{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T06:17:20Z","timestamp":1772605040364,"version":"3.50.1"},"reference-count":150,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T00:00:00Z","timestamp":1641340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RTI2018-096884-B-C32"],"award-info":[{"award-number":["RTI2018-096884-B-C32"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Community of Madrid Region under the framework of the multi-year agreement with the University of Alcal\u00e1 (Stimulus to Excellence for Permanent University Professors)","award":["EPU-INV\/2020\/010"],"award-info":[{"award-number":["EPU-INV\/2020\/010"]}]},{"name":"Spanish Ministry of Science &quot;Juan de la Cierva Formaci\u00f3n&quot;","award":["FJC2018-037870-I"],"award-info":[{"award-number":["FJC2018-037870-I"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer\u2019s accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region.<\/jats:p>","DOI":"10.3390\/rs14010235","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Identifying Forest Structural Types along an Aridity Gradient in Peninsular Spain: Integrating Low-Density LiDAR, Forest Inventory, and Aridity Index"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9576-4780","authenticated-orcid":false,"given":"Juli\u00e1n","family":"Tijer\u00edn-Trivi\u00f1o","sequence":"first","affiliation":[{"name":"Grupo de Ecolog\u00eda Forestal y Restauraci\u00f3n (FORECO), Departamento de Ciencias de la Vida, Universidad de Alcal\u00e1, Alcal\u00e1 de Henares, 28805 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9597-6609","authenticated-orcid":false,"given":"Daniel","family":"Moreno-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Grupo de Ecolog\u00eda Forestal y Restauraci\u00f3n (FORECO), Departamento de Ciencias de la Vida, Universidad de Alcal\u00e1, Alcal\u00e1 de Henares, 28805 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1456-0132","authenticated-orcid":false,"given":"Miguel A.","family":"Zavala","sequence":"additional","affiliation":[{"name":"Grupo de Ecolog\u00eda Forestal y Restauraci\u00f3n (FORECO), Departamento de Ciencias de la Vida, Universidad de Alcal\u00e1, Alcal\u00e1 de Henares, 28805 Madrid, Spain"}]},{"given":"Julen","family":"Astigarraga","sequence":"additional","affiliation":[{"name":"Grupo de Ecolog\u00eda Forestal y Restauraci\u00f3n (FORECO), Departamento de Ciencias de la Vida, Universidad de Alcal\u00e1, Alcal\u00e1 de Henares, 28805 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6260-5791","authenticated-orcid":false,"given":"Mariano","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Grupo de Teledetecci\u00f3n Ambiental, Departamento de Geolog\u00eda, Geograf\u00eda y Medioambiente, Universidad de Alcal\u00e1, Alcal\u00e1 de Henares, 28805 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"160521","DOI":"10.1098\/rsos.160521","article-title":"The importance of forest structure to biodiversity-productivity relationships","volume":"4","author":"Bohn","year":"2017","journal-title":"R. Soc. Open Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.foreco.2018.09.057","article-title":"Biodiversity response to forest structure and management: Comparing species richness, conservation relevant species and functional diversity as metrics in forest conservation","volume":"432","author":"Lelli","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1002\/hyp.9907","article-title":"Wildfire effects on extractable elements in ash from a Pinus pinaster forest in Portugal","volume":"28","author":"Pereira","year":"2014","journal-title":"Hydrol. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1071\/WR17168","article-title":"Experimental evaluation of the initial effects of large-scale thinning on structure and biodiversity of river red gum (Eucalyptus camaldulensis) forests","volume":"45","author":"Gonsalves","year":"2018","journal-title":"Wildl. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10342-012-0673-y","article-title":"Productivity of mixed versus pure stands of oak (Quercus petraea (M att.) L iebl. and Quercus robur L.) and European beech (Fagus sylvatica L.) along an ecological gradient","volume":"132","author":"Pretzsch","year":"2013","journal-title":"Eur. J. For. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21289","DOI":"10.1073\/pnas.0914211107","article-title":"Forest responses to increasing aridity and warmth in the southwestern United States","volume":"107","author":"Williams","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2400","DOI":"10.1111\/j.1365-2486.2011.02421.x","article-title":"Disentangling the relative importance of climate, size and competition on tree growth in Iberian forests: Implications for forest management under global change","volume":"17","author":"Zavala","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ruiz-Benito, P., Lines, E.R., G\u00f3mez-Aparicio, L., Zavala, M.A., and Coomes, D.A. (2013). Patterns and Drivers of Tree Mortality in Iberian Forests: Climatic Effects Are Modified by Competition. