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: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central 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	±%	1000 m³	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1021	13	184	27	316	24	530	16	888	15	449	14	983	10	601	14	673	12	306	20	251	24	947	10	1972	8	626	15	9748	4
	fir	818	9	68	31	118	20	441	20	325	25	214	22	560	11	261	19	74	30	130	30	56	54	118	28	25	32	96	33	3303	6
	pine	8	146	15	48	0	0	21	106	9	48	0	.	23	100	1	100	36	63	16	42	7	71	272	23	308	16	7	66	723	13
	larch	9	91	2	100	21	79	7	58	8	65	14	74	8	100	6	90	16	42	0	.	13	46	394	14	259	20	260	16	1018	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	1	100	1	100	0	.	3	100	0	.	82	31	140	24	2	65	229	19
	other conifers	20	86	7	53	4	69	17	51	16	126	0	.	4	64	13	88	7	78	12	53	5	60	9	50	40	32	2	73	157	22
	beech	1017	9	303	17	265	23	375	16	612	13	125	33	441	15	346	15	229	22	80	29	132	25	145	34	97	26	1024	11	5192	4
	maple	342	14	121	27	132	25	140	25	294	20	106	20	167	21	186	24	177	18	57	36	67	26	126	19	50	42	157	19	2120	6
	ash	178	20	112	28	149	33	101	32	262	23	11	160	100	22	118	26	72	35	69	39	46	39	119	39	78	32	242	19	1657	8
	oak	77	31	17	51	74	38	19	50	48	35	1	100	0	100	5	78	23	52	2	71	0	100	72	26	6	55	125	25	468	12
	sweet chestnut	0	.	0	.	4	79	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	76	0	.	667	16	687	15
	other broadleaves	272	12	111	33	160	23	131	23	326	15	98	32	152	16	127	27	203	18	148	30	83	33	502	13	499	16	1314	9	4127	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	3762	5	940	11	1242	11	1784	9	2789	8	1017	11	2440	6	1663	9	1511	9	822	14	662	15	2803	7	3473	6	4522	5	29431	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2028	9	389	28	812	19	974	17	2121	12	1374	14	2094	10	1672	11	1387	13	604	17	692	22	2418	10	4070	7	1163	12	21798	3
	fir	1451	10	155	28	193	31	688	18	383	19	392	24	1065	12	465	19	152	26	188	28	91	50	296	27	109	26	159	28	5787	5
	pine	170	33	72	54	21	70	31	47	97	37	0	0	73	48	7	75	17	59	75	41	41	57	578	18	432	18	47	47	1662	10
	larch	18	100	18	60	42	59	10	75	69	38	0	.	0	.	15	82	30	60	0	.	30	59	853	12	533	15	753	14	2370	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	10	100	7	100	0	.	0	.	0	.	273	19	273	20	2	100	566	13
	other conifers	38	70	35	44	16	80	34	48	78	37	0	.	10	59	9	72	26	62	0	.	0	0	2	100	23	40	0	.	271	19
	beech	2618	9	763	14	618	15	745	14	1268	13	246	32	1237	12	896	14	381	24	266	26	287	22	348	28	204	30	1547	12	11425	4
	maple	594	13	192	23	145	35	220	21	625	17	270	21	430	20	371	21	254	22	150	38	214	28	133	28	106	45	127	28	3830	6
	ash	344	19	182	27	309	24	176	26	432	18	162	33	207	31	422	21	105	35	108	32	59	37	151	27	70	37	405	25	3132	7
	oak	357	27	132	32	149	29	68	34	155	28	14	60	15	61	0	0	37	60	5	100	0	.	68	29	8	59	242	23	1250	11
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	64	3	100	1253	12	1278	12
	other broadleaves	300	17	153	32	153	30	178	28	425	16	48	34	135	24	177	29	218	27	62	35	81	32	465	16	239	25	1498	10	4133	6
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	7920	5	2092	10	2467	10	3124	9	5653	7	2505	11	5275	6	4041	8	2608	9	1458	14	1494	14	5600	6	6068	6	7197	5	57502	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3202	9	363	22	818	19	1352	15	2884	11	2305	13	3466	8	2722	10	2429	11	1178	17	1085	19	3293	9	5876	7	2005	12	32979	3
	fir	2528	9	235	30	329	26	1019	15	719	16	512	19	1644	10	972	15	244	25	197	32	155	35	236	26	139	34	250	31	9180	5
	pine	497	21	178	28	110	42	86	48	390	19	0	.	155	34	19	59	8	63	125	49	51	70	571	16	683	19	166	59	3041	8
	larch	18	58	41	44	66	62	96	43	123	42	0	.	0	.	24	50	29	59	0	.	100	41	1452	11	919	14	1130	12	3997	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	23	100	3	100	5	100	13	100	0	.	255	19	368	22	5	100	671	14
	other conifers	40	100	66	56	39	65	15	72	48	74	0	.	0	.	8	100	3	100	0	.	-12	100	0	.	0	.	0	.	208	35
