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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NFI4
number of stems
development stage · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
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	41	100	39	70	669	46	25	100	239	42	139	72	1152	30
	fir	0	.	0	.	93	73	0	.	0	.	0	.	93	73
	pine	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	larch	0	.	8	100	0	.	85	92	35	53	15	100	143	57
	Arolla pine	0	.	0	.	0	.	42	100	0	0	0	.	42	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	45	86	47	61	70	59	0	.	7	100	0	.	169	38
	maple	27	100	0	.	54	63	0	.	0	.	0	.	81	53
	ash	0	.	0	.	17	100	0	.	0	.	0	.	17	100
	oak	0	.	8	100	0	.	0	.	0	.	0	.	8	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	19	100	20	100	45	63	0	.	0	.	0	.	84	47
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	131	58	121	45	948	40	152	81	282	40	154	71	1789	25
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	142	44	886	24	1772	20	238	39	597	37	87	85	3724	13
	fir	247	48	389	31	376	35	55	100	66	71	19	100	1152	20
	pine	23	100	59	100	7	100	0	0	113	68	0	.	201	49
	larch	0	.	55	54	43	72	203	48	22	75	19	100	342	32
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	20	100	15	100	69	100	0	.	0	.	104	71
	beech	294	37	1009	19	349	33	35	100	0	.	11	100	1697	15
	maple	71	61	259	34	541	33	17	100	17	100	8	100	913	22
	ash	89	54	243	31	177	43	55	100	0	.	0	.	564	23
	oak	7	100	25	75	0	.	0	0	0	.	0	.	31	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	other broadleaves	100	42	466	27	464	31	124	65	136	64	40	60	1330	17
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	.	0	0
	total	972	27	3410	14	3745	15	797	38	951	30	196	61	10070	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4105	27	12557	13	7247	19	1589	36	4188	26	1353	38	31039	9
	fir	1292	34	3481	23	1441	32	120	76	25	78	241	68	6600	16
	pine	278	60	539	54	155	90	1969	42	1817	35	25	100	4784	23
	larch	17	100	645	56	490	45	1059	34	236	37	1080	37	3527	20
	Arolla pine	0	.	0	.	0	.	178	91	130	88	0	.	308	64
	other conifers	149	52	339	34	17	100	17	100	447	47	51	75	1020	25
	beech	2949	25	3198	15	1936	30	74	100	35	100	2715	34	10908	13
	maple	1463	27	2321	23	1646	23	390	45	265	61	579	39	6664	13
	ash	752	33	1836	23	1332	27	387	48	70	71	957	31	5333	13
	oak	669	62	709	54	123	100	422	56	0	.	1108	43	3033	26
	sweet chestnut	0	.	0	0	15	100	10	100	0	.	5859	23	5884	23
	other broadleaves	663	40	2139	19	1847	24	2459	25	2676	34	7982	19	17767	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	12337	14	27765	9	16250	13	8675	18	9888	18	21951	12	96866	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3167	25	4931	17	8941	16	2288	45	4067	25	880	41	24275	10
	fir	1389	23	1945	28	1922	28	0	.	193	56	302	70	5751	15
	pine	635	41	376	43	153	73	1334	37	1110	42	0	.	3608	21
	larch	21	75	134	55	35	100	703	37	1022	33	940	35	2855	19
	Arolla pine	0	.	0	.	0	.	61	59	492	48	0	.	552	43
	other conifers	85	100	243	46	229	84	17	100	17	100	0	.	591	40
	beech	4745	16	3105	19	4273	21	404	61	440	67	4065	26	17033	10
	maple	702	22	993	24	2651	23	45	100	133	63	194	52	4719	15
	ash	481	30	1693	23	1463	28	54	75	184	74	650	61	4525	16
	oak	782	47	1045	30	141	42	155	49	0	.	416	32	2539	20
	sweet chestnut	0	.	7	100	0	.	55	100	0	.	1709	35	1771	34
	other broadleaves	698	34	1231	24	687	26	492	43	522	35	2622	25	6252	13
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	12705	13	15703	11	20496	12	5609	24	8180	19	11778	14	74471	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3421	15	4669	15	10050	13	2412	25	7243	15	2396	26	30190	7
