Combining double sampling for stratification and cluster sampling to a three-level sampling design for continuous forest inventories

Lüpke, Nikolas von; Saborowski, Joachim

doi:10.1007/s10342-013-0743-9

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…However, besides the need to present reliable and accurate information, these inventories must present reduced costs (Von Lüpke & Saborowski, 2014), which can be achieved when determining, for example, the optimal time interval between monitoring. The determination of this interval depends on the correlation between measurements, and the intervals can be increased without affecting the estimates of population dynamics (Brena, 1979).…”

Section: Introduction and Objectivesmentioning

confidence: 99%

Evaluation of Sampling with Partial Replacement and Double Sampling in a Managed Forest in the Brazilian Amazon

Reategui-Betancourt,

Mazzei,

Rezende

et al. 2023

Floresta Ambient.

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We compared sampling techniques in a managed native forest in Paragominas, Pará, Brazil. Our goal in this study was to evaluate the feasibility of using Double Sampling (DS) and Sampling with Partial Replacement (SPR), when compared to Continuous Forest Inventory (CFI), to estimate the wood stock for trees with DBH ≥ 20 cm in a managed forest. In our results, DS had the best volume prediction, generating a sampling error of 5.20% (11.48 m³ ha -1 ) on the second occasion 3.86% (8.78 m³ ha -1 ). The average volume increment, estimated for the forest in the monitored period (2014-2016) was 6.88 m³ ha -1 , with a relative sampling error of 63.09%. Therefore, as an alternative and of low cost, we suggest using DS in successive forest inventories in monitoring areas of forest resources in the Brazilian Amazon.

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Section: Introduction and Objectivesmentioning

confidence: 99%

Evaluation of Sampling with Partial Replacement and Double Sampling in a Managed Forest in the Brazilian Amazon

Reategui-Betancourt,

Mazzei,

Rezende

et al. 2023

Floresta Ambient.

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“…Regression estimators using this concept can consider either one additional sample of plot locations (two-phase or double-sampling) or two additional samples available in different sample sizes (three-phase or triple-sampling), see Gregoire and Valentine (2007); Saborowski, Marx, Nagel, and Böckmann (2010); Mandallaz (2013a,d); von Lüpke, Hansen, and Saborowski (2012). Their application to existing forest inventory systems has already showed their efficiency in terms of cost reduction and gain in estimation precision (Breidenbach and Astrup 2012;von Lüpke and Saborowski 2014;Mandallaz, Breschan, and Hill 2013;Magnussen, Mandallaz, Breidenbach, Lanz, and Ginzler 2014;Massey, Mandallaz, and Lanz 2014).…”

Section: Introductionmentioning

confidence: 99%

The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories

Hill¹,

Massey²,

Mandallaz³

2021

J. Stat. Soft.

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Forest inventories provide reliable evidence-based information to assess the state and development of forests over time. They typically consist of a random sample of plot locations in the forest that are assessed individually by field crews. Due to the high costs of these terrestrial campaigns, remote sensing information available in high quantity and low costs is frequently incorporated in the estimation process in order to reduce inventory costs or improve estimation precision. With respect to this objective, the application of multiphase forest inventory methods (e.g., double-and triple-sampling regression estimators) has proved to be efficient. While these methods have been successfully applied in practice, the availability of open-source software has been rare if not non-existent. The R package forestinventory provides a comprehensive set of global and small area regression estimators for multiphase forest inventories under simple and cluster sampling. The implemented methods have been demonstrated in various scientific studies ranging from small to large scale forest inventories, and can be used for post-stratification, regression and regression within strata. This article gives an extensive review of the mathematical theory of this family of design-based estimators, puts them into a common framework of forest inventory scenarios and demonstrates their application in the R environment.

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“…The specific scope of double-sampling is to enlarge the terrestrial sample size by a much larger sample of predictions of the target variable in order to gain higher estimation precision without performing additional expensive terrestrial measurements. Model-dependent and design-based regression estimators are used in a broad range of doublesampling concepts and methods (Gregoire and Valentine 2007;Köhl et al 2006;Mandallaz 2013a, b;Saborowski et al 2010;Schreuder et al 1993) and have been applied to existing inventory systems (Breidenbach and Astrup 2012;von Lüpke and Saborowski 2014;Mandallaz et al 2013;Magnussen et al 2014;Massey et al 2014). While double-sampling methods provide reliable estimates for a given spatial unit, e.g., a forest district, they do not provide information about the spatial distribution of the estimated quantity within this area.…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

2018

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A timber volume regression model applicable to the state and communal forest area of the federal German state of RhinelandPalatinate is identified using a combination of airborne laser scanning (ALS)-derived metrics and information from a satellitebased tree species classification map available on the federal state level. As is common in many forest inventory datasets, strong heterogeneity in the ALS data due to different acquisition dates and misclassifications in the tree species classification map had noticeable effects on the regression model's performance. This article specifically addresses techniques that improve the performance of ordinary least square regression models under such restricting conditions. We introduce a calibration technique to neutralize the effect of misclassifications in the tree species variable that originally caused a residual inflation of 0.05 in adjusted R 2 . Incorporating the calibrated tree species information improved the model accuracy by up to 0.07 in adjusted R 2 and suggests the use of such information in forthcoming inventories. We also found that including ALS quality information as categorical variables within the regression model considerably mitigates issues with time lags between the ALS and terrestrial data acquisition and ALS quality variations (increase of 0.09 in adjusted R 2 ). The model achieved an adjusted R 2 of 0.48 and a cross-validated root-mean-square error (RMSE cv ) of 46.7% under incorporation of the tree species and ALS quality information and was thus improved by 0.12 in adjusted R 2 (5% in RMSE cv ) compared to the simple model only containing ALS height metrics (adjusted R 2 = 0.36 , RMSE cv = 51.7%).

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Combining double sampling for stratification and cluster sampling to a three-level sampling design for continuous forest inventories

Cited by 7 publications

References 9 publications

Evaluation of Sampling with Partial Replacement and Double Sampling in a Managed Forest in the Brazilian Amazon

Evaluation of Sampling with Partial Replacement and Double Sampling in a Managed Forest in the Brazilian Amazon

The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories

Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

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