{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:05:36Z","timestamp":1770897936293,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,13]],"date-time":"2017-06-13T00:00:00Z","timestamp":1497312000000},"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>Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA\u2014Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)\u2014in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R2) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.<\/jats:p>","DOI":"10.3390\/rs9060598","type":"journal-article","created":{"date-parts":[[2017,6,14]],"date-time":"2017-06-14T03:19:32Z","timestamp":1497410372000},"page":"598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA"],"prefix":"10.3390","volume":"9","author":[{"given":"Ram","family":"Deo","sequence":"first","affiliation":[{"name":"Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave. North, St. Paul, MN 55108, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7044-9650","authenticated-orcid":false,"given":"Matthew","family":"Russell","sequence":"additional","affiliation":[{"name":"Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave. North, St. Paul, MN 55108, USA"}]},{"given":"Grant","family":"Domke","sequence":"additional","affiliation":[{"name":"Northern Research Station, Forest Inventory and Analysis, USDA Forest Service, 1992 Folwell Ave., St. Paul, MN 55108, USA"}]},{"given":"Hans-Erik","family":"Andersen","sequence":"additional","affiliation":[{"name":"Pacific Northwest Research Station, USDA Forest Service, University of Washington, Seattle, WA 98195, USA"}]},{"given":"Warren","family":"Cohen","sequence":"additional","affiliation":[{"name":"USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97731, USA"}]},{"given":"Christopher","family":"Woodall","sequence":"additional","affiliation":[{"name":"Northern Research Station, Center for Research on Ecosystem Change, USDA Forest Service, 271 Mast Road, Durham, NH 03824, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2009JG000935","article-title":"Importance of biomass in the global carbon cycle","volume":"114","author":"Houghton","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5125","DOI":"10.5194\/bg-9-5125-2012","article-title":"Carbon emissions from land use and land-cover change","volume":"9","author":"Houghton","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.foreco.2015.05.036","article-title":"Changes in forest production, biomass and carbon: Results from the 2015 UN FAO Global Forest Resource Assessment","volume":"352","author":"Lasco","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"235","DOI":"10.5194\/essd-6-235-2014","article-title":"Global carbon budget 2013","volume":"6","author":"Peters","year":"2014","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_5","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_6","unstructured":"White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D., and Woods, M. (2017, June 12). A Best Practice Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach. Available online: http:\/\/www.cfs.nrcan.gc.ca\/pubwarehouse\/pdfs\/34887.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2012.02.001","article-title":"LiDAR sampling for large-area forest characterization: A review","volume":"121","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/07038992.2017.1259556","article-title":"Using Landsat time-series and LiDAR to inform aboveground forest biomass baselines in northern Minnesota, USA","volume":"43","author":"Deo","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_9","unstructured":"Deo, R.K. (2008). Modeling and mapping of aboveground biomass and carbon sequestration in the cool temperate forest of north-east China. [Master\u2019s Thesis, ITC]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1641\/0006-3568(2002)052[0019:LRSFES]2.0.CO;2","article-title":"LiDAR remote sensing for ecosystem studies","volume":"52","author":"Lefsky","year":"2002","journal-title":"BioScience"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.rse.2013.12.013","article-title":"Influence of LiDAR, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure","volume":"143","author":"Zald","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/S0034-4257(02)00056-1","article-title":"Integration of LiDAR and Landsat ETM+ data for estimating and mapping forest canopy height","volume":"82","author":"Hudak","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1016\/j.rse.2008.03.004","article-title":"Estimation of above- and below- ground biomass across regions of the boreal forest zone using airborne laser","volume":"112","author":"Gobakken","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"McGaughey, R.J. (2014). FUSION\/LDV: Software for LiDAR Data Analysis and Visualization, Version 3.21."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1080\/07038992.2016.1220826","article-title":"Optimizing variable radius plot size and LiDAR resolution to model standing volume in conifer forests","volume":"42","author":"Deo","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2007.02.028","article-title":"Estimating field-plot data of forest stands using airborne laser scanning and SPOT HRG data","volume":"110","author":"Wallerman","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1093\/forestscience\/49.1.12","article-title":"National-scale biomass estimators for United States tree species","volume":"49","author":"Jenkins","year":"2003","journal-title":"Forest Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Falster, D.S., Duursma, R.A., Ishihara, M.I., Barneche, D.R., FitzJohn, R.G., Varhammar, A., Aiba, M., Ando, M., Anten, N., and Aspinwall, M.J. (2015). BAAD: A biomass and allometry database for woody plants. Ecology, 96.","DOI":"10.1890\/14-1889.1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1111\/gcb.12629","article-title":"Improved allometric models to estimate the aboveground biomass of tropical trees","volume":"20","author":"Chave","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.foreco.2012.01.022","article-title":"Consequences of alternative tree-level biomass estimation procedures on U.S. forest carbon stock estimates","volume":"270","author":"Domke","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Weiskittel, A.R., Hann, D.W., Kershaw, J.A., and Vanclay, J.K. (2011). Forest Growth and Yield Modeling, Wiley.","DOI":"10.1002\/9781119998518"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1139\/X09-042","article-title":"Growing stock estimation for alpine forests in Austria: A robust LiDAR-based approach","volume":"39","author":"Hollaus","year":"2009","journal-title":"Can. J. For. