Result query system

back

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). n/a: located above the forest boundary modelled by Zischg et al. and sometimes in the area of lakes, which were relatively roughly cut out by Zischg et al.

canton #827
Regional demarcation with the cantons as a unit, with the two half-cantons, Basel-Land and Basel-Stadt, combined into one canton for statistical reasons.

accessible forest without shrub forest NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is 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).

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)

ownership (2 categories)

regional demarcation: production region

unit: m³/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
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

top

2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5

volume of deadwood (stemwood)

ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

top

3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5

growing stock (stemwood)

ownership (2 categories)

regional demarcation: production region

unit: m³/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
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest and the total forest (pairs of values in ), but they do in the private forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest and the total forest of the Plateau has decreased. For the private forest the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest and total forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

top

4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

top

5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protective forest [2022]”) – is assigned to all measurement cycles.

top

6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

top

7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

top

8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

top

9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

top

NFI5
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
development stage	altitudinal vegetation belts (NaiS; 6 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
	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	.
	subalpine	0	.	0	.	0	.	169	94	0	.	0	.	0	.	0	.	225	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	419	50
	high-montane	0	.	0	.	0	.	2	71	0	.	0	.	0	.	17	.	430	44	0	.	4	.	0	.	0	.	0	.	68	.	0	.	0	.	0	.	0	.	59	81	0	.	0	.	21	73	0	.	0	.	600	35
	lower and upper montane	0	.	0	.	36	.	34	100	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	.	12	100	0	.	0	.	0	.	189	92	10	.	0	.	0	.	379	54
	submontane	0	.	0	.	0	.	73	.	0	.	34	.	0	.	0	.	0	.	18	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	141	83	0	.	0	.	104	71	0	.	0	.	3	.	387	42
	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	.
	total	0	.	0	.	36	.	279	64	0	.	34	.	0	.	19	90	655	35	18	.	4	100	0	.	0	.	0	.	68	.	0	.	111	88	12	100	141	83	59	81	0	.	293	64	55	55	0	.	3	.	1786	22
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	.	0	.	0	.	5	100	0	.	0	.	88	55
	subalpine	0	.	0	.	0	.	267	64	0	.	0	.	0	.	0	.	618	44	0	.	0	.	0	.	0	.	23	.	1	100	0	.	0	.	0	.	0	.	2	100	6	100	0	.	40	68	0	.	0	.	957	34
	high-montane	0	.	0	.	0	.	466	45	0	.	167	62	0	.	0	.	1526	35	0	.	0	.	3	.	0	.	7	.	28	100	0	.	0	.	72	.	0	.	9	55	38	100	91	82	93	59	0	.	0	.	2500	24
	lower and upper montane	0	.	0	.	133	72	853	44	0	.	156	75	0	.	46	100	192	60	35	73	420	57	49	100	0	.	14	77	128	58	66	.	24	94	155	57	117	100	56	92	25	100	255	56	0	.	81	71	243	50	3048	18
	submontane	312	45	0	.	0	.	595	44	74	68	133	80	0	.	0	.	0	.	65	77	132	70	52	.	0	.	0	.	29	74	13	.	33	.	0	.	0	.	0	.	0	.	165	63	0	.	0	.	180	70	1784	21
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	27	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	0	.	0	.	2	100	0	.	0	.	55	59
