Species Composition Affects the Accuracy of Stand-Level Biomass Models in Hemiboreal Forests

Jānis Liepiņš, Andis Lazdiņš, Santa Kalēja and Kaspars Liepiņš
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Jānis Liepiņš: Latvian State Forest Research Institute ‘Silava’, Rigas Str. 111, LV-2169 Salaspils, Latvia
Andis Lazdiņš: Latvian State Forest Research Institute ‘Silava’, Rigas Str. 111, LV-2169 Salaspils, Latvia
Santa Kalēja: Latvian State Forest Research Institute ‘Silava’, Rigas Str. 111, LV-2169 Salaspils, Latvia
Kaspars Liepiņš: Latvian State Forest Research Institute ‘Silava’, Rigas Str. 111, LV-2169 Salaspils, Latvia

Land, 2022, vol. 11, issue 7, 1-13

Abstract: Various tree species contribute differently to total biomass stock, making the development of species-specific stand-level equations critical for better estimation of forest biomass and quantification of carbon stocks. Previously derived dry weight biomass models did not assess the effect of dominant species composition according to stand growing stock. Growing stock definitions and forest species composition differ by country, justifying the need for national stand-level biomass equations. We explored the relationship between growing stock volume and stand biomass density of above- and below-ground components in six common forest categories in Latvia using plot-level data from the National Forest Inventory from 2016 to 2020. Additionally, we explored model dependence on region, forest type, and species composition index. Models that considered growing stock and dominant species composition index performed better than models with growing stock as the only variable, especially for heterogeneous deciduous forests with greater species diversity. The elaborated models are a useful alternative to individual-level assessment for estimating forest biomass stocks in circumstances where individual tree data are not available.

Keywords: composition index; forest biomass; National Forest Inventory; biomass density; growing stock (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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