{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T14:35:37Z","timestamp":1780583737754,"version":"3.54.1"},"reference-count":80,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,20]],"date-time":"2019-08-20T00:00:00Z","timestamp":1566259200000},"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>Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.<\/jats:p>","DOI":"10.3390\/rs11161944","type":"journal-article","created":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T11:19:06Z","timestamp":1566386346000},"page":"1944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":92,"title":["Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Jessica","family":"Esteban","sequence":"first","affiliation":[{"name":"Departamento de Topograf\u00eda y Geom\u00e1tica, Universidad Polit\u00e9cnica de Madrid, 28040 Madrid, Spain"},{"name":"Agresta Soc. Coop., 28012 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald","family":"McRoberts","sequence":"additional","affiliation":[{"name":"Northern Research Station, U.S. Forest Service, St. Paul, MN 55108, USA"},{"name":"Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4725-8044","authenticated-orcid":false,"given":"Alfredo","family":"Fern\u00e1ndez-Landa","sequence":"additional","affiliation":[{"name":"Agresta Soc. Coop., 28012 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Tom\u00e9","sequence":"additional","affiliation":[{"name":"Agresta Soc. Coop., 28012 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"N\u04d5sset","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 \u00c5s, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.rse.2015.08.029","article-title":"A questionnaire-based review of the operational use of remotely sensed data by national forest inventories","volume":"174","author":"Barrett","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/02827581.2017.1416666","article-title":"Remote sensing and forest inventories in Nordic countries\u2013roadmap for the future","volume":"33","author":"Kangas","year":"2018","journal-title":"Scand. J. For. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1080\/02827581.2010.496739","article-title":"Advances and emerging issues in national forest inventories","volume":"25","author":"McRoberts","year":"2010","journal-title":"Scand. J. For. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tomppo, E., Gschwantner, T., Lawrence, M., and McRoberts, R.E. (2010). National Forest Inventories: Pathways for Common Reporting, Springer.","DOI":"10.1007\/978-90-481-3233-1"},{"key":"ref_5","unstructured":"Maltamo, M., N\u00e6sset, E., and Vauhkonen, J. (2010). Forestry Applications of Airborne Laser Scanning. Managed Forest Ecosystems, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/S0034-4257(97)00041-2","article-title":"Estimating timber volume of forest stands using airborne laser scanner data","volume":"61","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0034-4257(01)00290-5","article-title":"Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data","volume":"80","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1080\/02827580701672147","article-title":"Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia","volume":"22","year":"2007","journal-title":"Scand. J. For. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1139\/X07-219","article-title":"Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data","volume":"38","author":"Gobakken","year":"2008","journal-title":"Can. J. For. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/02827580410019472","article-title":"Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning","volume":"19","author":"Holmgren","year":"2004","journal-title":"Scand. J. For. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1093\/forestry\/cpl007","article-title":"Estimation of stem volume using laser scanning-based canopy height metrics","volume":"79","author":"Maltamo","year":"2006","journal-title":"Forestry"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.10.008","article-title":"Model-assisted estimation of change in forest biomass over an 11year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide \u201cactivity data\u201d","volume":"128","author":"Gobakken","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.foreco.2018.06.041","article-title":"Direct and indirect site index determination for Norway spruce and Scots pine using bitemporal airborne laser scanner data","volume":"428","author":"Noordermeer","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.rse.2017.09.007","article-title":"Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux","volume":"204","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2015.02.018","article-title":"Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data","volume":"164","author":"McRoberts","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Domingo, D., Alonso, R., de la Riva, J., Lamelas, M.T., Rodr\u00edguez, F., and Montealegre, A.L. (2019). Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data. Remote Sens., 11.","DOI":"10.3390\/rs11030261"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.rse.2014.11.020","article-title":"Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information","volume":"158","author":"Saarela","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.14358\/PERS.75.12.1451","article-title":"A Two Stage Method to Estimate Species-specific Growing Stock","volume":"75","author":"Suvanto","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40663-016-0064-9","article-title":"Use of models in large-area forest surveys: Comparing model-assisted, model-based and hybrid estimation","volume":"3","author":"Saarela","year":"2016","journal-title":"For. Ecosyst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s13595-016-0590-1","article-title":"Hierarchical model-based inference for forest inventory utilizing three sources of information","volume":"73","author":"Saarela","year":"2016","journal-title":"Ann. For. