{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:34:07Z","timestamp":1768473247438,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T00:00:00Z","timestamp":1593734400000},"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>In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study aims at predicting microhabitats in a mixed and multi-layered Mediterranean forest using Airborne Laser Scanning data through the implementation of a Machine Learning algorithm. The study focuses on the identification of LiDAR metrics useful for detecting microhabitats according to the recent hierarchical classification system for Tree-related Microhabitats, from single microhabitats to the habitat trees. The results demonstrate that Airborne Laser Scanning point clouds support the prediction of microhabitat abundance. Better prediction capabilities were obtained at a higher hierarchical level and for some of the single microhabitats, such as epiphytic bryophytes, root buttress cavities, and branch holes. Metrics concerned with tree height distribution and crown density are the most important predictors of microhabitats in a multi-layered forest.<\/jats:p>","DOI":"10.3390\/rs12132142","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T09:49:11Z","timestamp":1594028951000},"page":"2142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Machine Learning Algorithms to Predict Tree-Related Microhabitats using Airborne Laser Scanning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5373-5970","authenticated-orcid":false,"given":"Giovanni","family":"Santopuoli","sequence":"first","affiliation":[{"name":"Dipartimento Agricoltura, Ambiente e Alimenti, Universit\u00e0 degli Studi del Molise, Via De Sanctis s.n.c., 86100 Campobasso, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8898-7046","authenticated-orcid":false,"given":"Mirko","family":"Di Febbraro","sequence":"additional","affiliation":[{"name":"Dipartimento di Bioscienze e Territorio, Universit\u00e0 degli Studi del Molise, Cda Fonte Lappone, s.n.c., 86090 Pesche (IS), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4325-951X","authenticated-orcid":false,"given":"Mauro","family":"Maesano","sequence":"additional","affiliation":[{"name":"Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, via San Camillo de Lellis snc, 01100 Viterbo, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3571-2353","authenticated-orcid":false,"given":"Marco","family":"Balsi","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Elettronica e Telecomunicazioni, Universit\u00e0 \u201cLa Sapienza\u201d, 00184 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5275-5769","authenticated-orcid":false,"given":"Marco","family":"Marchetti","sequence":"additional","affiliation":[{"name":"Dipartimento di Bioscienze e Territorio, Universit\u00e0 degli Studi del Molise, Cda Fonte Lappone, s.n.c., 86090 Pesche (IS), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1150-8064","authenticated-orcid":false,"given":"Bruno","family":"Lasserre","sequence":"additional","affiliation":[{"name":"Dipartimento di Bioscienze e Territorio, Universit\u00e0 degli Studi del Molise, Cda Fonte Lappone, s.n.c., 86090 Pesche (IS), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"ref_1","unstructured":"Kraus, D., and Krumm, F. (2013). Habitat trees: Key elements for forest biodiversity. Integrative Approaches as an Opportunity for the Conservation of Forest Biodiversity, European Forest Insititute."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gro\u00dfmann, J., Schultze, J., Bauhus, J., and Pyttel, P. (2018). Predictors of Microhabitat Frequency and Diversity in Mixed Mountain Forests in South-Western Germany. Forests, 9.","DOI":"10.3390\/f9030104"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108437","DOI":"10.1016\/j.biocon.2020.108437","article-title":"Formerly managed forest reserves complement integrative management for biodiversity conservation in temperate European forests","volume":"242","author":"Leidinger","year":"2020","journal-title":"Biol. Conserv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.foreco.2018.09.043","article-title":"Predicting abundance and diversity of tree-related microhabitats in Central European montane forests from common forest attributes","volume":"432","author":"Asbeck","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"76","DOI":"10.3832\/ifor2617-011","article-title":"Biodiversity conservation and wood production in a Natura 2000 Mediterranean forest. A trade-off evaluation focused on the occurrence of microhabitats","volume":"12","author":"Santopuoli","year":"2019","journal-title":"iForest-Biogeosciences For."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Paillet, Y., Debaive, N., Archaux, F., Cateau, E., Gilg, O., and Guilbert, E. (2019). Nothing else matters? Tree diameter and living status have more effects than biogeoclimatic context on microhabitat number and occurrence: An analysis in French forest reserves. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216500"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1016\/j.foreco.2008.11.027","article-title":"Tree microhabitat structures as indicators of biodiversity in Douglas-Fir forests of different stand ages and management histories in the Pacific Northwest, U.S.A","volume":"257","author":"Michel","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_8","unstructured":"Kraus, D., Schuck, A., Bebi, P., Blaschke, M., B\u00fctler, R., Flade, M., Heintz, W., Krumm, F., Lachat, T., and Larrieu, L. (2017). Spatially Explicit Database of Tree Related Microhabitats (TreMs). Version 1.2. Integrate+ Project, Institut National de la Recherche Agronomique (INRA)."