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	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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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
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 2004/07
		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	8654	10	14200	7	22784	5	6969	10	15018	8	4491	10	72116	3
	fir	6579	7	5348	9	6705	7	587	28	377	22	751	23	20347	4
	pine	357	35	351	42	803	29	2258	19	2719	17	59	62	6547	11
	larch	10	100	306	29	319	35	3251	13	1717	16	3053	12	8655	7
	Arolla pine	0	.	0	.	33	75	609	22	891	19	30	75	1562	14
	other conifers	415	30	494	34	604	29	46	64	709	31	20	71	2289	15
	beech	8629	6	9399	6	10063	7	769	30	1024	18	6799	10	36683	3
	maple	2946	11	3090	11	5061	9	486	24	406	28	922	20	12911	6
	ash	1442	15	3169	12	2622	12	455	28	537	28	1770	16	9996	6
	oak	860	29	1048	22	283	38	698	26	32	58	1205	24	4126	12
	sweet chestnut	40	100	39	100	84	71	0	0	0	.	5742	13	5905	13
	other broadleaves	3116	11	4264	10	6920	9	3555	15	3420	17	11480	8	32755	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	33048	4	41707	4	56282	3	19682	6	26850	6	36322	4	213891	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4909	8	8529	7	16814	5	4669	10	8528	7	2956	10	46404	3
	fir	3229	9	2829	9	4148	8	329	27	295	27	365	26	11195	5
	pine	532	22	483	25	497	31	1683	16	1245	17	137	44	4577	9
	larch	40	79	307	30	156	37	2146	13	1141	14	2044	12	5834	7
	Arolla pine	0	.	0	.	105	59	694	19	909	20	30	58	1738	13
	other conifers	211	38	288	26	121	46	13	100	91	40	10	100	733	18
	beech	7194	7	6236	6	6843	7	317	31	762	22	3430	11	24781	4
	maple	1683	10	1819	12	3277	8	109	51	224	30	335	26	7447	5
	ash	1138	16	1995	11	2373	14	160	44	282	36	733	24	6680	7
	oak	778	23	1142	18	288	33	171	36	31	75	775	21	3184	10
	sweet chestnut	0	.	19	100	11	100	20	100	0	.	3215	12	3266	12
	other broadleaves	994	14	1764	11	2040	11	853	17	673	26	4386	9	10710	5
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	20707	4	25410	4	36673	3	11163	7	14181	5	18415	4	126550	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3330	7	4750	7	11452	5	2886	9	5444	6	1844	11	29706	3
	fir	2450	8	1923	9	2557	7	160	33	174	26	247	31	7511	5
	pine	689	18	661	16	356	22	716	16	831	18	200	54	3453	8
	larch	36	48	186	25	59	51	1438	12	1092	13	1208	13	4020	7
	Arolla pine	0	.	0	.	43	80	335	20	432	21	10	100	820	14
	other conifers	19	71	122	42	10	100	0	.	0	.	0	.	151	36
	beech	5445	6	4135	7	3748	8	119	37	448	22	1296	12	15191	4
	maple	891	12	739	13	1024	11	46	55	97	37	151	31	2949	6
	ash	601	19	1455	11	886	17	115	56	97	45	205	24	3358	8
	oak	421	19	812	16	122	36	33	50	30	87	331	23	1749	10
	sweet chestnut	0	.	22	71	27	87	16	73	0	.	1159	13	1223	12
	other broadleaves	425	22	743	14	499	18	175	28	131	40	1250	12	3223	7
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	0
	total	14307	3	15549	4	20782	3	6038	7	8778	5	7901	5	73354	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2516	6	3511	6	7844	4	1491	9	3404	7	1199	11	19965	2
	fir	1792	7	1629	8	1990	7	113	28	129	21	150	24	5803	4
	pine	428	16	624	13	208	29	218	24	416	16	80	40	1975	8
	larch	24	53	166	26	88	38	1097	11	864	10	768	11	3007	6
	Arolla pine	0	.	0	.	9	71	150	24	246	18	0	.	405	14
	other conifers	0	.	81	36	0	.	0	.	0	.	4	100	85	35
	beech	2933	6	2788	6	2182	8	43	56	216	24	597	12	8758	3
	maple	363	15	299	14	376	12	8	71	26	47	17	80	1089	8
	ash	242	17	839	10	385	21	8	100	21	51	42	42	1537	8
	oak	153	21	553	13	61	36	11	100	28	65	131	31	938	10
	sweet chestnut	0	.	12	100	8	70	0	.	0	.	455	16	474	15
	other broadleaves	76	28	342	17	149	22	12	58	20	53	256	15	856	10
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	8528	3	10842	3	13299	3	3152	7	5370	5	3700	5	44892	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1638	7	2166	7	5043	5	662	11	1811	7	725	11	12045	3
	fir	1019	11	1300	8	1427	7	67	37	143	22	105	26	4062	5
	pine	164	24	286	15	50	36	102	30	188	20	16	61	806	10
	larch	8	71	115	26	83	30	750	11	595	11	559	10	2109	6
	Arolla pine	0	.	0	.	0	0	33	36	99	24	4	100	136	20
	other conifers	4	100	48	39	0	.	0	.	0	.	4	100	57	35
	beech	1067	8	1548	7	1098	10	14	72	93	26	240	16	4060	4
	maple	67	25	85	28	109	23	0	.	15	61	4	100	281	14
	ash	88	23	345	14	79	26	4	100	19	51	16	60	551	11
	oak	76	36	356	14	26	41	4	100	12	58	51	38	526	12
	sweet chestnut	0	.	4	100	10	100	0	.	0	.	271	17	285	17
	other broadleaves	32	35	100	29	146	35	39	44	10	72	53	35	381	17