	beech	4559	7	1446	14	1029	15	1190	14	1685	12	531	23	1690	13	1417	12	396	23	238	27	477	21	180	24	235	25	1211	12	16286	4
	maple	768	13	197	26	166	41	138	33	447	18	102	35	336	20	379	19	270	21	115	35	164	26	39	51	63	56	94	36	3279	7
	ash	422	17	302	24	505	23	320	26	710	15	142	44	233	36	445	19	153	40	52	58	49	45	81	37	46	44	278	24	3741	7
	oak	301	27	183	30	202	26	149	31	209	24	17	62	16	72	1	2166	36	73	15	74	0	.	42	52	20	86	290	20	1481	10
	sweet chestnut	0	.	0	.	13	100	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	23	64	0	.	949	14	988	14
	other broadleaves	310	20	194	34	124	37	147	27	410	24	9	72	88	38	94	30	121	30	47	36	30	48	216	29	154	31	1116	12	3060	7
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	12645	4	3207	9	3402	10	4512	8	7627	6	3618	10	7651	6	6086	7	3694	9	1981	13	2100	13	6389	6	8504	5	7494	5	78911	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4564	7	722	22	1001	18	1636	15	2721	10	2850	11	4678	8	3687	10	3054	11	1140	17	1467	15	2966	9	6914	6	2301	12	39700	2
	fir	3450	8	336	25	390	23	1309	14	1080	14	791	18	2209	11	1612	13	374	27	323	29	239	26	344	27	308	23	292	23	13058	4
	pine	545	19	221	28	162	41	132	31	529	18	0	.	194	59	39	61	32	75	81	43	39	53	320	21	633	17	111	47	3038	8
	larch	2	100	60	67	48	54	72	43	101	43	8	100	19	71	35	76	51	56	0	.	171	59	1938	11	1519	13	1163	13	5187	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	4	218	4	100	0	.	164	26	337	18	0	.	523	14
	other conifers	34	100	71	62	51	60	60	79	29	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	256	32
	beech	4614	7	1855	11	1228	15	1786	11	2033	10	582	22	1524	13	1847	13	493	26	287	26	460	23	116	35	243	39	954	12	18022	3
	maple	523	14	170	31	44	52	110	39	266	21	88	34	123	32	245	21	121	28	65	36	139	30	45	53	31	61	31	79	2004	8
	ash	461	21	240	25	352	22	343	30	1031	13	79	51	162	28	432	22	73	59	37	89	147	34	47	52	16	76	146	29	3568	7
	oak	297	25	166	33	381	25	174	38	390	20	0	.	39	59	9	100	29	75	17	73	14	100	27	47	61	57	191	31	1794	10
	sweet chestnut	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	9	73	3	100	595	15	620	14
	other broadleaves	182	24	99	48	175	38	89	52	253	26	0	.	58	39	69	42	59	51	19	71	46	55	24	125	42	47	504	16	1619	10
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	14673	4	3941	9	3841	9	5710	8	8433	5	4399	10	9021	6	7976	7	4294	9	1972	14	2723	11	5998	7	10108	5	6300	6	89388	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3982	8	661	22	978	22	1847	15	2718	12	2616	13	4717	10	4111	10	2742	11	1235	20	1322	17	2425	11	5940	8	1932	12	37227	3
	fir	3146	9	229	37	558	26	1679	13	1187	16	905	20	2573	11	1707	14	506	23	167	38	286	34	294	31	454	27	442	27	14134	4
	pine	352	29	87	70	88	52	166	33	590	20	0	.	55	64	61	72	14	73	32	58	45	83	302	31	468	20	60	61	2321	10
	larch	26	71	29	66	134	45	40	58	215	32	38	100	44	72	62	61	49	52	0	.	71	55	2290	11	1865	15	1172	11	6034	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	119	31	184	24	7	100	310	19
	other conifers	0	.	40	100	51	82	44	77	63	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	214	41
	beech	2817	9	1609	15	916	18	1561	14	2426	10	441	25	1291	17	1190	15	612	31	184	34	373	26	76	59	171	34	713	16	14382	4
	maple	216	24	47	51	49	52	41	74	208	32	75	51	42	51	114	39	67	64	18	71	47	59	0	.	47	55	21	71	992	13
	ash	271	24	145	37	278	27	359	37	663	19	65	61	68	68	151	33	34	52	30	100	50	55	7	123	7	100	58	61	2186	11
	oak	153	38	136	38	307	35	356	27	335	23	14	100	19	100	13	100	13	100	10	100	0	.	17	74	63	53	66	41	1501	13
	sweet chestnut	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	526	19	570	19
	other broadleaves	102	76	103	58	173	45	32	58	82	44	0	.	22	71	36	59	244	56	18	75	28	58	79	46	20	87	109	34	1048	19
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	11065	5	3085	11	3570	11	6127	8	8488	6	4153	11	8831	7	7446	7	4281	10	1694	17	2223	13	5609	7	9225	7	5122	6	80919	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2415	11	291	36	555	24	1066	20	1148	19	1276	19	2716	11	2535	13	1303	15	667	22	860	19	1297	16	3330	11	1172	16	20632	4
