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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

development stage #20
Stage of stand development, defined by the dominant diameter at breast height (dbhdom = dbh of the 100 largest [thickest] trees per hectare). Young growth/thicket: dbhdom <12 cm, pole timber: dbhdom 12-30 cm, young timber: dbhdom 31-40 cm, medium timber : dbhdom 41-50 cm, old timber: dbhdom >50 cm, mixed: trees of different development stages, no development stage predominant or groups of different development stages covering < 500 m². Reference: Field Survey (MID 261: Entwicklungsstufe)

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

economic region #366
Division of Switzerland into 14 regions (2 in the Jura, 3 on the Plateau, 3 in the Pre-Alps, 5 in the Alps and 1 in the Southern Alps). The economic regions are a subdivision of the production regions according to economic-geographical criteria.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

Estimates and standard error of the estimates

The results published in the NFI tables are estimates for variables in the Swiss forest (population parameters) whose true values are not known and must therefore be extrapolated (estimated) from the NFI sample data.

The extrapolations carry uncertainties. However, the accuracy of the extrapolations can be reliably estimated from the NFI sample, which is designed to be a random sample. To this end, all NFI tables include a second figure in addition to the estimate itself, namely the standard error of the estimate.

In most tables the percent standard error is reported (±%), but the absolute standard error (±) is also reported occasionally, especially for estimated percentages. The relationship between absolute and percent standard error is as follows:

percent standard error = absolute standard error / estimate × 100

absolute standard error = percent standard error × estimate / 100

Information on the forest road network is based on a full survey of all forest roads. In this case, there is no need to specify a standard error because there is no uncertainty caused by sampling.

Confidence interval of the estimate

With the estimate and the standard error of the estimate, the limits of the so-called confidence interval of the estimate can be calculated as:

lower limit = estimate – tQ × absolute standard error

upper limit = estimate + tQ × absolute standard error

If the simple standard error is used for the calculation (tQ = 1), then the 68% confidence interval is formed. In this case, the true value of the population parameter has a 68% probability of lying within this confidence interval of the estimate. If the double standard error is used (tQ = 2), then there is a 95% probability that the true value lies within this 95% confidence interval.

Significance of the estimate

The confidence interval can be used to statistically test whether the estimated population parameter is larger or smaller than a specific reference or target value, or whether two estimated population parameters truly differ (in the real population). For practical applications, proceed as follows: if a reference value lies outside the confidence interval, the estimated population value can be interpreted as being significantly different from it; if it lies within the confidence interval, the difference between the estimated population parameter and the reference value can be interpreted as random or not significant. When two population parameters are compared, they can be interpreted as differing significantly if their confidence intervals do not overlap.

Dealing with missing values

When calculating a results table, data are not always available for all combinations of characteristics^* of the classification variables and regional demarcation applied. In most cases, this indicates that the attribute estimated with the respective target variable does not occur or occurs only very rarely. Usually, the value 0 is used (imputed). However, as this value is not based on direct measurements, the associated standard error is represented with a full stop [.]. If the imputed value of 0 is referred to in the calculation, e.g. in the case of percentages or certain change estimates, no value can be entered. In this case, the estimated value and standard error are both represented with a full stop [.].

For example, no Arolla pines have been found and measured in the Swiss Plateau to date (growing stock of Arolla pines by production region). The values can be assumed to be missing because Arolla pine does not occur in the Swiss Plateau and therefore the growing stock there must be 0.

^* e.g. ‘Arolla pine’ characteristic of the classification variable ‘tree species’ and ‘Swiss plateau’ characteristic of the ‘regional demarcation’

Changes

There are two types of changes in the NFI:

The first type involves specific target variables relating to components of change, such as increment, fellings, mortality and losses. These target variables are only available for two consecutive inventory cycles, e.g. NFI3–NFI4. In the evaluation of components of change, the classification variable characteristic of the second inventory cycle is assigned to the first inventory cycle. These evaluations therefore do not take into account potential changes in a classification variable characteristic from the earlier to the later inventory (e.g. from private to public ownership).

