Selection from the list of topics

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 (“±”).

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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	23-25calculated 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).

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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 (pairs of values in ), but they do in the private forest and the total 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 of the Plateau has decreased. For the private forest, as well as for the total 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.

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).

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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.

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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. “protection forest [2022]”) – is assigned to all measurement cycles.

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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

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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.

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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.

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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.

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NFI4
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9271	9	12471	7	28825	4	8070	8	17255	7	6034	10	81927	3
	fir	7390	6	6780	9	8041	7	696	28	336	23	768	26	24011	4
	pine	367	33	304	42	1097	27	2870	18	3624	16	138	48	8400	10
	larch	71	51	441	46	449	33	3994	12	2489	17	3844	11	11289	7
	Arolla pine	0	.	0	.	75	67	787	20	1459	17	58	53	2380	12
	other conifers	535	29	396	20	809	28	82	59	939	29	50	66	2810	14
	beech	9520	6	9364	6	10505	7	841	28	1125	18	7241	10	38597	3
	maple	3306	10	3517	10	5716	8	606	23	482	26	1302	19	14930	5
	ash	1420	14	3609	10	2951	11	508	24	639	25	1837	15	10964	6
	oak	1011	29	1018	22	324	41	925	27	33	58	1223	24	4532	12
	sweet chestnut	10	100	40	61	152	79	20	100	0	.	7802	12	8024	11
	other broadleaves	3565	9	5050	9	9316	8	4362	13	4605	14	13285	7	40183	4
	indeterminable	171	29	67	38	352	19	79	35	122	34	164	31	955	12
	total	36637	4	43057	4	68614	3	23841	6	33108	5	43746	4	249002	1
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5112	8	7732	7	18717	5	5800	9	10337	6	3448	9	51145	3
	fir	3736	8	3065	10	4228	7	416	26	326	25	452	23	12224	4
	pine	618	22	414	25	616	25	1995	15	1560	18	148	42	5351	9
	larch	40	79	266	28	146	41	2346	11	1640	13	2576	11	7013	6
	Arolla pine	0	.	0	.	52	73	871	17	1011	20	20	71	1954	13
	other conifers	186	39	257	24	134	43	10	100	132	37	10	100	730	17
	beech	6543	7	5564	6	6819	7	347	33	733	20	3851	11	23858	4
	maple	1743	10	2160	11	3613	8	157	41	299	32	280	25	8252	5
	ash	934	15	1901	10	1936	12	314	29	261	37	1020	21	6366	7
	oak	773	22	1100	17	206	34	241	28	51	60	670	19	3041	10
	sweet chestnut	0	.	29	100	30	58	20	100	0	.	4050	11	4130	10
	other broadleaves	969	14	2004	12	2422	11	1218	15	897	19	4879	9	12389	5
	indeterminable	40	50	19	71	166	25	51	45	71	38	30	58	377	17
	total	20695	4	24511	4	39086	3	13786	6	17317	5	21435	4	136830	1
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3289	7	4800	7	12833	4	3162	9	6264	6	2238	10	32586	2
	fir	2495	8	1974	9	2819	7	128	33	150	29	272	28	7837	4
