References

The methods Sylva implements, the datasets it is validated against, and the software it interoperates with. Cite the original work when you publish results, not just Sylva. Each entry says where in Sylva it is used.

Methods

Åkerblom, M., Raumonen, P., Casella, E., Disney, M. I., Danson, F. M., Gaulton, R., Schofield, L. A., & Kaasalainen, M. (2018). Non-intersecting leaf insertion algorithm for tree structure models. Interface Focus, 8(2), 20170045. https://doi.org/10.1098/rsfs.2017.0045 In Sylva: the collision-free leaf insertion that leaves.add_leaves does not attempt.

Amanatides, J., & Woo, A. (1987). A fast voxel traversal algorithm for ray tracing. In Eurographics 1987: Technical papers (pp. 3–10). The Eurographics Association. https://doi.org/10.2312/egtp.19871000 In Sylva: the voxel walk of canopy.density_grid and sylva.voxels.

Andrew, A. M. (1979). Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters, 9(5), 216–219. https://doi.org/10.1016/0020-0190(79)90072-3 In Sylva: the monotone-chain convex hull behind trees.convex_hull_area, crown_metrics and crown_shape.

Armston, J., Disney, M., Lewis, P., Scarth, P., Phinn, S., Lucas, R., Bunting, P., & Goodwin, N. (2013). Direct retrieval of canopy gap probability using airborne waveform lidar. Remote Sensing of Environment, 134, 24–38. https://doi.org/10.1016/j.rse.2013.02.021 In Sylva: the 1/n weighting of the echoes of a pulse in canopy.GapProfile.

Bailey, B. N., & Mahaffee, W. F. (2017). Rapid, high-resolution measurement of leaf area and leaf orientation using terrestrial LiDAR scanning data. Measurement Science and Technology, 28(6), 064006. https://doi.org/10.1088/1361-6501/aa5cfd In Sylva: the bailey attenuation estimator and triangle-facet G in sylva.voxels.

Baskerville, G. L. (1972). Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research, 2(1), 49–53. https://doi.org/10.1139/x72-009 In Sylva: the back-transformation exp(σ²/2) of log-log models in fusion.fit_model and fusion.upscale.

Beaton, A. E., & Tukey, J. W. (1974). The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data. Technometrics, 16(2), 147–185. https://doi.org/10.1080/00401706.1974.10489171 In Sylva: the Tukey biweight option (robust="tukey") of sylva.coreg.icp.

Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256. https://doi.org/10.1109/34.121791 In Sylva: iterative closest point, registration.icp (method="point") and sylva.coreg.icp.

Bouvier, M., Durrieu, S., Fournier, R. A., & Renaud, J.-P. (2015). Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment, 156, 322–334. https://doi.org/10.1016/j.rse.2014.10.004 Used by: the gap-fraction profile of leaf area density, which als.gap_profile reproduces with weighting="all".

Calders, K., Armston, J., Newnham, G., Herold, M., & Goodwin, N. (2014). Implications of sensor configuration and topography on vertical plant profiles derived from terrestrial LiDAR. Agricultural and Forest Meteorology, 194, 104–117. https://doi.org/10.1016/j.agrformet.2014.03.022 In Sylva: the robust ground plane of canopy.fit_ground_plane.

Campbell, G. S. (1990). Derivation of an angle density function for canopies with ellipsoidal leaf angle distributions. Agricultural and Forest Meteorology, 49(3), 173–176. https://doi.org/10.1016/0168-1923(90)90030-A In Sylva: the ellipsoidal χ and its projection function in sylva.leaves and the ellipsoidal leaf angle distribution of sylva.voxels.

Chen, Y., & Medioni, G. (1992). Object modelling by registration of multiple range images. Image and Vision Computing, 10(3), 145–155. https://doi.org/10.1016/0262-8856(92)90066-C In Sylva: point-to-plane ICP, registration.icp (method="plane") and sylva.coreg.icp.

