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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NFI5
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 2018/26
		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	2783	10	2006	11	891	14	966	15	1775	12	477	18	8898	5
	fir	1690	9	799	17	352	22	89	36	18	39	64	36	3011	7
	pine	18	182	37	61	4	100	220	33	190	29	8	100	477	21
	larch	128	49	46	40	6	67	332	28	82	28	161	34	755	17
	Arolla pine	0	.	0	.	0	.	114	59	89	40	3	100	206	37
	other conifers	133	26	3	74	35	37	9	56	28	40	1	100	209	19
	beech	2596	7	816	16	573	19	108	33	58	31	894	15	5045	6
	maple	1006	13	391	17	187	24	120	30	49	36	109	38	1862	9
	ash	584	16	155	31	167	39	74	34	51	39	197	26	1228	11
	oak	227	28	15	79	2	100	61	35	3	100	93	43	401	20
	sweet chestnut	0	.	0	.	0	.	0	0	5	100	664	20	669	20
	other broadleaves	975	10	372	16	238	20	393	18	350	25	1026	14	3355	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	10140	4	4640	7	2455	10	2487	10	2697	9	3697	7	26115	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5121	9	3751	9	2585	13	2106	16	3417	11	1098	17	18078	4
	fir	3491	10	1787	14	779	23	165	36	32	50	105	36	6360	7
	pine	411	28	99	73	68	63	628	24	446	26	5	100	1657	14
	larch	178	40	70	63	60	71	610	19	217	27	690	23	1825	12
	Arolla pine	0	.	0	.	0	.	169	31	284	27	5	100	458	20
	other conifers	136	27	18	71	16	71	4	100	8	73	0	.	182	23
	beech	5062	7	1980	12	1287	15	274	38	178	36	1643	18	10424	5
	maple	1842	11	751	17	794	19	139	34	76	44	80	42	3681	8
	ash	862	16	465	30	420	25	78	42	90	90	300	35	2215	12
	oak	713	22	48	46	7	100	75	44	6	100	270	26	1119	16
	sweet chestnut	0	.	0	.	0	.	9	100	5	100	1376	16	1389	16
	other broadleaves	1109	12	360	18	333	31	329	21	317	32	1365	17	3814	8
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	18926	4	9329	6	6349	9	4586	10	5077	9	6937	7	51203	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6949	8	6707	8	4582	11	2814	13	5038	10	2097	19	28188	4
	fir	4318	10	2382	13	1193	18	312	34	133	36	357	36	8695	7
	pine	1016	18	180	47	140	47	547	22	519	28	42	106	2444	12
	larch	120	48	82	52	23	100	1150	20	780	18	986	21	3140	11
	Arolla pine	0	.	12	100	0	.	174	34	356	25	0	.	542	20
	other conifers	139	39	0	.	-25	100	0	0	0	.	0	.	114	52
	beech	8539	6	2318	12	2172	14	247	34	234	29	1407	16	14918	5
	maple	1850	11	895	18	861	18	34	60	79	46	94	62	3813	8
	ash	1841	12	677	34	529	24	121	50	44	57	102	39	3314	11
	oak	768	18	34	59	26	59	53	50	6	100	251	27	1138	14
	sweet chestnut	0	.	0	.	0	.	33	74	0	.	897	19	931	19
	other broadleaves	842	15	118	46	213	24	49	44	81	37	1094	19	2396	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	26382	4	13406	6	9713	8	5534	9	7268	8	7329	8	69632	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8602	7	9052	8	5156	11	3116	13	6491	9	2532	17	34950	4
	fir	6185	8	2693	11	1873	15	340	34	185	35	300	32	11577	6
	pine	1289	14	259	59	47	53	333	26	618	26	57	63	2603	12
	larch	258	32	70	84	13	100	1710	18	1269	16	1041	16	4361	9
	Arolla pine	0	.	0	.	0	.	126	36	287	26	0	.	413	21
	other conifers	175	39	0	.	0	.	0	.	0	.	16	100	191	36
	beech	9902	6	2667	13	2923	13	89	70	360	36	949	16	16890	5
	maple	1178	12	407	22	548	19	84	74	38	59	52	49	2307	9
	ash	1886	13	345	27	556	27	54	85	0	0	106	61	2946	11
	oak	1280	16	123	53	37	74	20	76	22	100	192	34	1674	14
	sweet chestnut	0	.	6	100	0	.	10	100	5	100	838	23	859	22
	other broadleaves	849	17	168	38	62	51	56	51	17	71	663	21	1815	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	31604	3	15788	6	11216	8	5937	10	9293	7	6746	7	80585	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7385	8	8086	8	5939	10	2703	13	5272	11	2375	19	31761	4
	fir	6582	8	4093	11	2320	18	432	40	320	33	593	29	14340	6
	pine	1117	18	119	55	93	71	145	42	498	28	27	100	1999	13
	larch	337	29	88	60	102	71	1950	16	1532	17	1049	16	5057	9
	Arolla pine	0	.	0	.	0	.	69	68	246	27	0	.	315	26
	other conifers	408	41	0	.	0	.	0	.	0	.	28	100	436	39
	beech	9028	7	2463	15	1660	17	71	82	175	47	686	21	14082	5
	maple	725	18	302	29	338	28	0	.	101	46	0	.	1465	13
	ash	1815	16	149	50	360	30	15	100	0	.	73	74	2412	13
	oak	1239	16	0	.	19	100	22	100	70	76	99	46	1450	15
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	537	27	545	27
	other broadleaves	557	29	166	64	145	39	54	60	20	100	79	51	1020	21
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	29193	3	15465	6	10974	8	5462	10	8243	8	5545	10	74883	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5544	10	5206	11	4437	13	1381	20	2966	14	1372	18	20906	5
	fir	4486	11	2982	15	1106	21	99	75	290	40	138	50	9101	8
