{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:38:44Z","timestamp":1762508324829,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf\u2013tree matrix derived from allometric relations of trees. Using the leaf\u2013tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha\u22121\/normalized RMSE 18.8%\/R\u00b2 0.76; 50 ha: 22.8 trees ha\u22121\/6.2%\/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha\u22121, bias 0.8 m\u00b2 ha\u22121) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.<\/jats:p>","DOI":"10.3390\/rs13010131","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T19:54:46Z","timestamp":1609703686000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deriving Tree Size Distributions of Tropical Forests from Lidar"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7594-8152","authenticated-orcid":false,"given":"Franziska","family":"Taubert","sequence":"first","affiliation":[{"name":"Department of Ecological Modelling, Helmholtz Centre for Environmental Research\u2014UFZ, 04318 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0482-0095","authenticated-orcid":false,"given":"Rico","family":"Fischer","sequence":"additional","affiliation":[{"name":"Department of Ecological Modelling, Helmholtz Centre for Environmental Research\u2014UFZ, 04318 Leipzig, Germany"}]},{"given":"Nikolai","family":"Knapp","sequence":"additional","affiliation":[{"name":"Department of Ecological Modelling, Helmholtz Centre for Environmental Research\u2014UFZ, 04318 Leipzig, Germany"}]},{"given":"Andreas","family":"Huth","sequence":"additional","affiliation":[{"name":"Department of Ecological Modelling, Helmholtz Centre for Environmental Research\u2014UFZ, 04318 Leipzig, Germany"},{"name":"German Centre for Integrative Biodiversity Research (iDiv), 04103 Halle-Leipzig-Jena, Germany"},{"name":"Institute of Environmental Systems Research, University Osnabr\u00fcck, 49076 Osnabr\u00fcck, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,2]]},"reference":[{"key":"ref_1","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_2","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_3","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.baae.2008.08.010","article-title":"Biodiversity change and ecosystem function in tropical forests","volume":"10","author":"Lewis","year":"2009","journal-title":"Basic Appl. Ecol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1126\/science.aaa9932","article-title":"Increasing human dominance of tropical forests","volume":"349","author":"Lewis","year":"2015","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1126\/science.aac6759","article-title":"Forest health and global change","volume":"349","author":"Trumbore","year":"2015","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nclimate3303","article-title":"Forest disturbances under climate change","volume":"7","author":"Seidl","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/s41586-018-0300-2","article-title":"The tropical forest carbon cycle and climate change","volume":"559","author":"Mitchard","year":"2018","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1038\/nature25508","article-title":"Global patterns of tropical forest fragmentation","volume":"554","author":"Taubert","year":"2018","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1126\/science.aau3445","article-title":"Classifying drivers of global forest loss","volume":"361","author":"Curtis","year":"2018","journal-title":"Science"},{"key":"ref_11","first-page":"10185","article-title":"Degradation in carbon stocks near tropical forest edges","volume":"6","author":"Ramler","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"14855","DOI":"10.1038\/ncomms14855","article-title":"High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle","volume":"8","author":"Brinck","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1111\/gcb.12712","article-title":"CTFS-Forest GEO: A worldwide network monitoring forests in an era of global change","volume":"21","author":"Davies","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H., and Hurtt, G.C. (2009). Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J. Geophys. Res. Biogeosci., 114.","DOI":"10.1029\/2008JG000911"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote sensing technologies for enhancing forest inventories: A review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8837","DOI":"10.1080\/01431161.2010.547533","article-title":"Earth science applications of ICESat\/GLAS: A review","volume":"32","author":"Wang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/TGRS.2007.900693","article-title":"TanDEM-X: A satellite formation for high-resolution SAR interferometry","volume":"45","author":"Krieger","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1109\/TGRS.2007.901027","article-title":"ALOS PALSAR: A pathfinder mission for global-scale monitoring of the environment","volume":"45","author":"Rosenqvist","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat satellite: The Landsat data continuity mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gascon, F., Cadau, E., Colin, O., Hoersch, B., Isola, C., L\u00f3pez Fern\u00e1ndez, B., and