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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NFI4
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2965	9	2109	9	1134	13	980	16	1860	10	532	15	9582	4
	fir	1751	10	863	13	370	23	96	52	33	36	56	41	3169	7
	pine	83	54	18	186	26	46	362	29	257	24	7	100	751	18
	larch	49	63	46	44	25	52	364	18	162	23	265	22	911	11
	Arolla pine	0	.	0	.	0	.	91	46	185	31	2	100	277	26
	other conifers	69	42	12	82	11	152	11	56	35	36	0	.	138	27
	beech	2387	7	805	15	633	15	197	44	67	38	929	15	5019	6
	maple	931	13	430	17	259	22	140	26	54	37	126	28	1941	8
	ash	702	16	183	23	275	21	173	48	85	44	214	31	1633	11
	oak	235	26	19	78	10	64	62	32	2	100	150	34	479	17
	sweet chestnut	2	100	0	.	0	.	19	100	0	.	836	19	857	18
	other broadleaves	867	10	496	15	419	24	447	17	527	23	1298	14	4054	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	10041	4	4980	6	3163	9	2943	9	3270	8	4416	7	28812	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6436	8	4835	9	3149	12	2233	15	3497	10	1229	16	21379	4
	fir	2842	10	1527	13	667	19	212	36	74	41	158	41	5480	7
	pine	477	26	83	60	125	46	595	23	369	28	45	61	1692	13
	larch	113	47	54	61	52	59	751	17	401	23	818	17	2188	10
	Arolla pine	0	.	0	.	0	.	233	29	294	24	3	100	531	19
	other conifers	163	31	34	70	6	100	4	100	10	59	0	.	217	26
	beech	6268	7	1950	12	1552	13	320	33	175	35	1641	17	11907	5
	maple	1689	11	958	17	742	19	106	34	133	56	127	39	3755	8
	ash	1375	13	553	25	791	20	175	34	79	49	251	29	3224	9
	oak	872	19	65	38	10	100	78	40	6	100	259	26	1289	14
	sweet chestnut	0	.	0	.	0	.	23	75	5	100	1454	16	1481	16
	other broadleaves	984	13	421	22	366	26	349	23	263	30	1592	14	3975	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	21218	4	10481	6	7461	8	5078	9	5305	8	7576	6	57120	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8765	7	7972	8	5325	10	3103	12	5605	9	2126	16	32896	4
	fir	4752	9	2449	11	1454	16	205	39	110	39	254	35	9224	6
	pine	1289	16	130	48	128	42	659	21	688	28	39	67	2932	11
	larch	266	29	41	71	86	62	1300	16	879	19	1160	17	3732	9
	Arolla pine	0	.	0	.	0	.	178	31	364	23	0	.	542	19
	other conifers	151	42	6	100	-19	100	0	.	0	.	0	.	137	48
	beech	9274	6	2515	13	2192	13	148	36	265	31	996	16	15389	5
	maple	1399	12	778	17	851	16	27	95	89	67	64	53	3207	8
	ash	2343	11	709	28	661	21	35	77	42	63	95	40	3885	9
	oak	1024	15	31	59	37	61	26	63	6	100	231	29	1355	13
	sweet chestnut	6	100	0	.	0	.	43	64	0	.	997	20	1045	19
	other broadleaves	1197	16	214	31	216	24	126	36	99	36	1050	16	2903	10
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	30466	3	14844	5	10930	7	5850	8	8145	7	7011	7	77247	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10530	6	10410	7	6379	9	2948	12	6745	9	2389	15	39400	3
	fir	6882	8	2811	11	2276	15	405	36	242	32	296	30	12911	5
	pine	1598	14	280	69	129	44	233	31	565	24	45	53	2851	12
	larch	245	35	88	60	19	100	1827	17	1515	15	955	16	4649	9
	Arolla pine	0	.	-7	100	0	.	145	36	312	23	0	.	451	20
	other conifers	239	43	0	.	0	.	0	.	0	.	20	100	259	40
	beech	11087	5	2819	13	2575	14	120	52	337	45	788	18	17726	4
	maple	959	14	371	24	510	19	48	82	47	71	9	100	1944	10
	ash	2414	12	279	34	629	24	40	71	9	100	109	50	3480	10
	oak	1405	15	65	62	46	59	19	76	54	79	231	40	1819	13
	sweet chestnut	0	.	7	100	0	.	10	100	5	100	644	21	666	20
	other broadleaves	851	19	135	42	141	42	22	231	57	59	380	23	1587	13
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	36209	3	17258	6	12704	7	5816	9	9890	7	5867	7	87743	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10232	7	9358	8	6775	10	2615	14	5419	11	2140	16	36538	4
	fir	6734	8	3438	12	2062	17	240	41	370	38	518	30	13361	6
	pine	1139	16	102	59	63	75	172	48	420	29	38	100	1935	13
	larch	344	34	73	60	141	55	2136	14	1818	16	1085	14	5598	8
	Arolla pine	0	.	0	.	0	.	45	50	166	32	0	.	210	27
	other conifers	248	51	0	.	0	.	0	.	0	.	30	100	278	47
	beech	9175	7	2173	16	1558	18	91	72	242	39	568	24	13806	6
	maple	562	23	260	41	123	41	0	.	83	53	20	100	1049	17
	ash	2095	16	153	54	238	36	15	100	0	.	33	100	2534	14
	oak	1075	19	0	.	16	100	21	100	50	71	39	71	1201	18
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	455	28	463	28
	other broadleaves	515	36	436	54	110	43	43	73	18	145	103	52	1225	25
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	32118	4	15995	6	11086	8	5377	10	8593	8	5030	9	78198	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5301	10	5317	11	4265	12	1268	21	2797	13	1422	18	20372	5
	fir	4983	11	2942	16	1047	24	46	100	179	41	99	59	9296	8
