Single tree-level aboveground biomass estimation using mobile LİDAR in scots pine (Pinus sylvestris L.) stands

Öz

Forest ecosystems play a pivotal role in balancing the global carbon cycle by storing a substantial proportion of terrestrial organic carbon stocks; therefore, aboveground biomass (AGB) measurements are of critical importance for climate policy development and sustainable forest management. This study assesses the accuracy of AGB estimates derived from GeoSLAM ZEB-Horizon mobile LiDAR (Light Detection and Ranging) system data in pure Scots pine (Pinus sylvestris L.) stands in the Bolu region, through a comparative evaluation with terrestrial inventory measurements. Across 23 sample plots, a total of 443 trees were measured. Terrestrial diameter at breast height data were applied to the allometric models of Çömez (2010) and Muukkonen (2007) to obtain reference AGB values. Mobile LiDAR point clouds underwent pre-processing, including ground classification, height normalization, and CSP (Comparative Shortest Path) based segmentation, to isolate individual tree stems. During the mobile LiDAR survey, individual tree matching was performed by utilizing the temporal trajectory information based on the tree numbering sequence, enabling successful matching between field-measured trees and trees in the point cloud. For each tree, LiDAR-based DBH was extracted through a circle-fitting procedure at 1.30 m height, and AGB were generated using the same allometric equations. The performance indicators of the mobile LiDAR-based estimates were notably high (Çömez: MAE: 4.20 kg, RMSE: 48.72 kg, R²: 0.9930; Muukkonen: MAE: 2.79 kg, RMSE: 31.75 kg, R²: 0.9931), indicating that in homogeneous Scots pine stands, mobile LiDAR achieves accuracy comparable to that of terrestrial inventory while providing significant time efficiency. The study recommends the development of national protocol standards for operationalizing mobile LiDAR applications, integration of multi-sensor fusion approaches, comprehensive validation across diverse forest types, and enhanced operator training.

Anahtar Kelimeler

• LiDAR
• Point cloud
• Forest inventory
• SLAM
• AGB

Mobil LiDAR ile sarıçam (Pinus sylvestris L.) meşcerelerinde tek ağaç düzeyinde toprak üstü biyokütle tahmini

Öz

Orman ekosistemleri, karasal organik karbon stoklarının büyük bölümünü barındırarak küresel karbon döngüsünün dengelenmesinde önemli bir rol oynar; bu nedenle toprak üstü biyokütle ölçümleri iklim politikaları ve sürdürülebilir orman yönetimi için kritik öneme sahiptir. Bu çalışma, Bolu yöresindeki saf sarıçam (Pinus sylvestris L.) meşcerelerinde GeoSLAM ZEB-Horizon mobil LiDAR (Light Detection and Ranging) sistemi verilerinin toprak üstü biyokütle tahminlerindeki doğruluğunu yersel envanterle karşılaştırmalı olarak değerlendirmektedir. 23 adet örnek alanda toplam 443 ağaç ölçülmüş; yersel göğüs çapı verileri Çömez (2010) ve Muukkonen (2007) allometrik modellerine uygulanarak referans toprak üstü biyokütle değerleri elde edilmiştir. Mobil LiDAR nokta bulutları ön işlemden geçirilmiş, zemin sınıflaması, yüksekliğe göre normalizasyon ve CSP (Comparative Shortest Path) tabanlı segmentasyon uygulanarak bireysel ağaç gövdeleri ayrıştırılmıştır. Mobil LiDAR taraması sırasında ağaç numaralandırma sırasına dayalı zamansal rota bilgisinden yararlanılarak tek ağaç eşleştirmesi yapılmış ve böylece arazide ölçülen ağaçlar ile nokta bulutu üzerindeki ağaçlar başarıyla eşleştirilmiştir. Her ağaca ilişkin 1.30 m yükseklikte daire uydurma (circle-fitting) işlemi ile LiDAR-tabanlı göğüs çapları çıkarılmış ve aynı allometrik denklemler kullanılarak toprak üstü biyokütle tahminleri oluşturulmuştur. Mobil LiDAR tabanlı tahminlerin performans göstergeleri oldukça yüksek olup (Çömez: Ortalama Mutlak Hata: 4.20 kg, Kök Ortalama Kare Hatası: 48.72 kg, R²: 0.9930; Muukkonen: Ortalama Mutlak Hata: 2.79 kg, Kök Ortalama Kare Hatası: 31.75 kg, R²: 0.9931), homojen sarıçam meşcerelerinde mobil LiDAR’ın yersel envantere eşdeğer doğruluk ve belirgin zaman-verimliliği sağladığı sonucuna varılmıştır. Çalışma, mobil LiDAR uygulamalarının operasyonelleştirilmesi için ulusal protokol standartlarının oluşturulmasını, çoklu sensör füzyonunu, farklı orman tiplerinde kapsamlı doğrulama çalışmalarını ve operatör eğitiminin güçlendirilmesini önermektedir.