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1093\/forestry\/cpv001","article-title":"Spatial-scale considerations for a large-area forest inventory regression model","volume":"88","author":"Westfall","year":"2015","journal-title":"Forestry"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.rse.2012.07.006","article-title":"Forest biomass estimation from airborne LiDAR data using machine learning approaches","volume":"125","author":"Gleaspm","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1093\/forestry\/cpq022","article-title":"Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical\/LiDAR-derived predictors","volume":"83","author":"Latifi","year":"2010","journal-title":"Forestry"},{"key":"ref_28","first-page":"229","article-title":"Stratified aboveground forest biomass estimation by remote sensing data","volume":"38","author":"Latifi","year":"2015","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_29","first-page":"1","article-title":"Methods for variable selection in LiDAR-assisted forest inventories","volume":"89","author":"Moser","year":"2016","journal-title":"Forestry"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.01.021","article-title":"Integration of airborne LiDAR and vegetation types derived from aerial photography for mapping aboveground live biomass","volume":"121","author":"Chen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5849\/forsci.12-134","article-title":"A review of methods for mapping and prediction of inventory attributes for operational forest management","volume":"60","author":"Brosofske","year":"2014","journal-title":"Forest Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","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_34","first-page":"18","article-title":"Classification and regression by random forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2016.01.015","article-title":"Integrating Landsat pixel composites and change metrics with LiDAR plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada","volume":"176","author":"Zald","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1093\/forestry\/cpv002","article-title":"Arguments for a model-dependent inference?","volume":"88","author":"Magnussen","year":"2015","journal-title":"Forestry"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2015.11.012","article-title":"Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass","volume":"173","author":"Gregoire","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.rse.2012.10.007","article-title":"Inference for LiDAR-assisted estimation of forest growing stock volume","volume":"128","author":"McRoberts","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"McCaskill, G.L., McWilliams, W.H., Barnett, C.J., Butler, B.J., Hatfield, M.A., Kurtz, C.M., Morin, R.S., Moser, W.K., Perry, C.H., and Woodall, C.W. (2011). Maine\u2019s Forests 2008.","DOI":"10.2737\/NRS-RB-48"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Miles, P.D., Heinzen, D., Mielke, M.E., Woodall, C.W., Butler, B.J., Piva, R.J., Meneguzzo, D.M., Perry, C.H., Gormanson, D.D., and Barnett, C.J. (2011). Minnesota\u2019s Forests 2008.","DOI":"10.2737\/NRS-RB-50"},{"key":"ref_41","unstructured":"Crocker, S.J. (2014). Forests of New Jersey 2013."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"McCaskill, G.L., McWilliams, W.H., Alerich, C.A., Butler, B.J., Crocker, S.J., Domke, G.M., Griffith, D., Kurtz, C.M., Lehman, S., and Lister, T.W. (2013). Pennsylvania\u2019s Forests 2009.","DOI":"10.2737\/NRS-RB-82"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rose, A.K. (2016). South Carolina\u2019s Forests 2011.","DOI":"10.2737\/SRS-RB-208"},{"key":"ref_44","unstructured":"USGS EROS (2017, March 16). Landsat Surface Reflectance Higher-Level Data Products. U.S. Geological Survey, Earth Resources Observation and Science Center, Available online: https:\/\/landsat.usgs.gov\/landsat-surface-reflectance-high-level-data-products."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.rse.2005.05.009","article-title":"Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection","volume":"97","author":"Healey","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A physically based transformation of Thematic Mapper data\u2014The TM Tasseled Cap","volume":"GE-22","author":"Crist","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","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_49","unstructured":"USGS EROS (2016, April 11). Landsat Surface Reflectance-Derived Spectral Indices, Available online: https:\/\/landsat.usgs.gov\/sites\/default\/files\/documents\/si_product_guide.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1080\/01431160500353858","article-title":"Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data","volume":"27","author":"Heiskanen","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","first-page":"1","article-title":"Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates","volume":"2012","author":"Lu","year":"2012","journal-title":"Int. J. For. Res."},{"key":"ref_52","unstructured":"R Core Team (2016, August 05). Available online: http:\/\/www.R-project.org\/."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1016\/j.rse.2010.07.010","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync\u2014Tools for calibration and validation","volume":"114","author":"Cohen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.02.009","article-title":"Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time","volume":"162","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"O\u2019Connell, B.M., LaPoint, E.B., Turner, J.A., Ridley, T., Pugh, S.A., Wilson, A.M., Waddell, K.L., and Conkling, B.L. (2016, August 01). The Forest Inventory and Analysis Database: Database Description and User Guide Version 6.0.2 for Phase 2, Available online: https:\/\/www.fia.fs.fed.us\/library\/database-documentation\/historic\/ver6\/FIADB%20User%20Guide%20P2_6-0-2_final-opt.pdf."