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0056843"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s10342-015-0912-0","article-title":"Climate modifies tree interactions in terms of basal area growth and mortality in monospecific and mixed Fagus sylvatica and Pinus sylvestris forests","volume":"134","year":"2015","journal-title":"Eur. J. For. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.foreco.2017.09.031","article-title":"Effects of thinning intensities on tree water use, growth, and resultant water use efficiency of 50-year-old Pinus koraiensis forest over four years","volume":"408","author":"Park","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2842","DOI":"10.1111\/j.1365-2486.2011.02452.x","article-title":"Unraveling the drivers of intensifying forest disturbance regimes in Europe","volume":"17","author":"Seidl","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1002\/eap.1631","article-title":"Last-century forest productivity in a managed dry-edge Scots pine population: The two sides of climate warming","volume":"28","author":"Zavala","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Madrigal-Gonz\u00e1lez, J., Ballesteros-C\u00e1novas, J.A., Zavala, M.A., Morales-Molino, C., and Stoffel, M. (2020). Forest stocks control long-term climatic mortality risks in Scots pine dry-edge forests. Ecosphere, 11.","DOI":"10.1002\/ecs2.3201"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5063","DOI":"10.1111\/gcb.15198","article-title":"Evidence of non-stationary relationships between climate and forest responses: Increased sensitivity to climate change in Iberian forests","volume":"26","author":"Astigarraga","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"McDowell, N.G., Allen, C.D., Anderson-Teixeira, K., Aukema, B.H., Bond-Lamberty, B., Chini, L., Clark, J.S., Dietze, M., Grossiord, C., and Hanbury-Brown, A. (2020). Pervasive shifts in forest dynamics in a changing world. Science, 368.","DOI":"10.1126\/science.aaz9463"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1111\/ele.12868","article-title":"Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality","volume":"21","author":"Plas","year":"2018","journal-title":"Ecol. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Torresan, C., Corona, P., Scrinzi, G., and Marsal, J.V. (2016). Using classification trees to predict forest structure types from LiDAR data. Ann. For. Res., 59.","DOI":"10.15287\/afr.2016.423"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Neuville, R., Bates, J., and Jonard, F. (2021). Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13030352"},{"key":"ref_19","first-page":"244","article-title":"Improved stand structure characterization from nested plot designs in the Spanish National Forest Inventory","volume":"94","author":"Alberdi","year":"2021","journal-title":"For. An Int. J. For. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1111\/j.1654-1103.2002.tb02066.x","article-title":"Trends in savanna structure and composition along an aridity gradient in the Kalahari","volume":"13","author":"Scholes","year":"2002","journal-title":"J. Veg. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1139\/x11-188","article-title":"Early responses to thinning treatments designed to accelerate late successional forest structure in young coniferous stands of western Oregon, USA","volume":"42","author":"Dodson","year":"2012","journal-title":"Can. J. For. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e01902","DOI":"10.1002\/eap.1902","article-title":"Forest structure and climate mediate drought-induced tree mortality in forests of the Sierra Nevada, USA","volume":"29","author":"Restaino","year":"2019","journal-title":"Ecol. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10113-014-0621-0","article-title":"Forest fires and adaptation options in Europe","volume":"16","author":"Khabarov","year":"2016","journal-title":"Reg. Environ. Chang."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.envexpbot.2014.11.006","article-title":"Functional traits and adaptive capacity of European forests to climate change","volume":"111","author":"Bussotti","year":"2015","journal-title":"Environ. Exp. Bot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.foreco.2009.09.023","article-title":"Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems","volume":"259","author":"Lindner","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chirici, G., Winter, S., and McRoberts, R.E. (2011). National Forest Inventories: Contributions to Forest Biodiversity Assessments, Springer Science & Business Media.","DOI":"10.1007\/978-94-007-0482-4"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108870","DOI":"10.1016\/j.ecolmodel.2019.108870","article-title":"Available and missing data to model impact of climate change on European forests","volume":"416","author":"Vacchiano","year":"2020","journal-title":"Ecol. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.foreco.2013.09.007","article-title":"National Forest Inventory and forest observational studies in Spain: Applications to forest modeling","volume":"316","author":"Canellas","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_29","first-page":"105","article-title":"Identifying forest structure types using National Forest Inventory Data: The case of sessile oak forest in the Cantabrian range","volume":"17","author":"Reque","year":"2008","journal-title":"Investig. Agrar. Sist. Recur."},{"key":"ref_30","first-page":"159","article-title":"Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery","volume":"66","author":"Garcia","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","first-page":"34","article-title":"Los inventarios forestales nacionales: Una herramienta para la gesti\u00f3n, la planificaci\u00f3n y la investigaci\u00f3n","volume":"57","year":"2013","journal-title":"Foresta"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Simard, M., Pinto, N., Fisher, J., and Baccini, A. (2011). Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci., 116.","DOI":"10.1029\/2011JG001708"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ecoinf.2017.01.005","article-title":"Regional mapping of vegetation structure for biodiversity monitoring using airborne lidar data","volume":"38","author":"Guo","year":"2017","journal-title":"Ecol. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/07038992.2017.1259556","article-title":"Using Landsat Time-Series and LiDAR to Inform Aboveground Forest Biomass Baselines in Northern Minnesota, USA","volume":"43","author":"Deo","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1139\/cjfr-2013-0401","article-title":"Mapping attributes of Canada\u2019s forests at moderate resolution through kNN and MODIS imagery","volume":"44","author":"Beaudoin","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1080\/10106049.2016.1265595","article-title":"An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery","volume":"33","author":"Ruiz","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1016\/j.rse.2009.03.004","article-title":"Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data","volume":"113","author":"Wulder","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2006.09.034","article-title":"Remote sensing support for national forest inventories","volume":"110","author":"McRoberts","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2017.12.017","article-title":"The shelf-life of airborne laser scanning data for enhancing forest inventory inferences","volume":"206","author":"McRoberts","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1080\/01431161.2020.1813346","article-title":"Using enhanced data co-registration to update Spanish National Forest Inventories (NFI) and to reduce training data under LiDAR-assisted inference","volume":"42","author":"Pascual","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.foreco.2018.12.012","article-title":"Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern","volume":"434","author":"Wiggins","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1139\/X10-064","article-title":"Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data","volume":"40","author":"Kane","year":"2010","journal-title":"Can. J. For. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1139\/X10-024","article-title":"Comparisons between field-and LiDAR-based measures of stand structural complexity","volume":"40","author":"Kane","year":"2010","journal-title":"Can. J. For. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1111\/2041-210X.12510","article-title":"Using discrete-return airborne laser scanning to quantify number of canopy strata across diverse forest types","volume":"7","author":"Wilkes","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.agrformet.2015.02.012","article-title":"Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems","volume":"205","author":"Woodgate","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.agrformet.2004.02.005","article-title":"Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests","volume":"124","author":"Valladares","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2018.04.015","article-title":"Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012\u20132016)","volume":"211","author":"Magnussen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","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":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1109\/JSTARS.2018.2816962","article-title":"Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study