{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T21:20:37Z","timestamp":1768771237286,"version":"3.49.0"},"reference-count":70,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T00:00:00Z","timestamp":1590624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Carbon Cycle Science Program funded jointly by NASA and USDA National Institute of Food and Agriculture","award":["2011-67003-30351"],"award-info":[{"award-number":["2011-67003-30351"]}]},{"name":"National Science Foundation Dynamics of Coupled Natural and Human Systems Program","award":["DEB-1313688"],"award-info":[{"award-number":["DEB-1313688"]}]},{"name":"National Science Foundation EPSCoR Program","award":["RII-1920908"],"award-info":[{"award-number":["RII-1920908"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>When forest conditions are mapped from empirical models, uncertainty in remotely sensed predictor variables can cause the systematic overestimation of low values, underestimation of high values, and suppression of variability. This regression dilution or attenuation bias is a well-recognized problem in remote sensing applications, with few practical solutions. Attenuation is of particular concern for applications that are responsive to prediction patterns at the high end of observed data ranges, where systematic error is typically greatest. We addressed attenuation bias in models of tree species relative abundance (percent of total aboveground live biomass) based on multitemporal Landsat and topoclimatic predictor data. We developed a multi-objective support vector regression (MOSVR) algorithm that simultaneously minimizes total prediction error and systematic error caused by attenuation bias. Applied to 13 tree species in the Acadian Forest Region of the northeastern U.S., MOSVR performed well compared to other prediction methods including single-objective SVR (SOSVR) minimizing total error, Random Forest (RF), gradient nearest neighbor (GNN), and Random Forest nearest neighbor (RFNN) algorithms. SOSVR and RF yielded the lowest total prediction error but produced the greatest systematic error, consistent with strong attenuation bias. Underestimation at high relative abundance caused strong deviations between predicted patterns of species dominance\/codominance and those observed at field plots. In contrast, GNN and RFNN produced dominance\/codominance patterns that deviated little from observed patterns, but predicted species relative abundance with lower accuracy and substantial systematic error. MOSVR produced the least systematic error for all species with total error often comparable to SOSVR or RF. Predicted patterns of dominance\/codominance matched observations well, though not quite as well as GNN or RFNN. Overall, MOSVR provides an effective machine learning approach to the reduction of systematic prediction error and should be fully generalizable to other remote sensing applications and prediction problems.<\/jats:p>","DOI":"10.3390\/rs12111739","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"1739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-Objective Support Vector Regression Reduces Systematic Error in Moderate Resolution Maps of Tree Species Abundance"],"prefix":"10.3390","volume":"12","author":[{"given":"Kasey","family":"Legaard","sequence":"first","affiliation":[{"name":"Center for Research on Sustainable Forests, University of Maine, Orono, ME 04469-5755, USA"},{"name":"School of Forest Resources, University of Maine, Orono, ME 04469-5755, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9815-2285","authenticated-orcid":false,"given":"Erin","family":"Simons-Legaard","sequence":"additional","affiliation":[{"name":"School of Forest Resources, University of Maine, Orono, ME 04469-5755, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2534-4478","authenticated-orcid":false,"given":"Aaron","family":"Weiskittel","sequence":"additional","affiliation":[{"name":"Center for Research on Sustainable Forests, University of Maine, Orono, ME 04469-5755, USA"},{"name":"School of Forest Resources, University of Maine, Orono, ME 04469-5755, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2007.08.021","article-title":"forest biomass using nationwide forest inventory data and moderate resolution information","volume":"112","author":"Blackard","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_2","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 modeling approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"3971","DOI":"10.1016\/j.rse.2008.07.005","article-title":"Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario","volume":"112","author":"Wolter","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.foreco.2012.02.002","article-title":"A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data","volume":"271","author":"Wilson","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2836","DOI":"10.1016\/j.rse.2010.07.015","article-title":"Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass","volume":"115","author":"Saatchi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"313","DOI":"10.14358\/PERS.75.3.313","article-title":"Effects of Mismatches of Scale and Location between Predictor and Response Variables on Forest Structure Mapping","volume":"75","author":"Xu","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6827","DOI":"10.5194\/bg-11-6827-2014","article-title":"Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks","volume":"11","author":"Detto","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_9","first-page":"304","article-title":"The Enhanced Forest Inventory and Analysis Program of the USDA Forest Service: Historical perspective and announcement of statistical documentation","volume":"103","author":"McRoberts","year":"2005","journal-title":"J. For."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3158","DOI":"10.1002\/sim.3713","article-title":"Linear mixed models for replication data to efficiently allow for covariate measurement error","volume":"28","author":"Bartlett","year":"2009","journal-title":"Stat. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/1467-985X.00164","article-title":"Correcting for regression dilution bias: Comparison of methods for a single predictor variable","volume":"163","author":"Frost","year":"2000","journal-title":"J. R. Stat. Soc. Ser. A"},{"key":"ref_12","first-page":"229","article-title":"The importance of measurement error for certain procedures in remote sensing at optical wavelengths","volume":"52","author":"Curran","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.3390\/rs5031001","article-title":"Impacts of Spatial Variability on Aboveground Biomass Estimation from L-Band Radar in a Temperate Forest","volume":"5","author":"Robinson","year":"2013","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/j.1654-1103.2001.tb02613.x","article-title":"Modeling spatially explicit forest structural attributes using generalized additive models","volume":"12","author":"Frescino","year":"2001","journal-title":"J. Veg. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2013.08.048","article-title":"Scale considerations for integrating forest inventory plot data and satellite image data for regional forest mapping","volume":"151","author":"Ohmann","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"725","article-title":"Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, USA Can","volume":"32","author":"Ohmann","year":"2002","journal-title":"J. For. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1016\/j.rse.2010.05.010","article-title":"An effective assessment protocol for continuous geospatial datasets of forest characteristics using USFS Forest Inventory and Analysis (FIA) data","volume":"114","author":"Riemann","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1111\/j.1467-9876.2011.01030.x","article-title":"Uncertainty in spatially predicted covariates: Is it ignorable?","volume":"61","author":"Foster","year":"2012","journal-title":"J. R. Stat. Soc. Ser. C"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10651-009-0130-3","article-title":"The Bayesian conditional independence model for measurement error: Applications in ecology","volume":"18","author":"Denham","year":"2010","journal-title":"Environ. Ecol. Stat."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1111\/j.2041-210X.2010.00077.x","article-title":"Fine-scale environmental variation in species distribution modelling: Regression dilution, latent variables and neighbourly advice","volume":"2","author":"McInerny","year":"2011","journal-title":"Methods Ecol. Evol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1002\/widm.1125","article-title":"Support vector machines in engineering: An overview","volume":"4","year":"2014","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/5254.671091","article-title":"Feature subset selection using a genetic algorithm","volume":"13","author":"Yang","year":"1998","journal-title":"IEEE Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","article-title":"Selection of relevant features and examples in machine learning","volume":"97","author":"Blum","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3374","DOI":"10.1109\/TGRS.2006.880628","article-title":"Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images","volume":"44","author":"Bazi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neucom.2004.11.022","article-title":"Evolutionary tuning of multiple SVM parameters","volume":"64","author":"Friedrichs","year":"2005","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.eswa.2005.09.024","article-title":"A GA-based feature selection and parameters optimization for support vector machines","volume":"31","author":"Huang","year":"2006","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"535","DOI":"10.5589\/m11-065","article-title":"Polarimetric Radarsat-2 imagery for soil moisture retrieval in alpine areas","volume":"37","author":"Pasolli","year":"2012","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1016\/j.ress.2005.11.018","article-title":"Multi-objective optimization using genetic algorithms: A tutorial","volume":"91","author":"Konak","year":"2006","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1525\/bio.2009.59.6.9","article-title":"Ecosystem thinking in the Northern Forest\u2014And beyond","volume":"59","author":"Likens","year":"2009","journal-title":"Bioscience"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1890\/07-0693.1","article-title":"Predicting Tree Diversity Across the United States as a Function of Modeled Gross Primary Production","volume":"18","author":"Nightingale","year":"2008","journal-title":"Ecol. Appl."},{"key":"ref_33","unstructured":"Barrett, J.W. (1995). The northeastern region. Regional Silviculture of the United States, Wiley."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/S0269-7491(01)00255-X","article-title":"Forest inventory and analysis: A national inventory and monitoring program","volume":"116","author":"Smith","year":"2002","journal-title":"Environ. Pollut."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"O\u2019Connell, B., Conkling, B.L., Wilson, A.M., Burrill, E.A., Turner, J.A., Pugh, S.A., Christiansen, G., Ridley, T., and Menlove, J. (2016). The Forest Inventory and Analysis Database: Database Description and User Guide for Phase 2 (Version 6.1).","DOI":"10.2737\/FS-FIADB-P2-6.1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TGRS.2005.852480","article-title":"SCS+C: A modified Sun-canopy-sensor topographic correction in forested terrain","volume":"43","author":"Soenen","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Archuleta, C.