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

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

扫码查看
原文链接
  • 万方数据
  • 维普
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 decision-making.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 under-estimating 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.

BiasModel-based inferenceDesign-based inference

G?ran St?hl、Terje Gobakken、Svetlana Saarela、Henrik J.Persson、Magnus Ekstr?m、Sean P.Healey、Zhiqiang Yang、Johan Holmgren、Eva Lindberg、Kenneth Nystr?m、Emanuele Papucci、Patrik Ulvdal、Hans Ole ?rka、Erik N?sset、Zhengyang Hou、H?kan Olsson、Ronald E.McRoberts

展开 >

Department of Forest Resource Management,Swedish University of Agricultural Sciences,901 83,Umeå,Sweden

Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences,Ås,Norway

USDA Forest Service,Rocky Mountain Research Station,Ogden,UT,USA

The Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University,Beijing 100083,China

Department of Forest Resources,University of Minnesota,St.Paul,MN,USA

展开 >

programme Mistra Digital Forests and of the Center for Researchbased Innovation SmartForest:Bringing Industry 4.0 to the Nor

309671

2024

10.1016/j.fecs.2023.100164

森林生态系统(英文版)
北京林业大学

森林生态系统(英文版)

CSTPCD
影响因子：0.09
ISSN：2095-6355
年,卷(期)：2024.11(1)
  • 49