	pine	325	34	81	71	41	100	17	100	219	35	0	.	684	22
	larch	167	45	23	100	27	100	1453	20	1343	20	669	22	3683	12
	Arolla pine	0	.	0	.	0	.	20	100	67	59	0	.	88	51
	other conifers	341	46	0	.	0	.	0	.	0	.	36	100	377	43
	beech	4299	10	843	27	846	26	67	74	167	42	475	30	6697	8
	maple	258	40	96	52	100	86	0	.	0	.	0	.	454	32
	ash	802	21	64	71	181	52	0	.	0	.	55	100	1103	19
	oak	655	29	0	0	0	.	0	.	32	100	34	100	721	27
	sweet chestnut	0	.	0	.	0	.	16	100	0	.	178	33	194	31
	other broadleaves	229	45	174	81	59	72	26	100	20	100	47	73	553	33
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	17106	5	9470	8	6799	11	3079	14	5103	11	3002	11	44559	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2736	15	2724	14	2809	17	917	30	1557	19	984	30	11727	7
	fir	2646	14	1802	19	1143	26	184	61	267	46	195	50	6238	10
	pine	202	45	0	.	0	.	0	.	62	71	0	.	264	38
	larch	41	100	62	71	135	58	718	28	505	27	404	29	1864	15
	Arolla pine	0	.	0	.	0	.	87	50	65	58	0	.	151	38
	other conifers	277	60	0	.	0	.	0	.	0	.	0	.	277	60
	beech	1746	18	648	29	224	72	30	100	118	70	170	41	2936	14
	maple	106	58	19	100	0	.	0	.	0	.	0	.	126	52
	ash	174	51	43	100	92	72	0	.	33	100	0	.	341	36
	oak	373	36	0	.	0	.	0	.	10	100	0	.	383	35
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	168	38	168	38
	other broadleaves	216	41	38	100	0	.	0	.	0	.	27	100	281	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8517	8	5336	10	4403	14	1934	19	2617	14	1948	17	24755	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1105	25	821	27	700	27	278	43	763	31	333	48	4000	13
	fir	1438	22	732	33	358	46	0	.	152	72	50	100	2731	16
	pine	49	100	0	.	0	.	0	.	0	.	0	.	49	100
	larch	0	.	0	.	0	.	398	42	303	44	270	51	972	26
	Arolla pine	0	.	0	.	0	.	46	100	70	71	0	.	116	58
	other conifers	296	52	81	100	0	.	0	.	0	.	0	.	376	46
	beech	217	50	44	100	162	59	0	0	0	.	171	45	593	29
	maple	0	.	0	.	27	100	0	.	0	.	0	.	27	100
	ash	142	71	0	.	140	71	0	.	0	.	0	.	282	50
	oak	215	51	0	.	83	71	0	.	0	.	0	.	298	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	153	46	153	46
	other broadleaves	70	100	0	.	0	.	0	.	0	.	0	.	70	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3531	14	1678	20	1469	20	723	29	1287	23	978	27	9667	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	295	45	338	41	210	51	124	61	317	41	154	71	1438	20
	fir	339	45	61	100	204	58	0	.	0	.	204	58	807	29
	pine	0	.	0	.	0	.	0	.	57	100	0	.	57	100
	larch	0	.	0	.	0	.	228	45	48	100	0	.	276	41
	Arolla pine	0	.	0	.	0	.	30	100	0	.	0	.	30	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	281	53	0	.	55	100	0	.	0	.	0	.	337	47
	maple	0	.	0	.	25	100	0	.	0	.	0	.	25	100
	ash	119	71	0	.	0	.	0	.	0	.	0	.	119	71
	oak	89	71	0	.	0	.	0	.	0	.	0	.	89	71
	sweet chestnut	0	.	0	.	0	.	29	100	0	.	145	59	174	52
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1124	24	399	38	494	34	412	32	422	35	502	36	3352	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	236	58	214	51	218	51	64	100	224	58	184	72	1141	25
	fir	174	72	133	100	0	.	0	.	0	.	0	.	307	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	52	100	129	100	182	77
	Arolla pine	0	.	0	.	0	.	39	100	36	100	0	.	75	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	194	100	0	.	0	.	0	.	0	.	0	.	194	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	374	60	374	60
	other broadleaves	66	100	0	.	0	.	0	.	0	.	0	.	66	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	670	41	347	50	218	51	103	73	312	46	687	42	2338	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	40757	5	38905	6	27528	8	14470	10	27821	7	11606	13	161087	3
	fir	31349	5	17464	8	9330	12	1621	26	1397	24	2006	24	63168	4
	pine	4427	11	775	42	393	41	1891	19	2609	18	138	56	10232	8
	larch	1230	18	440	34	365	37	8549	12	6131	12	5398	12	22114	6
	Arolla pine	0	.	12	100	0	.	874	24	1499	20	8	100	2393	15
	other conifers	1904	29	102	80	27	95	13	58	35	40	82	98	2163	26
	beech	41671	4	11778	9	9901	10	886	31	1290	27	6395	12	71921	3
	maple	6965	7	2862	11	2880	13	376	31	342	28	336	30	13760	5
	ash	8225	9	1898	22	2444	17	342	35	219	47	832	32	13959	7
	oak	5753	12	220	39	173	46	231	38	149	71	939	21	7465	10
	sweet chestnut	0	.	6	100	0	.	97	52	23	82	5330	13	5456	12
	other broadleaves	4913	9	1396	24	1050	17	906	17	805	26	4300	12	13369	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	147193	2	75857	4	54090	6	30257	7	42320	6	37372	5	387089	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. 1218049. Birmensdorf, Swiss Federal Research Institute WSL

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