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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NFI2
number of stems
development stage · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
development stage	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	.	24	100	330	35	55	53	179	51	9	100	597	25
	fir	0	.	0	.	21	62	0	.	0	.	0	0	21	62
	pine	0	.	0	.	4	100	10	100	0	.	0	.	14	77
	larch	0	.	0	.	0	0	8	71	4	100	21	73	33	51
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	16	100	110	55	0	.	0	.	45	72	170	41
	maple	15	100	0	.	6	100	0	.	0	.	0	.	21	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	54	100	54	100
	other broadleaves	0	0	0	.	13	100	20	100	0	.	0	.	33	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	15	100	40	72	485	28	93	49	183	50	129	57	944	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	257	30	563	30	1207	17	105	45	822	21	214	40	3168	11
	fir	249	43	143	35	253	33	60	86	54	52	39	79	798	20
	pine	60	71	99	41	0	.	10	100	114	61	0	.	284	32
	larch	4	100	78	51	77	76	207	41	202	31	193	37	760	19
	Arolla pine	0	.	0	.	0	.	48	74	0	.	0	.	48	74
	other conifers	18	100	58	85	51	82	0	.	41	100	0	.	168	47
	beech	101	34	337	33	399	31	35	76	24	62	119	40	1015	18
	maple	69	47	156	34	209	27	83	56	26	72	73	57	615	17
	ash	30	74	138	39	152	36	27	73	19	100	14	76	379	23
	oak	4	100	57	56	0	.	48	70	0	.	0	.	109	43
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	19	82	19	82
	other broadleaves	148	53	142	47	371	27	230	38	139	44	318	31	1348	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	940	20	1771	17	2719	13	853	25	1441	17	988	21	8712	7
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5489	16	18521	10	15141	10	1980	30	4165	20	2580	21	47876	6
	fir	1591	22	3008	17	2147	17	318	70	120	60	275	44	7458	10
	pine	385	37	862	27	778	42	3683	20	2300	24	19	71	8029	12
	larch	108	57	577	33	46	76	1492	22	923	26	1894	19	5039	12
	Arolla pine	0	.	0	.	122	83	429	42	389	52	0	.	940	31
	other conifers	299	56	479	39	190	52	21	71	317	44	138	97	1444	23
	beech	4916	15	3549	14	4757	16	361	51	394	54	6671	14	20648	7
	maple	937	21	1650	21	2253	16	168	35	373	45	630	25	6013	10
	ash	1084	23	2320	16	2109	20	354	34	275	40	1761	19	7902	9
	oak	1185	31	1868	26	280	41	373	33	18	81	1673	22	5397	14
	sweet chestnut	0	.	0	.	37	81	88	66	0	.	7347	14	7472	13
	other broadleaves	1236	22	2703	15	3827	15	2237	20	1857	20	11534	9	23394	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17231	9	35537	7	31688	7	11504	12	11131	12	34522	6	141613	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5072	12	6685	12	17946	9	3097	20	7339	12	947	31	41085	5
	fir	4067	12	2525	17	3817	13	136	55	191	39	212	62	10949	8
	pine	997	24	566	22	442	32	872	34	1322	25	149	84	4348	13
	larch	19	100	279	41	105	38	1500	21	1246	19	780	29	3929	12
	Arolla pine	0	.	0	.	4	100	336	57	722	33	0	.	1062	29
	other conifers	74	51	145	63	50	68	0	.	20	100	0	.	289	37
	beech	10617	8	6907	11	6265	12	525	48	504	45	1684	27	26502	6
	maple	2082	12	1123	17	1880	16	62	85	19	71	0	.	5167	8
	ash	1044	24	1775	14	967	19	30	100	68	72	31	75	3916	10
	oak	681	31	1060	23	302	49	91	46	35	100	178	54	2348	16
	sweet chestnut	0	.	54	100	19	100	0	.	0	.	1285	30	1358	29
	other broadleaves	1129	17	1424	20	1962	16	411	40	242	40	563	40	5731	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	25782	6	22543	7	33759	7	7063	14	11708	10	5830	14	106685	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6209	9	10985	7	15775	7	4160	14	8435	10	1847	19	47412	4
