{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:41:37Z","timestamp":1760179297218,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,25]],"date-time":"2020-10-25T00:00:00Z","timestamp":1603584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA\u2019s Land-Cover\/Land-Use Change (LCLUC) program","award":["NHX14AD92G"],"award-info":[{"award-number":["NHX14AD92G"]}]},{"name":"Carbon Monitoring System (CMS) projects","award":["NNH15AZ06I","NNH18ZDA001N"],"award-info":[{"award-number":["NNH15AZ06I","NNH18ZDA001N"]}]},{"name":"USDA NIFA","award":["2011-32100-06016"],"award-info":[{"award-number":["2011-32100-06016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Stand-level maps of past forest disturbances (expressed as time since disturbance, TSD) are needed to model forest ecosystem processes, but the conventional approaches based on remotely sensed satellite data can only extend as far back as the first available satellite observations. Stand-level analysis of airborne LiDAR data has been demonstrated to accurately estimate long-term TSD (~100 years), but large-scale coverage of airborne LiDAR remains costly. NASA\u2019s spaceborne LiDAR Global Ecosystem Dynamics Investigation (GEDI) instrument, launched in December 2018, is providing billions of measurements of tropical and temperate forest canopies around the globe. GEDI is a spatial sampling instrument and, as such, does not provide wall-to-wall data. GEDI\u2019s lasers illuminate ground footprints, which are separated by ~600 m across-track and ~60 m along-track, so new approaches are needed to generate wall-to-wall maps from the discrete measurements. In this paper, we studied the feasibility of a data fusion approach between GEDI and Landsat for wall-to-wall mapping of TSD. We tested the methodology on a ~52,500-ha area located in central Idaho (USA), where an extensive record of stand-replacing disturbances is available, starting in 1870. GEDI data were simulated over the nominal two-year planned mission lifetime from airborne LiDAR data and used for TSD estimation using a random forest (RF) classifier. Image segmentation was performed on Landsat-8 data, obtaining image-objects representing forest stands needed for the spatial extrapolation of estimated TSD from the discrete GEDI locations. We quantified the influence of (1) the forest stand map delineation, (2) the sample size of the training dataset, and (3) the number of GEDI footprints per stand on the accuracy of estimated TSD. The results show that GEDI-Landsat data fusion would allow for TSD estimation in stands covering ~95% of the study area, having the potential to reconstruct the long-term disturbance history of temperate even-aged forests with accuracy (median root mean square deviation = 22.14 years, median BIAS = 1.70 years, 60.13% of stands classified within 10 years of the reference disturbance date) comparable to the results obtained in the same study area with airborne LiDAR.<\/jats:p>","DOI":"10.3390\/rs12213506","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T03:51:47Z","timestamp":1603684307000},"page":"3506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimating Time Since the Last Stand-Replacing Disturbance (TSD) from Spaceborne Simulated GEDI Data: A Feasibility Study"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0455-3951","authenticated-orcid":false,"given":"Nuria","family":"Sanchez-Lopez","sequence":"first","affiliation":[{"name":"Department of Forest, Rangeland and Fire Sciences, College of Natural Resources, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6525-4413","authenticated-orcid":false,"given":"Luigi","family":"Boschetti","sequence":"additional","affiliation":[{"name":"Department of Forest, Rangeland and Fire Sciences, College of Natural Resources, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7480-1458","authenticated-orcid":false,"given":"Andrew T.","family":"Hudak","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Forest Service, U.S., Rocky Mountain Research Station, Forestry Sciences Laboratory, 1221 South Main St., Moscow, ID 83843, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Hancock","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"School of Geosciences, University of Edinburgh, Edinburgh EH8 9XP, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura I.","family":"Duncanson","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"715","DOI":"10.5194\/bg-8-715-2011","article-title":"Age structure and disturbance legacy of North American forests","volume":"8","author":"Pan","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1111\/j.1365-2486.2004.00866.x","article-title":"Carbon cycling and storage in world forests: Biome patterns related to forest age","volume":"10","author":"Pregitzer","year":"2004","journal-title":"Glob. Chang. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4382","DOI":"10.1073\/pnas.1810512116","article-title":"Role of forest regrowth in global carbon sink dynamics","volume":"116","author":"Pugh","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2882","DOI":"10.1111\/j.1365-2486.2008.01686.x","article-title":"Tree age, disturbance history, and carbon stocks and fluxes in subalpine Rocky Mountain forests","volume":"14","author":"Bradford","year":"2008","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9439","DOI":"10.1073\/pnas.0804042105","article-title":"Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data","volume":"105","author":"Hansen","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/17538940902801614","article-title":"Development of time series stacks of Landsat images for reconstructing forest disturbance history","volume":"2","author":"Huang","year":"2009","journal-title":"Int. J. Digit. Earth"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huo, L.