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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NFI5
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		production region
		Jura		Plateau		Pre-Alps		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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	974	14	1828	14	2049	11	4274	7	610	16	9734	5
	fir	901	12	798	15	939	15	343	20	66	35	3046	7
	pine	-10	300	28	53	28	75	474	21	14	65	534	20
	larch	7	88	122	51	29	51	573	17	263	24	993	13
	Arolla pine	0	.	0	.	0	.	260	30	3	85	263	30
	other conifers	58	36	75	37	24	41	75	25	1	100	233	17
	beech	1426	9	1208	10	924	13	652	18	934	14	5144	5
	maple	496	20	509	17	388	19	510	14	176	31	2078	8
	ash	234	20	366	23	179	25	364	22	215	24	1358	10
	oak	132	40	95	38	1	100	82	30	94	42	405	20
	sweet chestnut	0	.	0	.	0	.	16	77	668	20	684	20
	other broadleaves	336	15	683	13	447	14	1311	11	1181	12	3958	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	4554	6	5711	6	5007	6	8935	4	4224	6	28430	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1733	12	3454	11	4814	8	8910	6	1339	15	20250	4
	fir	2030	12	1495	16	2094	13	685	17	132	35	6435	7
	pine	299	37	112	33	99	73	1184	16	5	100	1699	14
	larch	14	100	164	42	42	82	1299	14	949	18	2469	11
	Arolla pine	0	.	0	.	0	.	571	18	5	100	576	18
	other conifers	60	44	76	35	24	58	42	56	0	.	202	23
	beech	2886	9	2198	11	2462	11	1404	15	1733	17	10682	5
	maple	904	12	1008	17	1108	14	801	18	91	39	3912	7
	ash	397	24	483	20	732	23	465	25	315	33	2391	11
	oak	339	34	385	27	52	48	123	33	303	24	1201	15
	sweet chestnut	0	.	0	.	0	.	20	59	1410	16	1431	15
	other broadleaves	388	20	787	15	515	22	1163	14	1679	16	4532	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	9048	5	10163	6	11941	5	16666	4	7961	6	55779	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3139	12	3929	11	8417	7	12637	5	2221	18	30344	4
	fir	2513	12	1826	15	2973	12	1132	17	372	34	8817	7
	pine	659	24	358	26	160	50	1273	16	42	106	2491	12
	larch	5	201	115	49	10	100	2390	12	1320	17	3839	10
	Arolla pine	0	.	0	.	0	.	654	18	0	.	654	18
	other conifers	18	71	120	44	0	.	-20	125	0	.	119	50
	beech	5012	8	3579	9	3427	10	1703	13	1459	15	15180	5
	maple	1033	15	833	16	1156	15	868	18	159	45	4048	8
	ash	457	20	1398	15	850	28	613	23	105	38	3423	10
	oak	412	24	356	27	42	52	98	34	267	26	1175	14
	sweet chestnut	0	.	0	.	0	.	52	60	904	19	956	19
	other broadleaves	261	27	643	18	203	27	356	24	1246	18	2710	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	13508	5	13157	5	17239	5	21758	4	8095	8	73757	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3841	9	4830	10	9903	7	15956	5	2691	16	37221	3
	fir	3523	10	2697	12	3448	9	1747	16	300	32	11716	5
	pine	534	21	755	19	250	61	1078	17	63	57	2681	11
	larch	41	58	217	36	13	100	3635	11	1237	14	5143	9
	Arolla pine	0	.	0	.	0	.	476	19	0	.	476	19
	other conifers	57	58	118	50	0	.	0	.	16	100	191	36
	beech	5635	8	4286	9	4279	11	2049	14	975	16	17225	5
	maple	824	15	378	22	603	18	518	21	52	49	2376	9
	ash	585	21	1377	16	662	24	292	32	106	61	3022	10
	oak	558	25	754	20	127	52	87	45	220	31	1745	13
	sweet chestnut	0	.	0	.	0	.	21	60	838	23	859	22
	other broadleaves	332	28	530	21	81	40	272	30	721	20	1936	11
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0
	total	15929	4	15942	5	19367	4	26131	4	7221	7	84590	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3757	10	3745	11	10001	7	13412	6	2453	18	33369	4
	fir	3341	11	3365	12	5384	10	1916	16	612	28	14617	6
	pine	376	25	741	23	172	49	744	22	27	100	2060	13
	larch	49	71	288	31	57	100	3925	10	1233	15	5553	8
	Arolla pine	0	.	0	.	0	.	338	25	0	.	338	25
	other conifers	142	68	266	51	0	.	0	.	28	100	436	39
	beech	3978	10	5242	8	3090	13	1496	17	697	21	14502	5
	maple	423	23	302	28	496	24	288	27	0	.	1509	13
	ash	249	35	1603	17	280	38	244	31	88	64	2464	13
	oak	282	32	995	19	0	.	112	54	99	46	1488	15
	sweet chestnut	0	.	0	.	0	.	20	72	537	27	557	26
	other broadleaves	132	53	468	32	117	45	290	40	104	42	1110	19
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	12728	5	17016	5	19598	5	22784	4	5878	10	78004	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2909	13	2694	14	6916	10	8154	8	1425	17	22098	5