Anahtar Kelimeler

• LiDAR
• Nokta bulutu
• Orman envanteri
• SLAM
• TÜB

Kaynakça

1. Aijazi, A., Checchin, P., Malaterre, L., Trassoudaine, L., 2017. Automatic detection and parameter estimation of trees for forest inventory applications using 3D Terrestrial LIDAR. Remote Sensing, 9(9): 946. https://doi.org/10.3390/rs9090946
2. Akgül, M., Yurtseven, H., Akburak, S., Çoban, S., 2016. Yersel lazer tarayıcı sistemler ile kentsel yeşil alanlarda bazı ağaç parametrelerinin belirlenmesi. İstanbul Üniversitesi Orman Fakültesi Dergisi, 66(2): 445-458. https://doi.org/ 10.17099/jffiu.96359
3. Alon, A.S., 2020. Tree extraction of airborne LiDAR data based on coordinates of deep learning object detection from orthophoto over complex mangrove forest. International Journal of Emerging Trends in Engineering Research, 8(5): 2107–2111. https://doi.org/10.30534/ijeter/2020/103852020
4. Alvarez, E., Duque, A., Saldarriaga, J., Cabrera, K., De Las Salas, G., Del Valle, I., Lema, A., Moreno, F., Orrego, S., Rodríguez, L., 2012. Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia. Forest Ecology and Management, 267: 297–308. https://doi.org/ 10.1016/j.foreco.2011.12.013
5. Asner, G.P., Powell, G.V.N., Mascaro, J., Knapp, D.E., Clark, J.K., Jacobson, J., Kennedy-Bowdoin, T., Balaji, A., Paez-Acosta, G., Victoria, E., Secada, L., Valqui, M., Hughes, R.F., 2010. High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Sciences, 107(38): 16738–16742. https://doi.org/10.1073/pnas. 1004875107 Bahadır, M., Yıldırım, F.S., Karslı, F., 2022. Görüntü eşleşti̇rme ve li̇dar kaynakli nokta bulutundan orman metri̇kleri̇ni̇n otomati̇k beli̇rlenmesi̇. UZALCBS 2022 Sempozyumu. 17-19 Kasım, Ankara, s. 12831. https://doi.org/ 10.15659/uzalcbs2022.12831
6. Beland, M., Parker, G., Sparrow, B., Harding, D., Chasmer, L., Phinn, S., Antonarakis, A., Strahler, A., 2019. On promoting the use of lidar systems in forest ecosystem research. Forest Ecology and Management, 450: 117484. https://doi.org/10.1016/ j.foreco.2019.117484
7. Brede, B., Lau, A., Bartholomeus, H., Kooistra, L., 2017. Comparing RIEGL RiCOPTER UAV LiDAR derived canopy height and DBH with terrestrial LiDAR. Sensors, 17(10): 2371. https://doi.org/10.3390/s17102371
8. Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L.P., Chave, J., Danson, F.M., Demol, M., Disney, M., Gaulton, R., Moorthy, S.M.K., Levick, S.R., Saarinen, N., Schaaf, C., Stovall, A., Terryn, L., Wilkes, P., Verbeeck, H., 2020. Terrestrial laser scanning in forest ecology: Expanding the horizon. Remote Sensing of Environment, 251: 112102. https://doi.org/10.1016/j.rse.2020.112102

9. Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., Culvenor, D., Avitabile, V., Disney, M., Armston, J., Kaasalainen, M., 2015. Nondestructive estimates of above‐ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution, 6(2): 198–208. https://doi.org/ 10.1111/2041-210x.12301
10. Chen, Q., Luo, H., Cheng, Y., Xie, M., Nan, D., 2024. An individual tree detection and segmentation method from TLS and MLS point clouds based on improved seed points. Forests, 15(7): 1083. https://doi.org/10.3390/f15071083
11. Corbera, E., Schroeder, H., 2011. Governing and implementing REDD+. Environmental Science & Policy, 14(2): 89–99. https://doi.org/10.1016/j.envsci.2010.11.002
12. Çakmak, Z., Akpınar, B., Selbesoğlu, M., 2022. Accuracy assessment of mobile lidar system in different environments. Afyon Kocatepe University Journal of Sciences and Engineering, 22(5): 1075-1086. https://doi.org/10.35414/ akufemubid.1139569
13. Çömez, A., 2010. Sündiken Dağlarında sarıçam (Pinus sylvestris L.) meşcerelerinde karbon birikiminin belirlenmesi. Doktora tezi, İstanbul Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
14. Dou, Y., Yang, Y., An, S., 2019. Above-ground biomass models of Caragana korshinskii and Sophora viciifolia in the Loess Plateau, China. Sustainability, 11(6): 1674. https://doi.org/ 10.3390/su11061674
15. Duncanson, L., Neuenschwander, A., Hancock, S., Thomas, N., Fatoyinbo, T., Simard, M., Silva, C.A., Armston, J., Luthcke, S. B., Hofton, M., Kellner, J.R., Dubayah, R., 2020. Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California. Remote Sensing of Environment, 242: 111779. https://doi.org/ 10.1016/j.rse.2020.111779
16. Durkaya, B., Durkaya, A., Makineci, E., Ülküdür, M., 2013. Estimation of above-ground biomass and sequestered carbon of Taurus Cedar (Cedrus libani L.) in Antalya, Turkey. iForest - Biogeosciences and Forestry, 6(5): 278–284. https://doi.org/ 10.3832/ifor0899-006
17. Ester, M., Kriegel, H. P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 2-4 Ağustos, Portland, s. 226-231.
18. FARO, 2025. Technical specifications sheet for ZEB Horizon, https://knowledge.faro.com/Hardware/GeoSlam/ZEB_Horizon_and_Horizon_RT/Technical_Specifications_Sheet_for_ZEB_Horizon, Erişim: 16.09.2025. Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H., Hurtt, G.C., 2009. Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research Atmospheres, 114 G00E02. https://doi.org/10.1029/2008jg000911
19. 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
20. Green Valley International, 2023. LiDAR360 User Guide, GreenValley International Ltd., https://www.greenvalleyintl. com/gvi/web/us/file/EN-User-Guide-LiDAR360.pdf, Erişim: 27.06.2025.
21. Guan, H., Su, Y., Sun, X., Xu, G., Li, W., Ma, Q., Wu, X., Wu, J., Liu, L., Guo, Q., 2020. A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments. ISPRS Journal of Photogrammetry and Remote Sensing, 166: 82–94. https://doi.org/10.1016/j.isprsjprs.2020.06.002
22. Guenther, M., Heenkenda, M.K., Morris, D., Leblon, B., 2024. Tree diameter at breast Height (DBH) estimation using an IPad Pro LiDAR Scanner: A case study in Boreal Forests, Ontario, Canada. Forests, 15(1): 214. https://doi.org/10.3390/f15010214
