{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:03:30Z","timestamp":1772766210079,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable measurements of the 3D distribution of Leaf Area Density (LAD) in forest canopy are crucial for describing and modelling microclimatic and eco-physiological processes involved in forest ecosystems functioning. To overcome the obvious limitations of direct measurements, several indirect methods have been developed, including methods based on Terrestrial LiDAR scanning (TLS). This work focused on various LAD estimators used in voxel-based approaches. LAD estimates were compared to reference measurements at branch scale in laboratory, which offered the opportunity to investigate in controlled conditions the sensitivity of estimations to various factors such as voxel size, distance to scanner, leaf morphology (species), type of scanner and type of estimator. We found that all approaches to retrieve LAD estimates were highly sensitive to voxel size whatever the species or scanner and to distance to the FARO scanner. We provided evidence that these biases were caused by vegetation heterogeneity and variations in the effective footprint of the scanner. We were able to identify calibration functions that could be readily applied when vegetation and scanner are similar to those of the present study. For different vegetation and scanner, we recommend replicating our method, which can be applied at reasonable cost. While acknowledging that the test conditions in the laboratory were very different from those of the measurements taken in the forest (especially in terms of occlusion), this study revealed existence of strong biases, including spatial biases. Because the distance between scanner and vegetation varies in field scanning, these biases should occur in a similar manner in the field and should be accounted for in voxel-based methods but also in gap-fraction methods.<\/jats:p>","DOI":"10.3390\/rs10101580","type":"journal-article","created":{"date-parts":[[2018,10,2]],"date-time":"2018-10-02T08:23:50Z","timestamp":1538468630000},"page":"1580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties"],"prefix":"10.3390","volume":"10","author":[{"given":"Maxime","family":"Soma","sequence":"first","affiliation":[{"name":"UR 629 Ecologies des For\u00eats M\u00e9diterran\u00e9ennes (URFM), INRA, 84000 Avignon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9842-6207","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Pimont","sequence":"additional","affiliation":[{"name":"UR 629 Ecologies des For\u00eats M\u00e9diterran\u00e9ennes (URFM), INRA, 84000 Avignon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6145-9614","authenticated-orcid":false,"given":"Sylvie","family":"Durrieu","sequence":"additional","affiliation":[{"name":"UMR Territoires, Environnement, T\u00e9l\u00e9d\u00e9tection et Information Spatiale (TETIS), IRSTEA, 34196 Montpellier, France"}]},{"given":"Jean-Luc","family":"Dupuy","sequence":"additional","affiliation":[{"name":"UR 629 Ecologies des For\u00eats M\u00e9diterran\u00e9ennes (URFM), INRA, 84000 Avignon, France"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pearcy, R.W., Ehleringer, J.R., Mooney, H.A., and Rundel, P.W. (1989). Canopy structure. Plant Physiological Ecology: Field Methods and Instrumentation, Springer.","DOI":"10.1007\/978-94-009-2221-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ecolmodel.2016.02.004","article-title":"Influence of vegetation spatial structure on growth and water fluxes of a mixed forest: Results from the NOTG 3D model","volume":"328","author":"Simioni","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.agrformet.2018.02.005","article-title":"Measuring and modelling energy partitioning in canopies of varying complexity using MAESPA model","volume":"253\u2013254","author":"Vezy","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1890\/070001","article-title":"Lidar: Shedding new light on habitat characterization and modeling","volume":"6","author":"Vierling","year":"2008","journal-title":"Front. Ecol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Keane, R. (2015). Wildland Fuel Fundamentals and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-319-09015-3"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","year":"2003","journal-title":"J. Exp. Bot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6211","DOI":"10.1364\/AO.34.006211","article-title":"Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index","volume":"34","author":"Chen","year":"1995","journal-title":"Appl. Opt."