	pine	778	17	575	16	445	26	817	15	874	18	195	49	3683	8
	larch	33	44	243	24	97	34	1759	11	1184	12	1515	11	4831	6
	Arolla pine	0	.	0	.	73	64	409	20	497	19	30	75	1009	13
	other conifers	45	69	171	33	13	76	0	.	0	.	0	.	229	28
	beech	5307	6	3475	6	4006	7	142	31	484	20	1387	12	14801	3
	maple	844	11	846	12	1336	10	32	58	146	36	163	32	3368	6
	ash	570	15	1410	11	961	15	183	65	56	44	293	24	3474	8
	oak	438	21	569	15	107	34	30	52	22	84	367	19	1532	10
	sweet chestnut	0	.	25	86	23	100	16	73	0	.	1446	13	1509	12
	other broadleaves	421	16	764	14	583	15	217	30	201	28	1618	10	3803	6
	indeterminable	29	74	21	54	60	37	0	.	52	56	33	50	195	24
	total	14248	3	14872	3	23355	3	6896	6	9932	5	9555	4	78857	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2623	6	3014	6	8769	4	1736	8	3870	6	1429	10	21440	2
	fir	1846	7	1511	8	2172	7	128	31	170	19	171	22	5998	4
	pine	434	16	455	13	253	26	260	20	422	15	73	42	1897	7
	larch	20	53	118	27	89	40	1207	11	960	12	904	11	3299	6
	Arolla pine	0	.	0	.	28	55	155	24	265	16	0	.	448	13
	other conifers	23	70	86	34	0	.	0	.	0	.	4	100	113	30
	beech	3118	5	2709	6	2319	7	47	56	217	23	715	12	9126	3
	maple	361	12	302	14	445	11	24	75	24	53	29	65	1186	7
	ash	288	17	882	9	422	14	17	61	23	53	94	27	1726	7
	oak	204	21	509	13	53	35	20	52	29	62	143	27	958	10
	sweet chestnut	0	.	8	100	12	58	0	.	0	.	568	14	588	13
	other broadleaves	151	26	345	16	176	19	29	38	31	39	372	13	1103	8
	indeterminable	8	71	12	58	52	32	4	100	22	45	8	71	106	21
	total	9076	3	9951	3	14790	3	3627	6	6032	5	4511	5	47987	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1582	7	1799	7	5509	4	826	10	2148	7	838	11	12703	3
	fir	1100	8	1224	8	1479	7	92	32	147	21	136	26	4178	4
	pine	144	25	268	16	70	33	134	29	208	19	23	52	847	10
	larch	16	50	118	22	69	33	888	10	705	14	614	10	2410	6
	Arolla pine	0	.	0	.	8	100	68	29	111	21	4	100	191	17
	other conifers	0	.	56	42	0	.	0	.	0	.	4	100	60	40
	beech	1303	8	1629	6	1217	9	8	100	85	27	333	14	4574	4
	maple	118	20	104	24	140	24	0	.	29	42	9	71	399	13
	ash	97	21	417	14	103	24	8	71	8	71	16	60	650	10
	oak	98	37	337	15	29	42	4	100	20	60	35	39	523	12
	sweet chestnut	0	.	15	100	0	.	0	.	0	.	310	17	325	17
	other broadleaves	20	53	159	27	119	40	33	49	14	59	61	29	407	17
	indeterminable	0	.	5	100	12	58	12	58	8	71	0	.	37	33
	total	4478	4	6132	4	8755	3	2073	7	3482	6	2384	5	27305	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	596	10	716	10	2196	6	269	16	841	10	373	14	4991	4
	fir	503	12	641	10	789	10	27	55	54	28	36	33	2050	6
	pine	38	40	83	25	14	59	12	58	62	31	4	100	214	16
	larch	0	.	53	34	45	57	363	14	225	16	245	14	931	8
	Arolla pine	0	.	0	.	18	51	19	45	59	30	0	.	97	23
	other conifers	4	100	36	40	0	.	0	.	0	.	0	.	40	37
	beech	274	15	665	9	367	13	8	71	24	41	117	22	1455	6
	maple	4	100	24	41	34	44	0	.	0	.	0	.	63	30
	ash	31	38	119	24	37	33	0	.	0	.	10	71	197	17
	oak	38	52	138	18	10	71	0	.	0	.	13	58	198	17
	sweet chestnut	0	.	0	.	20	82	0	.	0	.	123	22	142	22
	other broadleaves	16	50	48	37	15	50	0	.	6	100	29	48	114	23
	indeterminable	0	.	4	100	4	100	4	100	0	.	0	.	12	58
	total	1504	7	2528	5	3550	5	703	11	1271	8	949	8	10504	2
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	202	17	288	14	1115	8	128	28	378	14	218	17	2329	5
	fir	172	18	236	15	307	13	20	45	36	36	41	37	812	8
	pine	4	100	4	100	0	.	0	.	13	58	0	.	21	45
	larch	4	100	9	71	16	50	164	20	82	23	138	20	413	12
	Arolla pine	0	.	0	.	0	.	27	43	19	45	0	.	46	31
	other conifers	4	100	28	52	0	.	0	.	0	.	0	.	33	47
	beech	92	24	245	14	97	23	0	.	16	60	49	37	499	10
