{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T23:54:54Z","timestamp":1772150094620,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T00:00:00Z","timestamp":1564531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006959","name":"U.S. Forest Service","doi-asserted-by":"publisher","award":["PSW internal grant award"],"award-info":[{"award-number":["PSW internal grant award"]}],"id":[{"id":"10.13039\/100006959","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an     R 2     value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of     9 %     in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The     R 2     values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84\u20130.96. The results bode well for using this method for efficient, automatic, and accurate\/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS.<\/jats:p>","DOI":"10.3390\/rs11151791","type":"journal-article","created":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T11:37:07Z","timestamp":1564573027000},"page":"1791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner"],"prefix":"10.3390","volume":"11","author":[{"given":"Ali","family":"Rouzbeh Kargar","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"given":"Richard","family":"MacKenzie","sequence":"additional","affiliation":[{"name":"Institute of Pacific Islands Forestry of US Forest Service, Hilo, HI 96721, USA"}]},{"given":"Gregory P.","family":"Asner","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA"}]},{"given":"Jan","family":"van Aardt","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29429","DOI":"10.1029\/97JD01107","article-title":"Leaf area index of boreal forests: Theory, techniques, and measurements","volume":"102","author":"Chen","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1093\/treephys\/9.1-2.35","article-title":"Models of water flux through forest stands: Critical leaf and stand parameters","volume":"9","author":"Whitehead","year":"1991","journal-title":"Tree Physiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/BF00665600","article-title":"Autumnal leaf conductance and apparent photosynthesis by saplings and sprouts in a recently disturbed northern hardwood forest","volume":"84","author":"Amthor","year":"1990","journal-title":"Oecologia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1080\/01431160500227896","article-title":"Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands","volume":"27","author":"Davi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.2135\/cropsci2000.4041179x","article-title":"Comparison of three leaf area index meters in a corn canopy","volume":"40","author":"Wilhelm","year":"2000","journal-title":"Crop Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"923","DOI":"10.14358\/PERS.72.8.923","article-title":"Isolating individual trees in a savanna woodland using small footprint lidar data","volume":"72","author":"Chen","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2006.04.019","article-title":"Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction","volume":"104","author":"Morsdorf","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0378-1127(93)90192-P","article-title":"Estimation of tree canopy leaf area index by gap fraction analysis","volume":"61","author":"Martens","year":"1993","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jarvis, P.G., and Leverenz, J.W. (1983). Productivity of temperate, deciduous and evergreen forests. Physiological Plant Ecology IV, Springer.","DOI":"10.1007\/978-3-642-68156-1_9"},{"key":"ref_10","first-page":"67","article-title":"Detailed stem measurements of standing trees from ground-based scanning lidar","volume":"52","author":"Henning","year":"2006","journal-title":"For. Sci."},{"key":"ref_11","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_12","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_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1111\/j.1469-8137.1959.tb05340.x","article-title":"Analysis of the spatial distribution of foliage by two-dimensional point quadrats","volume":"58","author":"Wilson","year":"1959","journal-title":"New Phytol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/0168-1923(91)90108-3","article-title":"Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand","volume":"56","author":"Chen","year":"1991","journal-title":"Agric. For. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1093\/jxb\/erl142","article-title":"3D lidar imaging for detecting and understanding plant responses and canopy structure","volume":"58","author":"Omasa","year":"2006","journal-title":"J. Exp. Bot."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2015.10.007","article-title":"Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories","volume":"110","author":"Tao","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","first-page":"37","article-title":"Extracting and analyzing forest and woodland cover change in Eritrea based on landsat data using supervised classification","volume":"19","author":"Ghebrezgabher","year":"2016","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/S0890-6955(01)00120-1","article-title":"A new segmentation method for point cloud data","volume":"42","author":"Woo","year":"2002","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yao, W., Hinz, S., and Stilla, U. (2009, January 20\u201322). Object extraction based on 3D-segmentation of lidar data by combining mean shift with normalized cuts: Two