Journal Home > Volume 7 , Issue 2
Background

National forest inventories (NFI) have a long history providing data to obtain nationally representative and accurate estimates of growing stock. Today, in most NFIs additional data are collected to provide information on a range of forest ecosystem functions such as biodiversity, habitat, nutrient and carbon dynamics. An important driver of nutrient and C cycling is decomposing biomass produced by forest vegetation. Several studies have demonstrated that understory vegetation, particularly annual plant litter of the herb layer can contribute significantly to nutrient and C cycling in forests. A methodology to obtain comprehensive, consistent and nationally representative estimates of herb layer biomass on NFI plots could provide added value to NFIs by complementing the existing strong basis of biomass estimates of the tree and tall shrub layer. The study was based on data from the Swiss NFI since it covers a large environmental gradient, which extends its applicability to other NFIs.

Results

Based on data from 405 measurements in nine forest strata, a parsimonious model formulation was identified to predict total and non-ligneous herb layer biomass. Besides herb layer cover, elevation was the main statistically significant explanatory variable for biomass. The regression models accurately predicted biomass based on absolute percentage cover (for total biomass: R2=0.65, p=0; for non-ligneous biomass: R2=0.76; p=0) as well as on cover classes (R2=0.83; p=0; and R2=0.79, p=0), which are typically used in NFIs. The good performance was supported by the verification with data from repeated samples. For the 2nd, 3rd, and 4th Swiss NFI estimates of non-ligneous above-ground herb layer biomass 586.6±7.7, 575.2±7.6, and 586.7±7.9 kg·ha-1, respectively.

Conclusions

The study presents a methodology to obtain herb layer biomass estimates based on a harmonized and standardized attribute available in many NFIs. The result of this study was a parsimonious model requiring only elevation data of sample plots in addition to NFI cover estimates to provide unbiased estimates at the national scale. These qualities are particularly important as they ensure accurate, consistent, and comparable results.


menu
Abstract
Full text
Outline
About this article

Extending harmonized national forest inventory herb layer vegetation cover observations to derive comprehensive biomass estimates

Show Author's information Markus Didion( )
Forest Resources and Management, Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland

Abstract

Background

National forest inventories (NFI) have a long history providing data to obtain nationally representative and accurate estimates of growing stock. Today, in most NFIs additional data are collected to provide information on a range of forest ecosystem functions such as biodiversity, habitat, nutrient and carbon dynamics. An important driver of nutrient and C cycling is decomposing biomass produced by forest vegetation. Several studies have demonstrated that understory vegetation, particularly annual plant litter of the herb layer can contribute significantly to nutrient and C cycling in forests. A methodology to obtain comprehensive, consistent and nationally representative estimates of herb layer biomass on NFI plots could provide added value to NFIs by complementing the existing strong basis of biomass estimates of the tree and tall shrub layer. The study was based on data from the Swiss NFI since it covers a large environmental gradient, which extends its applicability to other NFIs.

Results

Based on data from 405 measurements in nine forest strata, a parsimonious model formulation was identified to predict total and non-ligneous herb layer biomass. Besides herb layer cover, elevation was the main statistically significant explanatory variable for biomass. The regression models accurately predicted biomass based on absolute percentage cover (for total biomass: R2=0.65, p=0; for non-ligneous biomass: R2=0.76; p=0) as well as on cover classes (R2=0.83; p=0; and R2=0.79, p=0), which are typically used in NFIs. The good performance was supported by the verification with data from repeated samples. For the 2nd, 3rd, and 4th Swiss NFI estimates of non-ligneous above-ground herb layer biomass 586.6±7.7, 575.2±7.6, and 586.7±7.9 kg·ha-1, respectively.

Conclusions

The study presents a methodology to obtain herb layer biomass estimates based on a harmonized and standardized attribute available in many NFIs. The result of this study was a parsimonious model requiring only elevation data of sample plots in addition to NFI cover estimates to provide unbiased estimates at the national scale. These qualities are particularly important as they ensure accurate, consistent, and comparable results.