With the second type of change, the difference in target variables, such as number of stems, growing stock or forest area, is used to assess the change between two inventory cycles. Target variables are generally used to represent states, such as that in NFI4, but can also indicate the net change between any two inventory cycles, e.g. NFI1–NFI4. In evaluations of change using these target variables, the change in a classification variable characteristic is considered in the analysis. Consequently, one can detect, for example, that the forest area without shrub forest increased between the two inventory cycles. This only has an effect on classification variables whose characteristic (class) can actually change, e.g. the affiliation with a forest area or the tree condition.

NFI5
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: economic region
unit: %, subtotal
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
development stage	altitudinal vegetation belts (NaiS; 6 classes)	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±	%	±
n/a	n/a	0.0	.	.	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	.	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	subalpine	0.0	.	.	.	0.0	.	0.0	.	0.0	.	0.0	.	72.3	28.3	0.0	.	64.1	28.2	0.0	.	0.0	.	57.8	30.0	30.1	17.5	40.0	29.7	23.9	10.1
	high-montane	0.0	.	.	.	0.0	.	0.0	.	0.0	.	0.0	.	27.7	28.3	65.5	32.0	0.9	0.9	0.0	.	0.0	.	18.8	19.3	69.9	17.5	60.0	29.7	32.4	10.1
	lower and upper montane	85.1	17.9	.	.	15.6	16.2	0.0	.	0.0	.	100.0	0.0	0.0	.	34.5	32.0	13.8	15.1	100.0	0.0	100.0	0.0	23.4	22.9	0.0	.	0.0	.	21.6	10.1
	submontane	14.9	17.9	.	.	84.4	16.2	100.0	0.0	100.0	0.0	0.0	.	0.0	.	0.0	.	21.1	21.7	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	22.1	8.4
	hyperinsubric and colline	0.0	.	.	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	total	100.0	.	.	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.
young growth/ thicket	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	30.4	23.6	11.4	8.3	0.0	.	0.4	0.5	0.0	.	0.8	0.9	21.6	19.7	29.4	13.5	0.0	.	9.8	3.6
	high-montane	0.5	0.5	0.0	.	0.0	.	0.0	.	0.0	.	37.1	20.7	8.8	8.1	13.0	11.9	52.8	18.7	48.9	31.1	27.5	24.6	78.4	19.7	62.7	14.4	87.0	10.6	30.3	5.9
	lower and upper montane	54.5	15.3	22.6	19.9	5.2	10.4	32.2	21.7	49.1	17.3	32.5	20.7	75.6	12.5	81.8	12.3	26.2	14.5	51.1	31.1	71.8	24.7	0.0	.	7.8	5.9	8.5	8.9	36.9	5.7
	submontane	45.0	15.3	77.4	19.9	85.8	12.8	67.8	21.7	50.9	17.3	0.0	.	4.2	4.3	5.2	4.0	20.6	17.6	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	22.4	4.4
	hyperinsubric and colline	0.0	.	0.0	.	9.1	7.4	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	4.4	4.8	0.7	0.4
	total	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.