Chetverikov, D., Svirko, D., Stepanov, D., & Krsek, P. (2002). The trimmed iterative closest point algorithm. In Proceedings of the 16th International Conference on Pattern Recognition (Vol. 3, pp. 545–548). IEEE. https://doi.org/10.1109/ICPR.2002.1047997 In Sylva: the trim of registration.icp and the trimmed correspondences of sylva.coreg.icp.

de Liocourt, F. (1898). De l'aménagement des sapinières. Bulletin trimestriel de la Société forestière de Franche-Comté et Belfort, 4, 396–409. In Sylva: the reverse-J (negative exponential) diameter distribution of synthetic.plot.

de Wit, C. T. (1965). Photosynthesis of leaf canopies (Agricultural Research Reports No. 663). Pudoc. https://edepot.wur.nl/187115 In Sylva: the planophile, erectophile, plagiophile and extremophile leaf angle distributions of sylva.leaves, sylva.voxels and synthetic.tree_model.

Dai, X., Liang, X., Qi, H., Chen, J., Wang, X., Wang, X., Zhang, Q., & Zhang, J. (2024). Automated registration of terrestrial point clouds through ground overlapping searching in forests. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–13. https://doi.org/10.1109/TGRS.2024.3471792 In Sylva: registering levelled forest scans on their shared ground: the height of a sylva.coreg stem match is taken from the terrain the two scans share.

Devereux, T., Lowe, T., Rivory, J., Bohn Reckziegel, R., Calders, K., Aryal, R. R., Eaton, G., Cooper, Z., Levick, S., Phinn, S., & Woodgate, W. (2026). RayExtract: A fast, scalable method for tree volume reconstruction from terrestrial laser scanning. Remote Sensing of Environment, 334, 115162. https://doi.org/10.1016/j.rse.2025.115162 In Sylva: raycloudtools' rayextract trees: the least-cost-path segmentation that trees.segment_trees and trees.merge_branches follow, and the power-mean section radius and Leonardo's rule at forks in sylva.qsm; also the raycloudtools baseline of the tree and QSM benchmarks.

Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P. L., Qi, W., & Silva, C. (2020). The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth's forests and topography. Science of Remote Sensing, 1, 100002. https://doi.org/10.1016/j.srs.2020.100002 In Sylva: the GEDI L2B canopy products (pavd_z, pai_z, cover_z, fhd_normal) that the profile metrics of ALSProfile.metrics follow, so ALS can calibrate models built on GEDI.

Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. https://doi.org/10.1145/358669.358692 In Sylva: RANSAC circle fits in trees.detect_stems, trees.fit_circle_ransac, trees.dbh_profile, trees.detect_buttress, quality.stem_noise and the stem maps of sylva.coreg.

Gatziolis, D., & McGaughey, R. J. (2019). Reconstructing aircraft trajectories from multi-return airborne laser-scanning data. Remote Sensing, 11(19), 2258. https://doi.org/10.3390/rs11192258 Used by: the trajectory estimate of als.estimate_trajectory, from the lines of multiple-return pulses.

Gibson, S. F. F. (1998). Constrained elastic surface nets: Generating smooth surfaces from binary segmented data. In Medical Image Computing and Computer-Assisted Intervention: MICCAI'98 (pp. 888–898). Springer. https://doi.org/10.1007/BFb0056277 In Sylva: the surface nets that close qsm.buttress_mesh.

Goel, N. S., & Strebel, D. E. (1984). Simple beta distribution representation of leaf orientation in vegetation canopies. Agronomy Journal, 76(5), 800–802. https://doi.org/10.2134/agronj1984.00021962007600050021x In Sylva: the beta fit of the leaf inclination distribution in sylva.leaves, the two-parameter beta distribution of sylva.voxels, and the leaf angles of synthetic.tree_model.

Hofton, M. A., Minster, J. B., & Blair, J. B. (2000). Decomposition of laser altimeter waveforms. IEEE Transactions on Geoscience and Remote Sensing, 38(4), 1989–1996. https://doi.org/10.1109/36.851780 In Sylva: the inflection-point initial estimates of waveform.decompose (peaks="inflection").

