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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NFI2
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	8608	14	1741	31	2318	23	5728	19	8808	16	5965	18	8081	13	6850	17	4431	15	2056	25	2330	23	8139	13	11613	10	2964	15	79632	4
	fir	6479	10	689	37	910	29	2280	17	1237	26	1118	26	4225	12	1676	18	736	29	1042	28	370	33	590	32	366	28	680	32	22399	5
	pine	306	43	432	59	190	75	152	71	243	39	0	.	318	70	0	.	141	76	108	62	0	.	3885	23	2410	24	81	100	8266	14
	larch	17	100	52	100	91	71	0	.	157	45	0	.	0	.	101	100	54	58	0	.	198	69	3023	18	1064	27	1924	22	6681	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	480	29	966	27	16	100	1462	20
	other conifers	213	41	69	61	121	100	137	50	273	55	15	100	38	71	74	50	0	.	70	78	49	73	277	82	247	51	0	.	1583	22
	beech	8512	10	2764	16	1816	19	3512	12	4177	11	1173	31	4299	16	2935	16	1650	28	920	29	1336	26	1144	33	889	29	6485	15	41611	5
	maple	1768	14	587	26	296	43	313	31	1338	23	612	28	963	28	825	24	956	31	239	42	536	34	636	30	289	48	348	32	9708	7
	ash	1307	24	500	36	700	41	329	28	1105	22	438	41	764	29	1149	29	263	38	258	37	327	62	508	35	418	66	1018	28	9085	9
	oak	1298	33	325	56	1384	38	213	53	309	41	147	64	58	58	21	100	143	66	18	100	0	.	528	32	15	100	1493	32	5953	15
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	8922	15	8957	15
	other broadleaves	2705	15	737	28	984	35	1184	35	1787	18	1171	23	1470	23	1580	26	1404	22	693	44	842	34	3378	18	3205	23	10126	12	31266	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	31213	6	7896	13	8827	13	13848	11	19433	9	10640	13	20216	8	15212	11	9777	12	5405	17	5988	17	22609	9	21482	8	34057	6	226602	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4772	12	837	29	1595	22	2254	17	3391	15	3605	17	6090	12	4233	14	3230	15	1453	23	1868	32	5046	12	8066	9	2583	15	49021	4
	fir	3504	11	278	34	408	37	1857	20	826	21	927	28	2003	13	787	20	388	29	353	34	213	37	306	37	175	45	272	36	12301	6
	pine	546	34	227	40	136	59	96	61	154	37	0	.	156	100	0	.	106	85	245	53	0	.	1715	21	1325	23	86	60	4790	12
	larch	34	100	35	70	71	61	34	71	171	53	0	.	0	.	0	.	55	74	0	.	89	72	2212	17	1075	21	1339	18	5117	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	456	36	746	25	0	.	1202	20
	other conifers	17	100	156	62	0	.	101	57	50	58	0	.	0	.	0	.	17	100	0	.	15	100	0	.	17	100	0	.	375	32
	beech	6395	10	1852	18	1043	20	2240	15	2719	12	775	37	2674	18	2153	18	800	22	468	32	726	24	640	33	597	31	2767	17	25849	5
	maple	1383	15	345	26	39	100	319	28	1073	19	328	32	598	21	815	23	519	32	115	43	567	32	149	47	88	45	150	40	6488	7
	ash	723	25	315	32	527	27	442	23	895	18	332	52	430	41	899	22	198	53	123	79	358	50	164	48	185	56	260	33	5850	9
	oak	752	29	156	51	718	33	318	30	283	28	21	100	32	100	75	77	54	58	18	100	0	.	108	57	15	100	507	31	3057	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	72	15	100	3200	16	3308	16
	other broadleaves	695	22	258	36	449	35	444	29	761	22	116	47	356	33	633	32	348	31	170	37	217	35	493	28	531	37	3177	13	8647	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18819	5	4459	13	4987	13	8104	9	10323	8	6104	14	12340	8	9596	10	5716	11	2944	17	4053	19	11381	8	12837	8	14342	6	126005	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3074	11	545	26	1704	22	2050	14	2642	12	2048	15	3897	10	2915	13	2384	15	1246	22	931	29	3030	13	5566	9	1892	15	33924	3
	fir	2487	12	247	32	221	36	1340	17	667	19	567	31	1382	14	776	19	256	31	281	34	107	38	230	48	112	41	171	38	8845	6
	pine	605	24	313	32	152	53	172	38	526	23	0	.	126	57	30	62	21	74	114	52	0	.	766	20	748	23	103	59	3677	9
	larch	17	100	52	74	43	62	81	84	67	54	0	.	0	.	7	100	51	75	0	.	18	100	1262	17	843	19	854	21	3296	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	257	29	403	26	0	.	675	19
