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您的检索式：作者名="Sean P.Healey"
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| 1 | The national forest inventory in China:history-results-international context显示文摘Background:National forest resource assessments and monitoring,commonly known as National Forest Inventories(NFI's),constitute an important national information infrastructure in many countries.Methods:This study presents details about developments of the NFI in China,including sampling and plot design,and the uses of alternative data sources,and specifically· reviews the evolution of the national forest inventory in China through the 20 th and 21 st centuries,with some reference to Europe and the US;· highlights the emergence of some common international themes:consistency of measurement; more efficient sampling designs; implementation of improved technology; expansion of the variables monitored; scientific transparency;· presents an example of how China's expanding NFI exemplifies these global trends.Results:Main results and important changes in China's NFI are documented,both to support continued trend analysis and to provide data users with historical perspective.Conclusions:New technologies and data needs ensure that the Chinese NFI,like the national inventories in other countries,will continue to evolve.Within the context of historical change and current conditions,likely directions for this evolution are suggested. | Wei Sheng Zeng Erkki Tomppo Sean P.Healey Klaus V.Gadow | 2015 | Forest Ecosystems2015，2，4： | 8 |
| 2 | Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time-and how this affects applications显示文摘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. | 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 | 2024 | Forest Ecosystems2024，11，1： | 0 |
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