Horn, B. K. P. (1981). Hill shading and the reflectance map. Proceedings of the IEEE, 69(1), 14–47. https://doi.org/10.1109/PROC.1981.11918 In Sylva: the slope estimate and shading of Raster.hillshade.

Hosoi, F., & Omasa, K. (2006). Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar. IEEE Transactions on Geoscience and Remote Sensing, 44(12), 3610–3618. https://doi.org/10.1109/TGRS.2006.881743 In Sylva: the contact-frequency PAD profile canopy.pad_profile_voxel.

Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. https://doi.org/10.1214/aoms/1177703732 In Sylva: the Huber weights of quality.stem_noise, sylva.coreg.icp, the coregistration pose graph and coreg.refine_joint.

Jupp, D. L. B., Culvenor, D. S., Lovell, J. L., Newnham, G. J., Strahler, A. H., & Woodcock, C. E. (2009). Estimating forest LAI profiles and structural parameters using a ground-based laser called 'Echidna®'. Tree Physiology, 29(2), 171–181. https://doi.org/10.1093/treephys/tpn022 In Sylva: the gap-probability profile, and the hinge, linear and weighted PAI estimators of canopy.GapProfile.

Kabsch, W. (1976). A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A, 32(5), 922–923. https://doi.org/10.1107/S0567739476001873 In Sylva: registration.kabsch and the point-to-point step of sylva.coreg.icp.

Kåsa, I. (1976). A circle fitting procedure and its error analysis. IEEE Transactions on Instrumentation and Measurement, IM-25(1), 8–14. https://doi.org/10.1109/TIM.1976.6312298 In Sylva: the algebraic circle fit that starts trees.fit_circle, the stem detector and quality.stem_noise.

Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1–2), 83–97. https://doi.org/10.1002/nav.3800020109 In Sylva: the optimal assignment of trees in change.match_trees and fusion.link_trees.

Lang, A. R. G., & Xiang, Y. (1986). Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agricultural and Forest Meteorology, 37(3), 229–243. https://doi.org/10.1016/0168-1923(86)90033-X In Sylva: the clumping index GapProfile.clumping.

Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 2(2), 164–168. https://doi.org/10.1090/qam/10666 In Sylva: the pose-graph solver of sylva.coreg and the circle and cylinder fits.

Low, K.-L. (2004). Linear least-squares optimization for point-to-plane ICP surface registration (Technical Report TR04-004). Department of Computer Science, University of North Carolina at Chapel Hill. https://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf In Sylva: the linearised point-to-plane step of registration.icp.

Lowe, T., Moghadam, P., Edwards, E., & Williams, J. (2021). Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM. Journal of Field Robotics, 38(4), 598–618. https://doi.org/10.1002/rob.22006 In Sylva: the ray-traced density of canopy.density_grid (raycloudtools' DensityGrid) and the formulation behind sylva.voxels.

Liu, H., Zhang, X., Xu, Y., & Chen, X. (2020). Efficient coarse registration of pairwise TLS point clouds using ortho projected feature images. ISPRS International Journal of Geo-Information, 9(4), 255. https://doi.org/10.3390/ijgi9040255 In Sylva: the vertical offset of levelled scans from the height difference of their overlap once aligned horizontally, as sylva.coreg sets a stem match's height.

Lu, F., & Milios, E. (1997). Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4(4), 333–349. https://doi.org/10.1023/A:1008854305733 In Sylva: the pose graph of sylva.coreg.

MacArthur, R. H., & Horn, H. S. (1969). Foliage profile by vertical measurements. Ecology, 50(5), 802–804. https://doi.org/10.2307/1933693 Used by: the layer-by-layer inversion of gap fraction into plant area density in als.gap_profile.

MacArthur, R. H., & MacArthur, J. W. (1961). On bird species diversity. Ecology, 42(3), 594–598. https://doi.org/10.2307/1932254 In Sylva: foliage height diversity, fhd, of ALSProfile.metrics.

Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441. https://doi.org/10.1137/0111030 In Sylva: with Levenberg (1944), the pose-graph solver, the circle and cylinder fits and the Gaussian fits of waveform.decompose.

