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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NFI3
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	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	23936	6	17906	6	10416	8	9024	9	16733	7	4636	11	82649	3
	fir	11596	6	6047	8	2891	12	1016	24	384	23	748	23	22681	4
	pine	888	23	817	30	265	35	3012	17	3456	17	86	60	8525	10
	larch	385	26	206	42	235	39	4155	11	2080	15	3148	12	10210	7
	Arolla pine	0	.	11	100	22	100	638	22	1084	18	10	100	1765	14
	other conifers	1164	22	193	51	245	34	67	49	860	31	10	100	2538	15
	beech	18289	4	6312	9	5100	9	1217	24	830	19	7037	10	38785	3
	maple	6126	8	3071	12	2066	15	874	19	388	29	944	20	13469	5
	ash	4713	9	1428	15	1555	17	551	24	529	30	1852	16	10627	6
	oak	2247	17	263	43	32	58	768	24	72	55	1337	23	4719	11
	sweet chestnut	89	71	0	.	0	.	127	64	0	.	8161	11	8376	11
	other broadleaves	8250	7	4615	10	3695	15	4900	13	4310	16	12758	8	38528	4
	indeterminable	96	32	320	28	79	35	188	26	201	25	170	27	1054	12
	total	77779	3	41189	4	26601	6	26536	6	30926	6	40896	4	243926	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13353	6	12149	6	7688	8	5722	9	9205	7	3077	10	51194	3
	fir	5911	6	3310	9	1750	12	487	21	264	30	431	24	12153	4
	pine	1230	15	318	43	388	30	1943	15	1445	19	96	47	5420	9
	larch	357	28	73	48	91	47	2527	12	1312	13	2009	12	6368	7
	Arolla pine	0	.	63	86	42	71	714	19	960	20	20	71	1799	13
	other conifers	518	22	72	62	40	50	23	71	81	43	0	.	733	18
	beech	13255	4	4321	9	3259	10	700	23	558	24	3550	11	25643	3
	maple	3515	8	1822	11	1563	13	248	32	147	35	345	26	7639	5
	ash	2951	9	1025	18	1567	19	348	31	263	38	753	24	6906	7
	oak	2012	14	306	33	41	79	180	34	29	75	784	21	3353	10
	sweet chestnut	19	100	0	.	0	.	52	61	9	100	3505	11	3586	11
	other broadleaves	2875	9	1257	15	1096	16	1177	15	800	23	4761	9	11966	5
	indeterminable	21	71	83	36	10	100	30	58	60	47	34	58	238	22
	total	46016	2	24798	4	17536	6	14153	6	15132	5	19364	4	136999	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7804	5	8647	6	4909	8	3620	8	5970	6	1898	11	32847	3
	fir	4145	6	2023	9	1131	11	237	26	172	27	247	31	7955	4
	pine	1482	13	200	29	239	33	858	16	858	18	194	56	3831	8
	larch	224	25	45	55	121	40	1613	11	1099	13	1200	13	4302	7
	Arolla pine	0	.	43	80	0	.	356	19	442	21	0	.	841	14
	other conifers	141	37	10	100	0	.	0	.	0	.	0	.	151	36
	beech	9253	4	2394	12	2123	9	175	30	246	24	1304	12	15495	3
	maple	1569	9	563	15	565	15	76	38	65	46	155	30	2993	6
	ash	2003	10	458	26	554	17	133	49	67	53	209	24	3424	8
	oak	1281	12	94	42	47	44	37	46	26	100	331	23	1817	10
	sweet chestnut	22	71	0	.	0	.	42	61	4	100	1300	12	1367	12
	other broadleaves	1223	12	307	25	223	22	266	24	182	35	1295	12	3496	7
	indeterminable	0	.	49	38	10	100	10	100	63	49	0	.	132	29
	total	29147	2	14835	4	9922	5	7422	6	9195	5	8133	5	78654	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5844	4	5814	5	3269	7	1870	8	3641	6	1246	10	21684	2
	fir	3166	5	1554	8	867	11	168	25	116	25	175	24	6046	4
	pine	1077	10	160	37	95	28	258	22	435	16	80	40	2104	8
	larch	190	24	56	37	57	43	1245	10	880	10	728	12	3156	6
	Arolla pine	0	.	8	71	4	100	154	24	258	17	0	.	425	14
	other conifers	81	36	0	.	0	.	0	.	0	.	4	100	85	35
	beech	5504	4	1290	9	1341	10	55	36	140	30	613	12	8943	3
	maple	623	11	172	18	263	16	16	62	17	61	17	80	1108	8
	ash	1026	9	158	21	300	26	16	61	16	59	46	39	1562	8
	oak	711	11	46	43	22	53	20	65	35	60	120	33	954	10
	sweet chestnut	19	100	4	100	0	.	9	71	0	.	507	15	540	15
	other broadleaves	433	15	65	33	71	32	35	40	24	47	286	15	913	9
	indeterminable	0	.	12	58	7	100	12	58	4	100	0	.	35	36
	total	18673	2	9340	4	6295	5	3858	6	5566	5	3824	5	47555	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3584	5	3473	6	2248	8	927	10	1990	7	758	11	12980	3
	fir	2157	7	1233	8	531	12	90	30	124	24	109	26	4244	5
	pine	465	13	29	57	46	46	106	30	183	20	16	61	845	10
	larch	118	26	55	36	44	35	856	10	620	11	504	11	2197	6
	Arolla pine	0	.	8	100	0	.	37	33	107	24	4	100	156	19
	other conifers	53	37	0	.	0	.	0	.	0	.	4	100	57	35
	beech	2543	5	693	14	560	12	42	49	66	29	256	16	4159	4
	maple	143	20	46	38	75	26	0	.	11	74	4	100	281	14
	ash	405	13	43	34	77	29	4	100	10	73	16	60	555	11
	oak	445	13	18	50	8	71	4	100	16	50	47	40	539	11
	sweet chestnut	4	100	0	.	0	.	10	100	4	100	284	17	302	16
	other broadleaves	118	25	90	50	61	44	49	38	10	72	53	35	381	17
	indeterminable	0	.	8	71	4	100	16	50	27	49	0	.	56	31