23. Hartley, R.J.L., Jayathunga, S., Massam, P.D., De Silva, D., Estarija, H.J., Davidson, S.J., Wuraola, A., Pearse, G.D., 2022. Assessing the potential of backpack-mounted mobile laser scanning systems for tree phenotyping. Remote Sensing, 14(14): 3344. https://doi.org/10.3390/rs14143344
24. Hasheminasab, S.M., Zhou, T., Habib, A., 2023. Linear feature-based image/LIDAR integration for a stockpile monitoring and reporting technology. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 2605–2623. https://doi.org/10.1109/jstars.2023.3250392
25. Heo, H.K., Lee, D.K., Park, J.H., Thorne, J.H., 2019. Estimating the heights and diameters at breast height of trees in an urban park and along a street using mobile LiDAR. Landscape and Ecological Engineering, 15(3): 253–263. https://doi.org/ 10.1007/s11355-019-00379-6
26. Hernández-Moreno, J.A., Velázquez-Martínez, A., Pérez-Salicrup, D.R., Bravo, F., MacFarlane, D.W., Reyes-Hernández, V.J., 2025. Terrestrial laser scanning for estimating the volume and biomass of coniferous stems in the Mariposa Monarca Biosphere Reserve, Mexico. Forests, 16(2): 334. https://doi.org/ 10.3390/f16020334
27. Herold, M., Carter, S., Avitabile, V., Espejo, A.B., Jonckheere, I., Lucas, R., McRoberts, R.E., Næsset, E., Nightingale, J., Petersen, R., Reiche, J., Romijn, E., Rosenqvist, A., Rozendaal, D.M.A., Seifert, F.M., Sanz, M.J., De Sy, V., 2019. The role and need for space-based forest biomass-related measurements in environmental management and policy. Surveys in Geophysics, 40(4): 757–778. https://doi.org/ 10.1007/s10712-019-09510-6
28. Houghton, R.A., Hall, F., Goetz, S.J., 2009. Importance of biomass in the global carbon cycle. Journal of Geophysical Research Atmospheres, 114,G00E03. https://doi.org/10.1029/2009jg000935
29. Hyyppä, E., Kukko, A., Kaijaluoto, R., White, J. C., Wulder, M.A., Pyörälä, J., Liang, X., Yu, X., Wang, Y., Kaartinen, H., Virtanen, J., Hyyppä, J., 2020a. Accurate derivation of stem curve and volume using backpack mobile laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 246–262. https://doi.org/10.1016/j.isprsjprs.2020.01.018
30. Hyyppä, E., Yu, X., Kaartinen, H., Hakala, T., Kukko, A., Vastaranta, M., Hyyppä, J., 2020b. Comparison of backpack, handheld, under-canopy UAV, and above-canopy UAV Laser scanning for field reference data collection in boreal forests. Remote Sensing, 12(20): 3327. https://doi.org/ 10.3390/rs12203327 Kankare, V., Holopainen, M., Vastaranta, M., Puttonen, E., Yu, X., Hyyppä, J., Vaaja, M., Hyyppä, H., Alho, P., 2013. Individual tree biomass estimation using terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 75: 64–75. https://doi.org/10.1016/j.isprsjprs.2012.10.003
31. Ko, C., Lee, S., Yim, J., Kim, D., Kang, J., 2021. Comparison of forest inventory methods at plot-level between a backpack personal laser scanning (BPLS) and conventional equipment in Jeju Island, South Korea. Forests, 12(3): 308. https://doi.org/10.3390/f12030308
32. Leboeuf, A., Riopel, M., Munger, D., Fradette, M., Bégin, J., 2022. Modeling merchantable wood volume using airborne LIDAR metrics and historical forest inventory plots at a provincial scale. Forests, 13(7): 985. https://doi.org/10.3390/f13070985
33. Lefsky, M.A., Cohen, W.B., Harding, D.J., Parker, G.G., Acker, S.A., Gower, S.T., 2002. Lidar remote sensing of above‐ground biomass in three biomes. Global Ecology and Biogeography, 11(5): 393–399. https://doi.org/10.1046/j.1466-822x.2002. 00303.x