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1093\/treephys\/tpn022","article-title":"Estimating forest LAI profiles and structural parameters using a ground-based laser called \u2019Echidna?","volume":"29","author":"Jupp","year":"2009","journal-title":"Tree Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.agrformet.2015.03.008","article-title":"Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution","volume":"209\u2013210","author":"Zhao","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1016\/j.agrformet.2011.05.004","article-title":"Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements","volume":"151","author":"Widlowski","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.rse.2017.01.032","article-title":"Estimation of 3D vegetation density with Terrestrial Laser Scanning data using voxels. A sensitivity analysis of influencing parameters","volume":"191","author":"Grau","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7995","DOI":"10.3390\/rs70607995","article-title":"Estimating leaf bulk density distribution in a tree canopy using terrestrial LiDAR and a straightforward calibration procedure","volume":"7","author":"Pimont","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rse.2018.06.024","article-title":"Estimators and confidence intervals for plant area density at voxel scale with T-LiDAR","volume":"215","author":"Pimont","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Durrieu, S., Allouis, T., Fournier, R., V\u00e9ga, C., and Albrech, L. (2008, January 17\u201319). Spatial quantification of vegetation density from terrestrial laser scanner data for characterization of 3D forest structure at plot level. Proceedings of the SilviLaser 2008, Edinburgh, UK."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1093\/jxb\/erm203","article-title":"Factors contributing to accuracy in the estimation of the woody canopy leaf area density profile using 3D portable lidar imaging","volume":"58","author":"Hosoi","year":"2007","journal-title":"J. Exp. Bot."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1088\/1361-6501\/aa5cfd","article-title":"Rapid, high-resolution measurement of leaf area and leaf orientation using terrestrial LiDAR scanning data","volume":"28","author":"Bailey","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.envsoft.2013.09.034","article-title":"A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR","volume":"51","author":"Widlowski","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.03.011","article-title":"Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning","volume":"194","author":"Bailey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.3390\/rs3081691","article-title":"Deriving fuel mass by size class in Douglas-fir (Pseudotsuga menziesii) using terrestrial laser scanning","volume":"3","author":"Seielstad","year":"2011","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.agrformet.2013.09.005","article-title":"On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR","volume":"184","author":"Baldocchi","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","first-page":"361","article-title":"Jensen\u2019s inequality predicts effects of environmental variation","volume":"5347","author":"Ruel","year":"1999","journal-title":"Tree"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1071\/WF07115","article-title":"Effect of vegetation heterogeneity on radiative transfer in forest fires","volume":"18","author":"Pimont","year":"2009","journal-title":"Int. J. Wildland Fire"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/0262-8856(92)90068-E","article-title":"3D measurements from imaging laser radars: How good are they?","volume":"10","author":"Hebert","year":"1992","journal-title":"Image Vis. Comput."},{"key":"ref_26","unstructured":"Newnham, G., Armston, J., Muir, J., Goodwin, N., Tindall, D., Culvenor, D., P\u00fcschel, P., Nystr\u00f6m, M., and Johansen, K. (2012). Evaluation of Terrestrial Laser Scanners for Measuring Vegetation Structure, CSIRO. CSIRO Sustainable Agriculture Flagship."},{"key":"ref_27","unstructured":"Leica Geosystems Cyclone Pointcloud Export Format\u2014Description of ASCII (2018, June 22). Ptx Format. Available online: http:\/\/w3.leica-geosystems.com\/kb\/?guid=5532D590-114C-43CD-A55F-FE79E5937CB2."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1469-8137.1960.tb06195.x","article-title":"Inclined point quadrats","volume":"59","author":"Wilson","year":"1960","journal-title":"New Phytol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, S., Dai, L., Wang, H., Wang, Y., He, Z., and Lin, S. (2017). Estimating leaf area density of individual trees using the point cloud segmentation of terrestrial LiDAR data and a voxel-based model. Remote Sens., 9.","DOI":"10.3390\/rs9111202"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2954","DOI":"10.1016\/j.rse.2010.08.030","article-title":"Measuring effective leaf area index, foliage profile, and stand height in New England forest stands using a full-waveform ground-based lidar","volume":"115","author":"Zhao","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Errington, A.F.C., and Daku, B.L.F. (2017). Temperature compensation for radiometric correction of terrestrial LiDAR intensity data. Remote Sens., 9.","DOI":"10.3390\/rs9040356"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2017.05.034","article-title":"Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor","volume":"198","author":"Vincent","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1580\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:23:32Z","timestamp":1760196212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,1]]},"references-count":32,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101580"],"URL":"https:\/\/doi.org\/10.3390\/rs10101580","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,1]]}}}