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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NFI3
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	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	.
	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	3328	10	1770	9	1083	15	943	17	1530	11	311	17	8965	5
	fir	1532	11	755	15	338	24	56	33	41	31	51	41	2774	8
	pine	128	32	39	60	10	62	318	27	243	27	5	100	742	16
	larch	27	54	25	82	21	72	286	18	99	24	188	24	645	12
	Arolla pine	0	.	0	.	0	.	107	56	88	43	1	100	197	36
	other conifers	83	25	3	100	24	40	8	55	26	44	0	.	144	18
	beech	2680	7	1057	19	547	15	113	38	63	31	941	17	5400	6
	maple	819	13	333	19	235	21	99	30	37	41	65	34	1588	9
	ash	776	18	178	21	216	22	65	43	73	46	159	36	1467	11
	oak	346	24	14	44	5	100	70	37	2	100	172	32	609	17
	sweet chestnut	8	100	0	.	0	.	0	0	0	.	898	21	907	21
	other broadleaves	818	11	416	16	347	22	412	18	365	26	1173	15	3530	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	10546	4	4590	7	2827	9	2476	10	2566	9	3963	8	26969	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6906	8	4757	9	3278	15	2166	14	3305	10	997	18	21409	4
	fir	3433	10	1695	13	664	20	167	42	55	60	152	39	6167	7
	pine	512	24	47	73	112	54	570	21	486	27	56	62	1783	13
	larch	140	41	42	62	28	77	632	19	272	24	533	19	1647	11
	Arolla pine	0	.	0	.	0	.	184	34	286	24	3	100	472	19
	other conifers	176	29	6	100	-15	133	15	76	6	73	0	.	189	30
	beech	6952	6	1814	12	1666	16	307	35	213	30	1403	19	12355	5
	maple	1641	11	902	19	719	17	56	40	25	108	105	40	3448	8
	ash	1560	13	593	26	890	26	139	52	84	60	165	36	3431	10
	oak	1071	18	53	44	33	79	54	70	6	100	278	31	1495	15
	sweet chestnut	0	.	0	.	0	.	17	100	6	100	1339	17	1362	17
	other broadleaves	993	14	394	25	441	26	217	27	181	36	1356	15	3581	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	23385	4	10303	6	7816	9	4524	10	4924	8	6386	7	57339	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8731	7	7137	8	5303	11	2627	13	5117	10	1914	18	30829	4
	fir	4929	9	2217	11	1400	16	189	44	153	43	179	36	9067	6
	pine	1548	15	143	49	100	56	609	22	750	26	35	80	3185	11
	larch	227	30	14	129	17	73	1064	19	874	21	928	24	3124	11
	Arolla pine	0	.	12	100	0	.	157	31	287	28	0	.	456	21
	other conifers	127	51	0	.	0	.	0	.	0	.	0	.	127	51
	beech	10351	6	2470	15	2444	13	109	55	233	33	796	18	16403	5
	maple	1501	12	567	18	686	18	60	53	24	85	56	54	2894	8
	ash	2484	13	526	33	779	23	-10	180	41	51	86	50	3906	10
	oak	1161	17	51	58	46	55	20	78	0	.	120	42	1398	15
	sweet chestnut	0	.	0	.	0	.	19	70	5	100	783	20	807	20
	other broadleaves	1230	16	241	33	151	31	140	38	83	41	661	19	2506	10
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	.	0	0
	total	32289	3	13379	6	10927	7	4984	9	7565	8	5558	8	74702	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11800	6	9864	8	6085	10	3008	12	6463	10	2206	15	39426	4
	fir	7311	7	2892	11	2010	16	331	37	168	38	353	31	13064	5
	pine	2289	15	307	62	78	46	235	35	657	25	77	63	3643	12
	larch	285	29	91	62	0	0	1693	16	1401	15	1006	18	4477	9
	Arolla pine	0	.	0	0	0	.	112	39	253	25	0	.	365	21
	other conifers	255	41	0	.	0	.	0	.	0	.	16	100	271	39
	beech	10841	5	2615	14	2390	16	72	59	326	37	722	18	16966	5
	maple	1185	14	420	22	478	21	21	72	45	70	18	100	2167	10
	ash	2335	14	249	32	682	37	45	71	9	100	43	138	3362	12
	oak	1393	16	45	75	13	100	36	71	48	71	205	53	1741	15
	sweet chestnut	0	.	5	100	0	.	7	100	0	.	537	28	549	28
	other broadleaves	794	20	71	50	132	49	0	12132	55	53	310	24	1361	14
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	38488	3	16558	6	11866	8	5560	9	9426	8	5494	8	87392	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11489	7	9073	8	6284	10	2267	16	4818	11	1964	16	35895	4
	fir	6300	8	3481	12	1832	15	183	45	368	37	329	33	12494	6
	pine	1173	16	121	51	40	71	178	43	439	29	20	100	1970	13
	larch	424	39	169	55	177	48	1672	17	1710	16	917	17	5068	9
	Arolla pine	0	.	0	.	0	.	12	100	182	30	0	.	194	29
	other conifers	315	40	0	.	0	.	0	.	0	.	26	100	341	38
	beech	8292	7	1755	17	1496	17	111	60	246	38	615	21	12515	6
	maple	429	30	114	43	96	53	0	.	48	72	0	.	687	22
	ash	1666	16	132	49	294	43	0	.	0	.	0	.	2092	14
	oak	999	18	0	.	15	100	0	.	35	74	78	62	1126	17
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	315	30	323	29
	other broadleaves	261	42	299	61	283	61	77	64	18	100	11	204	950	29
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	31347	4	15145	6	10517	8	4499	11	7872	8	4274	9	73654	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6039	11	4526	11	3327	14	965	23	2614	15	1175	19	18647	6