Keywords: Carbon, Model, ENFIN, Switzerland, Ground layer, Herbs, Ferns, Grasses, Shrubs, Litter

References(29)

Alberdi I, Condés S, Martínez-Millán J (2010) Review of monitoring and assessing ground vegetation biodiversity in national forest inventories. Environ Monit Assess 164:649-676. https://doi.org/10.1007/s10661-009-0919-4

Alberdi I, Condés S, Mcroberts RE, Winter S (2018) Mean species cover: a harmonized indicator of shrub cover for forest inventories. Eur J Forest Res 137:265-278. https://doi.org/10.1007/s10342-018-1110-7

Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Software 67:1-48. https://doi.org/10.18637/jss.v067.i01

Brändli U-B, Hägeli M (2019) Swiss NFI at a glance. In: Fischer C, Traub B (eds) Swiss National Forest Inventory - methods and models of the fourth assessment. Springer International Publishing, Cham, pp 3-35. https://doi.org/10.1007/978-3-030-19293-8_1
DOI
Chirici G, McRoberts RE, Winter S, Barbati A, Brändli U, Abegg M, Beranova J, Rondeux J, Bertini R, Asensio IA, Condés S (2011) Harmonization tests. In: Chirici G, Winter S, McRoberts RE (eds) National Forest Inventories: contributions to Forest biodiversity assessments. Springer Netherlands, Dordrecht, pp 121-190. https://doi.org/10.1007/978-94-007-0482-4_5
DOI

Clifford D, Cressie N, England JR, Roxburgh SH, Paul KI (2013) Correction factors for unbiased, efficient estimation and prediction of biomass from log-log allometric models. Forest Ecol Manag 310:375-381. https://doi.org/10.1016/j.foreco.2013.08.041

Core Team R (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna https://www.R-project.org/

Davis GE (1993) Design elements of monitoring programs: the necessary ingredients for success. Environ Monit Assess 26:99-105. https://doi.org/10.1007/bf00547489

de Wit HA, Palosuo T, Hylen G, Liski J (2006) A carbon budget of forest biomass and soils in Southeast Norway calculated using a widely applicable method. Forest Ecol Manag 225:15-26

Didion M, Baume M, Giudici F, Schneuwly J (2018) Herb layer cover and biomass data from Swiss forests. Swiss Federal Research Institute WSL, Birmensdorf https://doi.org/10.16904/envidat.52. Accessed 22 Dec 2019
Didion M, Thürig E (2018) Data on soil carbon stock change, carbon stock and stock change in surface litter and in coarse deadwood prepared for the Swiss GHGI 2019 (1990-2017). Swiss Federal Institute for Forest. Snow and Landscape Research WSL, Birmensdorf http://www.bafu.admin.ch/bafu/en/home/topics/climate/state/data/climate-reporting.html. Accessed 22 Dec 2019
Düggelin C, Keller M (eds) (2017) Schweizerisches Landesforstinventar: Feldaufnahme - Anleitung 2017 [Swiss National Forest Inventory: Field manual 2017]. Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf (ZH), p 220.
FOEN(Federal Office for the Environment) (2019) Switzerland's greenhouse gas inventory 1990-2017. National Inventory Report including reporting elements under the Kyoto protocol. Submission of April 2019 under the United Nations framework convention on climate change and under the Kyoto protocol. Federal Office for the Environment, Bern http://www.bafu.admin.ch/climatereporting. Accessed 22 Dec 2019

Gilliam FS (2007) The ecological significance of the herbaceous layer in temperate Forest ecosystems. BioScience 57:845-858. https://doi.org/10.1641/b571007

Heinrichs S, Bernhardt-Römermann M, Schmidt W (2010) The estimation of aboveground biomass and nutrient pools of understorey plants in closed Norway spruce forests and on clearcuts. Eur J For Res 129:613-624. https://doi.org/10.1007/s10342-010-0362-7

Johnson KD, Domke GM, Russell MB, Walters B, Hom J, Peduzzi A, Birdsey R, Dolan K, Huang W (2017) Estimating aboveground live understory vegetation carbon in the United States. Environ Res Lett 12: 125010. https://doi.org/10.1088/1748-9326/aa8fdb
DOI