-Z., Boschetti, L., and Sparks, A.M. (2019). Object-Based Classification of Forest Disturbance Types in the Conterminous United States. Remote Sens., 11.","DOI":"10.3390\/rs11050477"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1016\/j.rse.2008.02.010","article-title":"North American forest disturbance mapped from a decadal Landsat record","volume":"112","author":"Masek","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.foreco.2007.03.019","article-title":"Patterns of forest regrowth following clearcutting in western Oregon as determined from a Landsat time-series","volume":"243","author":"Schroeder","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_12","first-page":"38","article-title":"Reconstruction of the disturbance history of a temperate coniferous forest through stand-level analysis of airborne LiDAR data","volume":"93","author":"Boschetti","year":"2020","journal-title":"For. Int. J. For. Res."},{"key":"ref_13","first-page":"128","article-title":"Estimating Forest Stand Age from LiDAR-Derived Predictors and Nearest Neighbor Imputation","volume":"60","author":"Racine","year":"2014","journal-title":"For. Sci."},{"key":"ref_14","unstructured":"Poulter, B., Arag\u00e3o, L., Andela, N., Bellassen, V., Ciais, P., Kato, T., Lin, X., Nachin, B., Luyssaert, S., and Pederson, N. (2019). The global forest age dataset and its uncertainties (GFADv1.1). NASA Natl. Aeronaut. Space Adm."},{"key":"ref_15","first-page":"943","article-title":"Height Growth and Site Index Curves for Inland Douglas-fir Based on Stem Analysis Data and Forest Habitat Type","volume":"30","author":"Monserud","year":"1984","journal-title":"For. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Oliver, C.D., and Larson, B.C. (1996). Forest Stand Dynamics: Updated Edition, John Wiley and Sons.","DOI":"10.1093\/forestscience\/42.3.397"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1890\/03-4037","article-title":"An Experimental Test of the Causes of Forest Growth Decline with Stand Age","volume":"74","author":"Ryan","year":"2004","journal-title":"Ecol. Monogr."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.10.013","article-title":"Estimating forest canopy fuel parameters using LIDAR data","volume":"94","author":"Andersen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"126","DOI":"10.5589\/m06-007","article-title":"Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data","volume":"32","author":"Hudak","year":"2006","journal-title":"Can. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/S0034-4257(99)00052-8","article-title":"Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests","volume":"70","author":"Lefsky","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.rse.2005.01.004","article-title":"Patterns of covariance between forest stand and canopy structure in the Pacific Northwest","volume":"95","author":"Lefsky","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0924-2716(97)83000-6","article-title":"Determination of mean tree height of forest stands using airborne laser scanner data","volume":"52","year":"1997","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0034-4257(88)90028-4","article-title":"Estimating forest biomass and volume using airborne laser data","volume":"24","author":"Nelson","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1016\/j.rse.2009.03.006","article-title":"Lidar-based mapping of leaf area index and its use for validating globcarbon satellite LAI product in a temperate forest of the southern USA","volume":"113","author":"Zhao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1016\/j.rse.2009.01.003","article-title":"Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA","volume":"113","author":"Falkowski","year":"2009","journal-title":"Remote Sens. Environ."},{"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. Biogeosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/S0264-3707(02)00042-X","article-title":"ICESat\u2019s laser measurements of polar ice, atmosphere, ocean, and land","volume":"34","author":"Zwally","year":"2002","journal-title":"J. Geodyn."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lefsky, M.A. (2010). A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophys. Res. Lett., 37.","DOI":"10.1029\/2010GL043622"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/nclimate1354","article-title":"Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps","volume":"2","author":"Baccini","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Simard, M., Pinto, N., Fisher, J.B., and Baccini, A. (2011). Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci., 116.","DOI":"10.1029\/2011JG001708"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2012.11.016","article-title":"Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error","volume":"130","author":"Montesano","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rse.2009.08.018","article-title":"Estimating forest canopy height and terrain relief from GLAS waveform metrics","volume":"114","author":"Duncanson","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12409","DOI":"10.3390\/rs61212409","article-title":"Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR","volume":"6","author":"Peterson","year":"2014","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"175","DOI":"10.14358\/PERS.79.2.175","article-title":"Towards integration of GLAS into a national fuel mapping program","volume":"79","author":"Peterson","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1080\/014311697219015","article-title":"The pixel: A snare and a delusion","volume":"18","author":"Fisher","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer Science & Business Media.