Martimort, P. (2014, January 26). Copernicus Sentinel-2 mission: Products, algorithms and Cal\/Val. Proceedings of the SPIE 9218, Earth Observing Systems XIX, 92181E, San Diego, CA, USA.","DOI":"10.1117\/12.2062260"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0034-4257(02)00084-6","article-title":"An overview of MODIS Land data processing and products","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"111597","DOI":"10.1016\/j.rse.2019.111597","article-title":"Structure metrics to generalize biomass estimation from lidar across forest types from different continents","volume":"237","author":"Knapp","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1080\/02827581.2011.624116","article-title":"A fine-scale model for area-based predictions of tree-size-related attributes derived from LiDAR canopy heights","volume":"27","author":"Magnussen","year":"2012","journal-title":"Scand. J. For. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s13595-016-0581-2","article-title":"Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters","volume":"73","author":"Magnussen","year":"2016","journal-title":"Ann. For. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fu, L., Duan, G., Ye, Q., Meng, X., Luo, P., Sharma, R.P., Sun, H., Wang, G., and Liu, Q. (2020). Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12071066"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Maltamo, M., and Gobakken, T. (2014). Predicting tree diameter distributions. Forestry Applications of Airborne Laser Scanning, Springer.","DOI":"10.1007\/978-94-017-8663-8_9"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.020","article-title":"Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data","volume":"161","author":"Palace","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Spriggs, R.A., Coomes, D.A., Jones, T.A., Caspersen, J.P., and Vanderwel, M.C. (2017). An alternative approach to using LiDAR remote sensing data to predict stem diameter distributions across a temperate forest landscape. Remote Sens., 9.","DOI":"10.3390\/rs9090944"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1002\/2013GL058373","article-title":"Imaging spectroscopy-and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystem dynamics","volume":"41","author":"Antonarakis","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2016.05.028","article-title":"Lidar detection of individual tree size in tropical forests","volume":"183","author":"Ferraz","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ferraz, A., Saatchi, S., Longo, M., and Clark, D.B. (2020). Tropical tree size\u2013frequency distributions from airborne lidar. Ecol. Appl.","DOI":"10.1002\/eap.2154"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TGRS.2018.2865014","article-title":"A Local Projection-Based Approach to Individual Tree Detection and 3-D Crown Delineation in Multistoried Coniferous Forests Using High-Density Airborne LiDAR Data","volume":"57","author":"Harikumar","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Aubry-Kientz, M., Dutrieux, R., Ferraz, A., Saatchi, S., Hamraz, H., Williams, J., Coomes, D., Piboule, A., and Vincent, G. (2019). A comparative assessment of the performance of individual tree crowns delineation algorithms from ALS data in tropical forests. Remote Sens., 11.","DOI":"10.3390\/rs11091086"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1016\/j.rse.2010.03.019","article-title":"Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar","volume":"115","author":"Yao","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"Condit, R., Perez, R., Aguilar, S., Lao, S., Foster, R., and Hubbell, S. (2019). Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years. Dryad."},{"key":"ref_39","unstructured":"Condit, R., Perez, R., Aguilar, S., Lao, S., Foster, R., and Hubbell, S. (2019). BCI 50-ha plot taxonomy. Dryad."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Condit, R. (1998). Tropical Forest Census Plots, R. G. Landes Company.","DOI":"10.1007\/978-3-662-03664-8"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"554557","DOI":"10.1126\/science.283.5401.554","article-title":"Light gap disturbances, recruitment limitation, and tree diversity in a neotropical forest","volume":"283","author":"Hubbell","year":"1999","journal-title":"Science"},{"key":"ref_42","unstructured":"(2020, December 18). ForestGeo Global Earth Observatory Network. Available online: https:\/\/forestgeo.si.edu\/explore-data\/barro-colorado-island-termsconditionsrequest-forms."},{"key":"ref_43","unstructured":"Dryad (2020, December 18). Available online: https:\/\/datadryad.org\/stash\/dataset\/doi:10.15146\/5xcp-0d46."