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length of forest roads (NFI4) #336
Länge der im LFI4 erhobenen Waldstrassen. Die Waldstrassenlänge wird im Wald zu 100% gezählt, am Waldrand und in aufgelösten Bestockungen dagegen nur zu 50%. Zu den Waldstrassen des LFI4 zählen nur die zum Zeitpunkt der LFI4-Erhebung explizit verfügbaren Strassen-/Streckenabschnitte. Da es sich um eine Vollerhebung sämtlicher Strassen handelt, ist die Angabe eines Standardfehlers nicht nötig (es gibt keine stichprobenbedingte Unsicherheit).

forest area (forest roads) #339
Waldfläche basierend auf der LFI-Waldmaske mit Stand 2017, die für die Berechnung der Erschliessungsdichte verwendet wird. Die Kriterien für die Definition von Wald bei der Herstellung der Waldmaske sind annähernd vergleichbar mit denjenigen für den LFI-Waldentscheid, der jeweils im Rahmen der Feldaufnahme auf den Probeflächen durchgeführt wird.

trafficability of forest road #2239
Die Befahrbarkeit der Strasse gibt an, mit welchem Lastwagentyp eine Strasse befahren werden kann. Dabei sind Achsenzahl und Gesamtgewicht des LKW für die Klassierung ausschlaggebend.

forest road width #2238
Die Strassenbreite gibt an, wie breit der betreffende Strecken-/Strassenabschnitt ist. Lastwagenstrassen im Schweizer Wald müssen, um in der Erschliessungserhebung erfasst zu werden, eine Mindestbreite von 2.50 m aufweisen. Im Rahmen der Försterumfrage des LFI4 wurden zusätzliche Informationen erhoben. Dabei steht nicht die lastwagenbefahrbare Strasse nach LFI mit 10 t Achslast und einer Mindestbreite von 2.50 m im Fokus, sondern die für die Holzabfuhr relevante Strasse mit mindestens 3 m Fahrbahnbreite und genügender Tragfähigkeit für Fahrzeuge mit einem Gesamtgewicht ab 26 t.

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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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.

NFI4
length of forest roads (NFI4) divided by forest area (forest roads)
trafficability of forest road · forest road width
regional demarcation: economic region
unit: m/ha, column total
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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
trafficability of forest road	forest road width	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±	m/ha	±
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	.	0.0	.
	2.5 - 2.9 m	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	.
	3.0 - 3.4 m	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	.
	3.50 m and larger	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	.
	total	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	.
18 t, 2-axle truck	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	.
	2.5 - 2.9 m	1.2	.	2.8	.	1.2	.	1.9	.	2.3	.	0.4	.	0.9	.	0.9	.	0.8	.	0.3	.	1.2	.	0.5	.	3.1	.	1.9	.	1.5	.
	3.0 - 3.4 m	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	.
	3.50 m and larger	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	.
	total	1.2	.	2.8	.	1.2	.	1.9	.	2.3	.	0.4	.	0.9	.	0.9	.	0.8	.	0.3	.	1.2	.	0.5	.	3.1	.	1.9	.	1.5	.
26 t, 3-axle truck	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	.
	2.5 - 2.9 m	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	.
	3.0 - 3.4 m	1.3	.	1.6	.	0.5	.	0.8	.	1.2	.	3.1	.	1.9	.	1.0	.	4.9	.	6.2	.	1.9	.	7.0	.	4.7	.	1.4	.	2.7	.
	3.50 m and larger	0.0	.	0.1	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.0	.	0.1	.	0.2	.	0.1	.	0.7	.	0.0	.	0.3	.	0.2	.
	total	1.3	.	1.7	.	0.5	.	0.8	.	1.2	.	3.1	.	1.9	.	1.0	.	5.0	.	6.4	.	2.0	.	7.7	.	4.7	.	1.8	.	2.9	.
28 - 32 t, 4-axle truck	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	.
	2.5 - 2.9 m	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	.
	3.0 - 3.4 m	4.1	.	0.8	.	1.8	.	3.4	.	3.3	.	3.8	.	5.2	.	5.4	.	4.0	.	2.1	.	0.9	.	1.6	.	2.2	.	0.8	.	2.7	.
	3.50 m and larger	0.1	.	0.0	.	0.0	.	0.0	.	0.1	.	0.0	.	0.1	.	0.1	.	0.5	.	0.1	.	0.0	.	0.5	.	0.1	.	0.4	.	0.2	.
	total	4.2	.	0.8	.	1.8	.	3.4	.	3.4	.	3.8	.	5.4	.	5.5	.	4.5	.	2.2	.	0.9	.	2.1	.	2.3	.	1.2	.	2.9	.
40 - 44 t, min. 5-axle truck	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	.
	2.5 - 2.9 m	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	.
	3.0 - 3.4 m	23.3	.	58.8	.	32.4	.	51.5	.	63.3	.	8.5	.	9.5	.	10.0	.	3.8	.	1.6	.	6.1	.	2.4	.	1.3	.	0.2	.	16.4	.
	3.50 m and larger	2.6	.	7.2	.	4.1	.	2.6	.	4.6	.	0.7	.	0.8	.	1.0	.	0.6	.	0.1	.	1.0	.	1.4	.	0.2	.	0.1	.	1.6	.
	total	25.9	.	66.0	.	36.5	.	54.0	.	67.8	.	9.2	.	10.3	.	11.0	.	4.4	.	1.6	.	7.0	.	3.7	.	1.5	.	0.3	.	18.0	.
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	.
	2.5 - 2.9 m	1.2	.	2.8	.	1.2	.	1.9	.	2.3	.	0.4	.	0.9	.	0.9	.	0.8	.	0.3	.	1.2	.	0.5	.	3.1	.	1.9	.	1.5	.
	3.0 - 3.4 m	28.7	.	61.2	.	34.7	.	55.7	.	67.8	.	15.4	.	16.7	.	16.5	.	12.7	.	9.8	.	8.8	.	10.9	.	8.2	.	2.4	.	21.8	.
	3.50 m and larger	2.7	.	7.2	.	4.1	.	2.6	.	4.7	.	0.7	.	0.9	.	1.1	.	1.2	.	0.4	.	1.1	.	2.6	.	0.3	.	0.9	.	2.0	.
	total	32.5	.	71.3	.	40.0	.	60.1	.	74.7	.	16.5	.	18.5	.	18.4	.	14.6	.	10.5	.	11.2	.	14.1	.	11.6	.	5.2	.	25.4	.

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. 1991616. Birmensdorf, Swiss Federal Research Institute WSL

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