from Central Gabon","volume":"11","author":"Silva","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2012.07.007","article-title":"Measuring gap fraction, element clumping index and LAI in Sierra Forest stands using a full-waveform ground-based lidar","volume":"125","author":"Zhao","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2015.01.030","article-title":"Canopy clumping appraisal using terrestrial and airborne laser scanning","volume":"161","author":"Garcia","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"447","DOI":"10.5589\/m09-038","article-title":"A cross-comparison of field, spectral, and lidar estimates of forest canopy cover","volume":"35","author":"Smith","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111262","DOI":"10.1016\/j.rse.2019.111262","article-title":"Characterizing global forest canopy cover distribution using spaceborne lidar","volume":"231","author":"Tang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.rse.2016.10.024","article-title":"Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data","volume":"194","author":"Valbuena","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.foreco.2018.10.057","article-title":"A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions","volume":"433","author":"Adnan","year":"2019","journal-title":"For. Ecol. Manage."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111770","DOI":"10.1016\/j.rse.2020.111770","article-title":"Detection of sub-canopy forest structure using airborne LiDAR","volume":"244","author":"Jarron","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Shoot, C., Andersen, H.-E., Moskal, L., Babcock, C., Cook, B., and Morton, D. (2021). Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data. Remote Sens., 13.","DOI":"10.3390\/rs13101863"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-017-0073-1","article-title":"Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR","volume":"12","author":"Garcia","year":"2017","journal-title":"Carbon Balance Manag."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1016\/j.rse.2010.02.009","article-title":"Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests","volume":"114","author":"Disney","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-015-0013-x","article-title":"Airborne lidar-based estimates of tropical forest structure in complex terrain: Opportunities and trade-offs for REDD+","volume":"10","author":"Leitold","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3390\/f5050936","article-title":"Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates","volume":"5","author":"Ruiz","year":"2014","journal-title":"Forests"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2640","DOI":"10.1016\/j.rse.2011.05.020","article-title":"Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR","volume":"115","author":"Richardson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2020.11.008","article-title":"Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning","volume":"172","author":"Pourshamsi","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Douss, R., Ferah, I.R., Durrieu, S., and de Boissieu, F. (2020, January 9\u201311). Regression analyses to study the benefit of Sentinel and LIDAR data fusion for forest structure. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105240"},{"key":"ref_66","unstructured":"Lorite Mart\u00ednez, S., Ojeda Manrique, J.C., Rodr\u00edguez-Cuenca, B., Gonz\u00e1lez Crist\u00f3bal, E., and Mu\u00f1oz, P. (2017, January 4\u20136). Procesado y distribuci\u00f3n de nubes de puntos en el proyecto PNOA-LiDAR. Proceedings of the XVII Congreso de la Asociaci\u00f3n Espa\u00f1ola de Teledetecci\u00f3n, Murcia, Spain."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Gonzalez-Ferreiro, E., Arellano-P\u00e9rez, S., Castedo-Dorado, F., Hevia, A., Vega, J.A., Vega-Nieva, D.J., \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_68","doi-asserted-by":"crossref","unstructured":"Revilla, S., Lamelas, M., Domingo, D., de la Riva, J., Montorio, R., Montealegre, A., and Garc\u00eda-Mart\u00edn, A. (2021). Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation\u2014Application to Fuel Type Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13030342"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1109\/JSTARS.2018.2835483","article-title":"Classification of Airborne Multispectral Lidar Point Clouds for Land Cover Mapping","volume":"11","author":"Ekhtari","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Gorgoso-Varela, J.J., Ponce, R.A., and Rodr\u00edguez-Puerta, F. (2021). Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Arag\u00f3n (Northeast Spain). Remote Sens., 13.","DOI":"10.3390\/rs13122307"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1038\/s41598-021-81267-8","article-title":"Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong","volume":"11","author":"Chan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_72","first-page":"352","article-title":"The importance of small-scale heterogeneity in boreal forests: Variation in diversity in forest-floor invertebrates across the succession gradient","volume":"19","author":"Haila","year":"1996","journal-title":"Ecography"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s40663-017-0098-7","article-title":"Dead standing pine trees in a boreal forest landscape in the Kalevala National Park, northern Fennoscandia: Amount, population characteristics and spatial pattern","volume":"4","author":"Kuuluvainen","year":"2017","journal-title":"For. Ecosyst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s10533-007-9166-3","article-title":"Decomposition of soil organic matter from boreal black spruce forest: Environmental and chemical controls","volume":"87","author":"Wickland","year":"2008","journal-title":"Biogeochemistry"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.tree.2009.03.019","article-title":"Urgent preservation of boreal carbon stocks and biodiversity","volume":"24","author":"Bradshaw","year":"2009","journal-title":"Trends Ecol. Evol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.foreco.2017.10.011","article-title":"Factors affecting forest dynamics in the Iberian Peninsula from 1987 to 2012. The role of topography and drought","volume":"406","author":"Ninyerola","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_77","unstructured":"de Castro, M., Mart\u00edn-Vide, J., Contributing, S.A., Abaurrea, J., As\u00edn, J., Barriendos, M., Brunet, M., Creus, J., Gal\u00e1n, E., and Gaertner, M.A. (2005). Impacts of Climatic Change in Spain 1. The Climate of Spain: Past, Present and Scenarios for the 21 St Century. A Preliminary Assessment of the Impacts in Spain Due to the Effects of Climate Change, ECCE Project Report."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1139\/X06-317","article-title":"Soil organic carbon dynamics along a climatic gradient in a southern Appalachian spruce\u2013fir forest","volume":"37","author":"Tewksbury","year":"2007","journal-title":"Can. J. For. Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1111\/j.1466-8238.2010.00645.x","article-title":"Variation in above-ground forest biomass across broad climatic gradients. Glob","volume":"20","author":"Stegen","year":"2011","journal-title":"Ecol. Biogeogr."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.foreco.2018.01.017","article-title":"Intra- and inter-specific variation of the maximum size-density relationship along an aridity gradient in Iberian pinewoods","volume":"411","author":"Aguirre","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_81","first-page":"3","article-title":"L\u2019indice d\u2019aridit\u00e9","volume":"3","year":"1926","journal-title":"Bull. Assoc. G\u00e9ographes Fran\u00e7ais"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"118302","DOI":"10.1016\/j.foreco.2020.118302","article-title":"Crown plasticity of five pine species in response to competition along an aridity gradient","volume":"473","author":"Aguirre","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/2052-336X-12-2","article-title":"Changes in the forest ecosystems in areas impacted by aridization in south-western Romania","volume":"12","author":"Pravalie","year":"2014","journal-title":"J. Environ. Health Sci. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"88","DOI":"10.7818\/ECOS.2016.25-3.10","article-title":"El Inventario Forestal Nacional espa\u00f1ol, una herramienta para el conocimiento, la gesti\u00f3n y la conservaci\u00f3n de los ecosistemas forestales arbolados","volume":"25","author":"Alberdi","year":"2016","journal-title":"Ecosistemas"},{"key":"ref_86","unstructured":"McGaughey, R.J. (2018). FUSION\/LDV: Software for LIDAR Data Analysis and Visualization, FUSION Version 3.80."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). Data mining, inference, and prediction. The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.procs.2016.02.095","article-title":"Analysis of K-Means and K-Medoids Algorithm for Big Data","volume":"78","author":"Arora","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v061.i06","article-title":"NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set","volume":"61","author":"Charrad","year":"2014","journal-title":"J. Stat. Softw."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1111\/j.1541-0420.2007.00784.x","article-title":"Determining the Number of Clusters Using the Weighted Gap Statistic","volume":"63","author":"Yan","year":"2007","journal-title":"Biometrics"},{"key":"ref_91","unstructured":"Mohajer, M., Englmeier, K.