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Woodall, C.W., Heath, L.S., Domke, G.M., and Nichols, M.C. (2011). Methods and Equations for Estimating Aboveground Volume, Biomas, and Carbon for Trees in the US Forest Inventory 2010.","DOI":"10.2737\/NRS-GTR-88"},{"key":"ref_57","first-page":"127","article-title":"The national forest inventory of the United States of America","volume":"24","author":"McRoberts","year":"2008","journal-title":"J. For. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2015.07.002","article-title":"The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data","volume":"168","author":"Gobakken","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_59","unstructured":"Deo, R.K. (2014). Application of an imputation method for geospatial inventory of forest structural attributes across multiple spatial scales in the Great Lake States, USA. [Ph.D. Thesis, Michigan Technological University]."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Gentle, J.E., Hardle, W.K., and Mori, Y. (2012). Numerical linear algebra. Handbook of Computational Statistics: Concepts and Methods, Springer.","DOI":"10.1007\/978-3-642-21551-3"},{"key":"ref_61","unstructured":"Evans, J.S., and Murphy, M.A. (2016, November 10). rfUtilities: Random Forests Model Selection and Performance Evaluation. Available online: http:\/\/cran.r-project.org\/package=rfUtilities."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1890\/08-0879.1","article-title":"Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics","volume":"91","author":"Murphy","year":"2010","journal-title":"Ecology"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1139\/cjfr-2014-0405","article-title":"Temporal transferability of LiDAR-based imputation of forest inventory attributes","volume":"45","author":"Fekety","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S, Springer. [4th ed.].","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1093\/treephys\/25.7.903","article-title":"A regression-based equivalence test for model validation: shifting the burden of proof","volume":"25","author":"Robinson","year":"2005","journal-title":"Tree Physiol."},{"key":"ref_66","unstructured":"Robinson, A. (2017, January 17). Equivalence: Provides Tests and Graphics for Assessing Tests of Equivalence. Available online: https:\/\/cran.r-project.org\/web\/packages\/equivalence."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1139\/cjfr-2015-0192","article-title":"Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach","volume":"45","author":"White","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"99","DOI":"10.14214\/sf.68","article-title":"The suitability of leaf-off airborne laser scanning data in an area-based forest inventory of coniferous and deciduous trees","volume":"46","author":"Villikka","year":"2012","journal-title":"Silva Fennica."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005GL023971","article-title":"Estimates of forest canopy height and aboveground biomass using ICESat","volume":"32","author":"Lefsky","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_70","first-page":"287","article-title":"Regional forest inventory using an airborne profiling LiDAR","volume":"13","author":"Nelson","year":"2008","journal-title":"Jpn. Soc. For. Plan."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1139\/cjfr-2016-0086","article-title":"Large-scale prediction of aboveground biomass in heterogeneous mountain forests by means of airborne laser scanning","volume":"46","author":"Maltamo","year":"2016","journal-title":"Can. J. For. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.07.012","article-title":"Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data","volume":"98","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_73","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_74","first-page":"502","article-title":"Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data","volume":"52","author":"Fayad","year":"2016b","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"992","DOI":"10.3390\/f5050992","article-title":"Low-density LiDAR and optical imagery for biomass estimation over boreal forest in Sweden","volume":"5","author":"Shendryk","year":"2014","journal-title":"Forests"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2008.09.009","article-title":"LiDAR remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers","volume":"113","author":"Zhao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1080\/02827580410019490","article-title":"Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators","volume":"19","author":"Lim","year":"2004","journal-title":"Scand. J. For. Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jneumeth.2013.08.024","article-title":"A comparison of random forest regression and multiple linear regression for prediction in neuroscience","volume":"220","author":"Smith","year":"2013","journal-title":"J. Neurosci. Methods."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"5246","DOI":"10.1109\/JSTARS.2015.2478478","article-title":"Capability of GLAS\/ICESat data to estimate forest canopy height and volume in mountainous forests of Iran","volume":"8","author":"Pourrahmati","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-015-0030-9","article-title":"Local discrepancies in continental scale biomass maps: A case study over forested and non-forested landscapes in Maryland, USA","volume":"10","author":"Huang","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.1016\/j.rse.2010.03.001","article-title":"The effects of rectification and Global Positioning System errors on satellite image-based estimates of forest area","volume":"114","author":"McRoberts","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.rse.2010.10.008","article-title":"Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass","volume":"115","author":"Frazer","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modelling approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. 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