pole timber	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	2.7	2.2	8.0	3.8	1.4	1.0	0.9	0.3
	subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	4.2	4.4	0.0	.	17.1	8.0	21.7	8.8	4.2	3.0	3.4	1.0
	high-montane	2.0	1.6	0.0	.	0.0	.	0.0	.	0.0	.	30.5	18.7	7.2	6.6	7.5	5.4	50.8	12.3	38.3	21.3	3.9	4.2	58.0	9.5	68.7	9.2	10.9	4.1	16.3	2.1
	lower and upper montane	68.2	8.4	15.3	7.9	24.2	9.4	43.1	9.8	19.7	6.6	69.5	18.7	86.0	8.9	92.5	5.4	37.8	11.7	57.5	21.1	96.1	4.2	1.6	1.2	0.0	.	35.5	6.8	40.6	2.9
	submontane	29.8	8.3	84.7	7.9	72.3	9.9	56.9	9.8	79.8	6.6	0.0	.	6.8	6.5	0.0	.	11.4	7.0	0.0	.	0.0	.	11.7	6.1	1.7	1.7	0.0	.	30.6	2.5
	hyperinsubric and colline	0.0	.	0.0	.	3.5	3.5	0.0	.	0.5	0.5	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	8.9	3.9	0.0	.	48.1	7.1	8.1	1.4
	total	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.
young timber	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	15.2	6.9	10.8	4.7	1.2	1.2	2.5	0.8
	subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	28.6	12.5	0.0	.	13.6	9.1	5.4	5.5	6.0	6.0	12.5	5.8	8.1	5.7	5.2	2.6	5.4	1.4
	high-montane	0.0	.	0.0	.	8.9	8.5	0.0	.	0.0	.	10.6	7.5	6.5	4.7	0.0	.	23.1	12.6	0.0	.	4.1	4.2	55.6	10.1	63.4	9.5	4.0	2.4	13.3	2.1
	lower and upper montane	81.5	6.0	41.3	14.3	19.2	10.8	35.1	9.5	33.5	8.2	89.4	7.5	54.3	12.6	92.4	5.7	47.5	14.2	50.2	20.1	90.0	7.4	8.5	5.0	8.5	6.2	41.4	7.3	45.6	3.0
	submontane	17.0	5.9	58.7	14.3	71.9	12.7	64.9	9.5	66.5	8.2	0.0	.	10.6	7.2	7.6	5.7	15.8	11.0	44.4	20.3	0.0	.	0.3	0.3	9.2	5.3	0.0	.	25.1	2.5
	hyperinsubric and colline	1.5	1.5	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	7.9	4.1	0.0	.	48.2	7.2	8.1	1.4
	total	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.
medium timber	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	5.4	5.2	8.3	3.3	0.0	.	1.2	0.5
	subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	3.1	3.0	0.0	.	18.3	9.7	0.0	.	0.0	.	16.2	9.5	31.9	7.2	9.4	4.9	6.1	1.2
	high-montane	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	17.9	12.2	4.9	3.9	7.1	4.0	16.6	9.4	49.7	19.0	0.0	.	52.9	11.4	43.9	7.5	44.9	8.6	15.2	1.9
	lower and upper montane	86.2	3.8	33.7	8.9	20.1	10.1	22.7	9.6	51.8	7.9	82.1	12.2	79.9	9.0	84.5	5.8	61.9	12.1	50.3	19.0	100.0	0.0	20.0	8.5	14.5	5.7	19.8	6.3	52.9	2.6
	submontane	13.1	3.7	66.3	8.9	71.0	11.1	77.3	9.6	47.3	7.9	0.0	.	12.2	8.1	8.4	4.4	3.2	3.2	0.0	.	0.0	.	0.0	.	1.4	1.4	0.0	.	21.6	2.0
	hyperinsubric and colline	0.7	0.7	0.0	.	8.9	6.2	0.0	.	0.9	0.9	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	5.5	3.6	0.0	.	25.9	7.3	3.0	0.8
	total	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.
old timber	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	5.3	2.4	6.3	2.2	0.0	.	1.0	0.3
	subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	8.7	4.7	3.4	1.8	0.5	0.5	3.6	2.6	12.2	6.9	4.9	4.8	39.3	6.9	32.4	5.5	18.5	5.2	9.0	1.0
	high-montane	2.0	1.5	0.0	.	0.9	0.9	0.0	.	0.0	.	31.2	9.0	9.2	3.3	3.8	2.0	45.8	9.3	14.2	7.3	26.0	11.7	47.2	7.3	47.8	5.9	43.9	7.1	18.1	1.5
	lower and upper montane	83.1	4.3	33.4	11.0	65.2	9.1	35.9	6.7	36.3	5.8	58.2	9.2	79.2	4.6	94.8	2.2	41.2	9.0	69.6	10.3	65.6	12.0	6.1	3.6	12.7	3.5	21.8	6.1	51.1	1.9
	submontane	14.8	4.1	66.6	11.0	31.6	8.9	64.1	6.7	63.7	5.8	1.8	1.8	8.3	3.1	0.9	0.9	9.3	5.3	4.0	4.0	3.5	3.5	0.8	0.8	0.7	0.7	0.0	.	19.5	1.4
	hyperinsubric and colline	0.0	.	0.0	.	2.4	2.4	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	1.3	1.3	0.0	.	15.8	4.5	1.3	0.4
	total	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.
mixed	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	8.2	2.4	14.0	3.7	3.1	1.6	3.0	0.6
	subalpine	0.0	.	0.0	.	4.4	4.4	0.0	.	0.0	.	5.3	4.6	7.0	2.9	1.5	1.5	38.3	9.2	18.5	10.5	29.3	13.1	36.1	6.4	38.0	5.7	17.7	4.8	16.2	1.6
	high-montane	0.0	.	0.0	.	4.0	4.0	0.0	.	0.0	.	18.7	8.9	10.6	3.5	2.6	1.8	31.1	8.5	31.1	12.4	10.1	6.2	40.7	6.2	30.9	5.4	19.9	5.5	16.5	1.6
	lower and upper montane	86.5	3.2	31.5	13.7	19.2	11.0	31.1	10.6	31.7	9.4	76.0	9.7	74.2	5.0	73.8	9.2	29.5	8.4	25.3	13.1	57.2	13.2	12.2	4.2	13.9	4.2	32.0	9.7	46.9	2.2
	submontane	13.5	3.2	68.5	13.7	72.4	12.4	68.9	10.6	65.8	9.5	0.0	.	8.2	3.1	22.1	9.1	1.2	1.2	25.1	15.3	3.4	3.4	0.0	.	2.1	1.5	0.0	.	13.5	1.5
	hyperinsubric and colline	0.0	.	0.0	.	0.0	.	0.0	.	2.5	2.5	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	2.9	1.6	1.1	1.1	27.4	5.8	3.8	0.7
	total	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.
total	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	upper subalpine	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	7.2	1.6	8.7	1.5	1.2	0.5	1.6	0.2
	subalpine	0.0	.	0.0	.	0.5	0.5	0.0	.	0.0	.	5.7	2.4	6.3	1.6	0.5	0.3	15.9	3.5	9.7	3.8	10.3	4.5	30.7	3.6	30.8	3.0	12.8	2.1	9.3	0.6
	high-montane	0.8	0.5	0.0	.	1.6	1.0	0.0	.	0.0	.	23.8	5.0	8.8	1.9	4.6	1.4	36.1	4.9	25.1	6.5	13.9	5.3	47.5	3.9	46.3	3.3	27.9	3.1	16.9	0.8
	lower and upper montane	83.5	2.0	32.3	5.3	39.0	5.8	33.4	4.1	36.4	3.5	69.8	5.3	76.2	2.9	88.7	2.4	40.6	4.9	54.6	7.5	73.5	6.4	9.9	2.3	12.0	2.1	28.8	3.7	48.8	1.0
	submontane	15.4	1.9	67.7	5.3	55.3	5.8	66.6	4.1	63.2	3.5	0.7	0.7	8.6	1.9	6.2	1.9	7.4	2.5	10.6	4.6	2.3	1.6	1.1	0.5	2.0	0.7	0.0	.	20.1	0.7
	hyperinsubric and colline	0.3	0.2	0.0	.	3.6	1.8	0.0	.	0.5	0.3	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	3.6	1.0	0.3	0.3	29.3	2.5	3.4	0.3
	total	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.	100.0	.

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1343105. Birmensdorf, Swiss Federal Research Institute WSL

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