{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T22:10:07Z","timestamp":1772057407775,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"EU Interreg-Sudoe program","doi-asserted-by":"publisher","award":["SOE2\/P5\/E0811"],"award-info":[{"award-number":["SOE2\/P5\/E0811"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The characterization of aboveground biomass is important in forest management planning, with various objectives ranging from prevention of forest fires to restoration of burned areas, especially in fire-prone regions such as NW Spain. Although remotely sensed data have often been used to assess the recovery of standing aboveground biomass after perturbations, the data have seldom been validated in the field, and different shrub fractions have not been modelled. The main objective of the present study was to assess different vegetation parameters (cover, height, standing AGB and their fractions) in field plots established in five areas affected by wildfires between 2009 and 2016 by using Sentinel-2 spectral indices and LiDAR metrics. For this purpose, 22 sampling plots were established in 2019, and vegetation variables were measured by a combination of non-destructive measurement (cover and height) and destructive sampling (total biomass and fine samples of live and dead fractions of biomass).The structural characterization of gorse shrublands was addressed, and models of shrub cover\u2014height, total biomass, and biomass by fraction and physiological condition\u2014were constructed, with adjusted coefficients of determination ranging from 0.6 to 0.9. The addition of LiDAR data to optical remote sensing images improved the models. Further research should be conducted to calibrate the models in other vegetation communities.<\/jats:p>","DOI":"10.3390\/rs14236063","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T05:45:22Z","timestamp":1669787122000},"page":"6063","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain"],"prefix":"10.3390","volume":"14","author":[{"given":"Jos\u00e9 Mar\u00eda","family":"Fern\u00e1ndez-Alonso","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n Forestal de Louriz\u00e1n, Xunta de Galicia, 36156 Pontevedra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafael","family":"Llorens","sequence":"additional","affiliation":[{"name":"Global Change Unit, Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Antonio","family":"Sobrino","sequence":"additional","affiliation":[{"name":"Global Change Unit, Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana Dar\u00eda","family":"Ruiz-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Unidad de Gesti\u00f3n Ambiental y Forestal Sostenible (UXAFORES), Departamento de Ingenier\u00eda Agroforestal, Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Universidad de Santiago de Compostela, 27002 Lugo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5206-9128","authenticated-orcid":false,"given":"Juan Gabriel","family":"Alvarez-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Unidad de Gesti\u00f3n Ambiental y Forestal Sostenible (UXAFORES), Departamento de Ingenier\u00eda Agroforestal, Escuela Polit\u00e9cnica Superior de Ingenier\u00eda, Universidad de Santiago de Compostela, 27002 Lugo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Antonio","family":"Vega","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n Forestal de Louriz\u00e1n, Xunta de Galicia, 36156 Pontevedra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4134-8727","authenticated-orcid":false,"given":"Cristina","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n Forestal de Louriz\u00e1n, Xunta de Galicia, 36156 Pontevedra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.jenvman.2006.07.015","article-title":"Combining remote sensing imagery and forest age inventory for biomass mapping","volume":"85","author":"Zheng","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_2","unstructured":"Eggelston, S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. (2006). IPCC Guidelines for National Greenhouse Gas Inventories, IGES."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2017.01.016","article-title":"Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem","volume":"191","author":"Meng","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2018.03.019","article-title":"Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques","volume":"210","author":"Meng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, M., Saatchi, S., Casas, A., Koltunov, A., Ustin, S.L., Ramirez, C., and Balzter, H. (2017). Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data. Remote Sens., 9.","DOI":"10.3390\/rs9040394"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Engelstad, P.S., Falkowski, M., Wolter, P., Poznanovic, A., and Johnson, P. (2019). Estimating Canopy Fuel Attributes from Low-Density LiDAR. Fire, 2.","DOI":"10.3390\/fire2030038"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2017). Remote Sensing of Above-Ground Biomass. Remote Sens., 9.","DOI":"10.3390\/rs9090935"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"673","DOI":"10.5194\/nhess-10-673-2010","article-title":"Post-fire vegetation recovery in Portugal based on spot\/vegetation data","volume":"10","author":"Gouveia","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fatoyinbo, L. (2012). Advances in remote sensing of post-fire vegetation recovery monitoring\u2014A review. Remote Sensing Biomass Principles Applications, IntechOpen.","DOI":"10.5772\/696"},{"key":"ref_10","unstructured":"De Groot, W.J., Flanagan, D.C., and Stocks, B.J. (2012, January 5\u201311). Climate change and wildfires. Proceedings of the Fourth International Symposium on Fire Economics, Planning, and Policy: Climate Change and Wildfires, Albany, CA, USA."