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
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3418	7	1923	7	1160	10	1037	10	1797	8	492	15	9828	4
	fir	1459	8	761	11	357	16	147	31	37	25	118	46	2879	6
	pine	104	24	64	34	25	43	290	20	298	18	5	73	786	11
	larch	47	28	15	79	24	46	392	13	183	19	273	16	933	8
	Arolla pine	0	.	5	100	2	100	81	42	82	27	0	100	170	24
	other conifers	110	24	16	50	25	31	5	51	41	34	0	100	197	16
	beech	2689	6	940	13	525	11	127	33	72	21	1061	11	5414	4
	maple	895	10	413	13	293	16	97	22	44	31	98	22	1840	6
	ash	873	12	186	15	222	17	82	26	68	31	279	20	1710	8
	oak	315	19	25	39	4	81	76	24	6	50	173	25	600	13
	sweet chestnut	11	71	0	.	0	.	13	72	0	.	804	15	828	14
	other broadleaves	1000	9	421	12	333	19	489	14	403	18	1317	9	3963	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	10921	3	4767	5	2969	7	2837	7	3030	6	4623	5	29147	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6980	6	5058	7	3293	10	2226	10	3576	7	1108	12	22240	3
	fir	3154	7	1777	10	822	14	207	29	89	32	144	27	6194	5
	pine	477	17	61	43	140	34	591	16	475	19	50	47	1794	9
	larch	239	28	28	53	40	46	833	13	417	17	622	14	2180	8
	Arolla pine	0	.	20	83	12	71	216	21	296	19	6	77	550	14
	other conifers	252	23	17	59	-1	978	9	76	16	46	0	.	293	21
	beech	6823	5	1873	9	1596	12	274	25	220	28	1337	13	12123	4
	maple	1711	8	868	13	688	13	95	31	43	52	139	29	3545	6
	ash	1637	10	513	19	810	19	130	39	82	38	275	26	3447	8
	oak	956	14	111	34	19	80	70	41	6	71	299	23	1461	11
	sweet chestnut	8	100	0	.	0	.	13	79	4	100	1102	13	1127	13
	other broadleaves	1129	10	437	17	423	18	342	17	202	27	1499	10	4032	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	23369	3	10763	5	7841	6	5006	7	5426	6	6581	5	58986	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8950	5	8376	6	5107	9	3208	9	5744	7	1740	13	33126	3
	fir	5099	7	2556	9	1455	12	222	26	201	35	245	33	9778	5
	pine	1600	11	169	30	201	35	607	17	731	18	171	61	3480	8
	larch	253	23	9	115	155	46	1349	12	1009	14	1006	15	3781	7
	Arolla pine	0	.	32	71	0	.	222	19	317	22	0	.	571	15
	other conifers	155	37	5	100	0	.	0	.	0	.	0	.	160	35
	beech	10837	4	2499	10	2308	10	146	32	194	27	956	14	16940	3
	maple	1692	9	520	14	564	15	59	37	29	55	51	118	2916	7
	ash	2320	9	456	25	617	20	57	58	57	54	182	25	3689	8
	oak	1339	13	71	43	29	66	30	44	45	100	222	25	1736	11
	sweet chestnut	16	73	0	.	0	.	36	55	3	100	813	13	869	13
	other broadleaves	1255	12	268	28	150	24	195	28	117	35	874	13	2859	8
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	33514	2	14961	5	10586	6	6133	6	8448	6	6261	6	79903	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12087	5	10810	5	6393	7	2897	9	6699	7	1888	12	40773	3
	fir	6995	6	3392	9	2093	11	315	25	219	26	286	24	13299	4
	pine	2084	11	237	47	127	34	276	26	705	17	112	39	3540	8
	larch	424	24	102	44	125	47	1914	11	1459	11	1050	13	5074	6
	Arolla pine	0	.	4	100	5	100	175	24	295	19	0	.	479	14
	other conifers	157	41	0	.	0	.	0	.	0	.	9	100	166	39
	beech	11311	4	2531	10	2553	11	53	57	217	34	927	13	17592	3