Maurer, C. R., Jr., Qi, R., & Raghavan, V. (2003). A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2), 265–270. https://doi.org/10.1109/TPAMI.2003.1177156 In Sylva: nearest-cell gap filling of the coregistration terrain model, through SciPy's implementation (see Software).

Miller, J. B. (1967). A formula for average foliage density. Australian Journal of Botany, 15(1), 141–144. https://doi.org/10.1071/BT9670141 In Sylva: method="miller" in canopy.lai_from_gap_fraction.

Munkres, J. (1957). Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, 5(1), 32–38. https://doi.org/10.1137/0105003 In Sylva: with Kuhn (1955), the assignment of change.match_trees and fusion.link_trees.

Næsset, E. (2002). Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80(1), 88–99. https://doi.org/10.1016/S0034-4257(01)00290-5 In Sylva: the area-based approach of fusion.upscale, plot values regressed on ALS metrics and predicted over a grid.

Olofsson, K., Holmgren, J., & Olsson, H. (2014). Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. Remote Sensing, 6(5), 4323–4344. https://doi.org/10.3390/rs6054323 In Sylva: an earlier stem detector that fits RANSAC circles in height slices and links them into stems, the approach trees.detect_stems shares.

Pimont, F., Allard, D., Soma, M., & Dupuy, J.-L. (2018). Estimators and confidence intervals for plant area density at voxel scale with T-LiDAR. Remote Sensing of Environment, 215, 343–370. https://doi.org/10.1016/j.rse.2018.06.024 In Sylva: the FPL and PPL attenuation estimators and their bias correction in sylva.voxels.

Pimont, F., Soma, M., & Dupuy, J.-L. (2019). Accounting for wood, foliage properties, and laser effective footprint in estimations of leaf area density from multiview-LiDAR data. Remote Sensing, 11(13), 1580. https://doi.org/10.3390/rs11131580 In Sylva: the beam-section weighted FPL estimator and the finite-leaf-size free path in sylva.voxels.

Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H., Vastaranta, M., Holopainen, M., Disney, M., & Lewis, P. (2013). Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing, 5(2), 491–520. https://doi.org/10.3390/rs5020491 In Sylva: TreeQSM, the cylinder-model approach that QSMs such as sylva.qsm build on.

Richards, F. J. (1959). A flexible growth function for empirical use. Journal of Experimental Botany, 10(2), 290–301. https://doi.org/10.1093/jxb/10.2.290 In Sylva: the Chapman-Richards form of the height-diameter curves of synthetic.plot.

Roussel, J.-R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R. H., Meador, A. S., Bourdon, J.-F., de Boissieu, F., & Achim, A. (2020). lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of Environment, 251, 112061. https://doi.org/10.1016/j.rse.2020.112061 In Sylva: the catalogue of tiles processed in buffered chunks that sylva.als follows, and the reference its DTMs and CHMs were checked against; the definitions of the area-based metrics of sylva.als.metrics (stdmetrics, entropy).

Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008). Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems, 56(11), 927–941. https://doi.org/10.1016/j.robot.2008.08.005 In Sylva: filters.statistical_outlier_removal.

Shinozaki, K., Yoda, K., Hozumi, K., & Kira, T. (1964). A quantitative analysis of plant form, the pipe model theory: I. Basic analyses. Japanese Journal of Ecology, 14(3), 97–105. https://doi.org/10.18960/seitai.14.3_97 In Sylva: the pipe model that sets unmeasured branch radii in qsm.build_qsm, and the branch radii of synthetic.tree_model.

Smith, D. D., Sperry, J. S., Enquist, B. J., Savage, V. M., McCulloh, K. A., & Bentley, L. P. (2014). Deviation from symmetrically self-similar branching in trees predicts altered hydraulics, mechanics, light interception and metabolic scaling. New Phytologist, 201(1), 217–229. https://doi.org/10.1111/nph.12487 In Sylva: path_fraction in QSM.metrics.

