Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

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

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI3
number of stems
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 2004/07
		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	31	100	224	46	1235	34	7	100	94	64	76	91	1668	26
	fir	42	100	86	58	235	51	0	.	0	.	0	0	363	38
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	7	100	0	0	7	100	30	71	43	53
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	24	100	267	50	357	58	0	.	7	100	71	100	726	36
	maple	0	.	0	.	61	47	0	.	0	.	0	.	61	47
	ash	0	.	41	85	33	71	0	.	0	.	0	.	74	57
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	19	100	34	100	69	59	0	.	0	.	0	.	122	46
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0
	total	117	85	653	36	1996	28	7	100	108	57	177	71	3058	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	131	38	703	33	895	28	145	48	553	36	73	100	2500	16
	fir	186	44	264	46	203	39	0	.	69	71	0	.	723	24
	pine	23	100	80	46	0	.	381	100	58	100	0	.	541	72
	larch	0	.	20	72	0	.	240	44	58	50	33	72	350	32
	Arolla pine	0	.	0	.	0	.	67	78	17	100	0	.	84	66
	other conifers	0	.	0	.	0	.	65	100	0	.	0	.	65	100
	beech	354	32	969	22	218	33	22	100	0	.	196	87	1760	17
	maple	117	46	146	46	132	38	0	.	0	.	18	100	413	25
	ash	57	75	182	45	125	49	0	.	0	.	0	.	365	31
	oak	17	100	69	41	0	.	106	100	0	.	18	100	210	54
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	291	60	291	60
	other broadleaves	150	43	325	36	179	49	35	71	83	72	99	84	872	22
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	1036	26	2759	18	1753	21	1059	44	838	29	729	40	8173	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4676	28	12540	14	8006	18	2583	42	4952	24	866	43	33623	9
	fir	972	37	2601	26	855	37	112	80	83	69	302	56	4926	17
	pine	154	87	765	41	121	100	1696	40	1363	39	0	.	4100	23
	larch	38	71	381	42	500	42	1360	32	452	49	1478	35	4208	18
	Arolla pine	0	.	0	.	0	.	141	100	113	86	0	.	253	67
	other conifers	170	45	340	37	17	100	0	.	328	56	51	75	906	26
	beech	2328	31	2571	18	1846	33	11	100	37	100	3466	30	10259	15
	maple	782	30	2355	22	1427	25	179	44	159	66	459	38	5360	13
	ash	507	42	1744	24	1446	28	281	62	104	58	629	39	4713	14
	oak	786	56	671	44	128	85	495	50	0	.	1134	47	3213	25
	sweet chestnut	0	.	0	.	15	100	11	100	0	.	5226	25	5252	25
	other broadleaves	673	45	1429	26	2309	24	2476	28	2453	34	6941	20	16281	12
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	11085	17	25397	10	16671	13	9345	19	10044	17	20553	12	93094	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4753	21	5171	17	8627	19	2463	28	2627	32	1315	37	24956	10
	fir	2130	24	1736	28	1442	25	344	65	210	64	90	82	5952	14
	pine	625	41	399	41	36	100	1351	36	981	48	0	.	3393	22
	larch	14	100	179	53	35	100	1567	26	505	48	506	41	2806	19
	Arolla pine	0	.	0	.	0	.	254	67	58	100	0	.	311	58
	other conifers	34	100	156	45	40	71	17	100	0	.	0	.	247	34
	beech	6740	15	3997	17	4614	20	466	67	518	69	2731	30	19066	9
	maple	714	29	697	26	1885	23	64	77	126	64	94	54	3580	14
	ash	840	35	1638	22	1991	34	92	72	175	77	879	45	5615	17
	oak	121	45	1426	37	81	50	92	54	0	.	632	36	2353	24
	sweet chestnut	0	.	0	.	0	.	55	100	0	.	2949	27	3004	27
	other broadleaves	917	32	1403	25	1310	36	584	44	438	41	3824	22	8476	13
	indeterminable	0	.	0	0	0	0	0	.	0	0	0	0	0	0
	total	16888	12	16801	11	20061	14	7349	18	5639	24	13020	14	79759	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6210	13	5858	14	12392	11	3678	22	8150	15	2377	24	38666	6
	fir	5453	14	2736	17	3233	16	194	74	180	45	332	43	12128	9