	fir	2016	14	321	28	337	31	1279	16	1136	18	703	27	1987	15	860	19	451	28	247	40	59	58	104	59	234	32	129	38	9864	6
	pine	133	41	18	100	47	58	55	71	229	35	0	.	59	64	39	71	16	100	0	.	0	.	39	58	209	33	10	100	856	16
	larch	0	.	17	100	76	80	36	73	92	46	0	.	58	100	26	100	33	77	0	.	16	100	1417	14	828	16	700	17	3298	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	27	73	0	0	0	.	46	45	152	30	0	.	236	23
	other conifers	0	.	0	.	27	100	122	53	67	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	216	37
	beech	658	21	828	19	448	26	999	19	1188	15	228	34	674	25	613	22	208	40	113	43	178	56	28	71	93	46	357	24	6612	7
	maple	0	.	20	100	0	.	0	.	115	42	0	.	14	100	39	100	42	64	0	.	13	100	0	.	0	.	0	.	243	30
	ash	93	42	88	50	185	58	66	59	252	31	21	100	57	58	64	58	24	100	24	100	18	100	0	.	0	.	44	71	936	18
	oak	50	71	204	48	110	38	91	46	333	28	0	.	16	100	22	100	0	.	0	.	0	.	17	100	0	.	28	65	870	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	76	0	.	209	24	236	23
	other broadleaves	10	100	102	45	86	63	0	.	54	58	0	.	0	.	40	100	35	71	0	0	20	100	0	.	12	100	78	56	436	24
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	5376	8	1888	14	1870	15	3715	10	4613	9	2229	14	5582	9	4248	10	2140	13	1052	19	1164	18	2973	10	4858	8	2727	9	44435	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1074	18	77	58	260	38	475	25	657	23	705	22	1431	15	1397	19	878	23	731	28	563	26	1080	24	1923	15	917	20	12168	6
	fir	1031	19	44	71	228	39	425	31	493	31	210	52	1029	18	783	23	172	38	167	42	108	63	94	46	188	39	244	38	5217	8
	pine	16	100	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	59	0	.	95	46
	larch	24	100	0	.	0	.	0	.	0	.	0	.	35	100	63	59	20	100	0	.	34	100	867	19	415	22	409	24	1868	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	43	58	45	0	.	133	31
	other conifers	0	.	25	100	36	100	59	71	106	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	47
	beech	365	29	347	31	318	34	381	36	581	23	59	58	235	40	293	37	52	72	20	100	48	71	12	100	70	71	172	36	2955	11
	maple	0	.	34	100	0	.	0	.	0	.	8	100	0	.	0	.	39	71	0	.	17	100	0	.	0	.	0	.	99	49
	ash	62	58	34	100	90	58	92	79	132	45	0	.	0	.	92	58	24	100	0	.	0	.	0	.	18	100	0	.	544	24
	oak	16	100	110	50	110	77	0	.	210	44	0	.	0	.	16	100	0	.	16	100	0	.	0	.	18	73	16	100	512	27
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	250	25	260	24
	other broadleaves	0	.	25	100	76	62	31	100	0	.	0	.	0	.	0	.	21	100	0	.	0	.	22	100	0	.	52	82	228	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2589	12	697	22	1118	20	1488	17	2178	13	982	20	2731	11	2644	14	1206	18	934	24	771	24	2162	15	2745	11	2059	11	24303	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	291	41	65	71	75	72	166	45	393	31	225	36	426	29	410	46	551	27	89	71	118	42	351	32	813	20	531	23	4505	9
	fir	349	32	0	.	89	58	142	50	266	39	88	60	405	33	271	39	79	71	0	.	99	58	36	71	84	71	70	71	1976	14
	pine	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	56	71
	larch	0	.	0	.	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	342	27	202	41	272	35	847	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	50	69	45	0	.	120	34
	other conifers	0	.	34	100	39	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	58
	beech	78	58	54	71	0	.	104	58	406	34	0	.	25	100	134	58	82	72	25	100	0	.	21	100	0	.	69	51	997	20
	maple	0	.	0	.	0	.	43	100	0	.	0	.	12	100	0	.	0	.	0	.	14	100	0	.	0	.	0	.	69	68
	ash	0	.	0	.	10	100	48	100	34	100	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	130	54
	oak	16	100	68	72	61	71	29	100	114	51	23	100	0	.	68	72	0	.	0	.	0	.	0	.	0	.	0	.	378	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	175	34	192	33
	other broadleaves	0	.	84	77	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	124	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	733	23	305	35	303	33	561	26	1240	19	335	29	910	21	922	26	712	24	114	60	231	33	819	19	1197	16	1127	16	9509	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	100	0	.	34	100	0	.	43	100	72	58	106	59	134	59	177	61	115	81	156	41	27	100	127	58	174	43	1202	19