Taubin, G. (1995). A signal processing approach to fair surface design. In Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '95) (pp. 351–358). ACM. https://doi.org/10.1145/218380.218473 In Sylva: smoothing of QSM skeleton nodes and radii, and of buttress meshes.

Tian, Z., & Li, S. (2022). Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11. https://doi.org/10.1109/TGRS.2022.3218603 In Sylva: the default leaves.classify_leaf_wood labeller (GBSeparation), translated from the authors' code (see Software).

Tremblay, J.-F., & Béland, M. (2018). Towards operational marker-free registration of terrestrial lidar data in forests. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 430–435. https://doi.org/10.1016/j.isprsjprs.2018.10.011 In Sylva: vertical error dominates stem-based registration, traced to the terrain under the stems; why sylva.coreg takes height from the shared ground.

Umeyama, S. (1991). Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4), 376–380. https://doi.org/10.1109/34.88573 In Sylva: the reflection-safe rotation of registration.kabsch and sylva.coreg.

Verroust, A., & Lazarus, F. (2000). Extracting skeletal curves from 3D scattered data. The Visual Computer, 16(1), 15–25. https://doi.org/10.1007/PL00007210 In Sylva: the geodesic level-set skeleton of qsm.build_qsm.

Vicari, M. B., Disney, M., Wilkes, P., Burt, A., Calders, K., & Woodgate, W. (2019). Leaf and wood classification framework for terrestrial LiDAR point clouds. Methods in Ecology and Evolution, 10(5), 680–694. https://doi.org/10.1111/2041-210X.13144 In Sylva: the path-frequency cue of qsm.wood_points.

Vicari, M. B., Pisek, J., & Disney, M. (2019). New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species. Agricultural and Forest Meteorology, 264, 322–333. https://doi.org/10.1016/j.agrformet.2018.10.021 In Sylva: leaf angle distributions from point normals, in sylva.leaves and the inclination=True path of sylva.voxels.

Vincent, G., Antin, C., Laurans, M., Heurtebize, J., Durrieu, S., Lavalley, C., & Dauzat, J. (2017). Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor. Remote Sensing of Environment, 198, 254–266. https://doi.org/10.1016/j.rse.2017.05.034 In Sylva: AMAPVox, whose voxel traversal and outputs sylva.voxels mirrors.

Vogel, H. (1979). A better way to construct the sunflower head. Mathematical Biosciences, 44(3–4), 179–189. https://doi.org/10.1016/0025-5564(79)90080-4 In Sylva: the sub-beam pattern of the beam footprint in synthetic.scan.

Wagner, W. (2010). Radiometric calibration of small-footprint full-waveform airborne laser scanner measurements: Basic physical concepts. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 505–513. https://doi.org/10.1016/j.isprsjprs.2010.06.007 In Sylva: the Lambertian reference targets of waveform.calibration_constant.

Wagner, W., Ullrich, A., Ducic, V., Melzer, T., & Studnicka, N. (2006). Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 60(2), 100–112. https://doi.org/10.1016/j.isprsjprs.2005.12.001 In Sylva: the Gaussian decomposition of sylva.waveform, its backscatter cross-section, and the pulse-target convolution of synthetic.waveforms.

Wang, X., Yang, Z., Cheng, X., Stoter, J., Xu, W., Wu, Z., & Nan, L. (2023). GlobalMatch: Registration of forest terrestrial point clouds by global matching of relative stem positions. ISPRS Journal of Photogrammetry and Remote Sensing, 197, 71–86. https://doi.org/10.1016/j.isprsjprs.2023.01.013 In Sylva: the 4-degree-of-freedom stem registration of levelled scans, and the sensitivity of stem heights to the terrain model, behind sylva.coreg's terrain-based height.

Weinmann, M., Jutzi, B., Hinz, S., & Mallet, C. (2015). Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 286–304. https://doi.org/10.1016/j.isprsjprs.2015.01.016 In Sylva: the eigenvalue planarity and linearity of filters.planarity_linearity.

