Nie, S., Wang, C., Xi, X., Wang, J., Zhou, G. and Wang, H. (2022). Exploring the potential of uav lidar data for trunk point extraction and direct dbh measurement. Remote Sensing, 14(12), 2753.
  • Fitts, L., Russell, M., Domke, G., & Knight, J. (2020). Modeling land use change and forest carbon stock changes in temperate forests in the United States. Carbon Balance Management, 16(20).
  • Hui, Z. (2024). A reliable dbh estimation method using terrestrial lidar points through polar coordinate transformation and progressive outlier removal. Forests, 15(6), 1031.
  • Ige, P., Akinyemi, G., and Smith, A. (2013). Nonlinear growth functions for modeling tree height–diameter relationships forgmelina arborea(roxb.) in south-west nigeria. Forest Science and Technology, 9(1), 20-24.
  • Ivanova, N., Fomin, V., Kusbach, A. (2022). Experience of Forest Ecological Classification in Assessment of Vegetation Dynamics. Sustainability, 14(6), 1-11.
  • Kuo, K., Itakura, K., & Hosoi, F. (2019). Leaf segmentation based on k-means algorithm to obtain leaf angle distribution using terrestrial lidar. Remote Sensing, 11(21), 2536.
  • Özçelik, R., Brooks, J., Diamantopoulou, M., & Wiant, H. (2009). Estimating breast height diameter and volume from stump diameter for three economically important species in Turkey. Scandinavian Journal of Forest Research, 25(1), 32-45.
  • Räty, J., Hietala, A., Breidenbach, J., & Astrup, R. (2023). An analysis of stand-level size distributions of decay-affected norway spruce trees based on harvester data. Annals of Forest Science, 80(1).
  • Ravaglia, J., Fournier, R., Bac, A., Vega, C., Côté, J., Piboule, A., Rémillard, U. (2019). Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data. Forests, 10(7), 599.
  • Straka, T. and Layton, P. (2010). Natural resources management: life cycle assessment and forest certification and sustainability issues. Sustainability, 2(2), 604-623.
  • Vatandaşlar, C., Zeybek, M. (2020). Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey. Turkish Journal of Agriculture and Forestry, 44( 3), 229-242.
  • Wang, D., Takoudjou, S., & Casella, E. (2020). Lewos: a universal leaf‐wood classification method to facilitate the 3d modelling of large tropical trees using terrestrial lidar. Methods in Ecology and Evolution, 11(3), 376-389.
  • Wieser, M., Mandlburger, G., Hollaus, M., Otepka, J., Glira, P., & Pfeifer, N. (2017). A case study of uas borne laser scanning for measurement of tree stem diameter. Remote Sensing, 9(11), 1154.
  • Zhao, K., Garcı́a, M., Liu, S., Guo, Q., Chen, G., Zhang, X., and Meng, X. (2015). Terrestrial lidar remote sensing of forests: maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution. Agricultural and Forest Meteorology, 209-210, 100-113.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Orman Biyometrisi
Bölüm Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi 20(2)
Yazarlar

Hayati Zengin 0000-0002-6679-0063

Yayımlanma Tarihi 28 Aralık 2024
Gönderilme Tarihi 29 Kasım 2024
Kabul Tarihi 12 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 20 Sayı: 2

Kaynak Göster

APA Zengin, H. (2024). Yersel Lidar Verisinden 3DFin Yazılımı ile Ağaçların Göğüs Çapının Belirlenmesi. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, 20(2), 395-407. https://doi.org/10.58816/duzceod.1593528

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