	other conifers	0	.	59	100	0	.	43	62	40	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	150	49
	beech	4120	9	1424	15	878	23	1442	14	1435	13	375	34	1341	16	1682	16	546	26	174	36	351	30	177	62	261	35	979	18	15185	5
	maple	543	18	237	31	35	53	67	44	280	23	68	78	260	32	420	20	242	31	83	47	226	31	44	71	51	63	25	100	2582	9
	ash	575	25	148	41	277	33	507	50	684	19	72	53	113	45	328	27	120	41	43	100	81	47	32	83	34	64	19	72	3033	12
	oak	290	26	194	35	227	32	111	35	305	30	0	.	138	86	7	100	0	.	0	.	0	.	19	100	12	100	179	41	1483	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	70	6	100	801	20	849	19
	other broadleaves	246	28	193	35	146	43	111	39	443	23	18	100	29	77	131	44	128	42	40	71	62	56	157	43	140	44	729	21	2573	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11956	5	3412	11	3683	13	5924	9	7090	7	3148	13	7288	8	6297	9	3763	12	1981	17	1777	18	6016	9	8177	8	5760	7	76272	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2202	9	472	23	1106	18	1565	13	2160	11	1404	15	2653	10	2014	12	1739	14	602	22	592	19	1623	11	3353	9	1168	14	22655	3
	fir	1444	10	112	29	263	29	898	14	622	17	311	25	1128	13	673	18	245	27	113	39	106	40	134	41	116	32	222	31	6388	5
	pine	261	24	137	32	169	44	143	29	449	20	0	.	149	61	34	62	58	63	7	100	0	.	207	33	400	23	28	61	2044	10
	larch	7	100	7	100	21	58	21	74	77	32	0	.	7	100	0	.	56	51	0	.	12	70	1031	15	822	14	511	18	2574	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	40	219	26	0	.	366	22
	other conifers	0	.	7	100	0	.	40	47	27	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	40
	beech	1781	9	719	17	421	24	995	14	1075	12	101	36	854	18	695	19	254	29	120	39	220	24	49	62	180	33	393	19	7858	5
	maple	157	23	97	32	0	.	14	71	85	34	48	38	62	43	153	31	61	40	42	47	44	41	6	100	14	71	8	100	791	11
	ash	110	30	87	28	127	35	158	51	444	18	8	100	55	42	92	32	27	50	25	70	50	43	7	100	6	100	25	59	1222	11
	oak	107	33	50	54	125	46	169	33	188	22	0	.	0	.	0	.	0	.	7	100	0	.	0	.	41	76	74	64	762	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	369	23	376	23
	other broadleaves	72	38	43	57	35	60	72	58	76	30	0	.	29	50	59	51	75	69	6	100	18	74	30	62	14	71	153	25	684	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6141	5	1732	12	2268	12	4075	9	5204	7	1872	13	4938	8	3721	9	2516	11	923	18	1043	15	3243	9	5166	7	2952	8	45793	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1313	10	268	25	416	22	1005	14	952	13	684	18	1419	11	1066	13	787	14	382	22	348	24	727	15	1564	10	655	14	11586	4
	fir	542	13	49	42	98	38	484	17	356	19	186	31	664	15	294	18	158	27	100	40	50	46	76	52	72	36	44	46	3173	6
	pine	91	30	14	100	56	43	70	54	147	25	0	.	48	68	8	100	0	.	0	.	0	.	65	37	159	33	14	71	673	14
	larch	0	.	7	100	7	100	7	100	55	46	0	.	7	100	22	75	63	46	7	100	19	75	678	16	507	17	296	19	1674	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	26	60	99	34	0	.	132	28
	other conifers	0	.	0	.	16	100	27	61	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	51
	beech	539	15	339	20	98	28	410	18	493	16	53	54	372	23	281	23	136	31	53	47	117	40	7	100	65	45	158	27	3122	6
	maple	32	51	14	71	7	100	0	.	81	35	0	.	6	100	19	73	23	59	0	.	7	100	0	.	14	71	0	.	202	21
	ash	42	52	7	100	57	50	24	58	107	28	8	100	23	73	19	73	0	.	6	100	12	100	0	.	0	.	11	100	316	18
	oak	48	47	53	42	58	58	41	47	136	32	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	11	100	354	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	6	100	164	29	177	27
	other broadleaves	17	71	21	74	15	71	0	.	14	71	0	.	7	100	7	100	70	46	7	100	21	76	15	100	0	.	8	100	202	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2624	7	771	14	828	15	2068	10	2369	8	930	16	2546	9	1716	10	1243	12	561	19	575	20	1602	11	2486	8	1360	9	21679	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	323	18	71	44	116	30	286	20	333	18	205	24	499	15	423	19	240	21	151	32	159	36	250	23	500	15	281	23	3836	6
