Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time-and how this affects applications  

作　　者：Goran Ståhl Terje Gobakken Svetlana Saarela Henrik J.Persson Magnus Ekstrom Sean P.Healey Zhiqiang Yang Johan Holmgren Eva Lindberg Kenneth Nystrom Emanuele Papucci Patrik Ulvdal Hans OleØrka Erik Næsset Zhengyang Hou Håkan Olsson Ronald E.McRoberts 

机构地区：[1]Department of Forest Resource Management,Swedish University of Agricultural Sciences,90183,Umeå,Sweden [2]Faculty of Environmental Sciences and Natural Resource Management,Norwegian University of Life Sciences,Ås,Norway [3]USDA Forest Service,Rocky Mountain Research Station,Ogden,UT,USA [4]The Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University,Beijing,100083,China [5]Department of Forest Resources,University of Minnesota,St.Paul,MN,USA

出　　处：《Forest Ecosystems》2024年第1期24-31,共8页森林生态系统（英文版）

基　　金：part of the programme Mistra Digital Forests and of the Center for Research-based Innovation Smart Forest:Bringing Industry 4.0to the Norwegian forest sector(NFR SFI project no.309671,smartforest.no)。

摘　　要：Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking.However,wall-to-wall information typically relies on model-based prediction,and several features of model-based prediction should be understood before extensively relying on this type of information.One such feature is that model-based predictors can be considered both unbiased and biased at the same time,which has important implications in several areas of application.In this discussion paper,we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary data.From this point of view,model-based predictors are typically unbiased.Secondly,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered biased,and sometimes severely so.We suggest distinguishing between conventional model-bias,defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted,and design-bias of model-based estimators,defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted.We show that model-based estimators(or predictors)are typically design-biased,and that there is a trend in the design-bias from overestimating small true values to underestimating large true values.Further,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend.We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.

关 键 词：BIAS Model-based inference Design-based inference 

分 类 号：S718.5[农业科学—林学] S771.8