简介：

简介：Thispaperfocusesontheuseofmodelsforincreasingtheprecisionofestimatorsinlarge-areaforestsurveys.Itismotivatedbytheincreasingavailabilityofremotelysenseddata,whichfacilitatesthedevelopmentofmodelspredictingthevariablesofinterestinforestsurveys.Wepresent,reviewandcomparethreedifferentestimationframeworkswheremodelsplayacorerole:model-assisted,model-based,andhybridestimation.Thefirsttwoarewellknown,whereasthethirdhasonlyrecentlybeenintroducedinforestsurveys.Hybridinferencemixesdesignbasedandmodel-basedinference,sinceitreliesonaprobabilitysampleofauxiliarydataandamodelpredictingthetargetvariablefromtheauxiliarydata.Wereviewstudiesonlarge-areaforestsurveysbasedonmodel-assisted,modelbased,andhybridestimation,anddiscussadvantagesanddisadvantagesoftheapproaches.Weconcludethatnogeneralrecommendationscanbemadeaboutwhethermodel-assisted,model-based,orhybridestimationshouldbepreferred.Thechoicedependsontheobjectiveofthesurveyandthepossibilitiestoacquireappropriatefieldandremotelysenseddata.Wealsoconcludethatmodellingapproachescanonlybesuccessfullyappliedforestimatingtargetvariablessuchasgrowingstockvolumeorbiomass,whichareadequatelyrelatedtocommonlyavailableremotelysenseddata,andthuspurelyfieldbasedsurveysremainimportantforseveralimportantforestparameters.