	total	312	45	0	.	133	72	2182	24	74	68	456	42	27	71	46	100	2380	25	99	56	552	46	105	69	0	.	44	60	185	44	80	85	58	70	227	50	117	100	133	53	69	67	511	38	140	44	81	71	423	41	8434	11
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	365	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	48	0	.	0	.	74	65	0	.	0	.	564	23
	subalpine	0	.	0	.	0	.	38	.	0	.	0	.	0	.	0	.	976	33	0	.	0	.	0	.	0	.	94	.	0	.	0	.	0	.	16	.	0	.	332	47	39	85	0	.	510	41	0	.	0	.	2007	21
	high-montane	0	.	0	.	0	.	827	36	0	.	0	.	0	.	0	.	2079	20	0	.	163	.	0	.	0	.	8	.	72	74	0	.	0	.	77	.	0	.	379	40	326	70	420	51	1709	22	0	.	0	.	6060	12
	lower and upper montane	22	.	0	.	274	87	1998	23	194	54	470	42	0	.	452	64	300	52	533	62	1454	31	460	45	190	76	118	67	830	36	46	.	509	42	993	36	148	74	1546	22	486	49	1152	28	110	69	120	71	471	58	12876	9
	submontane	1399	24	0	.	0	.	1014	24	591	38	536	54	0	.	0	.	33	.	153	72	394	45	149	51	0	.	163	.	53	.	278	58	120	68	0	.	788	34	0	.	0	.	1285	31	242	56	0	.	1742	22	8942	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	196	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2067	20	0	.	73	.	186	43	0	.	24	.	2546	18
	total	1421	23	0	.	274	87	3877	15	785	32	1006	35	0	.	452	64	3950	14	687	51	2011	25	609	36	190	76	383	53	955	32	324	52	629	36	1085	34	937	31	4449	12	852	39	2931	19	2832	17	120	71	2237	21	32995	5
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	523	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	74	0	.	0	.	521	48	0	.	0	.	1255	29
	subalpine	0	.	0	.	0	.	276	58	0	.	166	.	0	.	0	.	474	57	0	.	20	.	0	.	203	.	523	76	200	72	0	.	0	.	0	.	0	.	298	54	53	.	122	.	442	46	0	.	0	.	2777	24
	high-montane	0	.	0	.	0	.	268	64	0	.	90	.	0	.	0	.	2805	24	0	.	76	.	0	.	64	.	118	.	52	.	0	.	0	.	0	.	0	.	296	55	132	.	260	71	2208	29	0	.	0	.	6369	16
	lower and upper montane	73	.	222	.	285	71	2914	22	445	52	1730	33	0	.	412	52	847	44	1183	39	834	44	286	81	434	78	437	49	1730	35	314	70	530	46	132	.	493	62	2600	24	497	55	1823	28	389	51	227	.	1400	36	20236	9
	submontane	1428	31	0	.	0	.	2174	27	163	.	543	49	0	.	0	.	362	60	18	351	719	49	0	.	0	.	153	.	172	74	588	46	364	51	89	.	544	46	0	.	356	77	1294	29	11	.	0	.	1444	31	10422	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	310	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2828	20	0	.	71	.	269	51	0	.	0	.	3478	17
	total	1501	30	222	.	285	71	5631	16	609	47	2529	26	0	.	412	52	5321	17	1201	39	1649	31	286	81	701	57	1232	40	2154	30	902	38	894	34	220	72	1038	38	6233	13	1037	40	3570	19	3839	19	227	.	2844	23	44537	6
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	768	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	212	.	0	.	0	.	981	36
	subalpine	0	.	0	.	0	.	602	57	0	.	0	.	0	.	99	.	2773	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	403	66	0	.	98	.	748	58	0	.	0	.	4723	20
	high-montane	0	.	0	.	0	.	577	60	0	.	585	74	0	.	0	.	5312	19	0	.	0	.	0	.	47	.	0	.	324	71	0	.	0	.	213	.	0	.	1175	37	1058	59	378	58	2111	32	0	.	0	.	11781	13
	lower and upper montane	258	75	829	59	1017	60	6532	18	721	48	2354	29	0	.	1101	38	1220	41	1572	32	996	39	1234	38	1000	53	664	59	4278	23	563	51	2398	28	1467	35	98	.	1060	36	714	60	4694	19	798	46	337	71	2679	27	38583	7
	submontane	1980	27	0	.	0	.	2281	28	995	43	1265	46	0	.	0	.	109	.	443	62	1562	40	121	.	0	.	0	.	71	.	981	41	1258	34	407	72	804	38	0	.	0	.	1623	33	0	.	303	71	1369	30	15572	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	305	71	0	.	90	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1436	33	0	.	96	.	217	66	0	.	61	.	2205	25
	total	2237	25	829	59	1017	60	9992	14	1716	32	4204	24	305	71	1201	36	10272	13	2015	28	2558	29	1355	36	1047	51	664	59	4673	22	1544	32	3656	22	2087	30	901	36	4074	19	1773	43	6889	16	4085	22	640	50	4109	20	73844	5
