Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1081	14	2023	12	2687	8	865	17	1584	11	492	17	8732	5
	fir	887	11	1095	15	986	12	74	66	50	37	64	38	3155	7
	pine	18	59	65	67	29	117	353	29	213	27	10	73	688	19
	larch	0	0	49	63	60	38	309	19	72	24	186	27	676	13
	Arolla pine	0	.	0	.	0	.	90	47	128	42	5	77	224	30
	other conifers	15	164	34	56	32	38	8	66	29	42	3	100	122	30
	beech	1314	10	1134	10	1342	11	151	54	96	30	884	16	4920	6
	maple	421	16	517	21	622	13	68	38	50	39	69	30	1747	9
	ash	245	27	463	20	361	17	59	45	84	45	195	33	1407	11
	oak	110	41	128	31	26	61	58	34	0	.	143	35	464	18
	sweet chestnut	2	100	0	0	0	.	0	0	0	.	813	19	814	19
	other broadleaves	299	16	576	12	624	13	377	19	423	26	1188	16	3488	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4392	6	6084	6	6767	5	2411	10	2730	9	4052	7	26435	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2530	13	4226	10	6750	8	2012	16	3146	11	1117	17	19780	4
	fir	1747	14	1391	13	1912	12	141	42	76	40	157	41	5423	7
	pine	208	44	269	31	164	42	565	24	369	28	68	53	1642	13
	larch	0	0	113	47	75	52	583	19	283	27	642	20	1695	12
	Arolla pine	0	.	0	.	0	.	213	31	219	26	7	71	440	20
	other conifers	83	40	81	48	17	60	0	0	10	59	4	100	195	27
	beech	3410	10	3133	8	3195	9	133	46	223	34	1580	18	11674	5
	maple	712	14	930	16	1561	13	43	42	143	41	120	41	3509	8
	ash	405	21	1153	16	1064	16	69	42	64	56	210	33	2966	9
	oak	223	28	650	23	43	44	59	49	0	0	240	27	1216	15
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	1440	16	1455	15
	other broadleaves	299	22	666	17	648	19	271	23	224	32	1358	14	3468	8
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	0	0	0
	total	9618	6	12612	5	15428	5	4105	10	4757	9	6943	7	53463	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3536	11	5785	9	11830	6	2604	13	5341	9	2060	17	31155	4
	fir	2781	12	2525	12	3310	10	146	49	148	32	245	36	9155	6
	pine	591	24	704	20	199	36	643	21	688	28	55	56	2879	11
	larch	55	54	223	32	61	53	1147	18	781	20	999	20	3265	10
	Arolla pine	0	.	0	.	0	.	159	32	287	26	28	100	474	20
	other conifers	12	101	139	44	-19	100	0	.	0	.	0	.	131	50
	beech	5493	8	4211	10	3956	10	141	38	397	27	996	16	15193	5
	maple	802	15	670	20	1359	12	0	100	117	51	64	53	3011	8
	ash	672	19	1781	14	1151	20	21	116	51	55	95	40	3770	9
	oak	316	27	722	19	36	62	28	62	6	100	208	31	1316	13
	sweet chestnut	0	.	6	100	0	.	19	71	0	.	997	20	1022	20
	other broadleaves	335	23	848	21	406	20	70	50	64	45	909	17	2632	10
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	14593	5	17613	5	22287	4	4976	9	7880	7	6654	7	74003	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4821	9	6317	8	14915	6	2592	13	6586	9	2291	16	37523	3
	fir	3866	10	3498	11	4490	10	227	41	308	27	282	31	12669	6
	pine	618	24	1027	18	290	67	219	32	548	25	36	61	2737	12
	larch	41	73	204	39	88	60	1602	18	1320	15	899	16	4154	9
	Arolla pine	0	.	0	.	-7	100	118	41	294	24	0	.	406	21
	other conifers	-6	100	245	42	0	.	0	.	0	.	20	100	259	40
	beech	5648	7	5970	8	4503	10	118	62	362	43	764	18	17365	4
	maple	646	18	397	23	751	16	38	100	35	73	9	100	1876	10
	ash	549	25	1912	14	813	21	21	100	21	72	109	50	3426	10
	oak	307	31	1103	18	59	65	19	76	13	100	267	38	1768	14
	sweet chestnut	0	.	0	.	17	71	0	.	0	.	649	20	666	20
	other broadleaves	167	32	644	24	246	29	-6	741	57	59	353	24	1461	14
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	16657	4	21316	4	26166	4	4948	10	9544	7	5679	8	84310	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4568	10	6413	10	14953	6	2016	15	5208	11	2100	16	35258	4
	fir	3288	12	4201	11	4621	10	132	57	416	34	518	30	13176	6
	pine	286	29	837	19	173	45	172	48	403	30	38	100	1910	13
	larch	66	58	305	37	126	54	1955	15	1682	18	1004	15	5138	9
	Arolla pine	0	.	0	.	0	.	34	58	166	32	0	.	200	28
	other conifers	0	.	248	51	0	.	0	.	0	.	30	100	278	47
	beech	3786	11	5442	9	3279	13	36	100	274	38	549	24	13366	6
	maple	289	28	297	35	322	35	0	.	113	43	20	100	1042	17
	ash	404	27	1722	18	340	33	15	100	20	100	0	.	2501	14
	oak	54	58	969	21	37	71	0	.	28	100	62	58	1150	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	463	28	463	28
	other broadleaves	95	60	309	40	335	56	43	73	30	75	69	60	882	27
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	12836	6	20744	5	24186	5	4403	11	8340	9	4854	9	75363	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2451	14	3639	13	8448	8	878	24	2621	14	1400	19	19437	5
	fir	2023	16	3185	14	3461	14	46	100	213	38	99	59	9027	8
