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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
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	8453	11	13479	10	29035	6	8276	12	16426	9	5132	13	80801	4
	fir	7782	8	6585	12	8033	9	421	34	405	28	623	32	23849	5
	pine	251	32	415	56	761	40	2944	25	2877	23	134	72	7383	14
	larch	37	71	472	72	617	38	3714	14	1761	18	3644	15	10245	9
	Arolla pine	0	.	0	.	0	.	629	31	1697	22	69	71	2396	17
	other conifers	541	34	442	24	588	42	139	58	871	38	17	100	2599	18
	beech	9496	8	9421	7	10471	9	1016	33	1001	24	6544	14	37950	4
	maple	3192	15	2913	14	5655	11	633	30	497	33	1133	29	14024	7
	ash	1331	18	3233	15	3150	14	645	29	660	37	1603	24	10622	8
	oak	1169	39	884	30	285	47	752	32	0	.	1411	34	4500	17
	sweet chestnut	17	100	20	100	210	100	37	100	0	.	8663	15	8947	15
	other broadleaves	3214	13	4710	11	9065	11	4076	16	4638	19	12982	11	38686	6
	indeterminable	124	48	67	50	359	25	109	41	161	40	151	48	971	16
	total	35608	5	42640	5	68230	4	23391	8	30995	7	42109	5	242973	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5262	11	7766	10	19102	7	5592	13	9628	9	3311	13	50660	4
	fir	3906	12	2927	12	4204	10	290	34	240	34	462	32	12028	6
	pine	627	28	563	30	550	38	2021	20	1262	25	191	51	5214	12
	larch	17	100	240	49	177	47	2134	16	1418	19	2674	14	6659	9
	Arolla pine	0	.	0	.	0	.	651	26	1052	26	35	71	1738	19
	other conifers	171	40	207	37	121	62	17	100	71	50	17	100	604	22
	beech	6715	9	6197	7	7090	8	373	40	804	28	4010	16	25189	5
	maple	1714	12	1941	16	3605	11	130	38	431	36	245	37	8067	7
	ash	761	20	1885	15	2432	15	421	35	334	48	656	28	6489	9
	oak	585	28	1336	22	179	38	270	38	36	100	741	23	3146	13
	sweet chestnut	0	.	0	.	21	100	37	100	0	.	4671	14	4729	14
	other broadleaves	804	21	1716	14	2314	15	1135	20	1009	25	5212	12	12190	7
	indeterminable	19	100	0	.	155	34	0	.	36	71	38	71	248	27
	total	20582	5	24778	5	39949	4	13070	9	16321	7	22263	5	136962	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3389	10	4721	9	13400	5	2981	12	6127	8	2292	15	32910	3
	fir	2410	11	2019	12	2943	9	139	51	129	30	289	33	7929	6
	pine	740	21	646	21	322	39	878	20	855	24	97	48	3537	10
	larch	45	54	199	31	99	52	1606	15	1167	16	1475	15	4591	8
	Arolla pine	0	.	0	.	0	.	275	33	510	25	37	100	821	19
	other conifers	14	71	113	45	22	76	0	.	0	.	0	.	149	36
	beech	5091	8	3528	9	3961	10	177	40	600	23	1226	17	14583	5
	maple	814	16	747	15	1507	12	36	71	203	42	74	43	3381	8
	ash	616	18	1534	14	1146	19	278	77	48	60	98	36	3721	10
	oak	338	25	651	19	40	52	47	59	7	100	304	27	1388	13
	sweet chestnut	0	.	7	100	42	100	29	73	0	.	1518	18	1595	17
	other broadleaves	420	21	751	19	543	19	144	37	172	39	1505	14	3536	9
	indeterminable	35	100	24	76	46	54	0	.	7	100	33	65	145	36
	total	13912	5	14941	5	24072	4	6590	9	9824	7	8948	6	78286	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2475	8	3050	8	8945	5	1664	12	3790	8	1427	13	21351	3
	fir	1929	10	1572	10	2166	9	116	43	147	26	161	29	6092	5
	pine	398	21	488	16	233	42	207	31	362	23	35	53	1724	11
	larch	29	61	88	38	43	59	1192	16	943	13	775	14	3069	8
	Arolla pine	0	.	0	.	10	100	133	37	231	22	0	.	373	19
	other conifers	7	100	102	40	0	.	0	.	0	.	8	100	117	36
	beech	2912	7	2702	8	2383	10	79	61	301	27	584	18	8962	4
	maple	383	16	233	21	473	14	30	100	36	61	7	100	1162	9
	ash	254	24	914	13	443	19	15	100	14	71	62	48	1702	9
	oak	175	29	509	16	47	49	21	75	7	100	171	34	931	13
	sweet chestnut	0	.	0	.	15	70	0	.	0	.	607	19	622	18
	other broadleaves	156	38	373	20	181	27	45	41	35	44	335	20	1125	11
	indeterminable	15	71	8	100	71	37	7	100	14	71	7	100	121	27
	total	8732	4	10040	4	15007	4	3510	9	5881	6	4180	6	47350	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1549	9	1902	9	5476	6	881	13	2002	9	833	15	12642	3
	fir	1045	11	1240	10	1428	9	84	43	112	31	163	30	4071	6
	pine	104	29	251	20	79	43	93	45	170	28	21	74	719	13
	larch	22	58	83	35	57	50	860	14	676	15	553	14	2251	8
	Arolla pine	0	.	0	.	0	.	35	45	100	30	0	.	135	25
	other conifers	0	.	68	49	0	.	0	.	0	.	8	100	76	45
	beech	1261	11	1576	9	1125	12	15	100	111	32	284	19	4372	5
	maple	119	24	91	34	172	33	0	.	52	41	8	100	441	17
	ash	111	25	469	18	120	30	7	100	7	100	13	100	727	14
	oak	21	58	309	20	21	58	0	.	14	100	24	59	389	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	265	24	265	24
	other broadleaves	28	61	158	40	171	47	23	57	26	59	55	43	461	24
	indeterminable	0	.	0	.	14	71	14	71	0	.	0	.	29	50