Kumar P, Chen HYH, Searle EB, Shahi C (2018) Dynamics of understorey biomass, production and turnover associated with long-term overstorey succession in boreal forest of Canada. Forest Ecol Manag 427:152-161. https://doi.org/10.1016/j.foreco.2018.05.066

Landuyt D, Lombaerde ED, Perring MP, Hertzog LR, Ampoorter E, Maes SL, Frenne PD, Ma S, Proesmans W, Blondeel H, Sercu BK, Wang B, Wasof S, Verheyen K (2019a) The functional role of temperate forest understorey vegetation in a changing world. Glob Chang Biol 25:3625-3641. https://doi.org/10.1111/gcb.14756

Landuyt D, Maes SL, Depauw L, Ampoorter E, Blondeel H, Perring MP, Brūmelis G, Brunet J, Decocq G, den Ouden J, Härdtle W, Hédl R, Heinken T, Heinrichs S, Jaroszewicz B, Kirby KJ, Kopecký M, Máliš F, Wulf M, Verheyen K (2019b) Drivers of aboveground understorey biomass and nutrient stocks in temperate deciduous forests. J Ecol. https://doi.org/10.1111/1365-2745.13318
DOI

McRoberts RE, Tomppo EO, Næsset E (2010) Advances and emerging issues in national forest inventories. Scand J Forest Res 25:368-381. https://doi.org/10.1080/02827581.2010.496739

Melo LC, Schneider R, Fortin M (2018) Estimating model- and sampling-related uncertainty in large-area growth predictions. Ecol Model 390:62-69. https://doi.org/10.1016/j.ecolmodel.2018.10.011

Muukkonen P, Mäkipää R (2006) Empirical biomass models of understorey vegetation in boreal forests according to stand and site attributes. Boreal Environ Res 11:355-369

Muukkonen P, Mäkipää R, Laiho R, Minkkinen K, Vasander H, Finér L (2006) Relationship between biomass and percentage cover in understorey vegetation of boreal coniferous forests. Silva Fenn 40:231-245

Schulze IM, Bolte A, Schmidt W, Eichhorn J (2009) Phytomass, litter and net primary production of herbaceous layer. In: Brumme R, Khanna P (eds) Functioning and Management of European Beech Ecosystems, Ecological Studies, vol 208. Springer, Berlin Heidelberg, pp 155-181. https://doi.org/10.1007/b82392_11
DOI

Shaver GR, Chapin FS (1991) Production: biomass relationships and element cycling in contrasting Arctic vegetation types. Ecol Monogr 61:1-31. https://doi.org/10.2307/1942997

Thürig E, Kaufmann E (2010) Increasing carbon sinks through forest management: a model-based comparison for Switzerland with its eastern plateau and eastern Alps. Eur J Forest Res 129:563-572. https://doi.org/10.1007/s10342-010-0354-7

Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (eds) (2010) National forest inventories : pathways for common reporting. Springer, Heidelberg. https://doi.org/10.1007/978-90-481-3233-1
DOI
Venables WN, Ripley BD (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York.https://doi.org/10.1007/978-0-387-21706-2
DOI

Welch NT, Belmont JM, Randolph JC (2007) Summer ground layer biomass and nutrient contribution to above-ground litter in an Indiana temperate deciduous Forest. Am Midl Nat 157(11-26):16

Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 22 December 2019
Accepted: 12 March 2020
Published: 01 June 2020
Issue date: June 2020

Copyright

© The Author(s) 2020.

Acknowledgements

Acknowledgements

J. Schneuwly (internship student) and NFI scientific collaborators M. Baume and F. Giudici were responsible for the meticulous and efficient work in the field. Valuable advice on the implementation of the project were made by F. Cioldi, C. Düggelin, M. Keller, A. Kupferschmid, P. Waldner and M. Walser (all WSL). Work material was provided by the NFI (F. Cioldi) and M. Walser. Comments to earlier versions of the manuscript by N. Rogiers and F. Giudici are gratefully acknowledged. We further appreciate the valuable comments of two anonymous reviewers.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Return