},{"key":"ref_11","unstructured":"Camia, A., Libert\u00e0, G., and San Miguel, J. (2017). Modeling the Impacts of Climate Change on Forest Fire Danger in Europe, Publications Office of the European Union."},{"key":"ref_12","unstructured":"D\u00edaz-Fierros, F. (2021). Os incendios forestais do cambio global xa estan aqu\u00ed. Un desaf\u00edo e unha ocasi\u00f3n para lograr unha resposta social consensuada. Unha Nova Xeraci\u00f3n de Lumes?, Consello da Cultura Galega."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.foreco.2012.10.050","article-title":"Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives","volume":"294","author":"Moreno","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.foreco.2012.10.022","article-title":"Global wildland fire season severity in the 21st century","volume":"294","author":"Flannigan","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_15","unstructured":"San-Miguel-Ayanz, J., Tracy, D., Boca, R., Libert\u00e0, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Art\u00e9s Vivancos, T., and Costa, H. (2018). Forest Fires in Europe, Middle East and North Africa 2017, Joint Research Center, European Union."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aranha, J., Enes, T., Calv\u00e3o, A., and Viana, H. (2020). Shrub Biomass Estimates in Former Burnt Areas Using Sentinel 2 Images Processing and Classification. Forests, 11.","DOI":"10.3390\/f11050555"},{"key":"ref_17","unstructured":"Ministerio de Medio Ambiente y Medio Rural yMarino (2011). Cuarto Inventario Forestal Nacional, Ministerio de Medio Ambiente y Medio Rural yMarino."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/978-94-007-2208-8_12","article-title":"Post-fire management of shrublands","volume":"Volume 24","author":"Moreira","year":"2012","journal-title":"Post-Fire Management and Restoration of Southern European Forests"},{"key":"ref_19","unstructured":"CMR (2022). Plan de Prevenci\u00f3n y Defensa Contra los Incendios Forestales de Galicia."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114","DOI":"10.3832\/ifor0931-008","article-title":"A model of shrub biomass accumulation as a tool to support management of Portuguese forests","volume":"8","author":"Botequim","year":"2015","journal-title":"Iforest Biogeosci. For."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"119926","DOI":"10.1016\/j.foreco.2021.119926","article-title":"Modelling aboveground biomass and fuel load components at stand level in shrub communities in NW Spain","volume":"505","author":"Vega","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.jenvman.2015.10.028","article-title":"Shrub recovery after fuel reduction treatments in a gorse shrubland in northern Spain","volume":"166","author":"Vega","year":"2016","journal-title":"J. Environ. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Keane, R.E. (2015). Wildland Fuel Fundamentals and Applications, Springer.","DOI":"10.1007\/978-3-319-09015-3"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Finney, M.A. (1998). FARSITE: Fire Area Simulator-Model Development and Evaluation, U.S. Department of Agriculture, Forest Service; Volume Research Paper RMRS-RP-4.","DOI":"10.2737\/RMRS-RP-4"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1071\/WF11139","article-title":"Describing wildland surface fuel loading for fire management: A review of approaches, methods and systems","volume":"22","author":"Keane","year":"2013","journal-title":"Int. J. Wildland Fire"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11258-005-3448-4","article-title":"Fire Risk and Vegetation Structural Dynamics in Mediterranean Shrubland","volume":"187","author":"Baeza","year":"2006","journal-title":"Plant Ecol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1071\/WF08123","article-title":"Flammability descriptors of fine dead fuels resulting from two mechanical treatments in shrubland: A comparative laboratory study","volume":"19","author":"Marino","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1071\/WF20092","article-title":"Medium-term effects of straw helimulching on post-fire vegetation recovery in shrublands in north-west Spain","volume":"30","year":"2021","journal-title":"Int. J. Wildland Fire"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s13595-011-0165-0","article-title":"Evaluation of the flammability of gorse (Ulex europaeus L.) managed by prescribed burning","volume":"69","author":"Madrigal","year":"2012","journal-title":"Ann. For. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"097696","DOI":"10.1117\/1.JRS.9.097696","article-title":"Review of the use of remote sensing for biomass estimation to support renewable energy generation","volume":"9","author":"Kumar","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.ecolmodel.2011.11.027","article-title":"Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models","volume":"226","author":"Viana","year":"2012","journal-title":"Ecol. Model."},{"key":"ref_33","first-page":"125","article-title":"Remote sensing of aboveground forest biomass: A review","volume":"57","author":"Mutanga","year":"2016","journal-title":"Trop. Ecol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Durante, P., Mart\u00edn-Alc\u00f3n, S., Gil-Tena, A., Algeet, N., Tom\u00e9, J.L., Recuero, L., Palacios-Orueta, A., and Oyonarte, C. (2019). Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale. Remote Sens., 11.","DOI":"10.3390\/rs11070795"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Vaglio, G., Pirotti, F., Callegari, M., Chen, Q., Cuozzo, G., Lingua, E., Notarnicola, C., and Papale, D. (2017). Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates. Remote Sens., 9.","DOI":"10.3390\/rs9010018"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, G., and Ranson, K.J. (2009, January 12\u201317). Forest biomass retrieval from lidar and radar. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417671"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s10712-019-09529-9","article-title":"New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar","volume":"40","author":"Kellner","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"107","DOI":"10.4996\/fireecology.0202107","article-title":"Spatial autocorrelation and pseudoreplication in fire ecology","volume":"2","author":"Bataineh","year":"2006","journal-title":"Fire Ecol."},{"key":"ref_41","first-page":"388","article-title":"Application of the Line Interception Method in Sampling Range Vegetation","volume":"39","author":"Canfield","year":"1941","journal-title":"J. For."},{"key":"ref_42","unstructured":"XdG (2021, December 03). Plan B\u00e1sico Auton\u00f3mico. Available online: http:\/\/mapas.xunta.gal."},{"key":"ref_43","unstructured":"McGaughey, R.J. (2009). FUSION\/LDV: Software for LiDAR Data Analysis and Visualization."},{"key":"ref_44","unstructured":"Core Team Development, R. (2022). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1080\/01431168808954942","article-title":"Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery","volume":"9","author":"Kaufman","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","unstructured":"Copernicus, H. (2022, February 24). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/dhus\/#\/home."},{"key":"ref_47","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment: Ground Measure of Severity, the Composite Burn Index; and Remote Sensing of Severity, the Normalized Burn Ratio, RMRS-GTR-164-CD: LA 1-51."},{"key":"ref_48","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite\u20131 Symposium, Washington, DC, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1080\/01431160210144598","article-title":"Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: Comparison of various indices through correlation analysis","volume":"24","author":"Xu","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","first-page":"e00479","article-title":"Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology","volume":"16","author":"Chen","year":"2018","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2015.10.024","article-title":"Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California","volume":"171","author":"Meng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2017.07.022","article-title":"Continental-scale quantification of post-fire vegetation greenness recovery in temperate and boreal North America","volume":"199","author":"Yang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Li, A., Dhakal, S., Glenn, N.F., Spaete, L.P., Shinneman, D.J., Pilliod, D.S., Arkle, R.S., and McIlroy, S.K. (2017). Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales. Remote Sens., 9.","DOI":"10.3390\/rs9090903"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2011.12.007","article-title":"Assessing post-fire vegetation recovery using red\u2013near infrared vegetation indices: Accounting for background and vegetation variability","volume":"68","author":"Veraverbeke","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/01431160701281072","article-title":"Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM\/ETM images","volume":"29","author":"Escuin","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1016\/j.rse.2011.03.001","article-title":"Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data","volume":"115","author":"Li","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1080\/01431160903380656","article-title":"Relationship between LiDAR-derived forest canopy height and Landsat images","volume":"31","author":"Pascual","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Vargas-Larreta, B., L\u00f3pez-S\u00e1nchez, C.A., Corral-Rivas, J.J., L\u00f3pez-Mart\u00ednez, J.O., Aguirre-Calder\u00f3n, C.G., and \u00c1lvarez-Gonz\u00e1lez, J.G. (2017). Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico. Forests, 8.","DOI":"10.20944\/preprints201705.0178.v1"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Jones, S., Soto-Berelov, M., Haywood, A., and Hislop, S. (2018). A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data. Remote Sens., 10.","DOI":"10.3390\/rs10111825"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Alonso-Rego, C., Arellano-P\u00e9rez, S., Cabo, C., Ordo\u00f1ez, C., \u00c1lvarez-Gonz\u00e1lez, J.G., D\u00edaz-Varela, R.A., and Ruiz-Gonz\u00e1lez, A.D. (2020). Estimating Fuel Loads and Structural Characteristics of Shrub Communities by Using Terrestrial Laser Scanning. Remote Sens., 12.","DOI":"10.3390\/rs12223704"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2012.10.017","article-title":"A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing","volume":"128","author":"Zolkos","year":"2013","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6063\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:29:57Z","timestamp":1760146197000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6063"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,30]]},"references-count":65,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236063"],"URL":"https:\/\/doi.org\/10.3390\/rs14236063","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,30]]}}}