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
number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 n
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
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	5906	12	9814	11	13859	10	31782	5	5288	16	66649	4
	fir	6692	10	4607	12	6588	12	2400	13	643	29	20930	6
	pine	131	47	204	45	239	71	4654	19	165	59	5392	17
	larch	74	80	771	53	231	48	5173	12	2891	18	9139	10
	Arolla pine	0	.	0	.	0	.	2292	21	49	74	2340	21
	other conifers	453	39	555	40	184	38	1282	24	20	100	2494	17
	beech	9568	8	8668	10	6292	10	4987	12	6155	14	35669	5
	maple	3708	14	3168	14	2652	17	4193	13	1282	30	15003	7
	ash	1444	19	1670	16	1091	20	2563	16	1525	22	8293	8
	oak	1056	44	621	31	21	100	892	32	870	41	3459	20
	sweet chestnut	0	.	0	.	0	.	93	83	4138	20	4230	20
	other broadleaves	2991	13	5043	12	3640	13	10862	10	10498	11	33034	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	32023	5	35119	5	34797	5	71172	4	33522	6	206634	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3212	11	5863	11	10034	8	20040	6	3179	14	42328	4
	fir	4032	10	2515	14	3815	10	1357	15	302	34	12020	6
	pine	373	32	276	43	278	79	3117	15	16	100	4060	14
	larch	17	100	327	40	67	78	3701	13	2569	15	6681	10
	Arolla pine	0	.	0	.	0	.	1809	17	16	100	1825	17
	other conifers	137	43	178	31	53	58	156	51	0	.	524	22
	beech	5529	9	4264	8	4627	10	3253	13	3930	16	21603	5
	maple	2009	11	1850	16	2259	13	2066	14	287	32	8470	7
	ash	756	21	814	17	1191	20	1110	23	649	34	4519	10
	oak	628	34	661	25	131	47	386	31	793	23	2598	13
	sweet chestnut	0	.	0	.	0	.	53	58	3758	15	3811	15
	other broadleaves	930	20	1702	14	1381	21	3561	13	4362	12	11936	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	17622	5	18449	5	23836	5	40608	4	19862	5	120376	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2728	11	3087	10	7569	7	12439	5	2015	15	27838	4
	fir	1953	12	1474	14	2460	12	1059	16	261	33	7208	6
	pine	678	24	282	26	195	55	1513	16	94	71	2761	12
	larch	14	71	82	50	9	100	3007	12	1527	16	4639	10
	Arolla pine	0	.	0	.	0	.	968	19	0	.	968	19
	other conifers	31	81	107	44	0	.	22	76	0	.	160	35
	beech	4398	8	2909	9	3079	10	1863	12	1712	15	13961	5
	maple	937	16	776	14	1174	15	878	18	204	40	3970	8
	ash	378	21	1096	16	668	29	710	20	151	34	3004	10
	oak	374	23	351	26	45	55	159	35	297	27	1226	13
	sweet chestnut	0	.	0	.	0	.	87	62	1382	20	1469	19
	other broadleaves	298	25	646	17	252	28	433	18	1617	17	3245	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	11789	5	10810	5	15451	5	23139	4	9262	7	70450	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1983	9	2239	9	4964	6	8422	5	1446	14	19054	3
	fir	1591	10	1170	11	1544	9	843	15	148	32	5295	5
	pine	319	21	326	19	124	56	724	16	41	52	1534	11
	larch	21	58	97	36	7	100	2175	11	835	14	3135	8
	Arolla pine	0	.	0	.	0	.	407	18	0	.	407	18
	other conifers	28	61	54	47	0	.	0	.	8	100	90	35
	beech	2718	7	1998	8	2021	11	1110	13	674	15	8520	4
	maple	431	15	209	21	343	17	374	17	44	47	1400	8
	ash	294	20	632	15	289	22	139	29	51	54	1404	10
	oak	297	25	381	18	61	48	66	42	155	31	960	12
	sweet chestnut	0	.	0	.	0	.	21	58	600	20	620	20
	other broadleaves	248	32	287	20	51	38	188	28	484	18	1258	11
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0
	total	7929	4	7393	4	9403	4	14469	3	4484	6	43678	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1214	10	1129	11	3170	7	4517	5	898	18	10928	4
	fir	981	11	933	12	1513	10	594	16	190	29	4210	6
	pine	136	25	229	23	60	48	303	21	14	100	742	13
	larch	14	70	84	31	14	100	1483	10	538	15	2134	8
	Arolla pine	0	.	0	.	0	.	198	25	0	.	198	25
	other conifers	42	62	74	49	0	.	0	.	8	100	124	36
	beech	1241	10	1546	8	984	12	524	17	309	19	4604	5
	maple	150	22	116	27	190	23	122	27	0	.	578	12
	ash	74	35	435	16	83	38	92	29	40	63	724	12
	oak	93	31	327	18	0	.	41	59	57	41	518	14
	sweet chestnut	0	.	0	.	0	.	15	72	297	26	312	25
	other broadleaves	43	47	151	29	45	47	113	33	57	42	409	17
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	3989	5	5024	4	6060	4	8003	4	2407	10	25483	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	649	13	577	13	1551	9	1974	7	371	16	5123	5
