Biomass; Forest; Geometric Feature; Leaf-Wood Separation; Segmentation; Tree Information Modeling; Circle fitting; Geometric feature; Information Modeling; Leaf-wood separation; Stem-volume; Tree information modeling; Tree stems; Wood separations; Information Systems; Geography, Planning and Development
Résumé :
[en] Developing a precise tree stem curve and robust estimation of stem volume are crucial for forest inventories with various applications. Laser scanned point clouds have been recognized as the most practical data for tree information modeling. Many methods for stem curve development involve estimating stem diameters at different heights and determining stem volume by utilizing fitted cylinders based on these diameters and the associated heights. The estimation of diameter depends on circle fitting. However, many circle fitting methods are non-robust and inaccurate in the presence of noise, outliers, and significant data gaps, resulting in faulty diameters and imprecise stem volume. Limited scanning, occlusions from the physical complexity, high tree density, and adjacent branches may cause data incompleteness, and generate outliers. To address these challenges, we employ robust statistical approaches to restrain the influence of outliers and data gaps. This paper contributes by (i) exploring the problems of robust diameter estimation for partial data, and in the presence of noise and outliers, (ii) understanding the impacts of using erroneous diameters in cylinder fitting, and later for stem curve and volume estimation, and (iii) developing a robust method that couples robust circle and cylinder fittings to derive precise stem curve and estimation of stem volume in the presence of outliers and partial data. We demonstrate the performance of the proposed algorithm through terrestrial laser scanning point clouds.
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
NURUNNABI, Abdul Awal Md ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Teferle, Felicia ; Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg
Novo, Ana; Forest Research Centre of Lourizán, Xunta de Galicia, Pontevedra, Spain
Balado, Jesús ; CINTECX, GeoTECH Group, University of Vigo, Vigo, Spain
Ientilucci, Emmett; Chester F. Carlson Center for Imaging Science, Rochester Inst. of Technology, Rochester, United States
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Derivation of Tree Stem Curve and Volume Using Point Clouds
Date de publication/diffusion :
27 juin 2024
Nom de la manifestation :
19th 3D GeoInfo Conference 2024, 1–3 July 2024, Vigo, Spain
Date de la manifestation :
01-07-2024 => 03-07-2024
Manifestation à portée :
International
Titre du périodique :
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
ISSN :
1682-1750
Maison d'édition :
International Society for Photogrammetry and Remote Sensing
Abegg, M., Ruedi Bösch, R., Kükenbrink, D., Morsdorf, F., 2023. Tree volume estimation with terrestrial laser scanning — Testing for bias in a 3D virtual environment. Agric For Meteorol., 331, 109348, 1–15.
Åkerblom, M., Raumonen, P., Kaasalainen, M., Casella, E., 2015. Analysis of geometric primitives in quantitative structure models of tree stems. Remote Sens., 7(4), 4581–4603.
Akossou, A. Y., Arzouma, S., Attakpa, E. Y., Fonton, N. H., Kokou, K., 2013. Scaling of teak (Tectona grandis) logs by the xylometer technique: accuracy of volume equations and influence of the log length. Diversity, 5(1), 99–113.
Akpo, H.A., Atindogbé, G., Obiakara, M.C., Adjinanoukon, A.B., Gbedolo, M, Fonton, N.H., 2021. Accuracy of common stem volume formulae using terrestrial photogrammetric point clouds: a case study with savanna trees in Benin. J. For. Res., 32, 2415–2422.
Al-Sharadqah, A., Chernov, N., 2009. Error analysis for circle fitting algorithms. Electron. J. Stat., 3, 886–911.
Bienert, A., Hess, C., Maas, H.G., Von Oheimb, G., 2014. A voxel-based technique to estimate the volume of trees from terrestrial laser scanner data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 40, 101–110.
Cabo, C., Del Pozo, S., Rodríguez-Gonzálvez, P., Ordóñez, C., González-Aguilera, D., 2018. Comparing terrestrial laser scanning and wearable laser scanning for individual tree modeling at plot level. Remote Sens., 10(4), 540.
Duda, R. O., Hart, P. E., 1972. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1), 11,15.6.
Ester, M., Kriegel, H. P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, 96(34), 226–231.
Fischler, M. A., Bolles, R. C., 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6), 381–395.
Gander, W., Golub, G. H., Strebel, R., 1994. Least-squares fitting of circles and ellipses. BIT Numer. Math., 34, 558-578.
Hyyppä, E., Kukko, A., Kaijaluoto, R., White, J.C., Wulder, M.A., Pyörälä, J., Liang, X., et al., 2020. Accurate derivation of stem curve and volume using backpack mobile laser scanning. ISPRS J Photogramm and Remote Sens., 161, 246–262.
Kawasaki, H., Masuda, H., 2022. Accurate Calculation of Tree STEM Traits in Forests by Local Correction of Point Cloud Registration. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 43, 209–214.
Kelly, J. F., Beltz, R. C., 1987. A comparison of tree volume estimation models for forest inventory. United States Department of Agriculture, Forest Service, Southern Forest Experiment Station, 233.
