{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:02:13Z","timestamp":1775559733664,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010198","name":"Ministerio de Econom\u00eda, Industria y Competitividad, Gobierno de Espa\u00f1a","doi-asserted-by":"publisher","award":["PTQ2018\u2013010043"],"award-info":[{"award-number":["PTQ2018\u2013010043"]}],"id":[{"id":"10.13039\/501100010198","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson\u2019s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg\/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.<\/jats:p>","DOI":"10.3390\/rs13122279","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T21:34:38Z","timestamp":1623360878000},"page":"2279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":126,"title":["Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests"],"prefix":"10.3390","volume":"13","author":[{"given":"Iv\u00e1n","family":"Dorado-Roda","sequence":"first","affiliation":[{"name":"Departamento de Tecnolog\u00eda Minera, Topograf\u00eda y de Estructuras, Escuela Superior y T\u00e9cnica de Ingenieros de Minas y Escuela de Ingenier\u00eda Agraria y Forestal, Universidad de Le\u00f3n, Av. de Astorga s\/n, Campus de Ponferrada, 24401 Ponferrada, Spain"}]},{"given":"Adri\u00e1n","family":"Pascual","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Hilo, HA 96720, USA"}]},{"given":"Sergio","family":"Godinho","sequence":"additional","affiliation":[{"name":"EaRSLab\u2014Earth Remote Sensing Laboratory, University of \u00c9vora, 7000-671 \u00c9vora, Portugal"},{"name":"Institute of Earth Sciences (ICT), Universidade de \u00c9vora, Rua Rom\u00e3o Ramalho, 59, 7002-554 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3560","authenticated-orcid":false,"given":"Carlos","family":"Silva","sequence":"additional","affiliation":[{"name":"School of Forest Resources and Conservation, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA"},{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA"},{"name":"Biosciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20707, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6661-190X","authenticated-orcid":false,"given":"Brigite","family":"Botequim","sequence":"additional","affiliation":[{"name":"Forest Research Centre, Instituto Superior de Agronomia (ISA), School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2657-813X","authenticated-orcid":false,"given":"Pablo","family":"Rodr\u00edguez-Gonz\u00e1lvez","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00eda Minera, Topograf\u00eda y de Estructuras, Escuela Superior y T\u00e9cnica de Ingenieros de Minas y Escuela de Ingenier\u00eda Agraria y Forestal, Universidad de Le\u00f3n, Av. de Astorga s\/n, Campus de Ponferrada, 24401 Ponferrada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4565-2155","authenticated-orcid":false,"given":"Eduardo","family":"Gonz\u00e1lez-Ferreiro","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00eda Minera, Topograf\u00eda y de Estructuras, Escuela Superior y T\u00e9cnica de Ingenieros de Minas y Escuela de Ingenier\u00eda Agraria y Forestal, Universidad de Le\u00f3n, Av. de Astorga s\/n, Campus de Ponferrada, 24401 Ponferrada, Spain"}]},{"given":"Juan","family":"Guerra-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Forest Research Centre, Instituto Superior de Agronomia (ISA), School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal"},{"name":"Centro de Iniciativas Empresariais, Fundaci\u00f3n CEL, O Palomar s\/n, 27004 Lugo, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.foreco.2015.06.014","article-title":"Dynamics of Global Forest Area: Results from the FAO Global Forest Resources Assessment 2015","volume":"352","author":"Keenan","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_2","unstructured":"Bourlion, N., Garavaglia, V., and Picard, N. (2020). Importance Des For\u00eats M\u00e9diterran\u00e9ennes. Etat des For\u00eats M\u00e9diterran\u00e9ennes 2018, Plan Bleu."},{"key":"ref_3","unstructured":"Guerra-Hern\u00e1ndez, J., Aviles, C., Botequim, B., Jurado-Varela, A., Sandoval, V., and Robla-Gonz\u00e1lez, E. (2019). Expansi\u00f3n Continua Del IFN4 de Extremadura y Canarias Mediante T\u00e9cnicas LiDAR. Teledetecci\u00f3n: Hacia una Visi\u00f3n Global del Cambio Clim\u00e1tico, Universidad de Valladolid."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pascual, A., Guerra-Hern\u00e1ndez, J., Cosenza, D.N., and Sandoval, V. (2020). The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning. Remote Sens., 12.","DOI":"10.3390\/rs12030413"},{"key":"ref_5","unstructured":"Espejo, A., Federici, S., Green, C., Amuchastegui, N., d\u2019Annunzio, R., Balzter, H., Bholanath, P., Brack, C., Brewer, C., and Birigazzi, L. (2020). Integration of Remote-Sensing and Ground-Based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests: Methods and Guidance from the Global Forest Observations Initiative, U.N. Food and Agriculture Organization. [3rd ed.]