检索结果-南通市图书馆

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

引用

Forest Ecosystems 2024年第1期11卷 24-31页

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 Department of Forest Resource Management Swedish University of Agricultural Sciences90183UmeåSweden Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life SciencesÅsNorway USDA Forest Service Rocky Mountain Research StationOgdenUTUSA The Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry UniversityBeijing100083China Department of Forest Resources University of MinnesotaSt.PaulMNUSA

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 ***,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 *** 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 *** this discussion paper,we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary *** this point of view,model-based predictors are typically ***,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered biased,and sometimes severely *** 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 *** 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 ***,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias *** 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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

The national forest inventory in China:history-results-international context

引用

Forest Ecosystems 2015年第4期2卷 288-303页

作者： Wei Sheng Zeng Erkki Tomppo sean.p.healey Klaus V.Gadow Academy of Forest Inventory and planning State Forestry Administration Natural Resources Institute Finland Forest Service United States Department of Agriculture University of G?ttingen Beijing Forestry University Department of Forestry and Wood Technology University of Stellenbosch

Background： National forest resource assessments Inventories （NFI＇s）, constitute an important nationa and monitoring, commonly known as National Forest 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 20th and 21st centuries, with some reference to Europe and the US; · highlights the emergence of some common international themes： consistency of measurement; sampling designs; implementation of improved technology; expansion of the variables monitored more efficient 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.

关键词： China Europe USA National forest inventories Forest inventory and analysis

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Use of models in large-area forest surveys： comparing model-assisted, model-based and hybrid estimation

引用

Forest Ecosystems 2016年第2期3卷 153-163页

作者： Goran Stahl Svetlana Saarela Sebastian Schnell Soren Holm Johannes Breidenbach sean p. healey paul L. patterson Steen Magnussen Erik Naesset Ronald E. McRoberts Timothy G. Gregoire Department of Forest Resource Management Swedish University of Agricultural Sciences UmeA Sweden. Norwegian Institute for Bioeconomy Research As Norway. USDA Forest Service Washington D.C. USA. Canadian Forest Service Pacific Forestry Centre British Columbia Canada Norwegian University of Life Sciences As Norway. School of Forestry and Fnvirnnmental Studies. Yale Universitv New Haven CT USA.

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role： model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design- based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary *** review studies on large-area forest surveys based on model-assisted, model- based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

关键词： Design-based inference Model-assisted estimation Model-based inference Hybrid inference Nationalforest inventory Remote sensing Sampling

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：