Kozak, A., 2004. My last words on taper equations. For. Chron., 80(4), 507–515.
Leão, F. M., Nascimento, R. M., Emmert, F., Santos, G. G. A., Caldeira, N. A. M., et al., 2021. How many trees are necessary to fit an accurate volume model for the Amazon Forest? A site-dependent analysis. For. Ecol. Manag., 480, 118652.
Liang, X., Hyyppä, J., Kaartinen, H., Lehtomäki, M., Pyörälä, J., Pfeifer, N., et al., 2018. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS J. Photogramm. Remote Sens., 144, 137–179.
Masuda, H., Hiraoka, Y., Saito, K., Eto, S., Matsushita, M., Takahashi, M., 2021. Efficient calculation method for tree stem traits from large-scale point clouds of forest stands. Remote Sens., 13(13), 2476.
Nurunnabi, A., West, G., Belton, D., 2015. Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data. Pattern Recognit., 48(4), 1404–1419.
Nurunnabi, A., West, G., Belton, D., 2016. Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three-dimensional point cloud data. IEEE Trans Geosci Remote Sens., 54(4), 2181–2193.
Nurunnabi, A., Sadahiro, Y., Laefer, D., 2018. Robust statistical approaches for circle fitting in laser scanning three-dimensional point cloud data. Pattern Recognit, 81, 417–431.
Nurunnabi, A., Teferle, F., Laefer, D., Chen, M., Ali, M. M., 2024. Development of a Precise Tree Structure from LiDAR Point Clouds. ISPRS Technical Commission II Symposium, June 11-14, 2024, Las Vegas, Nevada, USA.
Olofsson, K., Holmgren, J., Olsson, H., 2014. Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. Remote Sens., 6(5), 4323–4344.
Pitkänen, T.P., Raumonen, P., Kangas, A., 2019. Measuring stem diameters with TLS in boreal forests by complementary fitting procedure. ISPRS J Photogramm and Remote Sens., 147, 294–306.
Piermattei, L., Karel, W., Wang, D., Wieser, M., Mokroš, M., Surový, P., et al., 2019. Terrestrial structure from motion photogrammetry for deriving forest inventory data. Remote Sens., 11(8), 950.
Poudel, K. P., Temesgen, H., Gray, N., 2018. Estimating upper stem diameters and volume of Douglas-fir and Western hemlock trees in the Pacific northwest. Forest Ecosystems, 5, 1–12.
Prendes, C., Cabo, C., Ordoñez, C., Majada, J., Canga, E., 2021. An algorithm for the automatic parametrization of wood volume equations from Terrestrial Laser Scanning point clouds: application in Pinus pinaster. GIScience & Remote Sensing, 58(7), 1130–1150.
Pueschel, P., Newnham, G., Rock, G., Udelhoven, T., Werner, W., Hill, J., 2013. The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans. ISPRS J Photogrammetry and Remote Sens., 77, pp.44–56.
Ravaglia, J., Fournier, R.A., Bac, A., Véga, C., Côté, J.F., Piboule, A., Rémillard, U., 2019. Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data. Forests, 10(7), 599.
Rousseeuw, P. J., Leroy, A. M., 2003. Robust Regression and Outlier Detection. John Wiley & Sons.
Sumida, A., Miyaura, T., Torii, H., 2013. Relationships of tree height and diameter at breast height revisited: analyses of stem growth using 20-year data of an even-aged Chamaecyparis obtusa stand. Tree Physiol., 33(1), 106–118.
Shu, Q., Rötzer, T., Detter, A., Ludwig, F., 2022. Tree information modeling: a data exchange platform for tree design and management. Forests, 13(11), 1955.
Trochta, J., Krůček, M., Vrška, T., Král, K., 2017. 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PloS one, 12(5), 1–17.
Wang, D., Hollaus, M., Puttonen, E., Pfeifer, N., 2016. Automatic and self-adaptive stem reconstruction in landslide-affected forests. Remote Sens., 8(12), 974.
Weiser, H., Schäfer, J., Winiwarter, L., Krašovec, N., Fassnacht, F. E., Höfle, B., 2022. Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests. Earth Syst. Sci. Data, 14(7), 2989–3012.
Windrim, L., Bryson, M., 2020. Detection, segmentation, and model fitting of individual tree stems from airborne laser scanning of forests using deep learning. Remote Sens., 12(9), 1469.
Xu, L., Oja, E., 1993. Randomized Hough Transform (RHT): Basic mechanisms, algorithms, and computational complexities. CVGIP: Image Understanding, 57(2), 131–154.
You, L., Wei, J., Liang, X., Lou, M., Pang, Y., Song, X., 2021. Comparison of numerical calculation methods for stem diameter retrieval using terrestrial laser data. Remote Sens., 13(9), 1780.
Yusup, A., Halik, Ü., Keyimu, M., Aishan, T., Abliz, A., et al., 2023. Trunk volume estimation of irregular shaped Populus euphratica riparian forest using TLS point cloud data and multivariate prediction models. For. Ecosyst., 10, 100082.
