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forest area #44
All areas designated as forest according to the NFI forest definition. The forest definition includes shrub forest. The target variable "forest area" is also used when classifying the total area as forest or non-forest.

dominance of alien woody species (neophytes; 3 classes; from NFI3 on) #887
Indication as to whether invasive or non-invasive alien woody species («tree or shrub neophytes») ≥12 cm in diameter at breast height (dbh) dominate the growing stock, i.e. account for at least 50% of the growing stock – in three classes. The classification as «invasive» is based on the 2022 publication «Invasive alien species in Switzerland» of the Federal Office for the Environment (FOEN) (UW-2220-E). Reference: Field Survey (MID 50: Baumart, MID 1018: Baumzustand, MID 60: Brusthöhendurchmesser, MID 62: Umfang)

forest formations (NaiS; 10 classes) #2637
Combination of the 18 groups of NaiS site types, each with a similar objective for the main tree species (NAISGGROB20), into 10 large associations known as «forest formations». *As the characterisation of the site types in the NaiS-NFI project is on a small scale, it is possible that non-forest site types such as meadow, pasture and rock may be present in sample plots that are classified as «forest» in NFI. Similarly, «forest without shrub forest“ may also contain «shrub forest» site types.

production region #49
Demarcation of Switzerland into five regions (Jura, Plateau, Pre-Alps, Alps and Southern Alps) with relatively uniform growth and timber production conditions. The production regions were established by the Federal Office of Forestry long before the first National Forest Inventory (NFI1, 1983-1985). With a small exception along Lake Geneva, the boundaries of the production regions still follow the municipal boundaries of the time. Unlike the NFI, the Forestry Statistics of the Federal Statistical Office don't use the production regions as demarcation but rather the forestry zones, whose boundaries are somewhat different.

accessible forest without shrub forest #434
Forest of which less than two-thirds is covered with shrubs that can be accessed 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
forest area
dominance of alien woody species (neophytes; 3 classes; from NFI3 on) · forest formations (NaiS; 10 classes)
regional demarcation: production region
unit: 1000 ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
dominance of alien woody species (neophytes; 3 classes; from NFI3 on)	forest formations (NaiS; 10 classes)	1000 ha	±%	1000 ha	±%	1000 ha	±%	1000 ha	±%	1000 ha	±%	1000 ha	±%
n/a	n/a	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	beech forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	silver fir-beech forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	other boradleaved forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	silver fir-spruce forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	spruce forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	Arolla pine and larch forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	pine forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	pioneer forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	shrub forests*	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	non-forest*	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	total	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
not dominated by alien woody species	n/a	2.6	38	1.7	45	3.2	33	15.8	15	3.1	33	26.4	12
	beech forests	118.3	3	175.6	2	59.6	6	33.4	9	21.4	12	408.3	2
	silver fir-beech forests	56.6	6	11.8	17	66.5	6	38.5	9	7.2	22	180.7	4
	other boradleaved forests	10.0	19	24.1	11	20.4	13	26.1	11	43.9	7	124.4	5
	silver fir-spruce forests	3.8	31	3.4	31	34.3	9	61.2	7	19.8	12	122.5	5
	spruce forests	0.0	.	0.3	100	17.6	14	141.3	4	24.1	11	183.3	4
	Arolla pine and larch forests	0.0	.	0.0	.	0.0	.	30.8	10	8.9	19	39.7	9
	pine forests	0.4	100	0.7	71	3.2	33	15.8	15	1.0	58	21.1	13
	pioneer forests	0.0	.	0.0	.	0.8	71	13.8	16	15.1	14	29.6	10
	shrub forests*	0.0	.	0.4	100	2.6	38	13.5	16	5.5	25	22.0	13
	non-forest*	4.7	28	5.6	25	12.7	17	14.6	15	7.2	21	44.7	9
	total	196.3	1	223.6	1	220.8	2	404.7	1	157.2	2	1202.7	1
dominated by non-invasive alien woody species	n/a	0.0	.	0.0	.	0.0	.	0.4	100	0.0	.	0.4	100
	beech forests	1.0	58	2.1	41	0.4	100	0.0	.	0.0	.	3.4	32
	silver fir-beech forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	other boradleaved forests	0.3	100	1.4	50	0.0	.	0.0	.	0.4	100	2.1	41
	silver fir-spruce forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	spruce forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	Arolla pine and larch forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	pine forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	pioneer forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	shrub forests*	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	non-forest*	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	total	1.4	50	3.4	32	0.4	100	0.4	100	0.4	100	5.9	24
dominated by invasive alien woody species	n/a	0.0	.	0.0	.	0.0	.	0.7	71	0.0	.	0.7	71
	beech forests	0.0	.	0.3	100	0.0	.	0.0	.	0.4	100	0.7	71
	silver fir-beech forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	other boradleaved forests	0.0	.	0.0	.	0.0	.	0.0	.	0.7	71	0.7	71
	silver fir-spruce forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	spruce forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	Arolla pine and larch forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	pine forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	pioneer forests	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	shrub forests*	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.
	non-forest*	0.0	.	0.4	100	0.0	.	0.3	100	0.0	.	0.7	71
	total	0.0	.	0.7	71	0.0	.	1.1	58	1.1	58	2.9	35
total	n/a	2.6	38	1.7	45	3.2	33	16.9	15	3.1	33	27.5	11
	beech forests	119.3	3	178.0	2	59.9	6	33.4	9	21.8	12	412.4	2
	silver fir-beech forests	56.6	6	11.8	17	66.5	6	38.5	9	7.2	22	180.7	4
	other boradleaved forests	10.3	18	25.4	11	20.4	13	26.1	11	45.0	7	127.2	5
	silver fir-spruce forests	3.8	31	3.4	31	34.3	9	61.2	7	19.8	12	122.5	5
	spruce forests	0.0	.	0.3	100	17.6	14	141.3	4	24.1	11	183.3	4
	Arolla pine and larch forests	0.0	.	0.0	.	0.0	.	30.8	10	8.9	19	39.7	9
	pine forests	0.4	100	0.7	71	3.2	33	15.8	15	1.0	58	21.1	13
	pioneer forests	0.0	.	0.0	.	0.8	71	13.8	16	15.1	14	29.6	10
	shrub forests*	0.0	.	0.4	100	2.6	38	13.5	16	5.5	25	22.0	13
	non-forest*	4.7	28	5.9	24	12.7	17	15.0	15	7.2	21	45.4	9
	total	197.7	1	227.7	1	221.1	2	406.2	1	158.8	2	1211.5	1

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. 1341601. Birmensdorf, Swiss Federal Research Institute WSL

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