	maple	5	100	0	.	12	58	0	.	5	100	0	.	21	45
	ash	20	53	60	31	10	72	0	.	4	100	0	.	93	24
	oak	8	71	67	33	5	100	0	.	8	71	4	100	93	26
	sweet chestnut	0	.	0	.	5	100	0	.	0	.	110	23	115	22
	other broadleaves	0	.	19	51	12	58	0	.	0	.	10	73	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	511	11	958	8	1579	7	338	16	560	11	570	10	4515	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41	38	92	22	284	15	54	37	121	19	107	21	700	9
	fir	43	32	80	24	106	21	4	100	8	71	13	57	253	13
	pine	0	.	4	100	0	.	0	.	4	100	0	.	8	71
	larch	0	.	4	100	0	.	61	27	35	40	67	30	166	18
	Arolla pine	0	.	0	.	0	.	16	50	16	50	4	100	36	33
	other conifers	4	100	8	71	0	.	0	.	0	.	0	.	12	58
	beech	13	58	95	23	16	50	4	100	0	.	16	50	144	18
	maple	0	.	6	100	7	71	0	.	0	.	0	.	13	59
	ash	0	.	18	50	0	.	0	.	0	.	0	.	18	50
	oak	14	59	34	36	10	72	0	.	0	.	0	.	58	28
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	69	31	73	30
	other broadleaves	0	.	14	59	0	.	0	.	0	.	4	100	19	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	114	21	354	12	423	12	144	20	184	16	280	14	1500	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	12	58	97	24	12	58	16	50	23	40	164	17
	fir	28	43	13	58	21	45	0	.	8	71	14	58	83	23
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	35	36	13	58	18	65	66	28
	Arolla pine	0	.	0	.	0	.	12	58	4	100	0	.	16	50
	other conifers	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	beech	4	100	9	71	12	58	0	.	0	.	8	71	33	36
	maple	0	.	0	.	0	.	0	.	0	.	4	100	4	100
	ash	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	oak	6	100	13	77	0	.	0	.	0	.	0	.	19	61
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	25	47	25	47
	other broadleaves	0	.	17	80	4	100	0	.	0	.	0	.	21	67
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	42	35	71	30	134	20	59	27	41	32	92	24	439	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	12	58	44	30	4	100	56	47	35	39	155	22
	fir	0	.	8	71	16	50	0	.	4	100	8	71	36	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	8	71	19	53	32	39
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	maple	0	.	0	.	12	75	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	11	58	0	.	0	.	0	.	0	.	11	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	77	38	77	38
	other broadleaves	15	74	22	100	0	.	0	.	0	.	4	100	41	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	19	62	54	46	72	25	12	58	72	38	154	23	383	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22724	6	30936	5	78390	3	20062	7	41285	5	14743	7	208140	2
	fir	17312	5	15533	6	19977	5	1511	24	1239	16	1911	19	57483	3
	pine	2382	15	2107	12	2495	20	6088	14	6767	13	580	38	20420	7
	larch	184	42	1251	20	912	23	10820	8	7342	9	9941	8	30450	4
	Arolla pine	0	.	0	.	253	52	2369	16	3446	14	116	55	6184	10
	other conifers	801	27	1042	17	957	28	92	62	1071	28	69	56	4031	12
	beech	26175	4	23755	4	25358	5	1397	27	2684	16	13728	9	93098	3
	maple	6381	7	6959	8	11316	7	819	22	985	24	1787	17	28248	4
	ash	3360	10	8420	7	6421	9	1030	25	990	26	3271	14	23492	4
	oak	2590	18	3796	11	743	28	1220	25	162	51	2454	17	10965	8
	sweet chestnut	10	100	117	80	242	73	60	84	0	.	14579	9	15008	9
	other broadleaves	5156	9	8443	8	12647	7	5859	12	5754	14	20262	7	58122	4
	indeterminable	248	23	128	25	646	13	150	25	275	24	235	26	1681	9
	total	87324	3	102487	2	160359	2	51477	5	72000	4	83675	3	557321	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. 1296755. Birmensdorf, Swiss Federal Research Institute WSL

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