A constructive review of the State Forest Inventory in the Russian Federation

Research output: Contribution to journalReview ArticleScientificpeer-review

Researchers

  • Alexander Alekseev
  • Erkki Tomppo

  • Ronald E. McRoberts
  • Klaus von Gadow

Research units

  • University of Helsinki
  • St.Petersburg State Forest Technical University
  • United States Department of Agriculture
  • University of Göttingen
  • University of Stellenbosch

Abstract

The State Forest Inventory (SFI) in the Russian Federation is a relatively new project that is little known in the English-language scientific literature. Following the stipulations of the Forest Act of 2006, the first SFI sample plots in this vast territory were established in 2007. The 34 Russian forest regions were the basic geographical units for all statistical estimates and served as a first-level stratification, while a second level was based on old inventory data and remotely sensed data. The sampling design was to consist of a simple random sample of 84,700 circular 500m(2) sample plots over forest land. Each sample plot consists of three nested concentric circular subplots with radii of 12.62, 5.64 and 2.82m and additional subplots for assessing and describing undergrowth, regeneration and ground vegetation. In total, 117 variables were to be measured or assessed on each plot.Although field work has begun, the methodology has elicited some criticism. The simple random sampling design is less efficient than a systematic design featuring sample plot clusters and a mix of temporary and permanent plots. The second-level stratification is mostly ineffective for increasing precision. Qualitative variables, which are not always essential, are dominant, while important quantitative variables are under-represented. Because of very slow progress, in 2018 the original plan was adjusted by reducing the number of permanent sample plots from 84,700 to 68,287 so that the first SFI cycle could be completed by 2020.

Details

Original languageEnglish
Article number9
Number of pages14
JournalForest ecosystems
Volume6
Issue number1
Publication statusPublished - 1 Dec 2019
MoE publication typeA2 Review article in a scientific journal

    Research areas

  • Forest inventory, Sampling design, Stratification, Remote sensing, Bias, Accuracy, SAMPLE-SIZE

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