-H., and Schmid, V.J. (2011). A comparison of Gap statistic definitions with and without logarithm function. arXiv."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1002\/2015JG003315","article-title":"Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data","volume":"122","author":"Garcia","year":"2017","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Silva, C.A., Hudak, A.T., Vierling, L.A., Klauberg, C., Garcia, M., Ferraz, A., Keller, M., Eitel, J., and Saatchi, S. (2017). Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest. Remote Sens., 9.","DOI":"10.3390\/rs9101068"},{"key":"ref_94","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_95","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1037\/a0016973","article-title":"An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests","volume":"14","author":"Strobl","year":"2009","journal-title":"Psychol. Methods"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.rse.2006.10.010","article-title":"Comparative assessment of the measures of thematic classification accuracy","volume":"107","author":"Liu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Detto, M., Muller-Landau, H., Mascaro, J., and Asner, G. (2013). Hydrological Networks and Associated Topographic Variation as Templates for the Spatial Organization of Tropical Forest Vegetation. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0076296"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolmodel.2012.03.007","article-title":"Evaluating effectiveness of down-sampling for stratified designs and unbalanced prevalence in Random Forest models of tree species distributions in Nevada","volume":"233","author":"Freeman","year":"2012","journal-title":"Ecol. Model."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1016\/j.foreco.2008.02.055","article-title":"Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands","volume":"255","author":"Pascual","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_101","first-page":"36","article-title":"An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data\u2014A case study in complex temperate forest stands","volume":"57","author":"Abdullahi","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2018.04.005","article-title":"A data-driven framework to identify and compare forest structure classes using LiDAR","volume":"211","author":"Moran","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1556\/ComEc.6.2005.1.7","article-title":"Hierarchical clusters of vegetation types","volume":"6","author":"Wallace","year":"2005","journal-title":"Community Ecol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1111\/j.1654-1103.2010.01211.x","article-title":"The management of vegetation classifications with fuzzy clustering","volume":"21","author":"Font","year":"2010","journal-title":"J. Veg. Sci."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.agrformet.2015.04.013","article-title":"Novel forest structure metrics from airborne LiDAR data for improved snow interception estimation","volume":"208","author":"Moeser","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2013.07.041","article-title":"Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park","volume":"151","author":"Kane","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Karna, Y.K., Penman, T.D., Aponte, C., and Bennett, L.T. (2019). Assessing Legacy Effects of Wildfires on the Crown Structure of Fire-Tolerant Eucalypt Trees Using Airborne LiDAR Data. Remote Sens., 11.","DOI":"10.20944\/preprints201910.0145.v1"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"118255","DOI":"10.1016\/j.foreco.2020.118255","article-title":"Persistent changes in the horizontal and vertical canopy structure of fire-tolerant forests after severe fire as quantified using multi-temporal airborne lidar data","volume":"472","author":"Karna","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.compag.2015.07.004","article-title":"A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics","volume":"116","author":"Gorgens","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s13595-015-0457-x","article-title":"Stand volume models based on stable metrics as from multiple ALS acquisitions in Eucalyptus plantations","volume":"72","author":"Packalen","year":"2015","journal-title":"Ann. For. Sci."},{"key":"ref_111","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_112","first-page":"399","article-title":"Lidar remote sensing of vegetation biomass","volume":"399","author":"Chen","year":"2013","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Huesca, M., Roth, K.L., Garc\u00eda, M., and Ustin, S.L. (2019). Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California. Remote Sens., 11.","DOI":"10.3390\/rs11091100"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0034-4257(98)00071-6","article-title":"Surface Lidar Remote Sensing of Basal Area and Biomass in Deciduous Forests of Eastern Maryland, USA","volume":"67","author":"Lefsky","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.rse.2009.11.021","article-title":"Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data","volume":"114","author":"Chuvieco","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1111\/j.1600-0587.2012.00086.x","article-title":"Multi-scale patterns of forest structure and species composition in relation to climate in northeast China","volume":"35","author":"Fang","year":"2012","journal-title":"Ecography"},{"key":"ref_117","first-page":"27","article-title":"Comparative testing of single-tree detection algorithms under different types of forest","volume":"85","author":"Vauhkonen","year":"2012","journal-title":"For. Int. J. For. Res."},{"key":"ref_118","first-page":"397","article-title":"LiDAR remote sensing of structural properties of subtropical rainforest and eucalypt forest in complex terrain in North-eastern Australia","volume":"26","author":"Ediriweera","year":"2014","journal-title":"J. Trop. For. Sci."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"S338","DOI":"10.5589\/m08-055","article-title":"The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data","volume":"34","author":"Falkowski","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_120","unstructured":"Jeronimo, S. (2021, November 01). LiDAR Individual Tree Detection for Assessing Structurally Diverse Forest Landscapes. Doctoral Dissertation. Available online: https:\/\/digital.lib.washington.edu\/researchworks\/handle\/1773\/35211."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3390\/f5020363","article-title":"Assessing the Feasibility of Low-Density LiDAR for Stand Inventory Attribute Predictions in Complex and Managed Forests of Northern Maine, USA","volume":"5","author":"Hayashi","year":"2014","journal-title":"Forests"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"807","DOI":"10.5558\/tfc84807-6","article-title":"The role of LiDAR in sustainable forest management","volume":"84","author":"Wulder","year":"2008","journal-title":"For. Chron."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.foreco.2016.04.024","article-title":"Space\u2013time modeling of changes in the abundance and distribution of tree species","volume":"372","author":"Montes","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"118779","DOI":"10.1016\/j.foreco.2020.118779","article-title":"Differences in stem radial variation between Pinus pinaster Ait. and Quercus pyrenaica Willd. may release inter-specific competition","volume":"481","author":"Aldea","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_125","unstructured":"Garilleti, R., Calleja, J.A., and Lara, F. (2012). Vegetaci\u00f3n Ribere\u00f1a de los R\u00edos y Ramblas de la Espa\u00f1a Meridional (Pen\u00ednsula y Archipi\u00e9lagos), Ministerio de Agricultura, Alimentaci\u00f3n y Medio Ambiente, Centro de Publicaciones."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1111\/1365-2745.12813","article-title":"Tree-to-tree competition in mixed European beech-Scots pine forests has different impacts on growth and water-use efficiency depending on site conditions","volume":"106","author":"Camarero","year":"2018","journal-title":"J. Ecol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1016\/j.foreco.2018.10.023","article-title":"Negative synergistic effects of land-use legacies and climate drive widespread oak decline in evergreen Mediterranean open woodlands","volume":"432","author":"Ledo","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_128","first-page":"110","article-title":"Towards assessment of cork production through National Forest Inventories","volume":"91","author":"Alberdi","year":"2018","journal-title":"For. An Int. J. For. Res."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"del Castillo, E., Tejedor, E., Serrano-Notivoli, R., Novak, K., Saz, M.\u00c1., Longares, L.A., and de Luis, M. (2018). Contrasting patterns of tree growth of mediterranean pine species in the iberian peninsula. Forests, 9.","DOI":"10.3390\/f9070416"},{"key":"ref_130","first-page":"145","article-title":"The current situation and future perspectives of Quercus ilex and Pinus halepensis afforestation on agricultural land in Spain under climate change scenarios","volume":"52","author":"Quinto","year":"2021","journal-title":"N. For."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1016\/j.scitotenv.2017.09.133","article-title":"Disentangling the climate-driven bimodal growth pattern in coastal and continental Mediterranean pine stands","volume":"615","author":"Pacheco","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_132","unstructured":"Tiscar, P.A., and Linares, J.C. (2011). Pinus nigra subsp. salzmannii forests from Southeast Spain: Using structure and process information to guide management. Pine Forests: Types, Threats and Management, Nova Science Publishers, Inc."