Wilkes, P., Disney, M., Armston, J., Bartholomeus, H., Bentley, L., Brede, B., Burt, A., Calders, K., Chavana-Bryant, C., Clewley, D., Duncanson, L., Forbes, B., Krisanski, S., Malhi, Y., Moffat, D., Origo, N., Shenkin, A., & Yang, W. (2023). TLS2trees: A scalable tree segmentation pipeline for TLS data. Methods in Ecology and Evolution, 14(12), 3083–3099. https://doi.org/10.1111/2041-210X.14233 In Sylva: the cluster-gap edge weighting tried and rejected in the tree benchmark.

Wilson, J. W. (1960). Inclined point quadrats. New Phytologist, 59(1), 1–7. https://doi.org/10.1111/j.1469-8137.1960.tb06195.x In Sylva: the leaf projection function G (LeafAngleDistribution.g).

Wilson, J. W. (1963). Estimation of foliage denseness and foliage angle by inclined point quadrats. Australian Journal of Botany, 11(1), 95–105. https://doi.org/10.1071/BT9630095 In Sylva: the hinge angle (57.5°) used by canopy.lai_from_gap_fraction and GapProfile.

Xu, H., Gossett, N., & Chen, B. (2007). Knowledge and heuristic-based modeling of laser-scanned trees. ACM Transactions on Graphics, 26(4), 19. https://doi.org/10.1145/1289603.1289610 In Sylva: the geodesic-shell clustering behind qsm.build_qsm.

Zhang, K., Chen, S.-C., Whitman, D., Shyu, M.-L., Yan, J., & Zhang, C. (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(4), 872–882. https://doi.org/10.1109/TGRS.2003.810682 In Sylva: ground.classify_ground_pmf.

Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. https://doi.org/10.3390/rs8060501 In Sylva: ground.classify_ground_csf.

Validation data

Burt, A., Boni Vicari, M., da Costa, A. C. L., Coughlin, I., Meir, P., Rowland, L., & Disney, M. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science, 8(2), 201458. https://doi.org/10.1098/rsos.201458 In Sylva: felled tropical trees in the QSM benchmark, and two of the trees in the buttress example.

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 In Sylva: destructively harvested trees in the QSM benchmark.

Calders, K., Verbeeck, H., Burt, A., Origo, N., Nightingale, J., Malhi, Y., Wilkes, P., Raumonen, P., Bunce, R., & Disney, M. (2022). Terrestrial laser scanning data Wytham Woods: Individual trees and quantitative structure models (QSMs) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7307956

Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., Senf, C., Soenens, L., Terryn, L., Van den Broeck, W. A. J., & Calders, K. (2025). TLS forest instance segmentation benchmark: 2983 manually segmented trees from four plots [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16875688 In Sylva: the four plots of the tree detection benchmark.

Cherlet, W., Dayal, K., Chen, S., Cooper, Z., Disney, M., Hanzl, A., Levick, S., Nightingale, J., Origo, N., Senf, C., Soenens, L., Terryn, L., Van den Broeck, W. A. J., & Calders, K. (2026). Benchmarking tree instance segmentation of terrestrial laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 231, 230–247. https://doi.org/10.1016/j.isprsjprs.2025.10.033 In Sylva: the evaluation protocol and published baselines of the tree detection benchmark.

Demol, M., Gielen, B., & Verbeeck, H. (2021). QSMs, point cloud and harvest data from a destructive forest biomass experiment in Belgium using terrestrial laser scanning [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4557401

Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., Martius, C., Goodman, R. C., Disney, M., Manuri, S., Burt, A., & Calders, K. (2018). Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution, 9(2), 223–234. https://doi.org/10.1111/2041-210X.12904 In Sylva: felled tropical trees in the QSM benchmark.

Hackenberg, J. (2021). Hackenberg et al 2021 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5131717 In Sylva: SimpleForest clouds, QSMs and reference table.

Momo Takoudjou, S., Ploton, P., Sonké, B., Hackenberg, J., Griffon, S., de Coligny, F., Kamdem, N. G., Libalah, M., Mofack, G. I., Le Moguédec, G., Pélissier, R., & Barbier, N. (2018). Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods in Ecology and Evolution, 9(4), 905–916. https://doi.org/10.1111/2041-210X.12933 In Sylva: felled tropical trees in the QSM benchmark.