	maple	1170	11	318	19	442	17	16	59	24	70	39	79	2009	8
	ash	2209	10	294	24	602	27	33	59	24	60	60	63	3222	9
	oak	1447	11	66	45	39	56	21	71	57	62	172	39	1801	10
	sweet chestnut	15	100	3	100	0	.	4	100	0	.	546	20	567	20
	other broadleaves	744	17	104	36	132	34	19	135	37	47	329	16	1364	11
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	38642	2	17860	4	12510	6	5722	7	9736	5	5416	6	89885	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11486	5	10077	7	6472	8	2269	11	5364	8	1803	12	37471	3
	fir	7108	7	4169	9	1871	13	203	32	375	27	338	27	14064	5
	pine	1357	14	70	51	127	46	239	31	437	21	36	58	2266	10
	larch	406	28	182	40	169	37	1976	11	1616	12	1095	11	5443	6
	Arolla pine	0	.	0	0	0	.	46	38	181	25	7	100	233	21
	other conifers	216	36	0	.	0	.	0	.	0	.	14	100	230	35
	beech	8427	6	2235	14	1612	12	79	53	204	31	580	17	13137	5
	maple	406	22	110	38	154	32	0	.	28	73	6	100	703	16
	ash	1369	13	149	36	258	33	7	100	21	75	30	62	1833	11
	oak	1328	13	36	59	22	72	6	100	36	53	95	40	1522	12
	sweet chestnut	6	100	0	.	0	.	17	100	5	100	462	20	490	19
	other broadleaves	326	27	281	51	187	56	95	39	21	71	82	47	993	21
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	32434	3	17310	5	10872	7	4937	8	8287	6	4549	6	78387	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6179	8	4951	8	3500	10	1327	19	3126	10	1110	16	20194	4
	fir	3760	9	2333	13	1298	17	189	38	175	36	158	36	7912	6
	pine	445	22	54	61	39	71	18	71	213	33	11	100	779	17
	larch	139	36	102	62	80	51	1214	15	602	18	552	19	2688	9
	Arolla pine	0	.	13	73	10	100	52	41	139	32	0	.	214	24
	other conifers	236	48	0	.	0	.	0	.	0	.	0	.	236	48
	beech	3128	10	1035	20	688	22	10	100	33	71	303	26	5197	8
	maple	121	43	70	48	0	.	0	.	0	.	0	.	191	33
	ash	611	22	18	100	78	51	0	.	14	100	25	100	745	19
	oak	742	23	17	100	20	100	21	100	19	72	19	82	838	21
	sweet chestnut	0	.	0	.	0	.	18	71	0	.	198	26	216	25
	other broadleaves	147	36	56	59	0	.	0	.	0	.	38	59	241	28
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	15506	5	8649	6	5713	9	2849	11	4321	9	2414	9	39452	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2374	12	2578	11	2105	14	742	22	1597	15	879	22	10275	6
	fir	1859	13	1196	16	630	23	37	71	179	39	150	41	4050	9
	pine	41	71	0	.	0	.	0	.	0	.	0	.	41	71
	larch	48	71	17	100	54	71	721	20	332	27	306	26	1480	13
	Arolla pine	0	.	0	.	0	.	74	43	70	42	0	.	144	30
	other conifers	85	58	40	100	0	.	0	.	0	.	0	.	125	51
	beech	1487	16	151	44	156	48	12	100	66	71	119	36	1993	14
	maple	35	100	29	73	12	100	0	.	0	.	0	.	76	56
	ash	213	39	17	100	65	71	0	.	0	.	0	.	294	33
	oak	421	30	27	100	127	48	0	.	0	.	0	.	574	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	204	28	204	28
	other broadleaves	107	55	0	.	0	.	24	100	0	.	25	71	156	42
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6670	7	4055	9	3149	11	1611	14	2245	12	1683	13	19412	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	852	31	877	20	705	30	288	33	501	26	387	26	3610	12
	fir	712	22	354	32	152	45	18	100	102	58	113	58	1450	16