	pine	710	25	704	25	344	49	288	44	1089	36	50	100	3185	16
	larch	24	77	173	37	87	86	663	31	880	29	1470	32	3297	18
	Arolla pine	0	.	0	.	0	.	60	71	308	48	0	.	368	42
	other conifers	213	50	87	52	0	.	17	100	0	0	0	.	317	37
	beech	9692	10	6829	12	5022	15	77	100	621	41	1092	37	23333	7
	maple	1900	13	1049	23	2087	17	96	100	215	78	48	100	5395	10
	ash	1128	26	1595	20	1239	24	0	0	114	72	113	51	4189	13
	oak	676	39	541	33	28	78	0	0	43	100	144	45	1433	23
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	759	36	766	35
	other broadleaves	1238	20	831	25	1525	22	416	51	127	51	757	33	4893	12
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	27244	8	20404	9	25965	9	5489	19	11727	13	7142	15	97971	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2937	16	6995	11	15162	9	2069	22	7185	14	2658	20	37005	5
	fir	3148	15	5449	12	6737	11	221	75	312	38	458	52	16325	7
	pine	101	37	629	21	238	69	177	87	211	40	42	100	1398	20
	larch	30	62	70	45	121	42	1313	25	1211	22	837	32	3582	14
	Arolla pine	0	.	0	.	0	.	227	44	310	41	0	.	537	30
	other conifers	58	58	127	51	18	100	0	.	0	.	0	.	204	37
	beech	3322	12	8743	10	7923	11	0	.	702	31	731	39	21419	6
	maple	1219	22	773	23	1413	16	161	74	206	46	16	100	3788	11
	ash	476	29	1670	17	691	29	18	100	68	84	0	.	2924	13
	oak	158	32	1191	20	54	52	0	.	28	100	26	76	1456	17
	sweet chestnut	66	100	0	.	0	.	0	.	0	.	596	41	662	38
	other broadleaves	741	28	1772	24	852	23	126	55	241	56	360	41	4091	14
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	.	0	0
	total	12256	10	27419	7	33207	7	4312	17	10473	11	5725	15	93392	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2691	17	1012	28	14895	10	4640	18	7000	15	3174	22	33411	7
	fir	3562	18	939	33	4280	17	87	57	279	47	322	63	9470	11
	pine	291	51	300	50	639	70	1215	32	848	34	311	59	3603	20
	larch	7	100	17	100	23	100	2056	27	971	23	1782	24	4856	15
	Arolla pine	0	.	0	.	10	100	740	38	1493	28	113	77	2356	22
	other conifers	103	53	244	55	323	60	0	.	190	65	0	.	860	31
	beech	2668	19	1899	27	4049	20	845	48	692	47	3178	23	13332	11
	maple	643	24	577	30	2164	26	60	73	126	48	115	67	3685	17
	ash	196	48	710	38	803	30	366	88	487	72	139	50	2701	22
	oak	301	50	434	46	150	46	104	74	0	.	428	44	1417	23
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	2254	27	2262	26
	other broadleaves	432	35	645	32	1864	20	618	38	384	32	1960	21	5903	11
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	0	0	0
	total	10894	12	6777	18	29199	9	10740	14	12470	12	13776	12	83857	5
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21428	8	32504	6	61212	5	15585	11	30561	7	10540	11	171831	3
	fir	15493	7	13812	8	16984	7	958	34	1134	22	1505	25	49886	4
	pine	1904	19	2877	15	1378	38	5107	20	4551	19	403	49	16220	9
	larch	113	36	840	24	773	30	7199	12	4083	13	6136	14	19143	7
	Arolla pine	0	.	0	.	10	100	1488	25	2299	21	113	77	3910	16
	other conifers	579	26	955	22	398	49	100	70	518	43	51	75	2600	15
	beech	25128	6	25275	6	24029	7	1420	36	2577	23	11465	13	89894	3
	maple	5375	9	5596	11	9169	10	560	33	832	29	750	27	22282	5
	ash	3206	15	7580	10	6328	14	757	49	948	41	1761	27	20580	7
	oak	2060	26	4331	16	441	32	798	35	71	72	2382	26	10082	11
	sweet chestnut	66	100	0	.	22	74	74	77	0	.	12075	13	12238	13
	other broadleaves	4169	13	6440	11	8108	11	4254	19	3726	23	13942	11	40639	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	79520	4	100210	3	128852	3	38302	7	51299	6	61122	4	459304	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. 1356634. Birmensdorf, Swiss Federal Research Institute WSL

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