-A., Constance, E.W., Arundel, S.T., Lowe, A.J., Mantey, K.S., and Phillips, L.A. (2017). The National Map seamless digital elevation model specifications, Techniques and Methods.","DOI":"10.3133\/tm11B9"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1016\/j.rse.2007.07.013","article-title":"Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation","volume":"112","author":"Canty","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for Automated Geoscientific Analyses (SAGA) v. 2.1.4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1002\/esp.3290120107","article-title":"Quantititaive analysis of land surface topography","volume":"12","author":"Zevenbergen","year":"1987","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ollinger, S.V., Aber, J.D., Federer, C.A., Lovett, G.M., and Ellis, J.M. (1995). Modeling Physical and Chemical Climate of the Northeastern United States for a Geographic Information System.","DOI":"10.2737\/NE-GTR-191"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1002\/hyp.3360050106","article-title":"The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models","volume":"5","author":"Quinn","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1080\/13658810500433453","article-title":"An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling","volume":"20","author":"Wang","year":"2006","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rehfeldt, G.E. (2006). A Spline Model of Climate for the Western United States.","DOI":"10.2737\/RMRS-GTR-165"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1086\/507711","article-title":"Empirical Analyses of Plant-Climate Relationships for the Western United States","volume":"167","author":"Rehfeldt","year":"2006","journal-title":"Int. J. Plant Sci."},{"key":"ref_46","first-page":"691","article-title":"Aspect transformation in site productivity research","volume":"64","author":"Beers","year":"1966","journal-title":"J. For."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1023\/A:1009841519580","article-title":"GLM versus CCA spatial modeling of plant species distribution","volume":"143","author":"Guisan","year":"1999","journal-title":"Plant Ecol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1017\/S1350482705001489","article-title":"GIS-based regionalisation of radiation, temperature and coupling measures in complex terrain for low mountain ranges","volume":"12","author":"Goldberg","year":"2005","journal-title":"Meteorol. Appl."},{"key":"ref_49","unstructured":"Hepinstall, J.A., Sader, S.A., Krohn, W.B., Boone, R.B., and Bartlett, R.I. (1999). Development and Testing of a Vegetation and Land Cover Map of Maine, Maine Agricultural and Forest Experiment Station, University of Maine."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1039\/B918972F","article-title":"Support Vector Machines for classification and regression","volume":"135","author":"Brereton","year":"2010","journal-title":"Analyst"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"Libsvm: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csda.2009.04.009","article-title":"Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap","volume":"53","author":"Kim","year":"2009","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","article-title":"On the validation of models","volume":"2","author":"Willmott","year":"1981","journal-title":"Phys. Geogr."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Z\u00e4pfel, G., Braune, R., and B\u00f6gl, M. (2010). Metaheuristic Search Concepts: A Tutorial with Applications to Production and Logistics, Springer.","DOI":"10.1007\/978-3-642-11343-7"},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v023.i10","article-title":"yaImpute: An R package for \u03baNN imputation","volume":"23","author":"Crookston","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.rse.2007.10.009","article-title":"Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data","volume":"112","author":"Hudak","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_59","unstructured":"R Core Team (2020, April 01). R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing. Available online: https:\/\/www.R.-project.org\/."},{"key":"ref_60","first-page":"18","article-title":"Classification, and regression by randomforest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_61","unstructured":"Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O\u2019Hara, R.B., Simpson, G.L., and Solymos, P. (2020, April 01). Vegan: Community Ecology Package; R Package Version 2.4-3. Available online: https:\/\/CRAN.R-project.org\/package=vegan."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1111\/avsc.12085","article-title":"Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?","volume":"17","author":"Henderson","year":"2014","journal-title":"Appl. Veg. Sci."},{"key":"ref_63","unstructured":"Openshaw, S. (1984). The Modifiable Areal Unit Problem, GeoBooks."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Carroll, R.J., Ruppert, D., and Stefanski, L.A. (1995). Measurement Error in Nonlinear Models, Monographs on Statistics and Applied Probability 63, Chapman & Hall.","DOI":"10.1007\/978-1-4899-4477-1"},{"key":"ref_65","unstructured":"Johnson, E.A., and Miyanishi, K. (2007). Relationship between spruce budworm outbreaks and forest dynamics in eastern North America. Plant Disturbance Ecology: The Process and the Response, Elsevier Science."},{"key":"ref_66","first-page":"332","article-title":"Applying a spruce budworm decision support system to Maine: Projecting spruce-fir volume impacts under alternative management and outbreak scenarios","volume":"109","author":"Hennigar","year":"2011","journal-title":"J. For."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10980-013-9966-x","article-title":"Influence of environmental factors and spatio-temporal covariates during the initial development of a spruce budworm outbreak","volume":"29","author":"Bouchard","year":"2013","journal-title":"Landsc. Ecol."},{"key":"ref_68","first-page":"195","article-title":"The severity of budworm-caused growth reductions in balsam fir\/spruce stands varies with the hardwood content of surrounding forest landscapes","volume":"54","author":"Campbell","year":"2008","journal-title":"For. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1080\/10618600.2014.907095","article-title":"Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation","volume":"24","author":"Goldstein","year":"2015","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.foreco.2015.03.016","article-title":"An imputed forest composition map for New England screened by species range boundaries","volume":"347","author":"Duveneck","year":"2015","journal-title":"For. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1739\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:33Z","timestamp":1760175213000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,28]]},"references-count":70,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12111739"],"URL":"https:\/\/doi.org\/10.3390\/rs12111739","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,28]]}}}