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: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		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	919	12	288	31	347	22	635	15	1229	13	478	14	881	12	670	14	564	13	248	23	241	21	1037	10	1797	8	492	15	9828	4
	fir	753	9	67	44	79	32	343	17	217	36	211	25	499	13	242	20	50	23	87	30	28	35	147	31	37	25	118	46	2879	6
	pine	31	34	18	43	2	71	28	67	24	38	0	0	40	43	4	86	24	57	8	49	13	72	290	20	298	18	5	73	786	11
	larch	1	100	2	100	17	47	11	56	17	50	0	.	5	100	10	81	10	107	0	.	14	53	392	13	183	19	273	16	933	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	2	100	0	.	81	42	82	27	0	100	170	24
	other conifers	37	36	4	53	2	77	17	43	49	45	0	.	6	76	14	44	10	66	8	55	4	74	5	51	41	34	0	100	197	16
	beech	960	10	391	13	315	20	424	14	599	13	129	31	530	15	367	14	282	32	40	42	117	22	127	33	72	21	1061	11	5414	4
	maple	306	14	63	26	125	39	127	22	273	20	96	22	160	22	192	22	157	21	40	42	60	29	97	22	44	31	98	22	1840	6
	ash	197	22	109	29	137	26	99	46	331	22	26	36	95	23	145	23	64	25	51	34	26	36	82	26	68	31	279	20	1710	8
	oak	128	37	27	53	93	32	16	39	52	36	4	71	0	100	3	100	21	45	1	100	0	.	76	24	6	50	173	25	600	13
	sweet chestnut	0	.	0	.	11	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	72	0	.	804	15	828	14
	other broadleaves	285	13	139	26	130	26	132	24	314	17	109	25	159	19	149	21	152	22	102	41	82	46	489	14	403	18	1317	9	3963	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
	total	3617	5	1108	12	1259	12	1831	9	3105	7	1052	11	2382	7	1796	9	1333	11	587	16	587	14	2837	7	3030	6	4623	5	29147	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2254	9	369	25	680	20	1435	15	2243	12	1421	13	2275	10	1711	12	1362	12	650	18	932	24	2226	10	3576	7	1108	12	22240	3
	fir	1382	10	181	29	322	28	796	16	474	18	381	22	1209	13	532	18	187	29	176	31	114	34	207	29	89	32	144	27	6194	5
	pine	202	26	77	45	64	59	16	58	117	32	0	0	48	50	7	78	13	79	96	40	37	76	591	16	475	19	50	47	1794	9
	larch	24	78	12	71	46	56	57	61	100	49	0	.	0	.	4	100	28	53	0	.	36	51	833	13	417	17	622	14	2180	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	16	100	6	100	4	100	7	100	0	.	216	21	296	19	6	77	550	14
	other conifers	60	65	65	43	15	100	46	44	66	35	0	.	2	100	9	76	15	67	0	.	-10	120	9	76	16	46	0	.	293	21
	beech	2858	8	960	15	654	14	987	14	1364	12	188	26	1265	11	992	15	420	22	199	29	405	25	274	25	220	28	1337	13	12123	4
	maple	629	12	252	19	135	38	202	23	494	15	235	23	392	22	393	17	241	21	114	29	180	31	95	31	43	52	139	29	3545	6
	ash	463	19	173	27	334	25	182	23	486	19	168	31	256	30	505	18	89	38	107	35	197	59	130	39	82	38	275	26	3447	8
	oak	324	25	167	35	211	36	85	38	169	26	20	60	30	48	14	100	61	55	5	100	0	.	70	41	6	71	299	23	1461	11
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	79	4	100	1102	13	1127	13
	other broadleaves	348	16	121	32	136	34	177	29	347	17	40	34	166	27	266	25	231	26	70	33	87	35	342	17	202	27	1499	10	4032	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
	total	8543	4	2377	10	2605	10	3983	8	5861	6	2454	11	5660	7	4439	8	2650	9	1424	14	1979	16	5006	7	5426	6	6581	5	58986	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3294	8	459	22	1093	20	1507	15	2595	11	2215	13	3854	9	2844	11	2308	12	1157	18	1106	22	3208	9	5744	7	1740	13	33126	3
	fir	2673	9	212	32	265	30	1095	16	854	15	731	21	1552	11	1075	14	273	28	219	28	161	28	222	26	201	35	245	33	9778	5
	pine	566	21	244	29	130	38	134	41	526	17	0	.	137	34	17	58	32	61	137	46	47	68	607	17	731	18	171	61	3480	8
	larch	9	71	55	41	62	56	29	60	97	39	0	.	0	.	19	58	9	115	0	.	135	52	1349	12	1009	14	1006	15	3781	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	10	73	0	.	0	.	222	19	317	22	0	.	571	15
	other conifers	8	100	53	63	47	79	14	69	33	72	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	160	35