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2017.10.007","article-title":"Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference","volume":"204","author":"Puliti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1139\/X10-195","article-title":"Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway","volume":"41","author":"Holm","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2016.07.023","article-title":"Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference","volume":"184","author":"Chen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2013.08.049","article-title":"Monitoring selective logging in western Amazonia with repeat lidar flights","volume":"151","author":"Andersen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.rse.2014.08.028","article-title":"Estimation for inaccessible and non-sampled forest areas using model-based inference and remotely sensed auxiliary information","volume":"154","author":"McRoberts","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2017.12.020","article-title":"Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots","volume":"209","author":"Matasci","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4744","DOI":"10.1080\/01431161.2018.1471551","article-title":"Testing the quality of forest variable estimation using dense image matching: A comparison with airborne laser scanning in a Mediterranean pine forest","volume":"39","author":"Navarro","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.foreco.2017.04.046","article-title":"Updating national forest inventory estimates of growing stock volume using hybrid inference","volume":"400","author":"McRoberts","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/forestry\/cpx048","article-title":"Parametric bootstrap estimators for hybrid inference in forest inventories","volume":"91","author":"Fortin","year":"2018","journal-title":"Forestry"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1016\/j.rse.2011.07.002","article-title":"Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data","volume":"115","author":"McRoberts","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2018.02.039","article-title":"How much can natural resource inventory benefit from finer resolution auxiliary data?","volume":"209","author":"Hou","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1139\/cjfr-2017-0396","article-title":"Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications","volume":"48","author":"McRoberts","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"361","DOI":"10.4336\/2017.pfb.37.91.1337","article-title":"The Spanish National Forest Inventory: History, development, challenges and perspectives","volume":"37","author":"Alberdi","year":"2017","journal-title":"Pesqui. Florest. Bras."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.foreco.2013.09.007","article-title":"National Forest Inventory and forest observational studies in Spain: Applications to forest modeling","volume":"316","author":"Alberdi","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1139\/cjfr-2014-0266","article-title":"A general method for assessing the effects of uncertainty in individual-tree volume model predictions on large-area volume estimates with a subtropical forest illustration","volume":"45","author":"McRoberts","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s13595-015-0473-x","article-title":"Propagating uncertainty through individual tree volume model predictions to large-area volume estimates","volume":"73","author":"McRoberts","year":"2016","journal-title":"Ann. For. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.5424\/sjar\/2010084-1242","article-title":"Accuracy and precision of GPS receivers under forest canopies in a mountainous environment","volume":"8","author":"Valbuena","year":"2013","journal-title":"Span. J. Agric. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1139\/X10-164","article-title":"Influence of global navigation satellite system errors in positioning inventory plots for treeheight distribution studies","volume":"41","author":"Mauro","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1051\/forest\/2009078","article-title":"A merchantable volume system for Pinus sylvestris L. in the major mountain ranges of Spain","volume":"66","year":"2009","journal-title":"Ann. For. Sci."},{"key":"ref_43","first-page":"16","article-title":"Fusing LIDAR data, photographs, and other data using 2D and 3D visualization techniques","volume":"28","author":"McGaughey","year":"2003","journal-title":"Proc. Terrain Data Appl. Vis. Connect."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Maltamo, M., N\u00e6sset, E., and Vauhkonen, J. (2014). Chapter 1 Introduction to forestry applications of airborne laser scanning. Forestry Applications of Airborne Laser Scanning, Concepts and Case Studies, Springer.","DOI":"10.1007\/978-94-017-8663-8"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1080\/07038992.2014.987376","article-title":"Forest Monitoring Using Landsat Time Series Data: A Review","volume":"40","author":"Banskota","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1139\/cjfr-2018-0295","article-title":"Comparing the stock-change and gain\u2013loss approaches for estimating forest carbon emissions for the aboveground biomass pool","volume":"48","author":"McRoberts","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_47","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1080\/01621459.1973.10481440","article-title":"Robust Estimation in Finite Populations I","volume":"68","author":"Royall","year":"1973","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_49","unstructured":"Valliant, R., Dorfman, A.H., and Royall, R. (2000). Finite Population Sampling and Inference, Wiley."},{"key":"ref_50","unstructured":"S\u00e4rndal, C.-E., Swensson, B., and Wretman, J. (2019). Model Assisted Survey Sampling, Springer."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1214\/aos\/1015956706","article-title":"Local Polynomial Regression Estimators in Survey Sampling","volume":"28","author":"Breidt","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_52","first-page":"103","article-title":"Nonparametric and Semiparametric Estimation in Complex Surveys","volume":"Volume 28","author":"Pfeffermann","year":"2009","journal-title":"Handbook of Statistics\u2014Sample Surveys: Inference and Analysis"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1214\/16-STS589","article-title":"Model-Assisted Survey Estimation with Modern Prediction Techniques","volume":"32","author":"Breidt","year":"2017","journal-title":"Stat. Sci."