	fir	5284	9	4958	9	4633	11	270	49	203	34	382	32	15731	6
	pine	718	24	1070	17	279	45	460	31	699	30	135	84	3361	12
	larch	33	58	166	38	177	60	1676	20	1089	17	677	26	3818	11
	Arolla pine	0	.	0	.	0	.	309	38	632	28	0	.	940	23
	other conifers	97	44	99	45	10	100	0	.	0	.	0	.	206	30
	beech	7566	8	8628	8	6674	13	30	58	612	33	655	29	24164	5
	maple	1103	14	1168	17	1659	13	40	100	69	44	0	.	4039	8
	ash	821	21	1863	17	936	21	20	71	103	47	22	84	3764	11
	oak	483	19	1152	17	86	42	10	100	105	83	34	75	1871	12
	sweet chestnut	10	100	20	100	0	.	0	.	0	.	261	42	291	39
	other broadleaves	1215	16	1385	18	1426	20	218	42	231	35	231	39	4706	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	23539	6	31493	5	31657	6	7193	12	12176	9	4245	13	110303	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1825	13	5423	8	9800	9	1333	20	3938	12	1610	17	23930	5
	fir	1966	16	3249	11	3094	12	334	51	252	29	106	53	9001	7
	pine	124	49	428	18	35	91	94	55	249	50	38	67	969	18
	larch	24	85	93	32	87	40	743	22	640	23	473	21	2061	12
	Arolla pine	0	.	0	.	0	.	23	85	194	36	24	100	241	32
	other conifers	0	.	145	41	0	.	0	.	0	.	0	.	145	41
	beech	2271	13	6181	8	2770	13	126	100	502	32	549	41	12400	6
	maple	528	25	643	19	885	19	0	.	94	61	51	93	2201	12
	ash	73	43	681	20	275	32	0	.	4	100	10	100	1043	16
	oak	123	43	772	16	61	45	0	.	0	.	36	52	992	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	545	25	545	25
	other broadleaves	224	42	966	19	385	27	120	79	73	61	445	24	2213	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7159	10	18582	6	17393	7	2773	17	5947	11	3888	13	55741	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2799	14	753	19	12813	8	4950	13	6168	12	3055	16	30538	5
	fir	2026	16	1086	25	4781	11	150	36	332	34	314	41	8689	8
	pine	195	34	61	56	343	36	446	37	717	28	178	61	1940	16
	larch	0	.	37	71	44	66	2581	17	768	20	1865	16	5295	10
	Arolla pine	0	.	0	.	42	91	572	22	512	32	13	77	1140	18
	other conifers	99	57	98	49	111	50	10	100	184	71	0	.	502	32
	beech	1749	17	1387	19	4021	11	65	74	390	39	1562	20	9172	8
	maple	476	20	467	31	1416	15	53	64	112	55	78	50	2602	11
	ash	529	30	500	23	1079	21	134	50	255	81	181	38	2679	14
	oak	34	59	149	42	61	65	156	62	0	.	306	47	705	27
	sweet chestnut	0	.	0	.	101	100	0	.	0	.	1862	20	1962	20
	other broadleaves	620	26	640	27	2101	17	333	40	288	30	1826	17	5808	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8526	11	5177	14	26914	6	9449	11	9725	10	11241	9	71032	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21651	5	42953	5	73013	3	15680	8	31045	5	10263	8	194606	2
	fir	15183	5	14969	6	18747	5	1269	26	1150	16	1329	19	52647	3
	pine	2479	13	3087	11	1883	21	5575	15	5401	13	519	39	18945	7
	larch	189	37	1230	19	535	26	8207	9	4872	9	5903	9	20936	5
	Arolla pine	0	.	0	.	168	64	1717	18	2449	16	37	70	4371	12
	other conifers	587	32	1023	22	413	31	31	58	562	35	138	97	2754	14
	beech	27219	4	27004	4	24996	6	1142	30	2426	18	11285	10	94072	2
	maple	5209	7	5207	9	8309	7	407	26	693	27	832	21	20658	4
	ash	3581	12	7278	8	5519	10	564	25	724	34	2018	16	19683	5
	oak	2510	17	5057	12	790	25	678	24	159	60	2228	18	11422	8
	sweet chestnut	10	100	74	78	156	68	88	66	0	.	11373	9	11702	9
	other broadleaves	4573	9	7259	8	10085	8	3570	15	2830	14	14917	8	43235	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	83192	2	115143	2	144615	3	38928	5	52310	4	60843	3	495030	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. 1356624. Birmensdorf, Swiss Federal Research Institute WSL

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