	fir	3838	17	1832	18	3444	16	259	59	85	61	144	51	9602	10
	pine	477	29	641	22	232	63	331	48	455	39	14	100	2151	16
	larch	7	100	205	38	19	58	611	38	684	29	921	37	2447	19
	Arolla pine	0	.	0	.	0	.	0	.	372	36	0	.	372	36
	other conifers	174	74	106	46	36	71	0	.	0	.	0	.	316	44
	beech	7298	12	5933	12	4690	15	340	81	476	48	1154	38	19891	7
	maple	954	16	901	22	1309	17	18	100	33	71	18	100	3232	11
	ash	552	31	1545	21	1123	25	0	0	75	80	70	71	3365	14
	oak	377	30	546	25	0	.	18	100	35	100	76	54	1053	18
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	727	45	734	45
	other broadleaves	733	26	723	22	890	23	215	64	89	72	476	41	3126	13
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	17831	10	17101	9	21799	10	4204	23	9546	13	5996	17	76477	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2668	15	4774	11	12751	9	2117	22	5082	15	1949	22	29342	5
	fir	3704	14	4140	12	4761	12	94	94	308	38	245	81	13253	7
	pine	77	39	351	24	123	95	187	87	141	44	0	.	879	26
	larch	29	50	35	52	93	51	1193	25	1120	24	687	31	3157	14
	Arolla pine	0	.	0	.	0	.	192	49	169	53	0	.	361	36
	other conifers	76	60	159	45	18	100	0	.	0	.	0	.	253	34
	beech	3335	11	6964	9	5674	13	0	.	749	32	734	46	17456	6
	maple	855	19	662	23	1343	18	55	100	157	58	0	.	3072	11
	ash	387	32	1427	19	725	26	18	100	12	100	0	.	2570	14
	oak	74	41	731	25	6	100	0	.	0	.	8	100	819	22
	sweet chestnut	17	100	0	.	0	.	0	.	0	.	235	48	252	45
	other broadleaves	429	35	1221	27	644	27	55	74	39	71	448	38	2836	16
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	11651	10	20464	7	26138	7	3913	18	7777	12	4306	16	74250	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5737	14	1780	19	17839	10	7140	15	9356	14	4119	18	45971	6
	fir	5615	15	2620	19	5530	14	454	50	336	38	652	36	15207	8
	pine	238	34	201	45	679	71	1242	31	504	31	319	62	3184	21
	larch	21	100	38	68	199	80	3303	19	933	23	2236	23	6729	13
	Arolla pine	0	.	0	.	10	100	1092	30	1144	33	148	75	2394	21
	other conifers	219	38	143	63	167	60	18	100	121	77	0	.	667	28
	beech	5636	13	3053	17	6127	15	689	56	733	45	3226	21	19463	8
	maple	1715	20	625	31	2155	17	119	83	367	45	110	62	5090	11
	ash	667	29	1031	26	1109	23	380	85	524	64	310	41	4021	16
	oak	207	52	491	31	132	43	380	59	0	.	531	38	1741	21
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	3459	22	3467	22
	other broadleaves	1116	24	1145	26	2625	21	700	30	603	34	2711	18	8901	10
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	21171	9	11126	12	36571	8	15525	11	14622	12	17822	10	116835	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19281	8	29637	7	59269	5	15810	11	30773	7	10923	10	165693	3
	fir	16085	7	14408	8	17565	7	983	31	1013	21	1603	25	51657	4
	pine	1729	20	2167	18	1348	41	5064	21	4139	20	359	55	14807	10
	larch	94	37	1119	34	880	32	7156	12	4053	13	5898	14	19199	7
	Arolla pine	0	.	0	.	10	100	1566	24	2307	21	148	75	4030	15
	other conifers	702	28	1010	20	483	46	121	62	585	39	51	75	2952	15
	beech	24301	6	23309	5	23118	7	1541	35	2440	22	11906	13	86616	3
	maple	5786	10	5760	11	9698	9	646	33	972	27	909	28	23771	5
	ash	2928	13	7775	9	5946	11	895	42	865	43	1987	26	20396	6
	oak	2116	27	3554	16	403	37	976	34	35	100	2140	24	9224	11
	sweet chestnut	17	100	7	100	22	74	74	77	0	.	12000	13	12120	13
	other broadleaves	3758	13	6945	10	7203	11	4045	17	4065	23	14280	11	40295	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	76797	3	95690	3	125947	3	38875	7	51246	6	62203	4	450759	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. 1356660. Birmensdorf, Swiss Federal Research Institute WSL

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