{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:57:11Z","timestamp":1761897431179,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T00:00:00Z","timestamp":1606867200000},"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>Airborne lidar scanner (ALS) technology is used in a variety of applications, including forestry. ALS has enormous potential for the estimation of relevant biometric parameters in forest plantations. This study investigates the use of an object-oriented semi-automated segmentation algorithm for stands delineation, based on modeling ALS data, in plantations of Eucalyptus grandis and E. dunnii in Uruguay. The results show that non-parametric methods delivered more accurate and less biased results for total volume (TV) with R2 0.93, RMSE 20.04 m3 h\u22121 for E. grandis and R2 0.93, RMSE 18.43 m3 h\u22121 for E.dunnii; and above ground biomass (AGB) with R2 0.95, RMSE 70.2 kg h\u22121 for E. grandis and R2 0.96, RMSE: 71.2 Kg h\u22121 for E. dunnii. Parametric methods performed better for dominant height (Ho) with R2 0.98, RMSE 0.67 m and R2: 0.96, RMSE: 0.8 m for E. grandis and E. dunnii, respectively. The most informative ALS metrics for the estimation of AGB and TV were metrics related to the elevation in parametric models (Elev.70 and Elev.75), while for the non-parametric models (k-NN) they were Elev.75 and canopy density. For Ho, the ALS metrics selected were also related to elevation both in the parametric (Elev.90 and Elev.99) and random forest models (Elev.max and Elev.75). The segmentation methodology proposed here matched closely the segments delineated by human operators, and provides a low-cost, cost-effective, easy to apply and update model aimed at generating AGB or TV maps for harvest tasks, based on rasters derived from ALS metrics. The present research shows the capacity of ALS metrics to improve extensive strategic inventories; validating and promoting the adoption of ALS technology for inventory forest stands of Eucalyptus spp. in Uruguay.<\/jats:p>","DOI":"10.3390\/rs12233947","type":"journal-article","created":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T20:25:49Z","timestamp":1606940749000},"page":"3947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2116-1095","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Hirigoyen","sequence":"first","affiliation":[{"name":"National Institute of Agricultural Research (Instituto Nacional de Investigaci\u00f3n Agropecuaria\u2014INIA Uruguay) Tacuaremb\u00f3, Ruta 5 km 386, Tacuaremb\u00f3 45000, Uruguay"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7455-3412","authenticated-orcid":false,"given":"M\u00aa Angeles","family":"Varo-Martinez","sequence":"additional","affiliation":[{"name":"Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 C\u00f3rdoba, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8621-7061","authenticated-orcid":false,"given":"Cecilia","family":"Rachid-Casnati","sequence":"additional","affiliation":[{"name":"National Institute of Agricultural Research (Instituto Nacional de Investigaci\u00f3n Agropecuaria\u2014INIA Uruguay) Tacuaremb\u00f3, Ruta 5 km 386, Tacuaremb\u00f3 45000, Uruguay"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8082-4007","authenticated-orcid":false,"given":"Jorge","family":"Franco","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy, University of the Republic, Paysand\u00fa 11200, Uruguay"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3470-8640","authenticated-orcid":false,"given":"Rafael M\u00aa","family":"Navarro-Cerrillo","sequence":"additional","affiliation":[{"name":"Department of Forestry Engineering, Laboratory of Silviculture, Dendrochronology and Climate Change, DendrodatLab-ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 C\u00f3rdoba, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,2]]},"reference":[{"key":"ref_1","first-page":"313","article-title":"Light detection and ranging-based measures of mixed hardwood forest structure","volume":"56","author":"Hawbaker","year":"2010","journal-title":"For. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ho\u015bci\u0142o, A., and Lewandowska, A. (2019). Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11080929"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0924-2716(97)83000-6","article-title":"Determination of mean tree height of forest stands using airborne laser scanner data","volume":"52","author":"Naesset","year":"1997","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Ferreiro, E., Arellano-P\u00e9rez, S., Castedo-Dorado, F., Hevia, A., Vega, J.A., Vega-Nieva, D., \u00c1lvarez-Gonz\u00e1lez, J.G., and Ruiz-Gonz\u00e1lez, A.D. (2017). Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176114"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cao, L., Mulverhill, C., Liu, H., Pang, Y., and Li, Z. (2019). Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests. Forests, 10.","DOI":"10.3390\/f10020125"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.5589\/m13-011","article-title":"How did we get here? An early history of forestry lidar1","volume":"39","author":"Nelson","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2014.10.004","article-title":"Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data","volume":"156","author":"Bouvier","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2015.11.012","article-title":"Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass","volume":"173","author":"Gregoire","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"119","article-title":"Evolutionary feature selection to estimate forest stand variables using LiDAR","volume":"26","author":"Miranda","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","first-page":"335","article-title":"The stand: Revisiting a central concept in forestry","volume":"111","author":"Nagel","year":"2013","journal-title":"J. For."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"207","DOI":"10.5194\/isprs-archives-XLI-B3-207-2016","article-title":"Forest stand segmentation using airborne lidar data and very high resolution multispectral imagery","volume":"41","author":"Dechesne","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1080\/02827580260417215","article-title":"Tree species classification using semi-automatic delineation of trees on aerial images","volume":"17","author":"Haara","year":"2002","journal-title":"Scand. J. For. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sanchez-Lopez, N., Boschetti, L., and Hudak, A. (2018). Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy. Remote Sens., 10.","DOI":"10.3390\/rs10101622"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.isprsjprs.2011.02.006","article-title":"Unsupervised image segmentation evaluation and refinement using a multi-scale approach","volume":"66","author":"Johnson","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","unstructured":"Casta\u00f1o, J.P., Gim\u00e9nez, A., Ceroni, M., Furest, J., Aunchayna, R., and Bidegain, M. (2011). Caracterizaci\u00f3n Agroclim\u00e1tica del Uruguay 1980\u20132009, Instituto de Investigaciones Agropecuarias. Serie T\u00e9cnica INIA 193."},{"key":"ref_16","unstructured":"Andr\u00e9s, H., Rafael, N., Jorge, F., Maurizio, B., and Cecilia, R. (2020). Modelling Taper, Total and Merchantable Stem Volume Considering Stand Density Effects to Eucalyptus Grandis and Eucalyptus Dunnii in Uruguay. iForest, in press."},{"key":"ref_17","unstructured":"McGaughey, R.J. (2013). FUSION\/LDV: Software for LiDAR Data Analysis and Visualization. February 2013\u2013FUSION Version 3.30, USDA Forest Service, Pacific Northwest Research Station, University of Washington."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1590\/0001-3765201820160071","article-title":"Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation","volume":"90","author":"Silva","year":"2018","journal-title":"An. Acad. Bras. Ci\u00eanc."},{"key":"ref_19","unstructured":"Lumley, T. (2020, August 01). Using Fortran Code by Alan Miller. Leaps: Regression Subset Selection, Available online: http:\/\/CRAN.R-project.org\/package=leaps2009."},{"key":"ref_20","unstructured":"R Development Core Team (2013). R Core Development Team A Language and Environment for Statistical Computing: Reference Index Version 3.5.1, R Development Core Team."},{"key":"ref_21","first-page":"87","article-title":"Some comments on Cp","volume":"42","author":"Mallows","year":"2000","journal-title":"Technometrics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.agrformet.2012.05.019","article-title":"Allometric equation choice impacts lidar-based forest biomass estimates: A case study from the Sierra National Forest, CA","volume":"165","author":"Zhao","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","unstructured":"Elzhov, T.V., Mullen, K.M., Spiess, A.-N., Bolker, B., Mullen, M.K.M., and Suggests, M. (2020, November 20). Package \u2018minpack.lm.\u2019 Title R Interface Levenberg-Marquardt Nonlinear Least-Sq. Algorithm Found MINPACK Plus Support Bounds\u2019, Available online: https:\/\/cran.rproject.org\/web\/packages\/minpack.lm\/minpack.lm.pdf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Quinn, G.P., and Keough, M.J. (2002). Experimental Design and Data Analysis for Biologists, Cambridge University Press.","DOI":"10.1017\/CBO9780511806384"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v023.i10","article-title":"YaImpute: An R Package for kNN Imputation","volume":"23","author":"Crookston","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2016.01.015","article-title":"Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada","volume":"176","author":"Zald","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.rse.2017.11.018","article-title":"Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states","volume":"205","author":"Knapp","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"1137","article-title":"A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection","volume":"2","author":"Kohavi","year":"1995","journal-title":"Int. Jt. Conf. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.10.013","article-title":"Estimating Forest Canopy Fuel Parameters Using LIDAR Data","volume":"94","author":"Andersen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_30","unstructured":"Qgis, D.T. (2020, November 20). QGIS Geographic Information System, Open Source Geospatial Foundation, Available online: https:\/\/scholar.google.com\/scholar_lookup?title=QGIS%20Geographic%20Information%20System&author=QGIS%20Development%20Team&publication_year=2009#d=gs_cit&u=%2Fscholar%3Fq%3Dinfo%3AK9pErfIkywoJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Des."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, Z., Heikkinen, V., Hauta-Kasari, M., Parkkinen, J., and Tokola, T. (2013). Forest Stand Delineation Using a Hybrid Segmentation Approach Based on Airborne Laser Scanning Data, Springer.","DOI":"10.1007\/978-3-642-38886-6_10"},{"key":"ref_33","first-page":"54","article-title":"Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density","volume":"56","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1080\/15481603.2017.1287238","article-title":"A comparison of unsupervised segmentation parameter optimization approaches using moderate-and high-resolution imagery","volume":"54","author":"Grybas","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2015.01.009","article-title":"Segmentation quality evaluation using region-based precision and recall measures for remote sensing images","volume":"102","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1080\/01431160600617194","article-title":"Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation","volume":"27","author":"Espindola","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/LGRS.2011.2163056","article-title":"An unsupervised evaluation method for remotely sensed imagery segmentation","volume":"9","author":"Zhang","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2308","DOI":"10.3390\/rs5052308","article-title":"Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LIDAR","volume":"5","author":"Mora","year":"2013","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"B\u00f6ck, S., Immitzer, M., and Atzberger, C. (2017). On the Objectivity of the Objective Function\u2014Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy. Remote Sens., 9.","DOI":"10.3390\/rs9080769"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, L., Mansaray, L.R., Huang, J., and Wang, L. (2019). Optimal segmentation scale parameter, feature subset and classification algorithm for geographic object-based crop recognition using multisource satellite imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050514"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1016\/j.rse.2017.09.011","article-title":"Improving Lidar-based aboveground biomass estimation of temperate hardwood forests with varying site productivity","volume":"204","author":"Shao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.rse.2018.07.024","article-title":"Three decades of forest structural dynamics over Canada\u2019s forested ecosystems using Landsat time-series and lidar plots","volume":"216","author":"Matasci","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0034-4257(01)00243-7","article-title":"Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve","volume":"79","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.1109\/TGRS.2012.2211023","article-title":"Growth-competition-based stem diameter and volume modeling for tree-level forest inventory using airborne LiDAR data","volume":"51","author":"Lo","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5721\/EuJRS20164911","article-title":"Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest","volume":"49","author":"Rodriguez","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jayathunga, S., Owari, T., and Tsuyuki, S. (2019). Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass. Remote Sens., 11.","DOI":"10.3390\/rs11030338"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3947\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:40:45Z","timestamp":1760179245000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,2]]},"references-count":47,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233947"],"URL":"https:\/\/doi.org\/10.3390\/rs12233947","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,12,2]]}}}