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Swiss National Forest Inventory (NFI) produces estimates of characteristics related to the state and change of the forest resources in the whole country as well as in smaller regions and domains. The estimates are based on measurements on permanent field plots placed on a systematic grid over the country. Digital stereo aerial images are an important source of auxiliary information that can be used with post-stratified estimation to increase the precision of the estimates. We examined post-stratification based on stereo image information, available on a much denser grid than the field plots, for the estimation of forest area, total growing-stock volume and growing-stock volume per unit of accessible forest area. We considered 31 post-stratification schemes that consisted of (1) forest/non-forest stratification by four alternative methods based on manual or semi-automated stereo image interpretation, and (2) sub-stratification within the forest stratum by regression-tree models constructed between field-measured growing-stock volume and stereo-image-retrieved vegetation height characteristics. We applied the schemes in two real NFI estimation cycles, and found that the most precise schemes reduced the variance of the whole-country estimators of forest area, total growing-stock volume and growing-stock volume per unit of accessible forest area by 81%, 67% and 34%, respectively, compared to the estimator without post-stratification. Based on this study, we consider regression-tree modelling well-suited for post-stratification, as it produces stratum boundaries as a direct result of modelling, appears robust against deviating values in fitting data, and avoids problems with unrealistic model predictions in application.

In forestry, site productivity is commonly described using the concept of site index (SI) and its effect on the potential mean annual increment. Although a unified causal explanation between SI and productivity is still missing, operational methods have been developed to determine SI on the basis of the existing tree crop or other indirect indicators of environmental drivers. In ecology, progress has been made in deriving productivity from the carbon balance, including photosynthesis, respiration and tissue longevity, but considerable gaps remain in quantifying the impact of soil processes on below-ground carbon allocation which would be crucial for closure of the total carbon balance. Recent empirical studies in Finland have found significant and consistent differences in the foliage to fine-root ratio and fine-root lifetime between the conventional SI based on field layer vegetation in geographical vegetation zones. In this study, we utilised this information to bridge the gap between the large body of empirical information on SI and productivity, and productivity as derived from the carbon balance. We incorporated assumptions concerning the climate and soil response of the carbon balance components in a process-based model, PipeQual, and applied the approach to Norway spruce stands in Fenno–Scandian conditions. The results agreed well with an empirical growth and yield model and provided fairly accurate projections for two contrasting test sites. The study provided insights into the role of the component processes of productivity. Importantly, the north-south gradient of productivity was equally attributed to a northward decrease in temperature and an increase in carbon allocation to fine roots. This cautions against climate change projections on the basis of current growth variation with temperature and calls for improved mechanistic models incorporating plant, soil and microbial interactions in regulating the soil-forest carbon and nitrogen fluxes.

The photochemical reflectance index (PRI) is regarded as a promising proxy to track the dynamics of photosynthetic light use efficiency (LUE) via remote sensing. The implementation of this approach requires the relationship between PRI and LUE to scale not only in space but also in time. The short-term relationship between PRI and LUE is well known and is based on the regulative process of non-photochemical quenching (NPQ), but at the seasonal timescale the mechanisms behind the relationship remain unclear. We examined to what extent sustained forms of NPQ, photoinhibition of reaction centres, seasonal changes in leaf pigment concentrations, or adjustments in the capacity of alternative energy sinks affect the seasonal relationship between PRI and LUE during the year in needles of boreal Scots pine. PRI and NPQ were highly correlated during most of the year but decoupled in early spring when the foliage was deeply downregulated. This phenomenon was attributed to differences in the physiological mechanisms controlling the seasonal dynamics of PRI and NPQ. Seasonal adjustments in the pool size of the xanthophyll cycle pigments, on a chlorophyll basis, controlled the dynamics of PRI, whereas the xanthophyll de-epoxidation status and other xanthophyll-independent mechanisms controlled the dynamics of NPQ at the seasonal timescale. We conclude that the PRI leads to an underestimation of NPQ, and consequently overestimation of LUE, under conditions of severe stress in overwintering Scots pine, and most likely also in species experiencing severe drought. This severe stress-induced decoupling may challenge the implementation of the PRI approach.

