Comparison of the local pivotal method and systematic sampling for national forest inventories

Abstract Background The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design. Results The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally. Conclusions LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

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Harmonising, improving and using social and recreational data in National Forest Inventories across Europe

Annals of Forest Science ◽

10.1007/s13595-020-00952-2 ◽

2020 ◽

Vol 77 (3) ◽

Author(s):

Mark A. Atkinson ◽

David M. Edwards ◽

Frank Søndergaard Jensen ◽

Alexander P. N. van der Jagt ◽

Ben R. Ditchburn ◽

...

Keyword(s):

National Level ◽

European Countries ◽

Transport Infrastructure ◽

National Forest ◽

Qualitative Response ◽

Forest Inventories ◽

Recreational Services ◽

National Forest Inventories ◽

Funded Project ◽

General Transport

Abstract Key message National Forest Inventories (NFIs) hold promise for monitoring and valuing of non-productive forest functions, including social and recreational services. European countries use a range of methods to collect social and recreational information within their NFI methodologies. Data collected frequently included general and recreation-specific infrastructure, but innovative approaches are also used to monitor recreational use and social abuse. Context Social and recreational indicators are increasingly valued in efforts to measure the non-productive value of forests in Europe. National Forest Inventories (NFIs) can be used to estimate recreational and social usage of forest land at a national level and relate this use to other biophysical, spatial and topographical features. Nonetheless, there is little information concerning the extent. Aims The study aims to identify the coverage of social and recreational data present in European NFIs including the types of data recorded as part of the NFI methodologies across European countries. It also aims to examine contrasting methods used to record social and recreational data and present recommendations for ways forward for countries to integrate these into NFI practice. Methods A pan-European questionnaire was designed and distributed to 35 counties as part of the EU-funded project Distributed, Integrated and Harmonised Forest Information for Bioeconomy Outlooks (DIABOLO). The questionnaire probed countries on all social and recreational data that was included within NFIs. Qualitative response data was analysed and recoded to measure the extent of social and recreational data recoded in European NFIs both as a function of the number of variable categories per country and the number of countries recording particular variables. Results Thirty-one countries reported at least one social or recreational variable over 12 categories of data. The most frequently recorded variables included ownership, general transport infrastructure and recreation-specific infrastructure. Countries collecting data over many different categories included Switzerland, Great Britain, Czech Republic, Luxemburg and Denmark. Conclusion The study proposes a specific set of indicators, based upon countries with well-developed social and recreational data in their NFIs, which could be used by other countries, and report on the extent to which these are currently collected across Europe. It discusses results and makes a series of recommendations concerning priorities for the inclusion of social and recreational data in European NFIs.

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Integrating data from National Forest Inventories into socio-cultural forest monitoring – a new approach

Scandinavian Journal of Forest Research ◽

10.1080/02827581.2020.1799066 ◽

2020 ◽

Vol 35 (5-6) ◽

pp. 274-285

Author(s):

K. Tessa Hegetschweiler ◽

Christoph Fischer ◽

Marco Moretti ◽

Marcel Hunziker

Keyword(s):

Forest Monitoring ◽

National Forest ◽

New Approach ◽

Forest Inventories ◽

National Forest Inventories

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100 years of national forest inventories

Silva Fennica ◽

10.14214/sf.10643 ◽

2021 ◽

Vol 55 (4) ◽

Author(s):

Matti Maltamo

Keyword(s):

National Forest ◽

Forest Inventories ◽

National Forest Inventories

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Catering Information Needs from Global to Local Scales—Potential and Challenges with National Forest Inventories

Forests ◽

10.3390/f10090800 ◽

2019 ◽

Vol 10 (9) ◽

pp. 800 ◽

Cited By ~ 2

Author(s):

Kangas ◽

Räty ◽

Korhonen ◽

Vauhkonen ◽

Packalen

Keyword(s):

Information Needs ◽

National Forest ◽

Model Errors ◽

Sampling Errors ◽

Small Areas ◽

Model Based ◽

Forest Inventories ◽

National Forest Inventories ◽

Biased Estimators ◽

National Information

Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced by the multitude of scales the forest data are needed, and the challenges in acknowledging the errors due to the measurements and models in addition to sampling errors. Between NFIs, the challenges related to the different harmonization tasks were reviewed. While a design-based approach is often considered more attractive than a model-based approach as it is guaranteed to provide unbiased results, the model-based approach is needed for downscaling the information to smaller scales and acknowledging the measurement and model errors. However, while a model-based inference is possible in small areas, the unknown random effects introduce biased estimators. The NFIs need to cater for the national information requirements and maintain the existing time series, while at the same time providing comparable information across the countries. In upscaling the NFI information to continental and global information needs, representative samples across the area are of utmost importance. Without representative data, the model-based approaches enable provision of forest information with unknown and indeterminable biases. Both design-based and model-based approaches need to be applied to cater to all information needs. This must be accomplished in a comprehensive way In particular, a need to have standardized quality requirements has been identified, acknowledging the possibility for bias and its implications, for all data used in policy making.

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