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, Simončič P, Tobin B, Zhiyanski M
Centre for Ecosystems, Society and Biosecurity, Forest Research, Alice Holt Lodge, Farnham, GU10 4LH, UK.
DISAFA, Chimica Agraria e Pedologia, University of Torino, Via P. Braccini 2, 10095, Grugliasco, TO, Italy.
Environ Monit Assess. 2016 Nov;188(11):630. doi: 10.1007/s10661-016-5608-5. Epub 2016 Oct 21.
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.
了解森林近期和过去土壤有机碳（SOC）储量的空间明确信息，将有助于我们更好地理解人为和非人为引起的变化对森林碳通量的影响。对于SOC核算，必须定义一个最小可检测差异，以便充分确定SOC的时间变化和空间差异。这需要足够详细的数据，以便在所需精度内以适当尺度预测SOC储量，从而仅考虑显著变化。在设计采样活动时，需要考虑影响SOC时空分布的因素（如土壤类型、地形、气候和植被），以优化采样深度和样本数量，从而确保样本准确反映某一地点SOC的分布。此外，需要定义与研究问题相关的适当尺度：剖面、样地、森林、集水区、国家或更广泛的尺度。将SOC储量从点样本扩展到景观单元具有挑战性，因此需要可靠的基线数据。了解每个特定尺度上与SOC测量相关的不确定性以及如何减少这些不确定性，对于在每个尺度上尽可能准确地评估SOC储量至关重要。本综述确定了在五个不同尺度（样本、剖面、样地、景观/区域和欧洲尺度）上与森林SOC储量估计相关的潜在误差和不确定性来源。还提供了关于如何减少森林SOC不确定性并提高每个尺度上SOC评估效率的建议。
