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protection forest region  

Jura + Plateau  Northwestern Alps  Northeastern Alps  Southwestern Alps  Southeastern Alps  Southern Alps  Switzerland  
slope (40% classes)  %  ±  %  ±  %  ±  %  ±  %  ±  %  ±  %  ± 
to 40%  77.5  0.9  36.7  1.5  34.5  1.9  14.4  1.5  24.4  1.6  16.2  1.3  45.0  0.6 
4180%  21.2  0.9  52.2  1.6  51.8  2.0  66.9  2.0  62.8  1.8  56.1  1.8  44.1  0.6 
over 80%  1.3  0.2  11.1  1.0  13.7  1.4  18.7  1.6  12.7  1.2  27.8  1.6  10.9  0.4 
total  100.0  .  100.0  .  100.0  .  100.0  .  100.0  .  100.0  .  100.0  . 
 idefinitions (in German)
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 forest area #44
Gesamtheit aller Flächen, die gemäss Walddefinition des LFI als Wald bezeichnet werden. Die Walddefinition schliesst Gebüschwald mit ein. Die Zielgrösse «Waldfläche» wird auch verwendet für Auswertungen der Gesamtfläche, wenn es um die Unterscheidung von Wald und Nichtwald geht.  slope (40% classes) #1084
Hangneigung in Klassen zu 40%, abgeleitet aus dem digitalen Höhenmodell DHM25 von Swisstopo.  protection forest region #829
Die 6 Schutzwaldregionen sind eine Zusammenfassung der 14 Wirtschaftsregionen durch das Landesforstinventar LFI nach naturräumlichen und statistischen Aspekten.  accessible forest without shrub forest NFI3/NFI4 #1429
Wald, der sowohl im LFI3 (20042006) als auch im LFI4 (20092017) zu weniger als zwei Dritteln mit Sträuchern bedeckt war und zu Fuss aufgesucht werden konnte.  1.4 x 1.4 km grid #410
Stichprobennetz des LFI mit einer Maschenweite von 1,4 km. Beim 1,4kmNetz handelt es sich um das gemeinsame Netz aller bisherigen terrestrischen Inventuren, weshalb es auch als Basisnetz bezeichnet wird.

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 socalled 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.
Table citation
Abegg, M.; Brändli, U.B.; Cioldi, F.; Fischer, C.; Herold, A.; Meile, R.; Rösler, E.; Speich, S.; Traub, B., 2020: Swiss national forest inventory  Result table No. 800978. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1766591