	fir	1962	18	2576	15	3365	13	1170	21	138	50	9211	8
	pine	47	71	279	38	123	58	236	34	14	100	698	22
	larch	0	.	167	45	0	.	2935	13	738	21	3841	11
	Arolla pine	0	.	0	.	0	.	107	45	0	.	107	45
	other conifers	75	100	266	52	0	.	0	.	36	100	377	43
	beech	1638	17	2921	13	1113	22	809	26	500	29	6981	8
	maple	59	71	232	43	134	69	126	46	0	.	551	28
	ash	238	42	565	25	186	51	100	58	55	100	1143	19
	oak	120	60	561	32	0	0	32	100	34	100	747	26
	sweet chestnut	0	.	0	.	0	.	16	100	178	33	194	31
	other broadleaves	72	100	191	42	25	100	340	47	47	73	674	29
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	7119	8	10452	6	11861	7	14024	6	3164	10	46621	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1355	21	1429	20	3180	14	5276	11	1016	29	12255	7
	fir	1196	20	1569	18	1938	17	1459	23	195	50	6357	10
	pine	41	100	161	50	0	.	62	71	0	.	264	38
	larch	41	100	0	.	84	71	1466	18	434	28	2026	15
	Arolla pine	0	.	0	.	0	.	151	38	0	.	151	38
	other conifers	0	.	277	60	0	.	0	.	0	.	277	60
	beech	426	35	1399	21	738	32	364	35	170	41	3097	14
	maple	72	72	34	100	19	100	27	100	0	.	152	46
	ash	79	71	95	72	96	71	71	71	30	100	371	34
	oak	114	71	333	39	0	.	10	100	0	.	457	34
	sweet chestnut	0	.	0	.	0	.	18	100	168	38	186	36
	other broadleaves	74	71	141	51	0	.	38	100	27	100	281	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	3398	12	5438	10	6055	10	8942	8	2040	17	25874	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	345	48	760	29	1133	23	1790	18	440	39	4469	12
	fir	392	42	1046	26	993	29	347	45	50	100	2828	16
	pine	0	.	49	100	0	.	0	.	0	.	49	100
	larch	0	.	0	.	0	.	754	29	447	39	1202	23
	Arolla pine	0	.	0	.	0	.	138	51	0	.	138	51
	other conifers	61	100	235	60	81	100	0	.	0	.	376	46
	beech	48	100	169	58	109	72	96	71	171	45	593	29
	maple	0	.	0	.	0	.	27	100	0	.	27	100
	ash	66	100	76	100	140	71	0	.	0	.	282	50
	oak	0	.	215	51	45	100	37	100	0	.	298	42
	sweet chestnut	0	.	0	.	0	.	0	.	153	46	153	46
	other broadleaves	0	.	70	100	0	.	0	.	0	.	70	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	911	28	2619	16	2501	16	3189	13	1263	23	10484	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	127	71	169	58	228	51	947	24	215	58	1685	19
	fir	214	58	126	71	119	71	146	71	204	58	807	29
	pine	0	.	0	.	0	.	57	100	0	.	57	100
	larch	0	.	0	.	0	.	350	38	38	100	387	36
	Arolla pine	0	.	0	.	0	.	57	71	0	.	57	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	281	53	55	100	0	.	0	.	337	47
	maple	0	.	0	.	0	.	25	100	0	.	25	100
	ash	0	.	119	71	0	.	0	.	0	.	119	71
	oak	0	.	89	71	0	.	0	.	0	.	89	71
	sweet chestnut	0	.	0	.	0	.	29	100	145	59	174	52
	other broadleaves	0	.	55	100	0	.	0	.	0	.	55	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	340	45	838	27	402	38	1610	18	601	32	3792	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	81	100	155	71	345	41	508	36	184	72	1274	24
	fir	174	72	0	.	133	100	0	.	0	.	307	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	107	71	129	100	236	63
	Arolla pine	0	.	0	.	0	.	75	71	0	.	75	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	194	100	0	.	0	.	0	.	194	100
	sweet chestnut	0	.	0	.	0	.	0	.	411	55	411	55
	other broadleaves	0	.	223	59	0	.	0	.	0	.	223	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	255	59	571	45	478	41	690	30	725	40	2719	19
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18260	6	22994	6	46986	5	71864	4	12596	12	172699	2
	fir	16244	7	15498	8	21385	7	8945	11	2068	24	64140	4
	pine	1946	17	2482	15	832	40	5107	12	165	49	10531	8
	larch	156	39	1074	20	236	40	17433	7	6789	11	25688	6
	Arolla pine	0	.	0	.	0	.	2828	14	9	94	2836	13
	other conifers	470	56	1434	34	128	64	97	46	82	98	2211	25
	beech	21049	5	21282	6	16197	7	8574	9	6638	11	73741	3
	maple	3810	9	3296	10	3905	10	3188	10	479	25	14678	5
	ash	2305	14	6080	11	3125	16	2150	15	913	30	14573	7
	oak	1956	18	3978	15	267	36	581	27	1017	20	7799	10
	sweet chestnut	0	.	0	.	0	.	193	45	5412	13	5605	12
	other broadleaves	1596	15	3790	12	1388	13	3770	12	5004	11	15548	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	67792	3	81907	3	94450	3	124730	2	41172	4	410050	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. 1218068. Birmensdorf, Swiss Federal Research Institute WSL

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