Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales: review and recommendations

E. I. Vanguelova; E. Bonifacio; B. De Vos; M. R. Hoosbeek; T. W. Berger; Lars Vesterdal; K. Armolaitis; L. Celi; L. Dinca; O. J. Kjønaas; P. Pavlenda; J. Pumpanen; Ü. Püttsepp; B. Reidy; P. Simoncic; B. Tobin; M. Zhiyanski

doi:10.1007/s10661-016-5608-5

Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales: review and recommendations

E. I. Vanguelova, E. Bonifacio, B. De Vos, M. R. Hoosbeek, T. W. Berger, Lars Vesterdal, K. Armolaitis, L. Celi, L. Dinca, O. J. Kjønaas, P. Pavlenda, J. Pumpanen, Ü. Püttsepp, B. Reidy, P. Simoncic, B. Tobin, M. Zhiyanski

LUKKET: Skov, natur og biomasse

25 Citationer (Scopus)

Abstract

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales---sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

Originalsprog	Engelsk
Artikelnummer	630
Tidsskrift	Environmental Monitoring and Assessment
Vol/bind	188
Udgave nummer	11
Antal sider	24
ISSN	1573-2959
DOI	https://doi.org/10.1007/s10661-016-5608-5
Status	Udgivet - 1 nov. 2016

Adgang til dokumentet

10.1007/s10661-016-5608-5

Citationsformater

Vanguelova, E. I., Bonifacio, E., De Vos, B., Hoosbeek, M. R., Berger, T. W., Vesterdal, L., Armolaitis, K., Celi, L., Dinca, L., Kjønaas, O. J., Pavlenda, P., Pumpanen, J., Püttsepp, Ü., Reidy, B., Simoncic, P., Tobin, B., & Zhiyanski, M. (2016). Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales: review and recommendations. Environmental Monitoring and Assessment, 188(11), Artikel 630. https://doi.org/10.1007/s10661-016-5608-5

Vanguelova, EI, Bonifacio, E, De Vos, B, Hoosbeek, MR, Berger, TW, Vesterdal, L, Armolaitis, K, Celi, L, Dinca, L, Kjønaas, OJ, Pavlenda, P, Pumpanen, J, Püttsepp, Ü, Reidy, B, Simoncic, P, Tobin, B & Zhiyanski, M 2016, 'Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales: review and recommendations', Environmental Monitoring and Assessment, bind 188, nr. 11, 630. https://doi.org/10.1007/s10661-016-5608-5

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abstract = "Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales---sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.",

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AU - Vanguelova, E. I.

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AU - De Vos, B.

AU - Hoosbeek, M. R.

AU - Berger, T. W.

AU - Vesterdal, Lars

AU - Armolaitis, K.

AU - Celi, L.

AU - Dinca, L.

AU - Kjønaas, O. J.

AU - Pavlenda, P.

AU - Pumpanen, J.

AU - Püttsepp, Ü.

AU - Reidy, B.

AU - Simoncic, P.

AU - Tobin, B.

AU - Zhiyanski, M.

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N2 - Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales---sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

AB - Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales---sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

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