."},{"key":"ref_6","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":"Alberdi","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_7","unstructured":"Tomppo, E., Haakana, M., Katila, M., and Per\u00e4saari, J. (2008). Multi-Source National Forest Inventory: Methods and Applications, Springer."},{"key":"ref_8","unstructured":"Eggleston, S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. (2006). IPCC Guidelines for National Greenhouse Gas Inventories, IPCC National Greenhouse Gas Inventories Programme."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping Global Forest Canopy Height through Integration of GEDI and Landsat Data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5211","DOI":"10.1080\/01431161.2018.1486519","article-title":"Comparison of ALS-and UAV (SfM)-Derived High-Density Point Clouds for Individual Tree Detection in Eucalyptus Plantations","volume":"39","author":"Cosenza","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"118690","DOI":"10.1016\/j.foreco.2020.118690","article-title":"Developing a Site Index Model for P. Pinaster Stands in NW Spain by Combining Bi-Temporal ALS Data and Environmental Data","volume":"481","author":"Pascual","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5721\/EuJRS20164911","article-title":"Comparison of ALS Based Models for Estimating Aboveground Biomass in Three Types of Mediterranean Forest","volume":"49","author":"Rodriguez","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1071\/WF19001","article-title":"Improving Silvicultural Practices for Mediterranean Forests through Fire Behaviour Modelling Using LiDAR-Derived Canopy Fuel Characteristics","volume":"28","author":"Botequim","year":"2019","journal-title":"Int. J. Wildland Fire"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Narine, L.L., Popescu, S.C., and Malambo, L. (2019). Synergy of ICESat-2 and Landsat for Mapping Forest Aboveground Biomass with Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11121503"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Narine, L.L., Popescu, S.C., and Malambo, L. (2020). Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example. Remote Sens., 12.","DOI":"10.3390\/rs12111824"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111325","DOI":"10.1016\/j.rse.2019.111325","article-title":"The Ice, Cloud, and Land Elevation Satellite\u20142 Mission: A Global Geolocated Photon Product Derived from the Advanced Topographic Laser Altimeter System","volume":"233","author":"Neumann","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.srs.2020.100002","article-title":"The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth\u2019s Forests and Topography","volume":"1","author":"Dubayah","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s10712-019-09538-8","article-title":"The Importance of Consistent Global Forest Aboveground Biomass Product Validation","volume":"40","author":"Duncanson","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111779","DOI":"10.1016\/j.rse.2020.111779","article-title":"Biomass Estimation from Simulated GEDI, ICESat-2 and NISAR across Environmental Gradients in Sonoma County, California","volume":"242","author":"Duncanson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112234","DOI":"10.1016\/j.rse.2020.112234","article-title":"Fusing Simulated GEDI, ICESat-2 and NISAR Data for Regional Aboveground Biomass Mapping","volume":"253","author":"Silva","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2014.10.029","article-title":"The Uncertainty of Biomass Estimates from Modeled ICESat-2 Returns across a Boreal Forest Gradient","volume":"158","author":"Montesano","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","first-page":"102163","article-title":"High-Resolution Mapping of Forest Canopy Height Using Machine Learning by Coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 Data","volume":"92","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Adam, M., Urbazaev, M., Dubois, C., and Schmullius, C. (2020). Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters. Remote Sens., 12.","DOI":"10.3390\/rs12233948"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rishmawi, K., Huang, C., and Zhan, X. (2021). Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data. Remote Sens., 13.","DOI":"10.3390\/rs13030442"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s40663-021-00291-2","article-title":"Using GEDI Lidar Data and Airborne Laser Scanning to Assess Height Growth Dynamics in Fast-Growing Species: A Showcase in Spain","volume":"8","author":"Pascual","year":"2021","journal-title":"For. Ecosyst."