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	4165	4	6354	4	8073	4	1674	8	2987	5	2048	6	25300	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	545	12	898	9	1777	6	287	21	712	10	308	15	4529	4
	fir	410	12	486	11	600	11	51	36	31	35	39	34	1617	6
	pine	35	43	79	25	14	59	8	71	58	31	4	100	198	16
	larch	0	.	39	34	47	47	315	15	153	18	194	16	748	9
	Arolla pine	0	.	0	.	12	58	23	41	55	32	0	.	90	23
	other conifers	4	100	41	50	0	.	0	.	0	.	0	.	45	46
	beech	234	19	502	11	316	15	4	100	8	71	97	22	1160	8
	maple	10	72	17	50	25	47	0	.	0	.	0	.	51	31
	ash	34	39	95	26	25	46	0	.	4	100	6	100	164	19
	oak	41	50	143	24	12	71	0	.	8	71	15	72	218	20
	sweet chestnut	0	.	0	.	9	71	0	.	0	.	89	25	98	24
	other broadleaves	12	58	19	51	16	50	0	.	0	.	12	58	59	27
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	1325	7	2318	6	2853	5	689	12	1028	8	764	8	8976	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	178	18	264	14	856	9	91	23	292	14	191	18	1871	6
	fir	166	19	178	16	235	14	8	71	35	36	25	40	646	9
	pine	0	.	8	71	0	.	0	.	0	.	0	.	8	71
	larch	4	100	9	71	8	71	139	22	65	28	108	21	333	13
	Arolla pine	0	.	0	.	0	.	27	43	20	45	4	100	50	30
	other conifers	4	100	13	58	0	.	0	.	0	.	0	.	17	50
	beech	56	28	194	18	45	33	0	.	16	60	32	35	343	13
	maple	0	.	6	100	8	71	0	.	5	100	0	.	19	51
	ash	0	.	44	34	4	100	0	.	0	.	0	.	48	32
	oak	4	100	79	28	19	50	0	.	0	.	0	.	102	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	84	27	84	27
	other broadleaves	0	.	19	51	4	100	0	.	0	.	8	71	31	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	411	12	813	8	1180	7	265	16	432	11	451	11	3551	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	82	46	52	30	231	17	43	43	67	27	76	25	552	12
	fir	65	28	49	29	46	30	0	.	12	58	15	58	187	16
	pine	0	.	4	100	0	.	0	.	4	100	0	.	8	71
	larch	0	.	0	.	0	.	70	25	28	43	54	36	151	19
	Arolla pine	0	.	0	.	0	.	8	71	12	58	0	.	20	45
	other conifers	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	beech	4	100	61	34	25	41	4	100	0	.	4	100	97	25
	maple	0	.	0	.	4	100	0	.	0	.	4	100	8	71
	ash	0	.	10	72	0	.	0	.	0	.	0	.	10	72
	oak	0	.	26	42	0	.	0	.	0	.	0	.	26	42
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	57	32	62	30
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	151	29	206	16	306	14	129	21	123	20	210	16	1124	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	71	12	58	87	25	4	100	32	35	27	41	170	17
	fir	4	100	17	50	8	71	0	.	0	.	13	59	43	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	8	71	9	72	33	36
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	22	45	0	.	0	.	0	.	8	71	31	38
	maple	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	ash	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	oak	0	.	16	63	0	.	0	.	0	.	0	.	16	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	20	59	20	59
	other broadleaves	0	.	21	64	4	100	0	.	0	.	4	100	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12	58	94	25	103	23	23	41	45	30	82	25	358	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	13	58	23	41	4	100	17	50	21	53	82	24
	fir	0	.	0	.	19	51	0	.	4	100	4	100	27	42
	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	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	19	100	19	100
	maple	0	.	0	.	12	75	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	70	38	70	38
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	9	71	16	50	54	30	16	50	33	35	133	27	262	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21865	6	34395	5	66913	4	17106	8	35325	5	11837	8	187441	2
	fir	15714	5	13760	6	17735	5	1313	24	1202	16	1714	18	51437	3
	pine	2205	14	2495	11	1928	21	4985	14	5461	13	497	39	17570	7
	larch	121	36	1128	17	761	24	9225	9	5670	9	8017	8	24922	5
	Arolla pine	0	.	0	.	202	58	1891	16	2672	15	77	64	4843	11
	other conifers	657	28	1090	22	736	30	59	70	800	30	39	62	3381	13
	beech	25562	4	24885	4	24319	6	1269	28	2567	16	12521	9	91123	3
	maple	5959	7	6055	8	9898	7	649	22	774	24	1434	18	24770	4
	ash	3545	11	7956	7	6374	10	741	31	959	26	2772	15	22346	5
	oak	2333	18	4179	11	810	26	918	24	142	51	2507	18	10889	8
	sweet chestnut	40	100	95	90	149	69	40	77	0	.	11161	10	11487	9
	other broadleaves	4661	9	7273	8	9778	8	4635	14	4254	17	17449	7	48050	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	82663	3	103309	2	139603	3	42832	5	59827	4	70025	3	498258	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. 1326992. Birmensdorf, Swiss Federal Research Institute WSL

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