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
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1468	15	3485	11	3637	8	818	14	1381	13	305	17	11094	5
	fir	926	12	888	15	1179	11	29	45	39	28	67	38	3128	7
	pine	64	60	161	42	46	62	450	29	266	27	18	71	1005	17
	larch	3	100	32	44	21	64	273	22	73	26	199	24	600	13
	Arolla pine	0	.	0	.	0	.	62	53	79	33	3	76	144	29
	other conifers	38	32	34	44	24	38	3	58	8	57	17	100	124	22
	beech	1817	10	1794	9	1464	12	55	42	179	37	933	19	6242	6
	maple	371	14	302	25	640	16	23	88	54	47	36	32	1427	10
	ash	241	22	494	22	491	17	64	36	71	65	141	27	1501	10
	oak	105	42	409	27	40	35	46	37	0	.	208	42	809	19
	sweet chestnut	1	100	0	.	0	.	2	100	0	.	1034	17	1038	17
	other broadleaves	349	18	555	19	749	13	277	25	269	22	1050	12	3250	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5386	6	8156	6	8290	5	2104	11	2417	10	4010	7	30362	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2726	11	5199	10	9237	7	1835	13	3622	11	1060	18	23679	4
	fir	2179	12	2349	13	2039	11	77	51	106	37	95	45	6844	7
	pine	276	36	205	27	190	41	452	24	499	23	48	61	1670	13
	larch	17	72	185	35	19	73	733	20	493	24	581	20	2028	11
	Arolla pine	0	.	0	.	0	.	111	37	269	31	6	100	387	24
	other conifers	18	59	121	44	-14	168	0	.	2	100	0	.	127	46
	beech	4392	9	3765	9	3317	11	130	44	284	28	1010	19	12898	5
	maple	895	14	751	16	1420	14	-10	275	99	56	65	42	3221	8
	ash	642	22	1237	12	1045	19	28	60	96	46	100	37	3148	9
	oak	206	26	843	20	94	49	25	68	34	100	213	32	1416	14
	sweet chestnut	0	.	0	.	0	.	36	72	0	.	1162	17	1198	17
	other broadleaves	301	21	916	18	508	16	139	30	146	36	1045	14	3056	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11653	5	15572	5	17855	5	3556	10	5651	8	5385	6	59671	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4515	10	9936	8	14199	6	2557	14	5647	10	1736	16	38591	4
	fir	3398	11	3853	11	3480	10	151	62	148	36	177	37	11207	6
	pine	798	21	1255	18	200	30	526	21	654	24	93	60	3526	10
	larch	104	52	232	41	63	64	1084	18	767	18	742	21	2992	10
	Arolla pine	0	.	0	.	14	71	143	30	353	27	0	.	510	20
	other conifers	0	.	135	48	0	.	0	.	0	.	12	100	147	45
	beech	6235	8	5695	8	3976	11	148	63	421	31	741	18	17217	5
	maple	909	15	532	20	1052	14	30	100	113	43	17	100	2654	9
	ash	885	24	1877	17	687	21	24	100	47	53	28	73	3549	12
	oak	274	27	1054	18	88	95	13	100	6	100	174	40	1609	15
	sweet chestnut	0	.	0	.	0	.	29	72	0	.	574	24	603	23
	other broadleaves	316	25	831	19	387	24	75	50	104	44	533	21	2245	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17435	5	25400	4	24145	5	4780	10	8260	8	4828	8	84848	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5590	9	13102	7	16938	6	2189	13	6204	9	2046	16	46069	3
	fir	3490	10	4655	9	5388	10	140	49	322	30	457	32	14451	5
	pine	600	21	1742	15	397	49	272	38	665	25	30	57	3706	11
	larch	74	52	267	31	159	41	1639	16	1363	15	824	19	4327	9
	Arolla pine	0	.	0	.	0	.	168	40	252	26	0	.	420	22
	other conifers	0	.	197	41	0	.	0	.	0	.	0	.	197	41
	beech	4906	9	6389	8	3970	12	76	59	462	30	516	22	16319	5
	maple	421	21	316	26	555	19	0	.	45	62	16	100	1354	12
	ash	384	25	1759	16	412	22	12	100	23	72	-43	90	2547	12
	oak	179	39	1117	17	9	100	0	.	42	100	181	55	1528	15
	sweet chestnut	0	.	0	.	-5	100	0	.	0	.	410	25	406	25
	other broadleaves	119	42	392	22	270	41	19	124	27	77	147	25	975	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	15764	5	29937	4	28094	5	4514	10	9406	7	4582	9	92298	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4976	10	9930	8	13182	7	1351	17	4587	11	1765	15	35792	4
	fir	2214	13	3949	12	4097	11	165	57	352	34	130	47	10908	7
	pine	281	30	880	21	119	65	134	38	442	35	36	71	1891	14
	larch	19	100	243	38	310	42	1381	17	1377	18	748	20	4078	10
	Arolla pine	0	.	0	.	0	0	29	75	200	35	0	.	230	32
	other conifers	60	100	164	63	0	.	0	.	0	.	0	.	224	54
	beech	2490	13	4000	11	2625	16	16	100	280	40	325	36	9736	7
	maple	100	44	291	32	118	48	0	.	29	71	0	.	538	22
	ash	111	50	620	24	243	36	0	.	0	.	39	100	1014	18
	oak	87	60	985	21	13	100	0	.	0	.	17	100	1102	19
	sweet chestnut	0	.	0	.	11	100	0	.	0	.	267	30	278	29
	other broadleaves	94	64	78	59	275	37	34	100	0	.	13	100	494	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10432	6	21140	5	20993	5	3111	12	7267	9	3340	10	66283	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1652	18	4478	11	6791	9	718	30	1996	16	1086	23	16721	6