},{"key":"ref_28","unstructured":"Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., and Wegner, J.D. (2021). Global Canopy Height Estimation with GEDI LIDAR Waveforms and Bayesian Deep Learning. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112110","DOI":"10.1016\/j.rse.2020.112110","article-title":"Validation of ICESat-2 Terrain and Canopy Heights in Boreal Forests","volume":"251","author":"Neuenschwander","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","unstructured":"McGaughey, R.J. (2019). FUSION\/LDV: Software for LIDAR Data Analysis and Visualization, Version 3.60+."},{"key":"ref_31","unstructured":"Isenburg, M. (2020, April 15). LAStools\u2014Efficient Tools for LiDAR Processing, Available online: http:\/\/lastools.org."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting Forest Stand Characteristics with Airborne Scanning Laser Using a Practical Two-Stage Procedure and Field Data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","unstructured":"Dubayah, R., Hofton, M., Blair, J.B., Armston, J., and Tang, H. (2020, June 01). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V001 [Data Set]. 2020. NASA EOSDIS Land Processes DAAC, Available online: https:\/\/search.earthdata.nasa.gov\/search?q=C1656766463-LPDAAC_ECS."},{"key":"ref_34","unstructured":"Dubayah, R., Hofton, M., Blair, J.B., Armston, J., and Tang, H. (2020, June 01). GEDI L2B GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V001 [Data Set]. 2020. NASA EOSDIS Land Processes DAAC, Available online: https:\/\/search.earthdata.nasa.gov\/search?q=C1656767133-LPDAAC_ECS."},{"key":"ref_35","unstructured":"Silva, C.A. (2020, August 15). rGEDI: NASA\u2019s Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing. R Package. Available online: https:\/\/CRAN.R-project.org\/package=rGEDI."},{"key":"ref_36","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation Project for Statistical Computing. version 3.6.1."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"802","DOI":"10.2307\/1933693","article-title":"Foliage Profile by Vertical Measurements","volume":"50","author":"MacArthur","year":"1969","journal-title":"Ecology"},{"key":"ref_38","unstructured":"Lumley, T., and Miller, A. (2020, August 15). Leaps: Regression Subset Selection. R Package. Available online: https:\/\/CRAN.R-Project.Org\/Package=leaps."},{"key":"ref_39","unstructured":"Belsley, D.A., Kuh, E., and Welsch, R.E. (2005). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, John Wiley & Sons."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1093\/wjaf\/23.4.223","article-title":"A Comparison of Statistical Methods for Estimating Forest Biomass from Light Detection and Ranging Data","volume":"23","author":"Li","year":"2008","journal-title":"West. J. Appl. For."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1007\/s00442-011-2165-z","article-title":"A Universal Airborne LiDAR Approach for Tropical Forest Carbon Mapping","volume":"168","author":"Asner","year":"2012","journal-title":"Oecologia"},{"key":"ref_42","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_43","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_44","doi-asserted-by":"crossref","first-page":"111283","DOI":"10.1016\/j.rse.2019.111283","article-title":"Forest Biomass Estimation over Three Distinct Forest Types Using TanDEM-X InSAR Data and Simulated GEDI Lidar Data","volume":"232","author":"Qi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1029\/2018EA000506","article-title":"The GEDI Simulator: A Large-footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions","volume":"6","author":"Hancock","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.01.037","article-title":"Estimating Aboveground Biomass and Forest Canopy Cover with Simulated ICESat-2 Data","volume":"224","author":"Narine","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/36.951085","article-title":"Modeling Lidar Waveforms in Heterogeneous and Discrete Canopies","volume":"39","author":"Jupp","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Rosen, P., Hensley, S., Shaffer, S., Edelstein, W., Kim, Y., Kumar, R., Misra, T., Bhan, R., and Sagi, R. (2017, January 23\u201328). The NASA-ISRO SAR (NISAR) Mission Dual-Band Radar Instrument Preliminary Design. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127836"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2019.03.032","article-title":"The European Space Agency BIOMASS Mission: Measuring Forest above-Ground Biomass from Space","volume":"227","author":"Quegan","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:12:56Z","timestamp":1760163176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,10]]},"references-count":49,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122279"],"URL":"https:\/\/doi.org\/10.3390\/rs13122279","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,10]]}}}