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1110	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	564	46	0	.	0	.	1674	27
	subalpine	0	.	0	.	0	.	1061	42	0	.	0	.	0	.	0	.	6498	18	0	.	0	.	0	.	257	72	0	.	281	77	0	.	0	.	352	72	0	.	1038	37	234	.	304	61	4356	21	0	.	0	.	14380	11
	high-montane	0	.	0	.	0	.	3235	29	0	.	1127	42	0	.	875	63	10448	15	0	.	209	.	0	.	0	.	1012	48	500	72	0	.	0	.	617	53	0	.	2520	28	678	54	2589	36	5360	21	0	.	0	.	29170	9
	lower and upper montane	981	44	2457	36	2871	33	17202	12	713	65	4780	24	0	.	1023	49	2695	26	3521	25	5868	21	3582	26	952	51	2052	40	10339	17	289	.	1689	33	4977	23	945	50	1830	35	796	52	7045	17	727	57	743	60	3367	28	81443	5
	submontane	4793	20	0	.	0	.	7792	16	1180	38	1014	46	0	.	0	.	119	.	1666	39	1221	38	417	76	0	.	0	.	540	60	1211	42	1556	34	190	.	4097	23	0	.	219	.	1935	33	84	.	0	.	2629	24	30664	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	161	94	0	.	73	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1588	32	0	.	0	.	144	.	0	.	0	.	1966	28
	total	5774	18	2457	36	2871	33	29289	9	1893	34	6921	19	161	94	1897	39	20943	10	5187	21	7298	18	3999	24	1209	43	3064	31	11660	15	1500	39	3245	24	6136	20	5042	21	6976	16	1927	33	11873	14	11235	14	743	60	5996	19	159297	3
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2093	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	266	48	0	.	0	.	1058	26	0	.	0	.	3417	16
	subalpine	0	.	0	.	0	.	3084	26	0	.	0	.	0	.	503	58	5280	17	0	.	0	.	0	.	0	.	447	60	427	83	0	.	0	.	2	.	0	.	1785	26	401	58	312	51	3893	23	0	.	0	.	16134	10
	high-montane	0	.	0	.	0	.	2595	26	0	.	253	72	0	.	210	75	4559	17	0	.	378	60	0	.	0	.	849	45	114	71	0	.	0	.	382	48	0	.	1681	31	429	54	469	54	4516	18	0	.	0	.	16435	9
	lower and upper montane	189	71	293	.	203	71	9766	13	88	.	1473	32	0	.	1029	42	1924	28	1971	33	3399	23	4872	17	230	87	888	43	4104	23	123	.	781	46	1045	36	331	63	3140	44	370	74	4880	19	1252	37	251	73	350	46	42951	7
	submontane	838	34	0	.	0	.	1507	32	545	45	518	62	0	.	0	.	212	74	933	40	1727	36	323	59	0	.	0	.	911	49	340	71	492	47	0	.	472	51	0	.	516	75	974	45	120	.	0	.	1460	32	11888	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	2618	20	0	.	0	.	321	53	0	.	0	.	3426	18
	total	1027	30	293	.	203	71	16952	10	633	41	2244	27	0	.	1742	31	14468	9	2904	26	5503	18	5195	17	230	87	2185	27	5554	20	462	58	1273	33	1429	30	889	37	9490	17	1716	34	6636	16	11161	12	251	73	1809	28	94251	4
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4903	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	641	33	0	.	0	.	2435	20	0	.	0	.	7978	11
	subalpine	0	.	0	.	0	.	5496	18	0	.	166	.	0	.	602	51	16846	10	0	.	20	.	0	.	461	60	1088	45	908	48	0	.	0	.	369	68	0	.	3859	18	734	46	836	35	10013	13	0	.	0	.	41396	6
	high-montane	0	.	0	.	0	.	7971	16	0	.	2222	31	0	.	1101	52	27158	8	0	.	829	43	3	.	111	71	1993	31	1157	39	0	.	0	.	1361	32	0	.	6119	16	2662	30	4209	24	16020	10	0	.	0	.	72915	5
	lower and upper montane	1523	33	3802	28	4818	24	39300	7	2160	29	10962	14	0	.	4066	21	7178	15	8815	15	12971	12	10484	12	2806	30	4173	24	21408	10	1400	35	6028	16	8780	15	2131	29	10232	16	2888	26	20038	9	3284	23	1759	34	8510	15	199517	2
	submontane	10749	12	0	.	0	.	15436	10	3549	20	4044	22	0	.	0	.	836	37	3297	24	5755	18	1062	38	0	.	317	70	1775	32	3412	22	3838	20	686	53	6847	16	0	.	1090	48	7381	15	457	43	303	71	8827	12	79660	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	494	54	0	.	1070	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	10564	9	0	.	240	58	1138	27	0	.	84	77	13676	7
	total	12272	11	3802	28	4818	24	68203	5	5710	16	17393	11	494	54	5769	18	57991	5	12112	12	19575	10	11549	11	3378	26	7571	17	25248	9	4812	18	9867	12	11196	13	9064	13	31414	6	7374	17	32704	7	33346	7	2062	30	17421	9	415143	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. 2313470. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1942406