	pine	25	100	0	.	0	.	0	.	28	100	0	.	52	71
	larch	0	.	0	.	0	.	424	25	200	41	179	43	802	19
	Arolla pine	0	.	0	.	0	.	30	71	43	58	0	.	74	45
	other conifers	31	100	0	.	0	.	0	.	0	.	0	.	31	100
	beech	369	36	148	48	101	60	20	100	0	.	11	100	649	25
	maple	0	.	0	.	12	100	0	.	0	.	14	100	27	71
	ash	68	75	0	.	0	.	0	.	0	.	0	.	68	75
	oak	150	42	0	.	0	.	0	.	0	.	0	.	150	42
	sweet chestnut	0	.	0	.	0	.	11	100	0	.	183	31	194	30
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2208	16	1379	16	970	24	791	19	874	20	887	18	7108	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	111	58	307	35	321	33	64	71	247	36	168	43	1219	17
	fir	110	58	166	50	0	.	0	.	0	.	105	58	381	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	87	51	46	72	34	74	166	36
	Arolla pine	0	.	0	.	0	.	20	100	15	100	0	.	35	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	228	45	0	.	0	.	0	.	0	.	43	72	271	40
	maple	0	.	0	.	18	100	0	.	0	.	0	.	18	100
	ash	35	100	0	.	0	.	0	.	0	.	0	.	35	100
	oak	134	63	0	.	0	.	0	.	0	.	0	.	134	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	82	61	82	61
	other broadleaves	130	63	0	.	0	.	48	100	0	.	17	100	195	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	749	25	473	29	340	33	219	37	308	31	449	26	2538	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	114	58	41	71	166	46	26	100	168	46	156	61	671	24
	fir	55	100	115	71	46	100	0	.	42	100	29	100	287	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	27	100	72	59	139	62	239	42
	Arolla pine	0	.	0	.	0	.	34	71	19	100	0	.	53	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	432	100	432	100
	maple	0	.	51	100	22	100	0	.	0	.	0	.	73	76
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	31	100	0	.	0	.	0	.	0	.	0	.	31	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	606	41	606	41
	other broadleaves	47	100	0	.	0	.	0	.	0	.	0	.	47	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	247	42	206	48	234	39	88	51	300	33	1363	38	2439	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	52550	3	44999	4	29223	6	14085	7	28819	5	9732	8	179406	2
	fir	30312	4	16818	6	8722	9	1338	20	1419	18	1686	18	60295	3
	pine	6132	8	654	30	660	26	2021	14	2887	13	385	41	12739	6
	larch	1556	15	454	32	646	28	8937	8	5936	9	5256	8	22785	4
	Arolla pine	0	.	74	67	29	71	950	16	1456	15	13	63	2522	11
	other conifers	1241	21	78	57	24	58	14	64	56	36	23	98	1437	19
	beech	45300	3	11413	8	9538	8	722	21	1007	21	5769	11	73749	2
	maple	6029	5	2378	9	2206	10	268	20	169	28	348	24	11398	4
	ash	9336	7	1632	15	2650	15	310	26	266	32	851	17	15045	5
	oak	6863	8	352	27	259	30	224	25	169	54	981	18	8849	7
	sweet chestnut	56	82	3	100	0	.	112	49	12	100	5001	10	5184	9
	other broadleaves	4885	9	1567	16	1225	16	1213	14	780	18	4181	7	13850	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	164260	1	80423	3	55183	4	30193	5	42974	4	34225	3	407258	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. 1217962. Birmensdorf, Swiss Federal Research Institute WSL

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