{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T21:22:12Z","timestamp":1782422532749,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"French Space Study Center"},{"name":"National Research Institute for Agriculture, Food and the Environment"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>LiDAR technology has been widely used to characterize structural parameters of forest ecosystems, which in turn are valuable information for forest monitoring. GEDI is a spaceborne LiDAR system specifically designed to measure vegetation\u2019s vertical structure, and it has been acquiring waveforms on a global scale since April 2019. In particular, canopy height is an important descriptor of forest ecosystems, as it allows for quantifying biomass and other inventory information. This paper analyzes the accuracy of canopy height estimates from GEDI data over tropical forests in French Guiana and Gabon. The influence of various signal acquisition and processing parameters is assessed to highlight how they impact the estimation of canopy heights. Canopy height models derived from airborne LiDAR data are used as reference heights. Several linear and non-linear approaches are tested given the richness of the available GEDI information. The results show that the use of regression models built on multiple GEDI metrics allows for reaching improved accuracies compared to a direct estimation from a single GEDI height metric. In a notable way, random forest improves the canopy height estimation accuracy by almost 80% (in terms of RMSE) compared to the use of rh_95 as a direct proxy of canopy height. Additionally, convolutional neural networks calibrated on GEDI waveforms exhibit similar results to the ones of other regression models. Beam type as well as beam sensitivity, which are related to laser penetration, appear as parameters of major influence on the data derived from GEDI waveforms and used as input for canopy height estimation. Therefore, we recommend the use of only power and high-sensitivity beams when sufficient data are available. Finally, we note that regression models trained on reference data can be transferred across study sites that share identical environmental conditions.<\/jats:p>","DOI":"10.3390\/rs14246264","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:34:20Z","timestamp":1670819660000},"page":"6264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Influence of GEDI Acquisition and Processing Parameters on Canopy Height Estimates over Tropical Forests"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5359-8718","authenticated-orcid":false,"given":"Kamel","family":"Lahssini","sequence":"first","affiliation":[{"name":"UMR TETIS, INRAE, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"UMR TETIS, INRAE, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5227-958X","authenticated-orcid":false,"given":"Guerric","family":"le Maire","sequence":"additional","affiliation":[{"name":"UMR Eco&Sols, CIRAD, 34398 Montpellier, France"},{"name":"Eco&Sols, Universit\u00e9 de Montpellier, CIRAD, INRAE, IRD, Institut Agro, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Fayad","sequence":"additional","affiliation":[{"name":"Kayrros SAS, 75009 Paris, France"},{"name":"Laboratoire des Sciences du Climat et de l\u2019Environnement, LSCE\/IPSL, CEA-CNRS9 UVSQ, Universit\u00e9 Paris-Saclay, 91191 Gif-sur-Yvette, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A Large and Persistent Carbon Sink in the World\u2019s Forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.rse.2013.09.023","article-title":"Mapping Tropical Forest Carbon: Calibrating Plot Estimates to a Simple LiDAR Metric","volume":"140","author":"Asner","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree Allometry and Improved Estimation of Carbon Stocks and Balance in Tropical Forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"L22S02","DOI":"10.1029\/2005GL023971","article-title":"Estimates of Forest Canopy Height and Aboveground Biomass Using ICESat: Icesat Estimates of Canopy Height","volume":"32","author":"Lefsky","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3381","DOI":"10.5194\/bg-9-3381-2012","article-title":"Tree Height Integrated into Pantropical Forest Biomass Estimates","volume":"9","author":"Feldpausch","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.foreco.2012.04.028","article-title":"Allometric Models for Estimating Above- and below-Ground Biomass in Amazonian Forests at S\u00e3o Gabriel Da Cachoeira in the Upper Rio Negro, Brazil","volume":"277","author":"Lima","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1191\/0309133305pp432ra","article-title":"Satellite Remote Sensing of Forest Resources: Three Decades of Research Development","volume":"29","author":"Boyd","year":"2005","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2015.12.039","article-title":"On the Interest of Penetration Depth, Canopy Area and Volume Metrics to Improve Lidar-Based Models of Forest Parameters","volume":"175","author":"Renaud","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lahssini, K., Dayal, K.R., Durrieu, S., and Monnet, J.