	beech	4705	7	1664	13	1167	16	1483	13	1818	10	670	23	1422	14	1554	12	407	21	218	30	536	20	146	32	194	27	956	14	16940	3
	maple	772	13	189	26	82	45	159	34	490	18	87	36	209	22	345	20	224	22	60	36	159	25	59	37	29	55	51	118	2916	7
	ash	411	21	280	22	334	27	398	31	897	14	138	47	176	43	486	22	142	37	69	66	61	55	57	58	57	54	182	25	3689	8
	oak	364	22	166	35	324	27	153	32	331	31	11	100	15	82	13	115	45	57	16	76	0	.	30	44	45	100	222	25	1736	11
	sweet chestnut	0	.	0	.	16	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	55	3	100	813	13	869	13
	other broadleaves	376	24	204	32	189	33	121	34	364	22	0	0	126	48	74	36	142	32	25	51	51	39	195	28	117	35	874	13	2859	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	13177	4	3527	10	3710	10	5093	8	8005	6	3852	11	7512	6	6428	7	3598	9	1901	14	2258	14	6133	6	8448	6	6261	6	79903	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4278	7	764	21	1458	18	2342	13	3245	10	2899	11	5040	8	3863	9	2871	11	1092	18	1438	17	2897	9	6699	7	1888	12	40773	3
	fir	3581	8	265	26	433	26	1429	14	1287	13	752	19	2249	11	1648	13	390	25	215	35	230	31	315	25	219	26	286	24	13299	4
	pine	583	19	237	26	279	38	175	27	810	20	0	.	176	61	68	50	61	58	40	63	20	59	276	26	705	17	112	39	3540	8
	larch	11	100	65	71	179	44	39	59	130	33	30	100	23	58	37	81	49	63	0	.	88	57	1914	11	1459	11	1050	13	5074	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	0	5	100	0	.	175	24	295	19	0	.	479	14
	other conifers	0	.	51	74	8	100	79	62	19	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	166	39
	beech	4231	7	1761	11	1416	15	1800	12	2103	10	480	23	1593	14	1835	14	458	25	241	36	477	21	53	57	217	34	927	13	17592	3
	maple	521	16	148	28	128	46	115	33	258	20	78	36	130	32	288	23	110	30	40	48	114	29	16	59	24	70	39	79	2009	8
	ash	351	24	220	25	289	22	457	28	891	15	104	46	117	38	309	23	73	40	75	62	217	65	33	59	24	60	60	63	3222	9
	oak	187	28	179	30	434	25	233	29	414	19	5	100	31	64	6	100	30	72	25	77	7	100	21	71	57	62	172	39	1801	10
	sweet chestnut	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	4	100	0	.	546	20	567	20
	other broadleaves	94	37	168	50	107	39	110	48	265	23	0	.	34	42	74	53	69	49	25	58	33	54	19	135	37	47	329	16	1364	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	13839	4	3858	9	4745	10	6779	8	9421	6	4348	10	9397	6	8127	7	4115	9	1758	15	2624	13	5722	7	9736	5	5416	6	89885	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4425	8	572	21	1312	18	2414	13	2762	12	2649	16	4769	9	4258	11	2659	12	1017	17	1197	18	2269	11	5364	8	1803	12	37471	3
	fir	2924	12	339	32	470	26	1768	15	1607	15	893	19	2696	11	1438	15	581	24	164	41	269	33	203	32	375	27	338	27	14064	5
	pine	362	28	90	70	68	52	186	29	652	20	0	.	70	51	47	78	0	.	29	58	51	83	239	31	437	21	36	58	2266	10
	larch	14	100	19	71	73	61	36	58	263	38	14	100	56	86	59	62	111	47	13	100	96	50	1976	11	1616	12	1095	11	5443	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	46	38	181	25	7	100	233	21
	other conifers	0	.	0	.	24	100	116	47	76	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	230	35
	beech	2060	11	1504	14	914	19	1679	15	2270	11	380	26	1142	18	1083	15	713	32	142	40	388	27	79	53	204	31	580	17	13137	5
	maple	120	30	36	59	38	58	25	100	186	37	33	62	62	56	101	40	14	72	7	100	45	57	0	.	28	73	6	100	703	16
	ash	158	31	150	39	209	30	289	34	562	21	11	100	97	47	183	42	42	64	9	100	66	48	7	100	21	75	30	62	1833	11
	oak	128	42	128	44	330	29	267	27	475	21	13	100	24	71	13	100	-1	100	9	100	0	.	6	100	36	53	95	40	1522	12
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	5	100	462	20	490	19
	other broadleaves	60	42	82	45	122	58	36	52	25	71	0	.	23	71	44	65	258	55	25	62	118	85	95	39	21	71	82	47	993	21