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2004.07.009","article-title":"Estimating time since forest harvest using segmented Landsat ETM+ imagery","volume":"93","author":"Wulder","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"536","DOI":"10.5589\/m03-032","article-title":"Forest inventory height update through the integration of lidar data with segmented Landsat imagery","volume":"29","author":"Wulder","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2007.02.002","article-title":"Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization","volume":"110","author":"Wulder","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Hancock, S., Armston, J., Hofton, M., Sun, X., Tang, H., Duncanson, L.I., Kellner, J.R., and Dubayah, R. (2019). The GEDI simulator: A large-footprint waveform lidar simulator for calibration and validation of spaceborne missions. Earth Space Sci.","DOI":"10.1029\/2018EA000506"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1071\/WF17023","article-title":"Multidecadal trends in area burned with high severity in the Selway-Bitterroot Wilderness Area 1880\u20132012","volume":"26","author":"Morgan","year":"2017","journal-title":"Int. J. Wildland Fire"},{"key":"ref_46","unstructured":"(2018, February 28). USDA, Forest Service Forest Service Activity Tracking System (FACTs) Harvest Database, Available online: http:\/\/data.fs.usda.gov\/geodata\/edw\/datasets.php."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"013537","DOI":"10.1117\/1.2795724","article-title":"Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms","volume":"1","author":"Lefsky","year":"2007","journal-title":"J. Appl. Remote Sens."},{"key":"ref_49","unstructured":"Roberts, D.W.U.S.U., and Cooper, S.V. (1989). Concepts and Techniques of Vegetation Mapping, Food and agriculture Organization of the United Nation."},{"key":"ref_50","unstructured":"McGaughey, R.J. (2009). FUSION\/LDV: Software for LIDAR Data Analysis and Visualization."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"013107","DOI":"10.1117\/1.OE.53.1.013107","article-title":"Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images","volume":"53","author":"Sun","year":"2014","journal-title":"Opt. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/2150704X.2014.915434","article-title":"Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance","volume":"5","author":"Baig","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_53","first-page":"12","article-title":"Multiresolution Segmentation\u2013an optimization approach for high quality multi-scale image segmentation","volume":"2000","author":"Baatz","year":"2000","journal-title":"AGIT Symp. Salzbg."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1080\/01431169508954436","article-title":"Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, USA","volume":"16","author":"Cohen","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(92)90056-P","article-title":"Estimating structural attributes of Douglas-fir\/western hemlock forest stands from Landsat and SPOT imagery","volume":"41","author":"Cohen","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.rse.2004.10.012","article-title":"Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances","volume":"94","author":"Jin","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"31","DOI":"10.5194\/isprsannals-II-7-31-2014","article-title":"Parameter-based performance analysis of object-based image analysis using aerial and QuikBird-2 images","volume":"2","author":"Kavzoglu","year":"2014","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sanchez-Lopez, N., Boschetti, L., and Hudak, A. (2018). Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy. Remote Sens., 10.","DOI":"10.3390\/rs10101622"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"289","DOI":"10.14358\/PERS.76.3.289","article-title":"Others Accuracy assessment measures for object-based image segmentation goodness","volume":"76","author":"Clinton","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"B\u00f6ck, S., Immitzer, M., and Atzberger, C. (2017). On the Objectivity of the Objective Function\u2014Problems with Unsupervised Segmentation Evaluation Based on Global Score and a Possible Remedy. Remote Sens., 9.","DOI":"10.3390\/rs9080769"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1080\/01431160600617194","article-title":"Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation","volume":"27","author":"Espindola","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","first-page":"473","article-title":"Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogramm","volume":"66","author":"Johnson","year":"2011","journal-title":"Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"943","DOI":"10.14214\/sf.943","article-title":"Laser-assisted selection of field plots for an area-based forest inventory","volume":"47","author":"Gobakken","year":"2013","journal-title":"Silva Fenn."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"G00E04","DOI":"10.1029\/2008JG000870","article-title":"Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design","volume":"114","author":"Hawbaker","year":"2009","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"16","DOI":"10.18637\/jss.v023.i10","article-title":"yaImpute: An R package for kNN imputation","volume":"23","author":"Crookston","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/S0034-4257(99)00057-7","article-title":"Relationships between Leaf Area Index and Landsat TM Spectral Vegetation Indices across Three Temperate Zone Sites","volume":"70","author":"Turner","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. (2016). Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060469"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"045011","DOI":"10.1088\/1748-9326\/3\/4\/045011","article-title":"A first map of tropical Africa\u2019s above-ground biomass derived from satellite imagery","volume":"3","author":"Baccini","year":"2008","journal-title":"Environ. Res. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:27:57Z","timestamp":1760178477000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,25]]},"references-count":69,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213506"],"URL":"https:\/\/doi.org\/10.3390\/rs12213506","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,10,25]]}}}