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ecolind.2017.08.051","article-title":"Tree related microhabitats in temperate and Mediterranean European forests: A hierarchical typology for inventory standardization","volume":"84","author":"Larrieu","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1139\/x2012-077","article-title":"Species, live status, and diameter are important tree features for diversity and abundance of tree microhabitats in subnatural montane beech-fir forests","volume":"42","author":"Larrieu","year":"2012","journal-title":"Can. J. For. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.biocon.2010.09.030","article-title":"Influence of tree characteristics and forest management on tree microhabitats","volume":"144","author":"Vuidot","year":"2011","journal-title":"Biol. Conserv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/j.1523-1739.2009.01399.x","article-title":"Biodiversity differences between managed and unmanaged forests: Meta-analysis of species richness in Europe","volume":"24","author":"Paillet","year":"2010","journal-title":"Conserv. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.foreco.2016.08.037","article-title":"Land ownership affects diversity and abundance of tree microhabitats in deciduous temperate forests","volume":"380","author":"Johann","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.foreco.2013.01.009","article-title":"Which factors influence the occurrence and density of tree microhabitats in Mediterranean oak forests?","volume":"295","author":"Regnery","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_15","first-page":"223","article-title":"Reconciling the Tradeoff between Economic and Ecological Objectives in Habitat-Tree Selection: A Comparison between Students, Foresters, and Forestry Trainers","volume":"65","author":"Cosyns","year":"2019","journal-title":"For. Sci."},{"key":"ref_16","first-page":"294","article-title":"Assessing forest naturalness","volume":"58","author":"McRoberts","year":"2012","journal-title":"For. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.agrformet.2016.11.011","article-title":"Large-scale estimation of xylem phenology in black spruce through remote sensing","volume":"233","author":"Antonucci","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1080\/17445647.2016.1145151","article-title":"Copernicus high-resolution layers for land cover classification in Italy","volume":"12","author":"Congedo","year":"2016","journal-title":"J. Maps"},{"key":"ref_19","first-page":"288","article-title":"Airborne laser scanning of forest resources: An overview of research in Italy as a commentary case study","volume":"23","author":"Montaghi","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2015.09.016","article-title":"Estimating and mapping forest structural diversity using airborne laser scanning data","volume":"170","author":"Mura","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.07.038","article-title":"The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas","volume":"200","author":"Santi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.11.010","article-title":"Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework","volume":"174","author":"Chirici","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Giannetti, F., Puletti, N., Puliti, S., Travaglini, D., and Chirici, G. (2020). Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests. Ecol. Indic., 117.","DOI":"10.1016\/j.ecolind.2020.106513"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2018.05.016","article-title":"A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data","volume":"213","author":"Giannetti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15","DOI":"10.14214\/sf.10247","article-title":"Large-Scale two-phase estimation of wood production by poplar plantations exploiting sentinel-2 data as auxiliary information","volume":"54","author":"Marcelli","year":"2020","journal-title":"Silva Fenn."},{"key":"ref_26","first-page":"377","article-title":"Combination of optical and LiDAR satellite imagery with forest inventory data to improve wall-to-wall assessment of growing stock in Italy","volume":"26","author":"Maselli","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.biombioe.2010.10.013","article-title":"Assessment of potential bioenergy from coppice forests trough the integration of remote sensing and field surveys","volume":"35","author":"Lasserre","year":"2011","journal-title":"Biomass Bioenergy"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1080\/22797254.2018.1482733","article-title":"Integrating terrestrial and airborne laser scanning for the assessment of single-tree attributes in Mediterranean forest stands","volume":"51","author":"Giannetti","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rehush, N., Abegg, M., Waser, L.T., and Br\u00e4ndli, U.-B. (2018). Identifying Tree-Related Microhabitats in TLS Point Clouds Using Machine Learning. Remote Sens., 10.","DOI":"10.3390\/rs10111735"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, T., Popescu, S.C., Lawing, A.M., Eriksson, M., Strimbu, B.M., and B\u00fcrkner, P.C. (2018). Bayesian and classical machine learning methods: A Comparison for tree species classification with LiDAR waveform signatures. Remote Sens., 10.","DOI":"10.3390\/rs10010039"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1214\/11-AOAS509","article-title":"Properties of design-based estimation under stratified spatial sampling with application to canopy coverage estimation","volume":"6","author":"Barabesi","year":"2012","journal-title":"Ann. Appl. Stat."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3832\/ifor1529-008","article-title":"Spatially explicit estimation of forest age by integrating remotely sensed data and inverse yield modeling techniques","volume":"9","author":"Frate","year":"2016","journal-title":"IForest"},{"key":"ref_33","unstructured":"Kraus, D., B\u00fctler, R., Krumm, F., Lachat, T., Larrieu, L., Mergner, U., Paillet, Y., Rydkvist, T., Schuck, A., and Winter, S. (2016). Catalogue of Tree Microhabitats\u2014Reference Field List, European Forest Institute. Integrate+TechnicalPaper.16p."