34. Liang, X., Kankare, V., Hyyppä, J., Wang, Y., Kukko, A., Haggrén, H., Yu, X., Kaartinen, H., Jaakkola, A., Guan, F., Holopainen, M., Vastaranta, M., 2016. Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 115: 63–77. https://doi.org/10.1016/j.isprsjprs. 2016.01.006
35. Lu, J., Wang, H., Qin, S., Cao, L., Pu, R., Li, G., Sun, J., 2020. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and backpack LiDAR point clouds. International Journal of Applied Earth Observation and Geoinformation, 86: 102014. https://doi.org/10.1016/j.jag.2019.102014
36. Maltamo, M., Karjalainen, T., Repola, J., Vauhkonen, J., 2018. Incorporating tree- and stand-level information on crown base height into multivariate forest management inventories based on airborne laser scanning. Silva Fennica, 52(3) article id 10006. https://doi.org/10.14214/sf.10006
37. Muukkonen, P., 2007. Generalized allometric volume and biomass equations for some tree species in Europe. European Journal of Forest Research, 126(2): 157–166. https://doi.org/10.1007/ s10342-007-0168-4
38. Newnham, G.J., Armston, J.D., Calders, K., Disney, M.I., Lovell, J.L., Schaaf, C.B., Strahler, A.H., Danson, F.M., 2015. Terrestrial laser scanning for plot-scale forest measurement. Current Forestry Reports, 1(4): 239–251. https://doi.org/10.1007/s40725-015-0025-5
39. Oikawa, N., Nakagawa, Y., Owari, T., Tatsumi, S., Suzuki, S.N., 2025. Utilising LiDAR‐equipped iPhone in forestry: Constructing 3D models and measuring tree sizes in a planting site. Ecological Solutions and Evidence, 6(1), e12399.: https://doi.org/10.1002/2688-8319.12399
40. Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J. G., Ciais, P., Jackson, R.B., Pacala, S.W., McGuire, A.D., Piao, S., Rautiainen, A., Sitch, S., Hayes, D., 2011. A large and persistent carbon sink in the world’s forests. Science, 333(6045): 988–993. https://doi.org/10.1126/science.1201609
41. Petersson, H., Holm, S., Ståhl, G., Alger, D., Fridman, J., Lehtonen, A., Lundström, A., Mäkipää, R., 2012. Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass – A comparative study. Forest Ecology and Management, 270: 78–84. https://doi.org/ 10.1016/j.foreco.2012.01.004
42. Prasetyo, W.E., Handayani, H.H., Raharjo, A.B., Saptarini, D., 2024. Advancing carbon stock estimation and 3D tree modeling: harnessing the potential of low-cost backpack LIDAR technology. IOP Conference Series Earth and Environmental Science, 1406(1): 012013. https://doi.org/10.1088/1755-1315/1406/1/012013 Riano, D., Meier, E., Allgöwer, B., Chuvieco, E., Ustin, S. L., 2003. Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote sensing of Environment, 86(2), 177-186. https://doi.org/10.1016/S0034-4257(03)00098-1
43. Ruhan, A., Du, W., Ying, H., Wei, B., Shan, Y., Dai, H., 2023. Estimation of aboveground biomass of individual trees by backpack LIDAR based on parameter-optimized quantitative structural models (ADQSM). Forests, 14(3): 475. https://doi.org/10.3390/f14030475
44. Seidel, D., Beyer, F., Hertel, D., Fleck, S., Leuschner, C., 2011. 3D-laser scanning: A non-destructive method for studying above- ground biomass and growth of juvenile trees. Agricultural and Forest Meteorology, 151(10): 1305–1311. https://doi.org/ 10.1016/j.agrformet.2011.05.013
