Comparison of estimators in one-phase two-stage Poisson sampling in forest inventories

MandallazDaniel,; MasseyAlexander,

doi:10.1139/x2012-110

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…All the previous results can be easily generalized to cluster sampling and also to two-stage sampling (the local density Y(x) being simply replaced by the so-called generalized local density Y*(x)). The reader will find all the details and examples in Mandallaz (2013aMandallaz ( , 2013b and Mandallaz and Massey (2012).…”

Section: Generalization To Cluster and Two-stage Samplingmentioning

confidence: 99%

New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation

2013

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We consider two-phase sampling schemes where one component of the auxiliary information is known in every point ("wall-to-wall") and a second component is available only in the large sample of the first phase, whereas the second phase yields a subsample with the terrestrial inventory. This setup is of growing interest in forest inventory thanks to the recent advances in remote sensing, in particular, the availability of LiDAR data. We propose a new two-phase regression estimator for global and local estimation and derive its asymptotic design-based variance. The new estimator performs better than the classical regression estimator. Furthermore, it can be generalized to cluster sampling and two-stage tree sampling within plots. Simulations and a case study with LiDAR data illustrate the theory.Résumé : Cet article propose un nouvel estimateur pour les inventaires forestiers utilisant des plans de sondage à deux phases pour lesquels l'information auxiliaire consiste en une première composante exhaustive connue en chaque point et une seconde composante connue seulement aux points du grand échantillon de la première phase. La deuxième phase consiste en un sous-échantillon des points de la première phase dans lesquels l'inventaire terrestre est effectué. Ce contexte est appelé à jour un rôle croissant grâce aux développements récents dans l'aquisition de données par télédétection. Nous proposons un nouvel estimateur par regression, aussi bien pour l'estimation globale que locale, et nous donnons sa variance asymptotique sous le plan de sondage. Le nouvel estimateur peut être adapté aux inventaire par satellites et avec tirage double des arbres au niveau de la placette terrestre. Un exemple utilisant des données LiDAR et des simulations illustrent la théorie. Le nouvel estimateur a de meilleures performance que l'estimateur de régression classique.

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Section: Generalization To Cluster and Two-stage Samplingmentioning

confidence: 99%

New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation

2013

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“…In the context of a two-stage forest inventory, Mandallaz and Massey (2012) have shown that it is possible to correct for model misspecification by using a two-stage HT estimator. The method could be adapted for an inventory such as the Spanish NFI.…”

Section: A B C Dmentioning

confidence: 99%

Uncertainty assessment of large-scale forest growth predictions based on a transition-matrix model in Catalonia

Fortin

Naeije

Manso³

2016

Annals of Forest Science

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Abstract& Key message When predicting forest growth at a regional or national level, uncertainty arises from the sampling and the prediction model. Using a transition-matrix model, we made predictions for the whole Catalonian forest over an 11-year interval. It turned out that the sampling was the major source of uncertainty and accounted for at least 60 % of the total uncertainty. & Context With the development of new policies to mitigate global warming and to protect biodiversity, there is a growing interest in large-scale forest growth models. Their predictions are affected by many sources of uncertainty such as the sampling error, errors in the estimates of the model parameters, and residual errors. Quantifying the total uncertainty of those predictions helps to evaluate the risk of making a wrong decision. & Aims In this paper, we quantified the contribution of the sampling error and the model-related errors to the total uncertainty of predictions from a large-scale growth model in Catalonia. & Methods The model was based on a transition-matrix approach and predicted tree frequencies by species group and 5-cm diameter class over an 11-year time step. Using Monte Carlo techniques, we propagated the sampling error and the model-related errors to quantify their contribution to the total uncertainty. & Results The sampling variance accounted for at least 60 % of the total variance in smaller diameter classes, with this percentage increasing up to 90 % in larger diameter classes. & Conclusion Among the few possible options to reduce sampling uncertainty, we suggest improving the variance-covariance estimator of the predictions in order to better account for the multivariate framework and the changing plot size.

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“…In sampling theory, there are two types of inference: design-based and model-based. In design-based inference, randomisation is used to select the population units to be measured; for statistical validity, estimators are constructed based on this randomisation [3][4][5]. An immediate implication is that the values of the variables of interest are treated as fixed; furthermore, the randomness in the estimators is only due to the random selection of the samples [6].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Effect of Six Methods of Analysis and Different Sample Sizes for Biomass Estimation in Grasslands of the State of Puebla, Mexico

Velasco-Bautista¹,

Sánchez²,

Meza-Juárez³

et al. 2021

Land

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In the assessment of natural resources, such as forests or grasslands, it is common to apply a two-stage cluster sampling design, the application of which in the field determines the following situations: (a) difficulty in locating secondary sampling units (SSUs) precisely as planned, so that a random pattern of SSUs can be identified; and (b) the possibility that some primary sampling units (PSUs) have fewer SSUs than planned, leading to PSUs of different sizes. In addition, when considering the estimated variance of the various potential estimators for two-stage cluster sampling, the part corresponding to the variation between SSUs tends to be small for large populations, so the estimator’s variance may depend only on the divergence between PSUs. Research on these aspects is incipient in grassland assessment, so this study generated an artificial population of 759 PSUs and examined the effect of six estimation methods, using 15 PSU sample sizes, on unbiased and relative sampling errors when estimating aboveground, belowground, and total biomass of halophytic grassland. The results indicated that methods 1, 2, 4, and 5 achieved unbiased biomass estimates regardless of sample size, while methods 3 and 6 led to slightly biased estimates. Methods 4 and 5 had relative sampling errors of less than 5% with a sample size of 140 when estimating total biomass.

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Comparison of estimators in one-phase two-stage Poisson sampling in forest inventories

Cited by 10 publications

References 12 publications

New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation

New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation

Uncertainty assessment of large-scale forest growth predictions based on a transition-matrix model in Catalonia

Assessment of the Effect of Six Methods of Analysis and Different Sample Sizes for Biomass Estimation in Grasslands of the State of Puebla, Mexico

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