	fir	416	17	515	15	664	13	236	21	30	50	1860	8
	pine	15	71	61	37	24	58	65	33	7	100	172	21
	larch	0	.	34	45	0	.	778	13	209	21	1021	11
	Arolla pine	0	.	0	.	0	.	36	45	0	.	36	45
	other conifers	14	100	47	51	0	.	0	.	8	100	69	42
	beech	344	16	611	12	241	22	176	24	151	28	1523	8
	maple	15	71	48	43	34	65	43	46	0	.	140	27
	ash	53	43	113	25	35	53	29	59	10	100	241	19
	oak	26	60	116	31	0	0	6	100	8	100	156	26
	sweet chestnut	0	.	0	.	0	.	7	100	75	31	82	30
	other broadleaves	14	100	43	41	8	100	70	44	15	71	151	27
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	1546	8	2164	6	2558	7	3421	6	884	10	10572	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	232	20	233	20	544	13	1005	10	203	26	2216	7
	fir	197	20	257	18	288	17	226	24	30	49	999	10
	pine	7	100	27	50	0	.	14	71	0	.	48	38
	larch	7	100	0	.	15	71	319	18	93	27	433	15
	Arolla pine	0	.	0	.	0	.	50	38	0	.	50	38
	other conifers	0	.	41	58	0	.	0	.	0	.	41	58
	beech	73	34	220	21	130	29	58	36	44	41	525	13
	maple	16	71	7	100	6	100	7	100	0	.	36	45
	ash	15	71	16	72	15	71	12	70	6	100	65	34
	oak	18	70	51	38	0	.	7	100	0	.	76	32
	sweet chestnut	0	.	0	.	0	.	8	100	67	40	75	37
	other broadleaves	16	71	27	50	0	.	6	100	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	580	12	879	10	997	10	1713	8	450	15	4620	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	90	29	169	22	293	17	75	37	675	11
	fir	58	39	132	26	120	29	35	45	7	100	353	16
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	137	28	87	37	224	22
	Arolla pine	0	.	0	.	0	.	36	53	0	.	36	53
	other conifers	7	100	27	62	7	100	0	.	0	.	41	47
	beech	8	100	20	58	16	71	15	71	37	45	95	28
	maple	0	.	0	.	0	.	8	100	0	.	8	100
	ash	7	100	9	100	17	72	0	.	0	.	33	51
	oak	0	.	29	50	8	100	10	100	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	45	46	45	46
	other broadleaves	0	.	8	100	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	127	27	322	16	337	16	534	13	251	21	1571	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	20	58	33	51	138	24	25	59	231	19
	fir	22	58	14	71	12	71	22	58	25	58	95	28
	pine	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	54	38	7	100	61	36
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	44	46	7	100	0	.	0	.	51	42
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	17	71	0	.	0	.	0	.	17	71
	oak	0	.	14	71	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	7	100	38	60	45	53
	other broadleaves	0	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	37	45	116	26	53	38	249	17	95	33	549	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	14	71	43	40	59	35	18	72	140	23
	fir	26	59	0	.	7	100	0	.	0	.	33	51
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	15	71	14	100	29	61
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	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	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	50	51	50	51
	other broadleaves	0	.	27	61	0	.	0	.	0	.	27	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	33	51	58	44	50	37	87	29	83	39	311	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15992	7	23067	7	41936	6	80669	4	13518	12	175182	3
	fir	15968	7	11616	8	17011	8	6772	11	1637	24	53003	4
	pine	1658	19	1411	17	920	55	10396	13	338	53	14724	10
	larch	148	44	1395	35	342	44	16842	8	8770	11	27496	6
	Arolla pine	0	.	0	.	0	.	5824	15	65	79	5889	15
	other conifers	712	31	1083	25	245	31	1460	24	44	70	3543	14
	beech	23878	5	20280	6	17397	7	11985	10	13012	12	86553	3
	maple	7266	10	6173	10	6657	11	7698	10	1817	26	29611	5
	ash	3020	13	4802	10	3391	16	4655	14	2433	22	18301	6
	oak	2492	26	2566	15	265	32	1568	25	2180	24	9071	11
	sweet chestnut	0	.	0	.	0	.	291	56	10448	13	10739	13
	other broadleaves	4540	12	7940	10	5378	12	15234	9	17040	10	50132	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	75674	3	80333	3	93541	3	163395	2	71300	4	484244	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. 1327040. Birmensdorf, Swiss Federal Research Institute WSL

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