-M. (2022, January 14\u201316). Joint Use of Airborne LiDAR Metrics and Topography Information to Estimate Forest Parameters via Neural Networks. Proceedings of the 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy.","DOI":"10.1109\/MELECON53508.2022.9843039"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Karasiak, N., Sheeren, D., Fauvel, M., Willm, J., Dejoux, J.-F., and Monteil, C. (2017, January 27\u201329). Mapping Tree Species of Forests in Southwest France Using Sentinel-2 Image Time Series. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035215"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. (2019). Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11101197"},{"key":"ref_13","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."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1109\/JSTARS.2022.3175609","article-title":"Combining LiDAR Metrics and Sentinel-2 Imagery to Estimate Basal Area and Wood Volume in Complex Forest Environment via Neural Networks","volume":"15","author":"Lahssini","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Morin, D., Planells, M., Baghdadi, N., Bouvet, A., Fayad, I., Le Toan, T., Mermoz, S., and Villard, L. (2022). Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process. Remote Sens., 14.","DOI":"10.3390\/rs14092079"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/JSTARS.2013.2273563","article-title":"Viability Statistics of GLAS\/ICESat Data Acquired Over Tropical Forests","volume":"7","author":"Baghdadi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., and Riedi, J. (2021). Quality Assessment of Acquired GEDI Waveforms: Case Study over France, Tunisia and French Guiana. Remote Sens., 13.","DOI":"10.3390\/rs13163144"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1109\/TGRS.2013.2258350","article-title":"Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation for the ICESat-2 Mission","volume":"52","author":"Herzfeld","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., and Lahssini, K. (2022). An Assessment of the GEDI Lasers\u2019 Capabilities in Detecting Canopy Tops and Their Penetration in a Densely Vegetated, Tropical Area. Remote Sens., 14.","DOI":"10.3390\/rs14132969"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112652","DOI":"10.1016\/j.rse.2021.112652","article-title":"A CNN-Based Approach for the Estimation of Canopy Heights and Wood Volume from GEDI Waveforms","volume":"265","author":"Fayad","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rse.2015.12.037","article-title":"SAR Tomography for the Retrieval of Forest Biomass and Height: Cross-Validation at Two Tropical Forest Sites in French Guiana","volume":"175","author":"Rocca","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11883","DOI":"10.3390\/rs61211883","article-title":"Canopy Height Estimation in French Guiana with LiDAR ICESat\/GLAS Data Using Principal Component Analysis and Random Forest Regressions","volume":"6","author":"Fayad","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., Bailly, J.-S., Barbier, N., Gond, V., H\u00e9rault, B., El Hajj, M., Fabre, F., and Perrin, J. (2016). Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana. Remote Sens., 8.","DOI":"10.3390\/rs8030240"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"El Moussawi, I., Ho Tong Minh, D., Baghdadi, N., Abdallah, C., Jomaah, J., Strauss, O., and Lavalle, M. (2019). L-Band UAVSAR Tomographic Imaging in Dense Forests: Gabon Forests. Remote Sens., 11.","DOI":"10.3390\/rs11050475"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.crte.2019.01.001","article-title":"Mapping of Aboveground Biomass in Gabon","volume":"351","author":"Baghdadi","year":"2019","journal-title":"Comptes Rendus Geosci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Memiaghe, H.R., Lutz, J.A., Korte, L., Alonso, A., and Kenfack, D. (2016). Ecological Importance of Small-Diameter Trees to the Structure, Diversity and Biomass of a Tropical Evergreen Forest at Rabi, Gabon. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0154988"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., and Frappart, F. (2022). Comparative Analysis of GEDI\u2019s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies. Remote Sens., 14.","DOI":"10.3390\/rs14020340"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2009JG000933","article-title":"Estimation of Tropical Forest Height and Biomass Dynamics Using Lidar Remote Sensing at La Selva, Costa Rica: Forest Dynamics Using Lidar","volume":"115","author":"Dubayah","year":"2010","journal-title":"J. Geophys. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1111\/j.1461-0248.2008.01274.x","article-title":"Pervasive Canopy Dynamics Produce Short-Term Stability in a Tropical Rain Forest Landscape","volume":"12","author":"Kellner","year":"2009","journal-title":"Ecol. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1111\/j.1466-8238.2009.00489.x","article-title":"Environmental Correlates of Tree Biomass, Basal Area, Wood Specific Gravity and Stem Density Gradients in Borneo\u2019s Tropical Forests: Forest Carbon and Structure Gradients","volume":"19","author":"Slik","year":"2010","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1017\/S0266467408005075","article-title":"Above-Ground Biomass and Productivity in a Rain Forest of Eastern South America","volume":"24","author":"Chave","year":"2008","journal-title":"J. Trop. Ecol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2015.08.001","article-title":"Using Repeated Small-Footprint LiDAR Acquisitions to Infer Spatial and Temporal Variations of a High-Biomass Neotropical Forest","volume":"169","author":"Tymen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2012.06.019","article-title":"Accuracy of Small Footprint Airborne LiDAR in Its Predictions of Tropical Moist Forest Stand Structure","volume":"125","author":"Vincent","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.3390\/rs4082210","article-title":"Influence of Surface Topography on ICESat\/GLAS Forest Height Estimation and Waveform Shape","volume":"4","author":"Hilbert","year":"2012","journal-title":"Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Dorado-Roda, I., Pascual, A., Godinho, S., Silva, C., Botequim, B., Rodr\u00edguez-Gonz\u00e1lvez, P., Gonz\u00e1lez-Ferreiro, E., and Guerra-Hern\u00e1ndez, J. (2021). Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sens., 13.","DOI":"10.3390\/rs13122279"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2018.11.005","article-title":"The ATL08 Land and Vegetation Product for the ICESat-2 Mission","volume":"221","author":"Neuenschwander","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xi, Z., Xu, H., Xing, Y., Gong, W., Chen, G., and Yang, S. (2022). Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods. Remote Sens., 14.","DOI":"10.3390\/rs14020364"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., Alcarde Alvares, C., Stape, J.L., Bailly, J.S., Scolforo, H.F., Cegatta, I.R., Zribi, M., and Le Maire, G. (2021). Terrain Slope Effect on Forest Height and Wood Volume Estimation from GEDI Data. Remote Sens., 13.","DOI":"10.3390\/rs13112136"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Escobar Villanueva, J.R., Iglesias Mart\u00ednez, L., and P\u00e9rez Montiel, J.I. (2019). DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19.","DOI":"10.3390\/s19143205"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7095","DOI":"10.1109\/JSTARS.2021.3092836","article-title":"Assessment of GEDI\u2019s LiDAR Data for the Estimation of Canopy Heights and Wood Volume of Eucalyptus Plantations in Brazil","volume":"14","author":"Fayad","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112760","DOI":"10.1016\/j.rse.2021.112760","article-title":"Global Canopy Height Regression and Uncertainty Estimation from GEDI LIDAR Waveforms with Deep Ensembles","volume":"268","author":"Lang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"112571","DOI":"10.1016\/j.rse.2021.112571","article-title":"Performance Evaluation of GEDI and ICESat-2 Laser Altimeter Data for Terrain and Canopy Height Retrievals","volume":"264","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.foreco.2015.06.021","article-title":"Changes in Planted Forests and Future Global Implications","volume":"352","author":"Payn","year":"2015","journal-title":"For. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6264\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:53Z","timestamp":1760146673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,10]]},"references-count":46,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246264"],"URL":"https:\/\/doi.org\/10.3390\/rs14246264","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,10]]}}}