},{"key":"ref_54","first-page":"649","article-title":"Does the model matter? Comparing model-assisted and model-dependent estimators of class frequencies for domains","volume":"7","author":"Lehtonen","year":"2005","journal-title":"Stat. Transit."},{"key":"ref_55","first-page":"359","article-title":"Combined inference in survey sampling","volume":"27","year":"2011","journal-title":"Pak. J. Stat."},{"key":"ref_56","first-page":"209","article-title":"Penalized spline nonparametric mixed models for inference about a finite population mean from two-stage samples","volume":"30","author":"Zheng","year":"2004","journal-title":"Surv. Methodol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1214\/aos\/1176351062","article-title":"Bootstrap Procedures under some Non-I.I.D. Models","volume":"16","author":"Liu","year":"1988","journal-title":"Ann. Stat."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.csda.2004.05.018","article-title":"Bootstrapping heteroskedastic regression models: Wild bootstrap vs. pairs bootstrap","volume":"49","author":"Flachaire","year":"2005","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_59","first-page":"1218","article-title":"Bootstrapping Regression Models","volume":"6","author":"Freedman","year":"1981","journal-title":"Ann. Stat."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/scientificamerican0583-116","article-title":"Computer-Intensive Methods in Statistics","volume":"248","author":"Diaconis","year":"1983","journal-title":"Sci. Am."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1002\/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F","article-title":"Bootstrap confidence intervals: When, which, what? A practical guide for medical statisticians","volume":"19","author":"Carpenter","year":"2000","journal-title":"Stat. Med."},{"key":"ref_62","unstructured":"Ranalli, M.G., and Mecatti, F. (August, January 28). Comparing Recent Approaches For Bootstrapping Sample Survey Data: A First Step Towards A Unified Approach. Proceedings of the Joint Statistical Meeting (JSM), San Diego, CA, USA."},{"key":"ref_63","first-page":"1","article-title":"Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests","volume":"17","author":"Mentch","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.rse.2009.12.013","article-title":"Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data","volume":"114","author":"McRoberts","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.11.001","article-title":"Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_66","first-page":"0550004","article-title":"Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA","volume":"13","author":"Woodall","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/07038992.2018.1461557","article-title":"Transferability of Lidar-derived Basal Area and Stem Density Models within a Northern Idaho Ecoregion","volume":"44","author":"Fekety","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.rse.2019.04.006","article-title":"Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data","volume":"227","author":"Tompalski","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.rse.2008.09.001","article-title":"Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data","volume":"113","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"623","DOI":"10.5589\/m03-030","article-title":"Simulating the effects of lidar scanning angle for estimation of mean tree height and canopy closure","volume":"29","author":"Holmgren","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"S152","DOI":"10.5589\/m13-052","article-title":"Effect of scanning angle on vegetation metrics derived from a nationwide Airborne Laser Scanning acquisition","volume":"39","author":"Montaghi","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2017.06.013","article-title":"Effects of temporally external auxiliary data on model-based inference","volume":"198","author":"Hou","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"923","DOI":"10.3390\/rs11080923","article-title":"Estimation of changes of forest structural attributes at three different spatial aggregation levels in Northern California using multitemporal LiDAR","volume":"11","author":"Mauro","year":"2019","journal-title":"Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.11.010","article-title":"Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway","volume":"130","author":"Gobakken","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_75","first-page":"458","article-title":"Modeling and predicting aboveground biomass change in young forest using multi-temporal airborne laser scanner data","volume":"30","author":"Gobakken","year":"2015","journal-title":"Scand. J. For. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2012.02.023","article-title":"Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys","volume":"123","author":"Byrne","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.rse.2018.11.029","article-title":"Mapping forest disturbance intensity in North and South Carolina using annual Landsat observations and field inventory data","volume":"221","author":"Tao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_78","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_79","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. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Durante, P., Mart\u00edn-Alc\u00f3n, S., Gil-Tena, A., Algeet, N., Tom\u00e9, J.L., Recuero, L., Palacios-Orueta, A., and Oyonarte, C. (2019). Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale. Remote Sens., 11.","DOI":"10.3390\/rs11070795"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1944\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:12:24Z","timestamp":1760188344000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,20]]},"references-count":80,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11161944"],"URL":"https:\/\/doi.org\/10.3390\/rs11161944","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,20]]}}}