	pine	252	36	83	75	31	100	0	.	199	39	18	100	584	24
	larch	126	51	57	76	26	100	1150	23	842	21	734	24	2935	13
	Arolla pine	0	.	0	.	0	.	33	72	86	46	0	.	119	38
	other conifers	296	42	0	.	0	.	0	.	0	.	0	.	296	42
	beech	3754	11	1017	24	695	29	24	100	101	58	406	31	5997	9
	maple	167	45	101	54	92	79	0	.	0	.	0	.	360	33
	ash	615	31	151	50	113	58	0	.	0	.	46	100	926	24
	oak	583	26	0	.	0	.	0	.	0	.	46	71	628	24
	sweet chestnut	0	.	0	.	0	.	50	76	0	.	143	35	193	32
	other broadleaves	197	41	58	71	105	75	0	.	21	100	16	100	397	31
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	16274	6	9726	8	6375	11	2571	16	4226	10	2930	12	42102	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2591	15	2736	14	2628	17	700	32	1319	22	990	25	10964	8
	fir	1768	18	1310	20	1106	24	78	71	248	46	178	51	4689	11
	pine	73	72	0	.	0	.	0	.	55	71	0	.	127	51
	larch	43	100	0	.	130	60	890	24	512	27	448	32	2023	15
	Arolla pine	0	.	0	.	0	.	100	45	43	71	0	.	143	38
	other conifers	346	50	0	.	0	.	0	.	0	.	0	.	346	50
	beech	1649	20	329	41	349	46	20	100	116	71	158	41	2621	16
	maple	60	100	45	76	32	100	0	.	0	.	0	.	137	56
	ash	274	41	45	100	113	71	0	.	30	100	0	.	462	32
	oak	308	41	0	.	52	71	0	.	12	100	0	.	373	36
	sweet chestnut	0	.	0	.	0	.	19	100	0	.	162	36	181	34
	other broadleaves	130	62	34	100	0	.	0	.	0	.	21	100	184	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7242	9	4499	12	4412	13	1808	18	2334	15	1957	15	22251	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	910	27	1294	23	717	45	296	51	761	26	553	31	4531	13
	fir	852	27	400	47	229	50	29	100	84	100	67	100	1662	20
	pine	47	100	0	.	0	.	0	.	54	100	0	.	102	71
	larch	0	.	0	.	0	.	342	38	307	44	302	44	951	24
	Arolla pine	0	.	0	.	0	.	67	58	48	72	0	.	114	45
	other conifers	72	100	71	100	0	.	0	.	0	.	0	.	142	71
	beech	368	42	123	74	113	72	39	100	0	.	82	58	726	28
	maple	0	.	0	.	23	100	0	.	0	.	0	.	23	100
	ash	141	72	0	.	65	100	0	.	0	.	0	.	207	59
	oak	301	41	0	.	0	.	0	.	0	.	0	.	301	41
	sweet chestnut	0	.	0	.	0	.	30	100	0	.	134	46	164	42
	other broadleaves	92	71	0	.	0	.	0	.	0	.	0	.	92	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2783	15	1887	19	1148	31	803	26	1254	21	1138	20	9013	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	125	71	257	47	326	42	51	100	154	72	84	71	996	24
	fir	331	46	89	100	65	100	0	.	0	.	145	71	629	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	157	58	61	100	0	.	218	50
	Arolla pine	0	.	0	.	0	.	28	100	0	.	0	.	28	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	54	100	0	.	41	100	0	.	0	.	0	.	96	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	56	100	0	.	0	.	0	.	0	.	0	.	56	100
	oak	168	100	0	.	0	.	0	.	0	.	0	.	168	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	102	62	102	62
	other broadleaves	212	75	0	.	0	.	0	.	0	.	0	.	212	75
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	947	32	345	43	433	36	236	46	214	59	331	40	2506	17
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	208	58	106	74	131	58	53	100	267	50	204	71	970	27
	fir	215	58	126	100	0	.	0	.	0	.	0	.	341	52
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	107	71	163	100	270	67
	Arolla pine	0	.	0	.	0	.	38	100	35	100	0	.	73	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	.	417	49	417	49
	other broadleaves	400	100	0	.	0	.	0	.	0	.	0	.	400	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	823	53	232	64	131	58	91	72	409	39	784	38	2470	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	48063	4	44393	6	30830	7	14247	9	28423	7	11670	10	177626	2
	fir	31110	5	15954	8	9276	11	1311	28	1341	24	1771	25	60763	4
	pine	4958	11	696	50	502	32	2020	19	2606	18	192	45	10974	8
	larch	1185	19	360	33	479	34	8917	11	6604	12	5930	11	23475	6
	Arolla pine	0	.	-7	100	0	.	958	22	1532	18	5	100	2489	13
	other conifers	1583	30	121	66	-2	1200	15	58	45	35	50	100	1813	27
	beech	44017	4	11731	9	9708	10	958	29	1304	27	5569	12	73286	3
	maple	5767	7	2943	11	2633	12	322	27	406	43	347	25	12416	5
	ash	10015	8	2075	19	2886	15	437	31	244	37	748	25	16407	6
	oak	5972	11	180	34	172	36	205	37	130	71	957	20	7615	9
	sweet chestnut	8	82	7	100	0	.	194	59	18	100	5342	12	5569	12
	other broadleaves	5443	13	1794	22	1358	17	987	18	986	21	4460	11	15029	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	158121	2	80247	4	57842	5	30572	7	43639	5	37041	4	407462	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. 1218006. Birmensdorf, Swiss Federal Research Institute WSL

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