The maximum light use efficiency (LUE = gross primary production (GPP)/absorbed photosynthetic photon flux density (aPPFD)) of plant canopies has been reported to vary spatially and some of this variation has previously been attributed to plant species differences. The canopy nitrogen concentration [N] can potentially explain some of this spatial variation. However, the current paradigm of the N-effect on photosynthesis is largely based on the relationship between photosynthetic capacity (A(max)) and [N], i.e., the effects of [N] on photosynthesis rates appear under high PPFD. A maximum LUE-[N] relationship, if it existed, would influence photosynthesis in the whole range of PPFD. We estimated maximum LUE for 14 eddy-covariance forest sites, examined its [N] dependency and investigated how the [N]-maximum LUE dependency could be incorporated into a GPP model. In the model, maximum LUE corresponds to LUE under optimal environmental conditions before light saturation takes place (the slope of GPP vs. PPFD under low PPFD). Maximum LUE was higher in deciduous/mixed than in coniferous sites, and correlated significantly with canopy mean [N]. Correlations between maximum LUE and canopy [N] existed regardless of daily PPFD, although we expected the correlation to disappear under low PPFD when LUE was also highest. Despite these correlations, including [N] in the model of GPP only marginally decreased the root mean squared error. Our results suggest that maximum LUE correlates linearly with canopy [N], but that a larger body of data is required before we can include this relationship into a GPP model. Gross primary production will therefore positively correlate with [N] already at low PPFD, and not only at high PPFD as is suggested by the prevailing paradigm of leaf-level A(max)-[N] relationships. This finding has consequences for modelling GPP driven by temporal changes or spatial variation in canopy [N].

Transplanting recovery of urban trees is an essential first step in their acclimation from nursery to challenging urban growing sites. Changes in crown allometry during this period are not well known. We followed annual shoot extension, crown structure and leaf area of Alnus glutinosa and Tilia × vulgaris street trees for six years from transplanting into their growing site. The growing site of Tilia trees suffered from excessive soil moisture whereas the Alnus site was more normal regarding soil water relations with periods of drought. The transplanting recovery of Tilia trees was delayed due to the deleterious influence of excessive soil water, but Alnus trees recovered substantially within the first 2–3 years. Leaf area in relation to branch basal area changed throughout the research period, indicating tree adaptation to new growing conditions.

In this study we introduce and test a new simple approach for estimating annual stand-level gross primary production (GPP), net primary production (NPP) and stem biomass growth based on carbon acquisition and allocation, by combining existing summary models. The focus is on the variation of GPP and NPP across different parts of Finland caused by climate. A summary model for estimating light-use efficiency is applied to calculate the annual GPP of a fully closed canopy ( P 0 ) for different sites as a function of climatic conditions. The site-specific P 0 s are first determined for five sites using long-term series of daily weather data from the Finnish Meteorological Institute (FMI), and these results are further generalised to all sites using effective temperature sum (ETS) as a predictor. Actual canopy GPP is calculated as a fraction of the estimated site-specific P 0 using a summary model of the effect of shading on photosynthesis in non-homogeneous forests. Leaf area is estimated from measured tree data using the pipe model. NPP:GPP ratio is estimated on the stratum level from mean height. The allocation to stem growth is estimated from stand age and site type, using a summary equation based on results from earlier studies. The method was tested against data from permanent sample plots of the Finnish National Forest Inventory (NFI) from years 1985 and 1995. The results indicate that the approach produces realistic short-term estimations for stem growth. At the stand level the model was nearly unbiased (2.1% underestimation), with RMSE of 34% and R 2 of 0.52, and it provided a clearly better fit than a simple linear prediction of stem growth from the estimated GPP. More importantly, we showed in a model comparison that in the present data set our model provided results of similar accuracy as a well-established empirical tree-level growth model.