A three-phase sampling extension of the generalized regression estimator with partially exhaustive information

MandallazDaniel,

doi:10.1139/cjfr-2013-0449

Cited by 14 publications

(4 citation statements)

References 5 publications

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“…Hill et al (2018) applied the estimators for timber volume estimation in forest management units of two levels (forest districts and sub-districts) in the German state of Rhineland-Palatinate using data from the German NFI. Mandallaz (2014) extended the two-phase small-area estimators with partially exhaustive auxiliary data to three-phase sampling, where the auxiliary variable values come from nested null-and first-phase samples. He illustrated the estimators with a simulation example like those in the earlier papers.…”

Section: Development Of New Design-based Model-assisted Small Area Estimatorsmentioning

confidence: 99%

A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

Guldin¹

2021

Front. For. Glob. Change

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Small domain estimation (SDE) research outside of the United States has been centered in Canada and Europe—both in transnational organizations, such as the European Union, and in the national statistics offices of individual countries. Support for SDE research is driven by government policy-makers responsible for core national statistics across domains. Examples include demographic information about provision of health care or education (a social domain) or business data for a manufacturing sector (economic domain). Small area estimation (SAE) research on forest statistics has typically studied a subset of core environmental statistics for a limited geographic domain. The statistical design and sampling intensity of national forest inventories (NFIs) provide population estimates of acceptable precision at the national level and sometimes for broad sub-national regions. But forest managers responsible for smaller areas—states/provinces, districts, counties—are facing changing market conditions, such as emerging forest carbon markets, and budgetary pressures that limit local forest inventories. They need better estimates of conditions and trends for small sub-sets of a national-scale domain than can be provided at acceptable levels of precision from NFIs. Small area estimation research is how forest biometricians at the science-policy interface build bridges to inform decisions by forest managers, landowners, and investors.

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Section: Development Of New Design-based Model-assisted Small Area Estimatorsmentioning

confidence: 99%

A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

Guldin¹

2021

Front. For. Glob. Change

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“…The common component of all SAE applications is the use of auxiliary information that is exhaustive or partial exhaustive (for the whole population). Double-Sampling or two-phase is one of the most frequently used sampling design, characterized by its costefficiency for inventories in large remote forest areas [4,5,7,9,14,15] and [16] (section 6.3), three-phase sampling in smaller extend [5,17,18], stratified systematic (cluster) sampling [19], stratified random sampling [20], and post-stratification [15,[21][22][23] for design-unbiased estimates (mean and variance) when a reasonable amount of field plots is needed in a small area [24]. Systematic or grid (sample locations on a regular grid) is one of the most common sampling (including cluster) scheme in MFIs and especially in NFIs.…”

Section: Sampling Designs In Sae For Fismentioning

confidence: 99%

“…Double-sampling or two-phase sampling seems to be one of the major sampling design schemes in the applications of SAE in FIs. The advantage of two-phase sampling, compared to the two-stage sampling, relies on the very large sample units/points [4,9,18] of the first phase with high correlated variables of Remote sensing (ex. ALS) data that covers (nearly) the whole population.…”

Section: Sampling Designs In Sae For Fismentioning

confidence: 99%

Sampling Design Contribution to Small Area Estimation Procedure in Forest Inventories

Georgakis¹

2020

MCDA

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The sampling design is a crucial topic that would be considered in Small Area Estimation (SAE). Applications of sampling designs presented in Forest Inventories (FIs) for SAE, with the two-phase sampling to have the most references. Eventually, FIs that are applied for SAE is an open research topic. An important contribution to this topic would be the comparison and the optimization of sampling designs that aims to improve SAE in FIs.

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“…An example can be found in von Lüpke et al [5] who illustrated an extension of the existing two-phase FDI of Lower Saxony to a three-phase design that uses updates of past inventory data as additional auxiliary information and allows for a significant reduction of the terrestrial sample size in intermediate inventories. Another example is Massey et al [19] who developed a triple-sampling extension based on the ideas of Mandallaz [20] for the Swiss NFI that can significantly reduce the increase in estimation uncertainty caused by the new annual inventory design.…”

Section: Introductionmentioning

confidence: 99%

A Double-Sampling Extension of the German National Forest Inventory for Design-Based Small Area Estimation on Forest District Levels

Hill

Mandallaz

Langshausen³

2018

Remote Sensing

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Abstract:The German National Forest Inventory consists of a systematic grid of permanent sample plots and provides a reliable evidence-based assessment of the state and the development of Germany's forests on national and federal state level in a 10 year interval. However, the data have yet been scarcely used for estimation on smaller management levels such as forest districts due to insufficient sample sizes within the area of interests and the implied large estimation errors. In this study, we present a double-sampling extension to the existing German National Forest Inventory (NFI) that allows for the application of recently developed design-based small area regression estimators. We illustrate the implementation of the estimation procedure and evaluate its potential for future large-scale operational application by the example of timber volume estimation on two small-scale management levels (45 and 405 forest district units respectively) over the entire area of the federal German state of Rhineland-Palatinate. An airborne laserscanning (ALS) derived canopy height model and a tree species classification map based on satellite data were used as auxiliary data in an ordinary least square regression model to produce the timber volume predictions. The results support that the suggested double-sampling procedure can substantially increase estimation precision on both management levels: the two-phase estimators were able to reduce the variance of the one-phase simple random sampling estimator by 43% and 25% on average for the two management levels respectively.

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A three-phase sampling extension of the generalized regression estimator with partially exhaustive information

Cited by 14 publications

References 5 publications

A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

Sampling Design Contribution to Small Area Estimation Procedure in Forest Inventories

A Double-Sampling Extension of the German National Forest Inventory for Design-Based Small Area Estimation on Forest District Levels

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