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	10251	5	2921	11	3567	11	6817	8	8877	6	3993	12	8940	7	7226	9	4377	11	1415	16	2230	14	4937	8	8287	6	4549	6	78387	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2175	12	298	42	790	24	1437	18	1479	15	1004	20	2765	11	2363	13	1183	16	536	25	601	24	1327	19	3126	10	1110	16	20194	4
	fir	1553	13	223	33	362	31	847	18	775	23	458	35	1514	15	992	18	361	29	265	49	41	71	189	38	175	36	158	36	7912	6
	pine	124	41	16	100	44	59	48	71	212	35	0	.	38	75	39	71	16	100	0	.	0	.	18	71	213	33	11	100	779	17
	larch	0	.	14	100	55	59	22	100	48	58	0	.	52	100	64	58	50	71	0	.	16	100	1214	15	602	18	552	19	2688	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	13	73	0	.	0	.	52	41	139	32	0	.	214	24
	other conifers	0	.	18	100	25	100	97	63	96	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	236	48
	beech	548	22	709	24	244	34	697	22	930	18	180	41	605	26	534	23	250	42	64	51	90	87	10	100	33	71	303	26	5197	8
	maple	0	.	47	79	0	.	0	.	74	50	8	100	0	.	0	.	62	52	0	.	0	.	0	.	0	.	0	.	191	33
	ash	126	39	50	100	158	49	88	58	190	37	18	100	0	.	48	71	0	.	15	100	15	100	0	.	14	100	25	100	745	19
	oak	90	60	252	43	218	45	45	59	137	43	0	.	17	100	20	100	0	.	0	.	0	.	21	100	19	72	19	82	838	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	71	0	.	198	26	216	25
	other broadleaves	0	.	96	46	21	100	13	100	16	100	0	.	0	.	0	.	56	59	0	.	0	.	0	.	0	.	38	59	241	28
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	4616	8	1723	16	1916	15	3294	11	3957	10	1667	16	4991	8	4069	10	1990	13	880	22	763	22	2849	11	4321	9	2414	9	39452	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	941	18	80	58	224	40	498	27	631	23	603	22	1243	16	1161	19	731	20	554	27	390	33	742	22	1597	15	879	22	10275	6
	fir	919	20	21	100	188	35	469	27	261	34	140	42	994	18	421	27	62	58	30	100	179	47	37	71	179	39	150	41	4050	9
	pine	0	.	0	.	0	.	0	.	41	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	71
	larch	24	100	0	.	0	.	25	100	0	.	0	.	0	.	25	100	17	100	0	.	29	100	721	20	332	27	306	26	1480	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	43	70	42	0	.	144	30
	other conifers	0	.	28	100	28	100	29	100	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	51
	beech	160	36	169	40	190	48	394	34	574	28	0	0	83	64	91	72	68	58	9	100	56	61	12	100	66	71	119	36	1993	14
	maple	0	.	0	.	0	.	35	100	0	.	0	.	10	100	0	.	18	100	0	.	12	100	0	.	0	.	0	.	76	56
	ash	0	.	47	100	23	100	38	100	105	51	0	.	0	.	65	71	17	100	0	.	0	.	0	.	0	.	0	.	294	33
	oak	0	.	43	71	175	56	26	100	177	38	27	100	0	.	85	62	0	.	41	72	0	.	0	.	0	.	0	.	574	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	204	28	204	28
	other broadleaves	0	.	46	100	62	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	25	71	156	42
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2044	13	433	27	890	24	1514	16	1789	14	770	21	2370	12	1848	15	914	17	635	24	666	25	1611	14	2245	12	1683	13	19412	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	499	47	36	100	125	61	63	71	129	50	126	45	335	33	424	44	416	31	137	60	145	42	288	33	501	26	387	26	3610	12
	fir	540	27	0	.	30	100	88	58	55	71	39	100	244	38	120	51	70	71	31	100	0	.	18	100	102	58	113	58	1450	16
	pine	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	52	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	424	25	200	41	179	43	802	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	71	43	58	0	.	74	45
	other conifers	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100
	beech	38	100	58	100	35	100	80	100	158	46	25	100	90	64	76	73	33	100	25	100	0	.	20	100	0	.	11	100	649	25
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	14	100	27	71
	ash	0	.	0	.	22	100	46	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	75
	oak	0	.	23	100	0	.	61	71	66	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	183	31	194	30
