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

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 500 m 2 sample plots over forest land. Each sample plot consists of three nested concentric circular subplots with radii of 12.62, 5.64 and 2.82 m 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.

Russian forest sequesters substantially more carbon than previously reported

“…Once finalized, the NFI will be verified before adoption as the official source of information to the SFR and national reporting. The NFI has received some criticism 13 because of the relatively sparse sampling employed and the stratification method used, which is partially based on outdated FIP data.…”

Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model

“…According to the FRA report for Russia (FRA national report: http://www.fao. org/3/cb0053en/cb0053en.pdf, last access: 31 May 2021), almost 80 % of the total land area on which forests are located is in "hard-to-reach" places beyond the 60th parallel (Alekseev et al, 2019). Since most of the deforestation caused by wildfires takes place in those regions (Curtis et al, 2018), inventories could present large underestimations of the reported losses.…”

“…Including the soil moisture stress effect (γ SM ) based on ERA-Interim soil moisture data would lead to a further decrease of the order of 10 % (Table S5). Disparities among the inventories are primarily attributed to differences in meteorological fields and emission potential distributions (Arneth et al, 2011), with additional possible contributions of differences in the canopy environment models and in the LAI datasets.…”

“…Isoprene is primarily emitted from terrestrial vegetation, in particular broadleaf trees, and therefore, isoprene emissions are critically dependent on the land cover (e.g. tree, shrub, grass, crop) and on the plant species within those land covers (Arneth et al, 2011;Guenther et al, 2012).…”

Observations on the Biology and Structure of Three Dry Tropical Forests in South India

“…Examples of observational infrastructures are national forest inventories, field experiments and long-term observational studies. The objective of a national forest inventory is to prepare reports about the state of the forest resource at a given time and within a specific geographical context (Alekseev et al, 2019;Zeng et al, 2015). Field experiments are established to evaluate ecosystem response to specific treatments.…”