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s10584-011-0372-6","article-title":"Selective drought-induced decline of pine species in southeastern Spain","volume":"113","author":"Camarero","year":"2012","journal-title":"Clim. Chang."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.foreco.2007.03.059","article-title":"Forest planning and traditional knowledge in collective woodlands of Spain: The dehesa system","volume":"249","author":"Linares","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_135","first-page":"119","article-title":"Forest dynamics in the Spanish central mountain range","volume":"8","year":"2010","journal-title":"End Tradit."},{"key":"ref_136","first-page":"195","article-title":"Cambios en la cubierta vegetal y usos del suelo en el Sistema Ib\u00e9rico noroccidental entre 1956 y 2001: Los Cameros (La Rioja, Espa\u00f1a)","volume":"47","author":"Oserin","year":"2008","journal-title":"Bolet\u00edn Asoc. Ge\u00f3grafos Espa\u00f1oles"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0378-1127(00)00383-2","article-title":"Forests of the Mediterranean region: Gaps in knowledge and research needs","volume":"132","author":"Oswald","year":"2000","journal-title":"For. Ecol. Manag."},{"key":"ref_138","unstructured":"Seidler, R. (2013). Patterns of Biodiversity Change in Anthropogenically Altered Forests. Encyclopedia of Biodiversity, Elsevier."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"118462","DOI":"10.1016\/j.foreco.2020.118462","article-title":"Rethinking maximum stand basal area and maximum SDI from the aspect of stand dynamics","volume":"475","author":"Zhao","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"42","DOI":"10.5424\/fs\/2112211-02193","article-title":"Biomass models to estimate carbon stocks for hardwood tree species","volume":"21","year":"2012","journal-title":"For. Syst."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s13595-019-0824-0","article-title":"A general method for the classification of forest stands using species composition and vertical and horizontal structure","volume":"76","author":"Olabarria","year":"2019","journal-title":"Ann. For. Sci."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.rse.2010.10.003","article-title":"Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions","volume":"115","author":"Jaskierniak","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2006.03.003","article-title":"Assessment of forest structure with airborne LiDAR and the effects of platform altitude","volume":"103","author":"Goodwin","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.rse.2007.10.009","article-title":"Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data","volume":"112","author":"Hudak","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/S0378-1127(02)00051-8","article-title":"Regional impact assessment on forest structure and functions under climate change\u2014the Brandenburg case study","volume":"162","author":"Lasch","year":"2002","journal-title":"For. Ecol. Manag."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1111\/geb.12083","article-title":"The interaction between a drying climate and land use affects forest structure and above-ground carbon storage","volume":"22","author":"Bennett","year":"2013","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Palace, M., Sullivan, F.B., Ducey, M., and Herrick, C. (2016). Estimating Tropical Forest Structure Using a Terrestrial Lidar. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0154115"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1111\/geb.12803","article-title":"Pan-tropical prediction of forest structure from the largest trees","volume":"27","author":"Bastin","year":"2018","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_149","unstructured":"Ministerio de Medio Ambiente y Medio Rural y Marino (2021, November 01). Anuario de Estad\u00edstica Agraria y Agroalimentaria\u00a02010. Available online: http:\/\/www.mapa.es\/es\/estadistica\/pags\/anuario\/introduccion.htm."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.foreco.2013.07.004","article-title":"European Forest Types and Forest Europe SFM indicators: Tools for monitoring progress on forest biodiversity conservation","volume":"321","author":"Barbati","year":"2014","journal-title":"For. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/235\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:26:49Z","timestamp":1760362009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/235"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,5]]},"references-count":150,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14010235"],"URL":"https:\/\/doi.org\/10.3390\/rs14010235","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,5]]}}}