Owen, H. J. F., Grieve, S., & Lines, E. R. (2024). Plot-level semantically labelled terrestrial laser scanning point clouds [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13268500 In Sylva: plot-scale leaf/wood labels (CC BY-NC 4.0).

Van den Broeck, W. A. J., Terryn, L., Chen, S., Cherlet, W., Cooper, Z. T., & Calders, K. (2025). Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 227, 366–382. https://doi.org/10.1016/j.isprsjprs.2025.06.023 In Sylva: the manually labelled trees of the leaf/wood benchmark; data set at https://doi.org/10.5281/zenodo.13759407.

Wielgosz, M., Puliti, S., & Astrup, R. (2026). SegmentAnyTreeV2: Scaling transformer-based tree instance segmentation across sensors, platforms, and forests (arXiv:2606.08206) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2606.08206 In Sylva: published F1 on the four benchmark plots, quoted in the tree benchmark.

The canopy benchmark also uses TERN AusCover / Ecosystem Surveillance plot scans and hemispherical photography, which are not deposited per plot; see https://www.tern.org.au for access and citation.

Formats

American Society for Photogrammetry and Remote Sensing. (2019). LAS specification 1.4 – R15. https://github.com/ASPRSorg/LAS In Sylva: .las and .laz reading and writing, including typed extra bytes, in sylva.io, and the waveform data packets of sylva.waveform.

Isenburg, M. (2013). LASzip: Lossless compression of LiDAR data. Photogrammetric Engineering & Remote Sensing, 79(2), 209–217. https://doi.org/10.14358/PERS.79.2.209 In Sylva: .laz compression, through the las and laz crates.

Isenburg, M. (2012). PulseWaves: An open, vendor-neutral, stand-alone, LAS-compatible full waveform LiDAR standard (version 0.3) and its reference library. rapidlasso. https://github.com/PulseWaves/PulseWaves In Sylva: the .pls / .wvs reader and writer of sylva.waveform; its sample files are used in the tests.

Software

Lowe, T. D., & Stepanas, K. (2021). RayCloudTools: A concise interface for analysis and manipulation of ray clouds. IEEE Access, 9, 79712–79724. https://doi.org/10.1109/ACCESS.2021.3084954 In Sylva: ray clouds, which Sylva reads and writes, and rayextract trees, which it follows and is benchmarked against: https://github.com/csiro-robotics/raycloudtools.

AMAPVox (UMR AMAP): voxelisation of lidar data, the reference implementation of the attenuation estimators: https://github.com/umr-amap/AMAPVox. sylva.voxels is a port of rayvoxel, J. Rivory's unpublished reimplementation of AMAPVox on raycloudtools; see THIRD_PARTY_NOTICES.md. Its estimators are documented in Vincent, G., Pimont, F., & Verley, P. (2021). A note on PAD/LAD estimators implemented in AMAPVox 1.7. DataSuds. https://doi.org/10.23708/1AJNMP

pylidar-tls-canopy (J. Armston): Jupp gap-probability profiles from RIEGL and LEAF instruments, the reference for the canopy benchmark: https://github.com/armstonj/pylidar-tls-canopy.

Tian, Z., & Li, S. (2022). Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds: GBSeparation, a python package [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.6837613 In Sylva: the reference implementation (CC BY 4.0) that leaves.classify_leaf_wood(method="gbs") translates into Rust.

arbor (J.-R. Roussel, r-lidar): tree segmentation and QSMs for point clouds; the idea of seeding the trunk surface rather than its axis in trees.segment_trees (seed_ring) came from it: https://github.com/r-lidar/arbor.

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., … SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2 In Sylva: scipy.ndimage's feature transform, which the coregistration terrain model transcribes (BSD-3-Clause; see THIRD_PARTY_NOTICES.md).

Segfix: a GUI for correcting instance segmentation of tree point clouds; it opens the tree_id column Sylva writes and saves it back in place: https://github.com/UQ-EORC/Segfix.