	pine	53	72	258	36	56	100	0	.	199	39	0	.	565	24
	larch	0	.	126	51	57	76	1108	24	868	21	709	24	2868	13
	Arolla pine	0	.	0	.	0	.	19	100	86	46	0	.	105	42
	other conifers	48	100	247	47	0	.	0	.	0	.	0	.	296	42
	beech	992	21	2663	14	1528	20	0	.	121	51	406	31	5708	9
	maple	35	100	133	51	151	54	0	.	0	.	0	.	318	35
	ash	178	42	469	38	189	45	0	.	0	.	46	100	882	25
	oak	67	75	467	28	0	.	0	.	0	.	46	71	580	25
	sweet chestnut	0	.	0	.	15	100	0	.	0	.	143	35	158	33
	other broadleaves	58	71	212	48	91	58	0	.	0	.	16	100	376	32
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	5903	9	11398	7	13997	7	2052	18	4107	11	2864	12	40321	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	870	24	1864	18	5013	12	481	40	1202	23	946	26	10376	8
	fir	880	25	1283	22	1854	18	78	71	343	43	178	51	4616	11
	pine	29	100	43	100	0	.	0	.	55	71	0	.	127	51
	larch	43	100	73	72	57	100	672	26	454	28	533	32	1832	16
	Arolla pine	0	.	0	.	0	.	100	45	43	71	0	.	143	38
	other conifers	65	100	281	57	0	.	0	.	0	.	0	.	346	50
	beech	274	45	1242	24	556	33	0	.	116	71	158	41	2346	16
	maple	60	100	0	.	13	100	0	.	32	100	0	.	106	66
	ash	36	100	292	41	104	71	0	.	30	100	0	.	462	32
	oak	0	.	335	39	26	100	0	.	12	100	0	.	373	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	162	36	162	36
	other broadleaves	0	.	130	62	34	100	0	.	0	.	21	100	184	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2258	15	5544	11	7657	9	1331	21	2286	16	1997	16	21073	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	275	53	723	28	1631	25	260	57	686	26	433	36	4008	14
	fir	422	38	577	34	482	40	29	100	84	100	67	100	1662	20
	pine	0	.	47	100	0	.	0	.	54	100	0	.	102	71
	larch	0	.	0	.	0	.	240	46	226	48	191	54	657	28
	Arolla pine	0	.	0	.	0	.	23	100	28	100	19	100	70	58
	other conifers	72	100	71	100	0	.	0	.	0	.	0	.	142	71
	beech	0	.	436	39	169	60	39	100	0	.	82	58	726	28
	maple	0	.	0	.	23	100	0	.	0	.	0	.	23	100
	ash	0	.	207	59	0	.	0	.	0	.	0	.	207	59
	oak	0	.	301	41	0	.	0	.	0	.	0	.	301	41
	sweet chestnut	0	.	0	.	0	.	30	100	0	.	134	46	164	42
	other broadleaves	0	.	41	100	0	.	0	.	0	.	0	.	41	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	768	29	2402	16	2305	20	621	31	1079	22	927	22	8103	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	65	100	139	71	358	39	51	100	154	72	37	100	803	28
	fir	225	58	195	60	65	100	0	.	0	.	145	71	629	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	94	71	61	100	64	100	218	50
	Arolla pine	0	.	0	.	0	.	28	100	0	.	0	.	28	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	54	100	41	100	0	.	0	.	0	.	96	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	56	100	0	.	0	.	0	.	0	.	56	100
	oak	0	.	168	100	0	.	0	.	0	.	0	.	168	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	102	62	102	62
	other broadleaves	0	.	143	100	0	.	0	.	0	.	0	.	143	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	290	50	756	37	464	34	172	51	214	59	347	40	2244	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	67	100	209	58	169	50	53	100	195	58	204	71	897	28
	fir	0	.	153	71	126	100	0	.	0	.	0	.	279	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	215	80	215	80
	Arolla pine	0	.	0	.	0	.	38	100	35	100	0	.	73	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	385	52	385	52
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	67	100	363	45	295	52	91	72	230	51	804	37	1849	22
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20264	6	31338	6	66754	4	11811	10	26722	7	11081	11	167969	2
	fir	16119	8	18103	8	21305	7	873	34	1638	23	1755	25	59792	4
	pine	1802	19	3250	13	911	40	1952	20	2528	19	207	45	10650	8
	larch	204	39	1093	20	525	28	7709	12	5747	13	5440	12	20718	7
	Arolla pine	0	.	0	.	-7	100	823	24	1287	19	60	92	2164	15
	other conifers	290	66	1346	33	29	80	8	66	39	39	57	89	1770	27
	beech	20917	5	24284	6	18569	7	618	37	1588	24	5418	12	71394	3
	maple	2965	9	2943	11	4802	9	148	40	491	32	281	29	11631	5
	ash	2490	15	8055	10	4021	13	185	43	270	39	655	28	15676	7
	oak	1077	18	4844	13	227	32	164	44	59	100	966	21	7336	10
	sweet chestnut	2	100	6	100	33	80	65	63	0	.	5287	12	5391	12
	other broadleaves	1253	16	3569	13	2384	15	755	20	800	23	3914	11	12674	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	67382	3	98830	3	119552	3	25111	7	41168	6	35121	4	387165	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. 1217990. Birmensdorf, Swiss Federal Research Institute WSL

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