	fir	1010	21	1338	21	2032	16	60	71	214	46	151	51	4806	10
	pine	103	59	189	41	43	100	0	.	97	50	0	.	433	27
	larch	0	.	155	52	114	58	707	22	516	26	460	25	1954	13
	Arolla pine	0	.	0	.	0	.	40	71	49	58	0	.	88	45
	other conifers	36	100	100	77	0	.	0	.	0	.	0	.	137	63
	beech	664	28	1718	19	727	24	0	.	81	74	224	37	3414	13
	maple	59	73	0	.	36	71	0	.	0	.	0	.	95	53
	ash	51	71	364	35	31	100	0	.	43	100	0	.	489	29
	oak	26	100	260	39	0	.	0	.	0	.	0	.	286	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	121	36	121	36
	other broadleaves	0	.	142	52	49	71	0	.	0	.	26	100	217	40
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3602	12	8745	8	9823	8	1525	19	2995	12	2069	14	28760	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	599	29	1057	22	2325	15	313	38	996	21	736	30	6027	9
	fir	568	32	898	27	656	27	0	.	164	59	52	100	2338	16
	pine	0	.	40	100	0	.	0	.	65	72	0	.	106	59
	larch	38	100	44	100	34	100	462	34	361	32	282	39	1221	19
	Arolla pine	0	.	0	.	0	.	69	50	43	71	29	100	141	39
	other conifers	0	.	59	100	0	.	0	.	0	.	0	.	59	100
	beech	247	42	1117	26	106	64	0	.	0	.	66	58	1537	21
	maple	0	.	0	.	19	100	0	.	34	100	0	.	52	73
	ash	0	.	90	71	0	.	0	.	0	.	0	.	90	71
	oak	0	.	254	41	25	100	0	.	0	.	0	.	279	39
	sweet chestnut	0	.	0	.	0	.	27	100	0	.	197	36	224	34
	other broadleaves	0	.	35	100	0	.	0	.	0	.	20	100	55	73
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1453	19	3594	13	3166	12	871	24	1662	17	1382	20	12128	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	164	58	489	33	514	28	113	58	219	42	27	100	1527	17
	fir	109	71	267	45	522	36	0	.	0	.	124	72	1021	25
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	108	58	73	71	77	71	258	38
	Arolla pine	0	.	0	.	0	.	24	100	0	.	0	.	24	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	254	63	115	58	39	100	20	100	0	.	428	42
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	109	71	0	.	0	.	0	.	0	.	109	71
	oak	0	.	185	63	0	.	0	.	0	.	0	.	185	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	226	46	226	46
	other broadleaves	0	.	148	72	0	.	0	.	0	.	0	.	148	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	273	45	1453	21	1150	21	284	39	312	34	454	33	3926	12
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	57	100	233	58	359	39	48	100	61	100	85	100	844	27
	fir	57	100	0	.	142	71	0	.	0	.	0	.	198	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	39	100	0	.	108	71	147	59
	Arolla pine	0	.	0	.	0	.	35	100	0	.	0	.	35	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	228	58	0	.	0	.	0	.	0	.	228	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	60	100	0	.	0	.	0	.	0	.	60	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	121	53	121	53
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	114	71	521	38	501	34	123	58	61	100	313	42	1633	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	99	71	0	.	134	72	71	100	304	46
	fir	0	.	0	.	0	.	0	.	0	.	96	100	96	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	120	71	120	71
	Arolla pine	0	.	0	.	0	.	37	100	33	100	0	.	70	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	485	49	485	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	99	71	37	100	167	61	772	36	1075	29
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21748	6	47910	5	67281	4	9944	10	24846	7	8917	12	180646	2
	fir	13952	8	18198	8	19535	7	621	38	1343	24	1348	28	54997	4
	pine	2123	16	4473	13	995	35	1833	19	2688	17	224	43	12336	8
	larch	255	37	1159	23	720	27	6427	12	5022	12	4141	13	17724	6
	Arolla pine	0	.	0	.	14	71	718	23	1279	21	39	97	2050	15
	other conifers	152	65	809	33	10	220	3	58	10	58	29	72	1013	28
	beech	20751	6	24960	6	16300	8	465	37	1727	23	3816	13	68018	3
	maple	2756	10	2194	12	3840	10	44	60	375	37	133	34	9341	6
	ash	2315	18	6552	11	2909	14	127	44	279	43	265	29	12447	7
	oak	878	20	5166	11	269	38	84	42	83	71	793	24	7274	9
	sweet chestnut	1	100	0	.	6	100	94	60	0	.	4597	13	4698	13
	other broadleaves	1179	16	3098	13	2238	13	544	22	546	22	2834	11	10440	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	66112	3	114519	3	114116	3	20904	7	38198	6	27135	5	380985	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. 1217901. Birmensdorf, Swiss Federal Research Institute WSL

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