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e5457","DOI":"10.7717\/peerj.5457","article-title":"Global mapping of potential natural vegetation: An assessment of machine learning algorithms for estimating land potential","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_35","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.rse.2019.04.006","article-title":"Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data","volume":"227","author":"Tompalski","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building predictive models in R using the caret package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0224365","article-title":"Machine learning algorithm validation with a limited sample size","volume":"14","author":"Vabalas","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","article-title":"Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Stat. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1111\/2041-210X.12403","article-title":"Overcoming limitations of modelling rare species by using ensembles of small models","volume":"6","author":"Breiner","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolmodel.2018.10.023","article-title":"Using macroecological constraints on spatial biodiversity predictions under climate change: The modelling method matters","volume":"390","author":"Raia","year":"2018","journal-title":"Ecol. Model."},{"key":"ref_43","first-page":"45","article-title":"A new typology design of performance metrics to measure errors in machine learning regression algorithms","volume":"14","author":"Botchkarev","year":"2019","journal-title":"Interdiscip. J. Inf. Knowl. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.foreco.2018.10.040","article-title":"Relationships between stand structural attributes and saproxylic beetle abundance in a Mediterranean broadleaved mixed forest","volume":"432","author":"Parisi","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sa\u010dkov, I., Santopuoli, G., Bucha, T., Lasserre, B., and Marchetti, M. (2016). Forest inventory attribute prediction using lightweight aerial scanner data in a selected type of multilayered deciduous forest. Forests, 7.","DOI":"10.3390\/f7120307"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s10531-012-0415-y","article-title":"Factors influencing epiphytic bryophyte and lichen species richness at different spatial scales in managed temperate forests","volume":"22","author":"Nascimbene","year":"2013","journal-title":"Biodivers. Conserv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1080\/11263504.2014.948524","article-title":"First mapping of the main high conservation value forests (HCVFs) at national scale: The case of Italy","volume":"150","author":"Maesano","year":"2016","journal-title":"Plant Biosyst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/1523908X.2015.1065718","article-title":"Implementing Criteria and Indicators for Sustainable Forest Management in a Decentralized Setting: Italy as a Case Study","volume":"18","author":"Santopuoli","year":"2016","journal-title":"J. Environ. Policy Plan."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.ecolind.2015.09.009","article-title":"Evaluating the implementation of the Pan-European Criteria and indicators for sustainable forest management\u2014A SWOT analysis","volume":"60","author":"Wolfslehner","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1016\/j.foreco.2007.10.029","article-title":"Microhabitats in lowland beech forests as monitoring tool for nature conservation","volume":"255","author":"Winter","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"344","DOI":"10.2980\/19-4-3506","article-title":"Deadwood occurrence and forest structure as indicators of old-growth forest conditions in Mediterranean mountainous ecosystems","volume":"19","author":"Lombardi","year":"2012","journal-title":"ECOSCIENCE"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.foreco.2017.10.052","article-title":"Community fingerprinting reveals increasing wood-inhabiting fungal diversity in unmanaged Mediterranean forests","volume":"408","author":"Pioli","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_53","first-page":"1","article-title":"Mapping forest ecosystem functions for landscape planning in a mountain Natura2000 site, Central Italy","volume":"0568","author":"Vizzarri","year":"2014","journal-title":"J. Environ. Plan. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"366","DOI":"10.17221\/45\/2016-JFS","article-title":"Social perception of forest multifunctionality in southern Italy: The case of Calabria Region","volume":"62","author":"Pastorella","year":"2016","journal-title":"J. For. Sci."},{"key":"ref_55","unstructured":"Gasparini, P., and Tabacchi, G. (2011). L\u2019Inventario Nazionale delle Foreste e dei serbatoi forestali di Carbonio INFC 2005. Secondo inventario forestale nazionale italiano. Metodi e Risultati, Edagricole-Il Sole 24 ore."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3832\/ifor0603-009","article-title":"Application of indicators network analysis to support local forest management plan development: A case study in Molise, Italy","volume":"5","author":"Santopuoli","year":"2012","journal-title":"iForest-Biogeosci. For."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"526","DOI":"10.3832\/ifor2668-011","article-title":"Forest certification map of Europe","volume":"11","author":"Maesano","year":"2018","journal-title":"IForest"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/11263504.2014.948525","article-title":"Dynamics of the silver fir (Abies alba Mill.) natural regeneration in a mixed forest in the Central Apennine","volume":"150","author":"Santopuoli","year":"2016","journal-title":"Plant Biosyst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:47:00Z","timestamp":1760176020000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,3]]},"references-count":58,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12132142"],"URL":"https:\/\/doi.org\/10.3390\/rs12132142","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,3]]}}}