{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T05:52:13Z","timestamp":1776059533918,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Cooperation Project of Fujian, China","award":["2022I0007"],"award-info":[{"award-number":["2022I0007"]}]},{"name":"International Cooperation Project of Fujian, China","award":["2022N5008"],"award-info":[{"award-number":["2022N5008"]}]},{"name":"International Cooperation Project of Fujian, China","award":["MSK202214"],"award-info":[{"award-number":["MSK202214"]}]},{"name":"Industry-University Cooperation Project of the Science and Technology Department of Fujian","award":["2022I0007"],"award-info":[{"award-number":["2022I0007"]}]},{"name":"Industry-University Cooperation Project of the Science and Technology Department of Fujian","award":["2022N5008"],"award-info":[{"award-number":["2022N5008"]}]},{"name":"Industry-University Cooperation Project of the Science and Technology Department of Fujian","award":["MSK202214"],"award-info":[{"award-number":["MSK202214"]}]},{"name":"Science and Technology Project of Fujian Provincial Water Conservancy Department","award":["2022I0007"],"award-info":[{"award-number":["2022I0007"]}]},{"name":"Science and Technology Project of Fujian Provincial Water Conservancy Department","award":["2022N5008"],"award-info":[{"award-number":["2022N5008"]}]},{"name":"Science and Technology Project of Fujian Provincial Water Conservancy Department","award":["MSK202214"],"award-info":[{"award-number":["MSK202214"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.<\/jats:p>","DOI":"10.3390\/rs15020467","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T02:29:57Z","timestamp":1673576997000},"page":"467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4553-2978","authenticated-orcid":false,"given":"Xiaocheng","family":"Zhou","sequence":"first","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Youzhuang","family":"Hao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3953-9965","authenticated-orcid":false,"given":"Liping","family":"Di","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Xiaoqin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Chongcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Yunzhi","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9453-2291","authenticated-orcid":false,"given":"G\u00e1bor","family":"Nagy","sequence":"additional","affiliation":[{"name":"Institute of Geoinformatics, Alba Regia Technical Faculty, Obuda University, 8000 Sz\u00e9kesfeh\u00e9rv\u00e1r, Hungary"}]},{"given":"Tamas","family":"Jancso","sequence":"additional","affiliation":[{"name":"Institute of Geoinformatics, Alba Regia Technical Faculty, Obuda University, 8000 Sz\u00e9kesfeh\u00e9rv\u00e1r, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1016\/j.foreco.2008.11.016","article-title":"Aboveground biomass assessment in Colombia: A remote sensing approach","volume":"257","author":"Anaya","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1002\/ece3.2114","article-title":"Carbon storage in China\u2019s forest ecosystems: Estimation by different integrative methods","volume":"6","author":"Peng","year":"2016","journal-title":"Ecol. Evol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and climate change: Forcings, feedbacks, and the climate benefits of forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Lin, X., Xu, M., Cao, C., Dang, Y., Bashir, B., Xie, B., and Huang, Z. (2020). Estimates of forest canopy height using a combination of ICESat-2\/ATLAS data and stereo-photogrammetry. Remote Sens., 12.","DOI":"10.3390\/rs12213649"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112511","DOI":"10.1016\/j.rse.2021.112511","article-title":"Scaled biomass estimation in woodland ecosystems: Testing the individual and combined capacities of satellite multispectral and lidar data","volume":"262","author":"Campbell","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, M., Sun, R., and Xiao, Z. (2018). Estimation of forest canopy height and aboveground biomass from spaceborne LiDAR and Landsat imageries in Maryland. Remote Sens., 10.","DOI":"10.3390\/rs10020344"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100046","DOI":"10.1016\/j.fecs.2022.100046","article-title":"Retrieval of forest canopy height in a mountainous region with ICESat-2 ATLAS","volume":"9","author":"Pang","year":"2022","journal-title":"For. Ecosyst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.biocon.2019.01.032","article-title":"Combining behavioural and LiDAR data to reveal relationships between canopy structure and orangutan nest site selection in disturbed forests","volume":"232","author":"Davies","year":"2019","journal-title":"Biol. Conserv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4155\/cmt.11.18","article-title":"Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change","volume":"2","author":"Goetz","year":"2011","journal-title":"Carbon Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3876","DOI":"10.1016\/j.rse.2008.06.003","article-title":"Regional aboveground forest biomass using airborne and spaceborne LiDAR in Qu\u00e9bec","volume":"112","author":"Boudreau","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fayad, I., Baghdadi, N., and Ri\u00e9di, 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_13","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_14","doi-asserted-by":"crossref","first-page":"111423","DOI":"10.1016\/j.rse.2019.111423","article-title":"Combining allometry and landsat-derived disturbance history to estimate tree biomass in subtropical planted forests","volume":"235","author":"Fang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1016\/j.rse.2010.05.021","article-title":"Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat","volume":"114","author":"Helmer","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.rse.2013.05.033","article-title":"Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics","volume":"151","author":"Pflugmacher","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"G04021","DOI":"10.1029\/2011JG001708","article-title":"Mapping forest canopy height globally with spaceborne lidar","volume":"116","author":"Simard","year":"2011","journal-title":"J. Geophys. Res. Biogeosciences"},{"key":"ref_19","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_20","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_21","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_22","doi-asserted-by":"crossref","first-page":"100024","DOI":"10.1016\/j.srs.2021.100024","article-title":"The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring","volume":"4","author":"Roy","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112844","DOI":"10.1016\/j.rse.2021.112844","article-title":"Neural network guided interpolation for mapping canopy height of China\u2019s forests by integrating GEDI and ICESat-2 data","volume":"269","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MIE.2020.3034884","article-title":"Deep transfer learning for industrial automation: A review and discussion of new techniques for data-driven machine learning","volume":"15","author":"Maschler","year":"2021","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/0378-1127(94)06102-O","article-title":"Forest height growth modelling","volume":"71","author":"Rennolls","year":"1995","journal-title":"Forest Ecol. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1002\/2013JG002515","article-title":"Mapping forest stand age in China using remotely sensed forest height and observation data","volume":"119","author":"Zhang","year":"2014","journal-title":"J. Geophys. Res. Biogeosciences"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2006.02.022","article-title":"Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery","volume":"102","author":"Kayitakire","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2012.03.011","article-title":"Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions","volume":"70","author":"Cutler","year":"2012","journal-title":"ISPRS J. Photogramm."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1007\/s10661-020-08694-4","article-title":"Forest age mapping based on multiple-resource remote sensing data","volume":"192","author":"Yang","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_32","first-page":"101908","article-title":"Mapping tropical dry forest age using airborne waveform LiDAR and hyperspectral metrics","volume":"83","author":"Sun","year":"2019","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.rse.2019.02.001","article-title":"Seasonal dynamics of albedo across European boreal forests: Analysis of MODIS albedo and structural metrics from airborne LiDAR","volume":"224","author":"Hovi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.rse.2013.03.014","article-title":"Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery","volume":"134","author":"Dong","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.icarus.2018.10.018","article-title":"Spectral characterization of analog samples in anticipation of OSIRIS-REx\u2019s arrival at Bennu: A blind test study","volume":"319","author":"Hanna","year":"2019","journal-title":"Icarus"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2014.02.005","article-title":"Effects of forest age on albedo in boreal forests estimated from MODIS and Landsat albedo retrievals","volume":"145","author":"Kuusinen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shen, J., Chen, G., Hua, J., Huang, S., and Ma, J. (2022). Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method. Remote Sens., 14.","DOI":"10.3390\/rs14133238"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, H., Liu, L., Huang, W., Huete, A.R., Zhang, X., Wang, F., Yu, L., Xie, Q., and Wang, C. (2019). Estimating the aboveground biomass for planted forests based on stand age and environmental variables. Remote Sens., 11.","DOI":"10.3390\/rs11192270"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_40","unstructured":"Dubayah, R., Hofton, M., Blair, J.B., Armston, J., Tang, H., and Luthcke, S. (2021, April 12). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V001. NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/GEDI\/GEDI02_A.001."},{"key":"ref_41","unstructured":"Serna, E.H., and Hernandez-Serna, A. (2021, August 10). pyGEDI: NASA\u2019s Global Ecosystem Dynamics Investigation (GEDI) Mission Data Extraction, Analysis, Processing and Visualization. Version 0.2, 5 April 2020. Available online: https:\/\/pypi.org\/project\/pyGEDI."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., and Healey, S. (2018). Implementation of the LandTrendr algorithm on google earth engine. Remote Sens., 10.","DOI":"10.3390\/rs10050691"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101221","DOI":"10.1016\/j.ecoinf.2021.101221","article-title":"Developing a new disturbance index for tracking gradual change of forest ecosystems in the hilly red soil region of southern China using dense Landsat time series","volume":"61","author":"Ye","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"83622","DOI":"10.1117\/1.JRS.8.083622","article-title":"Identification of high temperature targets in remote sensing imagery based on factor analysis","volume":"8","author":"Yu","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11104-011-0766-2","article-title":"Potential for forest vegetation carbon storage in Fujian, China, determined from forest inventories","volume":"345","author":"Ren","year":"2011","journal-title":"Plant Soil"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/MGRS.2020.3032713","article-title":"Lidar boosts 3d ecological observations and modelings: A review and perspective","volume":"9","author":"Guo","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"10750","DOI":"10.3390\/rs61110750","article-title":"Estimation of airborne lidar-derived tropical forest canopy height using landsat time series in Cambodia","volume":"6","author":"Ota","year":"2014","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, M., Cao, C., Dang, Y., and Ni, X. (2019). Mapping forest canopy height in mountainous areas using ZiYuan-3 stereo images and Landsat data. Forests, 10.","DOI":"10.3390\/f10020105"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1111\/1365-2745.12510","article-title":"Regional and historical factors supplement current climate in shaping global forest canopy height","volume":"104","author":"Zhang","year":"2016","journal-title":"J. Ecol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.isprsjprs.2014.11.007","article-title":"Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm","volume":"101","author":"Ahmed","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2016.02.023","article-title":"Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data","volume":"185","author":"Hansen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_52","first-page":"101956","article-title":"Update and spatial extension of strategic forest inventories using time series remote sensing and modeling","volume":"84","author":"Shang","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1080\/15481603.2022.2085354","article-title":"Factors affecting relative height and ground elevation estimations of GEDI among forest types across the conterminous USA","volume":"59","author":"Wang","year":"2022","journal-title":"Gisci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1002\/2016EA000177","article-title":"Mapping spatial distribution of forest age in China","volume":"4","author":"Zhang","year":"2017","journal-title":"Earth Space Sci."},{"key":"ref_55","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:04:47Z","timestamp":1760119487000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,12]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020467"],"URL":"https:\/\/doi.org\/10.3390\/rs15020467","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,12]]}}}