	total	10035	3	5695	5	3655	6	2142	7	3169	5	2056	6	26752	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1295	7	1225	7	871	10	371	18	755	10	305	15	4822	4
	fir	812	9	483	12	254	17	51	36	35	33	43	32	1678	6
	pine	109	23	14	59	8	71	8	71	62	31	8	100	210	17
	larch	38	34	20	60	18	51	380	14	165	17	164	18	784	9
	Arolla pine	0	.	8	71	4	100	23	41	55	32	0	.	90	23
	other conifers	45	46	0	.	0	.	0	.	0	.	0	.	45	46
	beech	679	10	236	19	151	21	4	100	8	71	97	22	1175	8
	maple	27	41	25	47	0	.	0	.	0	.	0	.	51	31
	ash	132	22	4	100	18	51	0	.	4	100	6	100	164	19
	oak	182	22	6	100	5	100	6	100	8	71	15	72	221	19
	sweet chestnut	0	.	0	.	0	.	14	77	0	.	103	23	117	23
	other broadleaves	39	34	15	61	0	.	0	.	0	.	16	61	71	27
	indeterminable	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	total	3359	5	2040	6	1329	8	856	11	1092	8	756	8	9431	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	408	11	517	10	404	14	158	22	303	14	179	19	1968	6
	fir	307	13	187	16	97	23	12	58	27	38	25	40	654	9
	pine	8	71	0	.	0	.	0	.	0	.	0	.	8	71
	larch	8	71	4	100	9	71	175	20	77	25	71	25	344	13
	Arolla pine	0	.	0	.	0	.	27	43	24	41	0	.	50	30
	other conifers	13	58	4	100	0	.	0	.	0	.	0	.	17	50
	beech	246	15	37	36	29	43	4	100	15	61	32	35	363	12
	maple	6	100	8	71	5	100	0	.	0	.	0	.	19	51
	ash	39	36	4	100	8	71	0	.	0	.	0	.	52	31
	oak	71	29	7	100	24	45	0	.	0	.	0	.	102	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	102	25	102	25
	other broadleaves	19	51	0	.	0	.	4	100	0	.	8	71	31	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1123	7	767	8	577	11	379	14	446	11	416	11	3710	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	122	33	140	19	111	30	54	36	71	26	77	25	574	12
	fir	93	22	49	29	21	46	4	100	12	58	15	58	195	15
	pine	4	100	0	.	0	.	0	.	4	100	0	.	8	71
	larch	0	.	0	.	0	.	78	25	35	40	42	39	155	19
	Arolla pine	0	.	0	.	0	.	8	71	12	58	0	.	20	45
	other conifers	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	beech	64	32	24	47	14	58	4	100	0	.	4	100	109	23
	maple	0	.	0	.	4	100	0	.	0	.	4	100	8	71
	ash	10	72	0	.	0	.	0	.	0	.	0	.	10	72
	oak	26	42	0	.	0	.	0	.	0	.	0	.	26	42
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	61	30	66	29
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	323	16	213	15	150	24	153	19	134	19	202	16	1174	7
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	48	35	53	30	12	58	32	35	27	41	185	16
	fir	16	50	17	50	0	.	0	.	0	.	18	51	51	29
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	8	71	9	72	33	36
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	22	45	0	.	0	.	0	.	0	.	8	71	31	38
	maple	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	ash	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	oak	16	63	0	.	0	.	0	.	0	.	0	.	16	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	29	46	29	46
	other broadleaves	21	64	0	.	0	.	4	100	0	.	4	100	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	92	25	66	29	57	30	40	32	45	30	95	23	395	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	8	71	24	41	4	100	21	45	17	61	86	23
	fir	7	100	8	71	4	100	0	.	4	100	4	100	27	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	71	12	58	15	61	36	36
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	19	100	19	100
	maple	0	.	8	100	4	100	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	70	38	70	38
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	27	42	24	47	32	35	20	45	41	32	125	29	270	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	56370	4	49925	4	29992	6	21762	7	38722	5	12219	8	208990	2
	fir	28210	4	14911	6	7545	8	2065	19	1139	17	1814	18	55684	3
	pine	5263	9	1538	26	1042	25	6185	14	6443	13	480	44	20951	7
	larch	1321	16	459	26	575	28	11053	8	6289	9	7890	9	27586	5
	Arolla pine	0	.	141	74	73	75	1973	16	2949	15	33	66	5170	11
	other conifers	2017	17	279	49	285	31	90	53	941	31	18	71	3630	13
	beech	49856	3	15308	8	12577	8	2200	21	1863	17	12918	9	94722	3
	maple	12008	6	5715	9	4549	10	1213	18	628	25	1469	18	25584	4
	ash	11283	6	3120	14	4080	14	1052	24	888	30	2883	15	23304	5
	oak	6996	10	741	30	179	31	1015	23	186	56	2634	17	11751	8
	sweet chestnut	153	72	4	100	0	.	260	59	17	100	14122	9	14555	9
	other broadleaves	12983	6	6350	10	5146	13	6435	12	5326	16	19181	7	55420	4
	indeterminable	117	29	477	21	110	29	256	23	355	22	204	26	1518	10
	total	186575	2	98967	3	66153	4	55559	5	65745	4	75866	3	548866	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. 1358367. Birmensdorf, Swiss Federal Research Institute WSL

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