	fir	245	48	0	.	0	.	37	100	28	100	45	100	113	71	0	.	0	.	72	71	0	.	0	.	80	71	79	71	698	26
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	231	37	120	51	61	100	412	29
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	19	100	0	.	34	71
	other conifers	0	.	0	.	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100
	beech	37	100	0	.	0	.	0	.	83	72	27	100	19	100	24	100	0	.	0	.	0	.	0	.	0	.	54	71	244	37
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	ash	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100
	oak	0	.	71	73	0	.	0	.	95	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	65
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	55	67	55
	other broadleaves	0	.	0	.	41	100	79	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	167	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	317	41	71	73	153	50	116	75	248	50	145	47	238	43	159	52	177	61	187	57	156	41	320	32	345	33	446	28	3078	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	39	100	0	.	38	100	42	100	0	.	0	.	83	71	58	71	43	71	43	100	114	64	62	71	547	45	262	42	1329	23
	fir	0	.	0	.	0	.	0	.	123	58	62	100	73	100	0	.	0	.	0	.	46	100	0	.	79	100	68	71	450	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	78	59	157	64	260	43
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	19	100	0	.	40	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	214	100	214	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	0	.	21	100	0	.	0	.	0	.	74	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	39	100	0	.	0	.	0	.	66	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	105	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	660	39	660	39
	other broadleaves	126	100	60	100	0	.	0	.	242	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	447	63
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	204	67	60	100	38	100	42	100	431	60	62	100	156	60	58	71	96	64	43	100	180	49	108	51	722	36	1378	27	3579	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18651	5	2752	15	4885	12	8088	10	13572	7	11873	9	20701	6	17328	7	13239	9	6109	13	6628	12	14867	7	31510	5	11084	8	181288	2
	fir	15035	6	1388	21	2241	17	7020	10	5739	10	3921	15	11659	8	6931	10	2052	18	1492	22	1138	24	1523	20	1701	18	1829	18	63668	3
	pine	1722	15	593	23	429	33	515	23	1870	13	0	0	558	39	166	47	124	38	329	34	183	54	2082	15	2817	13	401	42	11791	6
	larch	98	42	167	50	386	36	290	30	608	21	60	79	164	79	231	39	228	30	0	.	434	38	9809	8	6738	9	6078	8	25291	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	48	100	22	100	35	87	20	100	0	.	1102	15	1619	14	15	60	2861	10
	other conifers	132	65	277	47	306	47	352	41	407	47	0	.	55	76	30	53	37	65	12	53	-7	141	12	50	63	31	29	93	1705	19
	beech	16764	5	7205	9	4823	12	7142	9	10283	7	2240	17	7137	10	6759	9	2453	19	1213	21	1956	16	926	21	1114	22	6315	9	76329	2
	maple	2442	8	782	16	536	23	693	18	1956	11	648	18	1124	13	1334	13	1022	15	404	25	698	19	344	19	297	35	442	18	12721	4
	ash	1831	13	1103	16	1914	16	1506	22	3516	10	480	27	827	22	1763	14	486	26	320	33	370	25	405	23	236	25	1173	16	15928	5
	oak	1306	17	1087	21	1393	20	885	18	1954	16	68	46	106	39	134	37	138	54	64	48	15	97	242	24	175	50	957	16	8524	7
	sweet chestnut	0	.	0	.	71	99	2	100	3	100	0	.	0	.	0	.	4	100	0	.	0	.	116	54	11	100	5352	10	5559	9
	other broadleaves	1303	15	932	29	1019	24	687	21	1793	18	154	25	454	15	544	20	901	26	294	24	288	25	1355	13	966	16	4698	7	15389	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	59284	3	16287	7	18004	7	27179	6	41701	4	19443	8	42834	5	35243	5	20720	7	10256	11	11704	10	32783	5	47246	4	38372	3	421054	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. 1218029. Birmensdorf, Swiss Federal Research Institute WSL

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