	fir	3504	12	2189	17	1260	23	121	58	74	71	115	50	7263	9
	pine	302	32	44	100	36	100	0	.	237	36	0	.	619	23
	larch	94	58	88	71	55	71	1162	22	615	24	558	26	2572	13
	Arolla pine	0	.	0	.	0	.	17	100	77	50	0	.	94	45
	other conifers	392	49	0	.	0	.	0	.	0	.	0	.	392	49
	beech	2410	13	1078	24	583	33	0	.	57	71	249	31	4377	11
	maple	181	48	55	59	0	.	0	.	0	.	0	.	236	39
	ash	540	30	31	100	71	74	0	.	22	100	45	100	710	25
	oak	587	33	0	.	0	.	0	.	15	100	0	.	601	32
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	167	37	182	35
	other broadleaves	111	50	95	58	0	.	0	.	0	.	19	100	226	36
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	14161	6	8106	9	5333	11	2279	16	3712	12	2328	12	35919	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2217	16	2165	15	2392	18	597	31	1136	21	909	32	9416	8
	fir	1888	17	1044	22	431	37	33	100	240	45	145	57	3781	12
	pine	37	100	0	.	0	.	0	.	0	.	0	.	37	100
	larch	42	100	0	.	92	71	694	26	442	31	244	39	1514	17
	Arolla pine	0	.	0	.	0	.	116	47	67	60	0	.	183	37
	other conifers	51	100	69	100	0	.	0	.	0	.	0	.	120	72
	beech	1100	27	229	48	130	75	20	100	109	71	164	41	1752	20
	maple	0	.	0	.	22	100	0	.	0	.	0	.	22	100
	ash	268	47	31	100	111	71	0	.	0	.	0	.	410	37
	oak	290	38	0	.	64	71	0	.	0	.	0	.	354	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	95	49	95	49
	other broadleaves	57	71	0	.	0	.	0	.	0	.	21	100	78	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5950	10	3537	13	3242	15	1460	19	1995	16	1578	20	17762	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	619	32	575	31	714	44	281	49	479	34	366	39	3034	16
	fir	603	30	445	38	204	51	0	.	72	100	144	71	1468	20
	pine	44	100	0	.	0	.	0	.	51	100	0	.	95	71
	larch	0	.	0	.	0	.	416	36	171	52	113	64	700	27
	Arolla pine	0	.	0	.	0	.	30	100	27	100	0	.	57	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	274	46	179	62	176	60	37	100	0	.	21	100	686	30
	maple	0	.	0	.	20	100	0	.	0	.	0	.	20	100
	ash	82	100	0	.	0	.	0	.	0	.	0	.	82	100
	oak	218	46	0	.	0	.	0	.	0	.	0	.	218	46
	sweet chestnut	0	.	0	.	0	.	21	100	0	.	150	46	171	42
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1840	18	1200	22	1114	31	784	26	800	26	794	25	6532	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	139	59	312	46	0	0	152	59	91	100	694	30
	fir	193	58	0	.	0	.	0	.	0	.	133	71	326	45
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	92	71	47	100	0	.	139	58
	Arolla pine	0	.	0	.	0	.	37	100	0	.	0	.	37	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	257	59	0	.	0	.	0	.	0	.	0	.	257	59
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	58	100	0	.	0	.	0	.	0	.	0	.	58	100
	oak	128	100	0	.	0	.	0	.	0	.	0	.	128	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	150	60	150	60
	other broadleaves	169	79	0	.	0	.	0	.	0	.	0	.	169	79
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	805	34	139	59	312	46	129	58	199	51	374	42	1957	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	200	58	38	100	136	75	48	100	123	72	88	100	634	33
	fir	0	.	91	100	0	.	0	.	0	.	0	.	91	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	50	100	134	100	184	78
	Arolla pine	0	.	0	.	0	.	35	100	35	100	0	.	69	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	.	379	57	379	57
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	200	58	130	76	136	75	83	72	208	51	601	45	1357	26
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	51329	4	40045	6	28914	8	12900	10	25736	7	10022	11	168948	2
	fir	29694	6	14809	8	8138	11	1080	30	1173	25	1601	25	56495	4
	pine	6032	10	701	45	375	40	1911	18	2862	18	192	50	12074	8
	larch	1239	20	430	36	391	35	7710	12	5680	12	4621	13	20071	7
	Arolla pine	0	.	12	100	0	.	807	24	1302	20	4	100	2124	15
	other conifers	1399	29	79	89	9	201	23	67	32	42	42	100	1584	27
	beech	43157	4	11196	10	9432	11	769	30	1248	26	4909	13	70711	3
	maple	5755	7	2391	12	2256	13	237	29	179	37	244	32	11061	5
	ash	9770	9	1739	21	3043	20	240	38	229	45	498	34	15518	7
	oak	6192	11	163	41	176	41	180	42	106	71	853	22	7670	10
	sweet chestnut	8	100	5	100	0	.	78	52	19	100	4814	13	4925	13
	other broadleaves	4434	10	1516	21	1354	21	844	20	702	22	3550	11	12401	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	159010	2	73087	4	54090	6	26778	7	39268	6	31349	4	383583	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. 1217942. Birmensdorf, Swiss Federal Research Institute WSL

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