	total	4260	5	6147	5	8664	4	2012	10	3270	7	2227	8	26579	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	563	14	753	12	2262	8	259	22	717	12	380	17	4934	5
	fir	434	16	650	13	792	14	15	100	54	35	29	50	1974	8
	pine	15	71	55	35	11	100	0	.	65	40	7	100	152	24
	larch	0	.	34	45	14	71	338	22	248	21	222	21	856	12
	Arolla pine	0	.	0	.	0	.	14	71	35	45	0	.	49	38
	other conifers	8	100	47	47	0	.	0	.	0	.	0	.	54	43
	beech	271	19	553	13	338	18	0	.	27	50	121	31	1311	9
	maple	7	100	28	50	62	44	0	.	0	.	0	.	96	32
	ash	45	41	102	36	44	41	0	.	0	.	10	100	201	23
	oak	20	73	114	26	0	.	0	.	0	.	15	71	148	23
	sweet chestnut	0	.	0	.	36	82	0	.	0	.	83	30	119	32
	other broadleaves	14	70	48	49	26	50	0	.	12	100	8	100	107	30
	indeterminable	0	.	0	.	6	100	7	100	0	.	0	.	13	71
	total	1377	9	2383	7	3590	6	634	16	1157	10	875	10	10016	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	167	23	307	17	1037	11	83	36	264	20	218	22	2075	7
	fir	144	24	212	21	292	17	15	71	47	42	30	49	741	11
	pine	7	100	7	100	0	.	0	.	14	71	0	.	28	50
	larch	7	100	16	71	7	100	173	24	112	27	130	29	444	15
	Arolla pine	0	.	0	.	0	.	35	44	14	71	0	.	50	37
	other conifers	8	100	41	58	0	.	0	.	0	.	0	.	49	51
	beech	82	39	205	22	103	29	0	.	18	74	44	41	452	15
	maple	9	100	0	.	13	71	0	.	8	100	0	.	30	50
	ash	7	100	48	41	18	72	0	.	6	100	0	.	79	32
	oak	0	.	54	38	8	100	0	.	7	100	0	.	69	33
	sweet chestnut	0	.	0	.	10	100	0	.	0	.	92	37	101	34
	other broadleaves	0	.	21	58	6	100	0	.	0	.	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	432	15	911	10	1494	9	306	18	491	14	520	14	4153	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	54	94	28	296	22	61	53	122	26	110	27	719	13
	fir	51	38	77	32	62	37	7	100	7	100	8	100	212	19
	pine	0	.	7	100	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	53	42	61	39	74	37	189	23
	Arolla pine	0	.	0	.	0	.	21	58	7	100	7	100	36	45
	other conifers	8	100	7	100	0	.	0	.	0	.	0	.	15	71
	beech	0	.	64	36	21	58	7	100	0	.	22	58	115	26
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	24	58	0	.	0	.	0	.	0	.	24	58
	oak	0	.	43	41	0	.	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	57	42	65	39
	other broadleaves	0	.	14	71	0	.	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	95	30	329	16	385	18	159	27	204	20	279	18	1451	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	14	71	87	29	15	71	15	72	14	71	153	22
	fir	29	50	22	58	7	100	0	.	0	.	18	72	75	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	8	100	7	100	30	50
	Arolla pine	0	.	0	.	0	.	21	58	0	.	0	.	21	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	7	100	0	.	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	oak	0	.	16	100	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	37	52	37	52
	other broadleaves	0	.	30	81	0	.	0	.	0	.	0	.	30	81
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	45	95	36	102	27	50	38	23	58	76	34	383	15
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	21	58	36	45	7	100	30	50	19	72	119	25
	fir	0	.	14	71	14	71	0	.	0	.	0	.	28	50
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	8	100	22	74	29	61
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	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	.	57	45	57	45
	other broadleaves	0	.	37	100	0	.	0	.	0	.	0	.	37	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	72	55	50	38	14	71	44	41	97	34	285	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21908	8	32107	6	79674	4	19819	10	39119	7	13736	10	206363	3
	fir	17729	7	15318	8	19942	7	1086	30	1140	21	1783	24	56999	4
	pine	2143	19	2433	17	1956	31	6142	19	5613	18	485	48	18771	9
	larch	156	44	1132	33	1013	29	10085	11	6401	11	9577	11	28363	6
	Arolla pine	0	.	0	.	10	100	1821	23	3655	19	148	75	5633	14
	other conifers	756	28	1027	21	732	42	156	62	942	36	51	75	3664	15
	beech	25829	6	24254	5	25499	7	1668	33	2863	21	12835	13	92948	3
	maple	6239	10	5952	11	11494	8	828	29	1228	29	1468	26	27208	5
	ash	3126	13	8216	9	7353	11	1366	32	1069	40	2442	23	23572	6
	oak	2308	26	3916	16	579	30	1090	32	71	71	2667	24	10631	11
	sweet chestnut	17	100	26	78	334	93	111	84	0	.	16050	12	16538	12
	other broadleaves	4637	12	7856	9	12306	10	5424	15	5892	19	20105	10	56220	5
	indeterminable	193	37	99	39	651	18	138	34	218	33	229	39	1528	12
	total	85040	3	102336	3	161543	3	49735	7	68210	5	81575	4	548438	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. 1296720. Birmensdorf, Swiss Federal Research Institute WSL

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