examples from urban areas. Proceedings of the 2009 Joint Urban Remote Sensing Event, Shanghai, China.","DOI":"10.1109\/URS.2009.5137673"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1109\/JSTARS.2015.2416001","article-title":"Single-scan stem reconstruction using low-resolution terrestrial laser scanner data","volume":"8","author":"Kelbe","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00023752","article-title":"Nutrient limitations to plant growth during primary succession in Hawaii Volcanoes National Park","volume":"23","author":"Vitousek","year":"1993","journal-title":"Biogeochemistry"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.agrformet.2008.08.004","article-title":"Evapotranspiration and energy balance of native wet montane cloud forest in Hawai\u2018i","volume":"149","author":"Giambelluca","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.agrformet.2006.09.007","article-title":"Influence of measurement set-up of ground-based LiDAR for derivation of tree structure","volume":"141","author":"Hoet","year":"2006","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","unstructured":"SICK (2009). LMS100\/111\/120\/151 Laser Measurement Systems Operating Instructions, SICK AG Waldkirch."},{"key":"ref_27","unstructured":"Van Aardt, J.A., Kelbe, D., Sacca, K., Giardina, C.P., Selmants, P.C., Litton, C.M., and Asner, G.P. (2017, January 10\u201312). A terrestrial lidar\u2019s assessment of climate change impacts on forest structure. Proceedings of the Silvilaser 2017, Blacksburg, VA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.robot.2008.08.005","article-title":"Towards 3D point cloud based object maps for household environments","volume":"56","author":"Rusu","year":"2008","journal-title":"Robot. Auton. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/34.3918","article-title":"Efficient component labeling of images of arbitrary dimension represented by linear bintrees","volume":"10","author":"Samet","year":"1988","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Douglas, E.S., Strahler, A., Martel, J., Cook, T., Mendillo, C., Marshall, R., and Yang, X. (2012, January 22\u201327). DWEL: A dual-wavelength echidna lidar for ground-based forest scanning. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352489"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"799","DOI":"10.5194\/isprs-archives-XLI-B5-799-2016","article-title":"Facets: A Cloudcompare Plugin to Extract Geological Planes from Unstructured 3D Point Clouds","volume":"41","author":"Dewez","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sen. Spat. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Canny, J. (1987). A computational approach to edge detection. Readings in Computer Vision, Morgan Kaufmann.","DOI":"10.1016\/B978-0-08-051581-6.50024-6"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","article-title":"On estimation of a probability density function and mode","volume":"33","author":"Parzen","year":"1962","journal-title":"Ann. Math. Stat."},{"key":"ref_34","unstructured":"Burkhart, H.E., Avery, T.E., and Bullock, B.P. (2019). Forest Measurements, Waveland Press. [6th ed.]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.2113\/gsecongeo.58.8.1246","article-title":"Principles of geostatistics","volume":"58","author":"Matheron","year":"1963","journal-title":"Econ. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Antonarakis, A.S., Richards, K.S., Brasington, J., and Muller, E. (2010). Determining leaf area index and leafy tree roughness using terrestrial laser scanning. Water Resour. Res.","DOI":"10.1029\/2009WR008318"},{"key":"ref_37","first-page":"61","article-title":"Three-dimensional forest cannopy structure from terrestrial laser scanning","volume":"13","author":"Danson","year":"2006","journal-title":"Koukal Schneider"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"320","DOI":"10.5589\/m08-027","article-title":"Retrieving crown leaf area index from an individual tree using ground-based lidar data","volume":"34","author":"Moorthy","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.isprsjprs.2017.06.006","article-title":"Retrieving the gap fraction, element clumping index, and leaf area index of individual trees using single-scan data from a terrestrial laser scanner","volume":"130","author":"Li","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/j.ecolind.2015.10.034","article-title":"Estimation of big sagebrush leaf area index with terrestrial laser scanning","volume":"61","author":"Olsoy","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1080\/01431160512331337961","article-title":"Measuring forest structure with terrestrial laser scanning","volume":"26","author":"Watt","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.rse.2015.02.023","article-title":"Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR","volume":"164","author":"Greaves","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1109\/TGRS.2015.2481492","article-title":"Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning data","volume":"54","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1791\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:33Z","timestamp":1760188293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,31]]},"references-count":44,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11151791"],"URL":"https:\/\/doi.org\/10.3390\/rs11151791","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,31]]}}}