	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	1077	27	117	61	243	40	338	36	433	26	190	39	669	24	619	33	519	27	194	47	157	39	791	19	874	20	887	18	7108	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	78	71	0	.	0	.	0	.	33	100	119	63	93	58	196	46	96	58	26	100	99	51	64	71	247	36	168	43	1219	17
	fir	39	100	0	.	0	.	0	.	72	71	40	100	126	58	0	.	0	.	0	.	0	.	0	.	0	.	105	58	381	32
	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	.	87	51	46	72	34	74	166	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	15	100	0	.	35	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	50	100	0	.	104	71	75	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	72	271	40
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	18	100
	ash	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100
	oak	35	100	0	.	0	.	0	.	99	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	61	82	61
	other broadleaves	0	.	24	100	0	.	106	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	17	100	195	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	151	50	73	75	35	100	210	51	279	40	159	53	218	41	215	45	96	58	26	100	99	51	219	37	308	31	449	26	2538	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	100	0	.	41	100	37	100	0	.	0	.	0	.	75	75	41	71	32	100	60	71	26	100	168	46	156	61	671	24
	fir	0	.	0	.	0	.	0	.	55	100	61	100	53	100	0	.	0	.	0	.	46	100	0	.	42	100	29	100	287	42
	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	.	27	100	72	59	139	62	239	42
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	71	19	100	0	.	53	58
	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	.	432	100	432	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	22	100	0	.	0	.	0	.	73	76
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	606	41	606	41
	other broadleaves	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	100	47	100	41	100	37	100	86	74	61	100	53	100	75	75	92	64	32	100	128	52	88	51	300	33	1363	38	2439	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18900	5	2866	15	6071	13	10367	9	14345	7	11512	10	21255	6	17564	7	12231	9	5449	13	6210	14	14085	7	28819	5	9732	8	179406	2
	fir	14364	6	1308	22	2150	19	6834	10	5656	11	3707	15	11137	8	6467	10	1974	18	1187	24	1068	24	1338	20	1419	18	1686	18	60295	3
	pine	1869	15	683	21	587	30	588	21	2407	13	0	0	509	37	181	45	146	39	310	36	169	64	2021	14	2887	13	385	41	12739	6
	larch	84	45	167	44	431	34	219	31	656	24	44	100	136	80	219	43	274	32	13	100	415	37	8937	8	5936	9	5256	8	22785	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	47	100	16	100	27	60	13	100	0	.	950	16	1456	15	13	63	2522	11
	other conifers	105	53	219	48	181	54	398	38	338	46	0	.	49	84	23	39	30	63	8	55	-6	152	14	64	56	36	23	98	1437	19
	beech	15559	5	7266	9	4936	12	7648	9	9891	7	2052	18	6730	10	6532	10	2631	20	939	21	2068	17	722	21	1007	21	5769	11	73749	2
	maple	2347	8	736	16	508	22	663	19	1774	11	536	17	963	14	1338	13	878	15	262	23	606	20	268	20	169	28	348	24	11398	4
	ash	1706	14	1028	19	1541	18	1597	22	3464	10	466	29	741	25	1741	15	425	24	326	40	583	45	310	26	266	32	851	17	15045	5
	oak	1255	17	985	25	1785	20	885	18	1952	16	80	45	117	38	155	37	155	49	97	53	7	100	224	25	169	54	981	18	8849	7
	sweet chestnut	0	.	0	.	56	82	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	112	49	12	100	5001	10	5184	9
	other broadleaves	1163	13	927	30	768	23	696	20	1331	13	149	23	508	20	607	18	909	26	247	25	372	38	1213	14	780	18	4181	7	13850	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	57353	3	16185	7	19013	8	29895	6	41814	4	18547	8	42193	5	34843	5	19683	7	8850	11	11491	11	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. 1217964. Birmensdorf, Swiss Federal Research Institute WSL

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