45. Shao, H., Cao, Z., Li, W., Chen, Y., Jiang, C., Hyyppa, J., Chen, J., Sun, L., 2022. Feasibility study of wood-leaf separation based on hyperspectral LiDAR technology in indoor circumstances. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 729–738. https://doi.org/ 10.1109/jstars.2021.3134651
46. Su, Y., Guo, Q., Jin, S., Guan, H., Sun, X., Ma, Q., Hu, T., Wang, R., Li, Y., 2020. The development and evaluation of a backpack LiDAR system for accurate and efficient forest inventory. IEEE Geoscience and Remote Sensing Letters, 18(9): 1660–1664. https://doi.org/10.1109/lgrs.2020.3005166
47. Tao, S., Wu, F., Guo, Q., Wang, Y., Li, W., Xue, B., Hu, X., Li, P., Tian, D., Li, C., Yao, H., Li, Y., Xu, G., Fang, J., 2015. Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories. ISPRS Journal of Photogrammetry and Remote Sensing, 110: 66–76. https://doi.org/10.1016/j.isprsjprs.2015.10.007
48. Tian, L., Wu, X., Tao, Y., Li, M., Qian, C., Liao, L., Fu, W., 2023. Review of remote sensing-based methods for forest aboveground biomass estimation: Progress, challenges, and prospects. Forests, 14(6): 1086. https://doi.org/ 10.3390/f14061086
49. Vandendaele, B., Martin-Ducup, O., Fournier, R.A., Pelletier, G., Lejeune, P., 2022. Mobile laser scanning for estimating tree structural attributes in a temperate hardwood forest. Remote Sensing, 14(18): 4522. https://doi.org/10.3390/rs14184522
50. 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.
51. Vatandaşlar, C., Zeybek, M., 2021. Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey. Measurement, 177: 109328. https://doi.org/10.1016/j.measurement.2021.109328
52. Vatandaşlar, C., Zeybek, M., Çankaya, E.Ç., Demi̇raslan, T., Şahi̇n, C., Gündüz, Y., Korkmaz, Ü., Avci, M.L., 2022a. El tipi mobil LiDAR teknolojisinin orman envanterlerinde kullanımı: Artvin-Şavşat örneği. Ormancılık Araştırma Dergisi, 9(1): 81–96. https://doi.org/10.17568/ogmoad.1016879
53. Vatandaşlar, C., Zeybek, Borucu, S. 2022b. Mobil LiDAR ile orman envanterlerinde farklı örnekleme tasarımlarının veri hassasiyeti ve iş verimliliğine etkisi: Rize Şenyuva örneği. Bartın Orman Fakültesi Dergisi, 24(2): 258–271. https://doi.org/10.24011/ barofd.1070484
54. Wilkes, P., Lau, A., Disney, M., Calders, K., Burt, A., De Tanago, J. G., Bartholomeus, H., Brede, B., Herold, M., 2017. Data acquisition considerations for Terrestrial Laser Scanning of forest plots. Remote Sensing of Environment, 196: 140–153. https://doi.org/10.1016/j.rse.2017.04.030
55. Xie, Y., Zhang, J., Chen, X., Pang, S., Zeng, H., Shen, Z., 2020. Accuracy assessment and error analysis for diameter at breast height measurement of trees obtained using a novel backpack LiDAR system. Forest Ecosystems, 7 (33), 1-11. https://doi.org/ 10.1186/s40663-020-00237-0
56. Yang, Z., Liu, Q., Luo, P., Ye, Q., Duan, G., Sharma, R.P., Zhang, H., Wang, G., Fu, L., 2020. Prediction of individual tree diameter and height to crown base using nonlinear simultaneous regression and airborne LIDAR data. Remote Sensing, 12(14): 2238. https://doi.org/10.3390/rs12142238
57. 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
58. Zolkos, S., Goetz, S., Dubayah, R., 2013. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment, 128: 289–298. https://doi.org/10.1016/j.rse.2012.10.017