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/gcb.13388","article-title":"Allometric equations for integrating remote sensing imagery into forest monitoring programmes","volume":"23","author":"Jucker","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bohlman, S., and O\u2019Brien, S. (2006). Allometry, adult stature and regeneration requirement of 65 tree species on Barro Colorado Island, Panama. J. Trop. Ecol., 123\u2013136.","DOI":"10.1017\/S0266467405003019"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"15125","DOI":"10.1073\/pnas.1513417112","article-title":"The structure of tropical forests and sphere packings","volume":"112","author":"Taubert","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20133218","DOI":"10.1098\/rspb.2013.3218","article-title":"Spatial scale and sampling resolution affect measures of gap disturbance in a lowland tropical forest: Implications for understanding forest regeneration and carbon storage","volume":"281","author":"Lobo","year":"2014","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_48","unstructured":"Campbell, G.S., and Norman, J. (2012). An Introduction to Environmental Biophysics, Springer Science & Business Media."},{"key":"ref_49","unstructured":"Strang, G. (2016). Introduction to linear algebra. Wellesley-Cambridge Press."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1890\/04-0150","article-title":"Development of tree size distributions in Douglas-fir forests under differing disturbance regimes","volume":"15","author":"Zenner","year":"2005","journal-title":"Ecol. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Purves, D.W., Lichstein, J.W., and Pacala, S.W. (2007). Crown plasticity and competition for canopy space: A new spatially implicit model parameterized for 250 North American tree species. PLoS ONE, 2.","DOI":"10.1371\/journal.pone.0000870"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.rse.2007.06.011","article-title":"A voxel-based lidar method for estimating crown base height for deciduous and pine trees","volume":"112","author":"Popescu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"847","DOI":"10.5194\/bg-16-847-2019","article-title":"Tropical tree height and crown allometries for the Barro Colorado Nature Monument, Panama: A comparison of alternative hierarchical models incorporating interspecific variation in relation to life history traits","volume":"16","author":"Wright","year":"2019","journal-title":"Biogeosciences"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Disney, M., Burt, A., Calders, K., Schaaf, C., and Stovall, A. (2019). Innovations in Ground and Airborne Technologies as Reference and for Training and Validation: Terrestrial Laser Scanning (TLS). Surv. Geophys.","DOI":"10.1007\/s10712-019-09527-x"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ma, X., Migliavacca, M., Wirth, C., Bohn, F.J., Huth, A., Richter, R., and Mahecha, M.D. (2020). Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space. Remote Sens., 12.","DOI":"10.3390\/rs12081248"},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1029\/1999GL010484","article-title":"Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data","volume":"26","author":"Blair","year":"1999","journal-title":"Geophys. Res. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.rse.2012.05.005","article-title":"Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica","volume":"124","author":"Tang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1111\/j.1461-0248.2012.01864.x","article-title":"Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment","volume":"15","author":"Stark","year":"2012","journal-title":"Ecol. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0034-4257(00)00210-8","article-title":"Laser altimeter canopy height profiles: Methods and validation for closed-canopy, broadleaf forests","volume":"76","author":"Harding","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1002\/2014JG002774","article-title":"Spatial variability in tropical forest leaf area density from multireturn lidar and modeling","volume":"120","author":"Detto","year":"2015","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_63","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_64","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_65","unstructured":"Adams, T., Beets, P., and Parrish, C. (2011). Another dimension from LiDAR\u2013Obtaining foliage density from full waveform data. Int. Conf. Lidar Appl. Assess. For. Ecosyst., 798. Available online: https:\/\/scholars.unh.edu\/ccom\/798."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5088","DOI":"10.1038\/s41467-019-13063-y","article-title":"From small-scale forest structure to Amazon-wide carbon estimates","volume":"10","author":"Knapp","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1111\/2041-210X.13171","article-title":"A scalable model of vegetation transitions using deep neural networks","volume":"10","author":"Rammer","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2016.10.018","article-title":"Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping","volume":"187","author":"Qi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1111\/j.1365-2486.2007.01323.x","article-title":"Distribution of aboveground live biomass in the Amazon basin","volume":"13","author":"Saatchi","year":"2007","journal-title":"Glob. Chang. Biol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:06:24Z","timestamp":1760159184000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,2]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13010131"],"URL":"https:\/\/doi.org\/10.3390\/rs13010131","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,1,2]]}}}