	fir	191	22	14	71	29	50	146	29	53	46	46	56	257	20	72	34	46	43	34	51	34	52	15	71	29	51	30	49	996	10
	pine	14	71	14	100	0	.	7	100	28	50	0	.	11	100	7	100	0	.	0	.	0	.	0	.	35	53	0	.	116	28
	larch	0	.	0	.	0	.	7	100	7	100	0	.	9	100	17	72	7	100	0	.	13	71	230	22	133	27	121	26	543	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	72	22	58	0	.	39	45
	other conifers	0	.	0	.	8	100	20	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	61
	beech	93	28	86	37	30	62	88	40	138	27	29	50	64	40	24	72	37	45	7	100	20	73	0	.	18	74	84	32	719	12
	maple	0	.	8	100	0	.	7	100	0	.	6	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	27	50
	ash	14	71	14	100	15	71	10	100	30	50	0	.	0	.	7	100	7	100	0	.	0	.	0	.	12	100	0	.	111	29
	oak	7	100	9	100	0	.	0	.	35	45	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	63	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	35	63	35
	other broadleaves	0	.	8	100	14	71	8	100	15	100	0	.	0	.	0	.	15	72	0	.	0	.	0	.	0	.	9	100	69	37
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	642	13	223	23	212	23	579	14	639	13	287	22	840	12	569	16	352	17	193	28	226	28	512	16	751	12	587	13	6610	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	107	29	8	100	36	45	27	50	68	34	61	40	41	41	181	25	97	30	64	37	91	32	85	35	186	21	134	28	1187	9
	fir	81	33	7	100	7	100	52	43	40	40	0	.	123	28	19	58	0	.	0	.	6	100	6	100	21	58	15	71	376	15
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	21	58
	larch	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	135	30	92	32	36	52	283	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	22	58	0	.	50	38
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	beech	14	71	36	54	7	100	36	52	93	33	0	.	30	60	10	100	9	100	0	.	0	.	0	.	0	.	23	58	257	19
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	73	0	.	0	.	0	.	17	73
	ash	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	oak	0	.	0	.	8	100	24	59	14	71	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	54	38
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	77	36	85	34
	other broadleaves	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	15	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	209	20	51	47	64	36	138	25	236	20	61	40	201	21	217	23	113	27	72	35	114	28	262	21	335	16	292	18	2366	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	7	100	13	71	30	50	7	100	16	71	21	57	19	75	22	58	0	.	36	45	34	45	41	41	14	71	276	16
	fir	14	71	0	.	0	.	14	71	16	71	0	.	45	41	0	.	17	72	7	100	0	.	0	.	0	.	25	58	138	24
	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	.	29	50	14	71	7	100	50	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	16	100	13	100	7	100	0	.	0	.	7	100	8	100	7	100	0	.	7	100	6	100	0	.	72	38
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	7	100	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	72
	oak	0	.	0	.	0	.	0	.	29	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	50	73	50
	other broadleaves	0	.	0	.	0	.	15	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	73
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	7	100	36	54	83	35	67	37	16	71	66	33	26	61	47	41	15	71	36	45	78	33	61	33	120	34	687	12
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	8	100	0	.	7	100	0	.	0	.	28	50	0	.	7	100	29	50	15	71	7	100	11	100	120	25
	fir	7	100	0	.	0	.	0	.	0	.	0	.	7	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	23	58
	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	.	14	71	0	.	7	100	21	58
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	8	100	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58
	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	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	50	31	50
	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	14	71	8	100	8	100	8	100	21	58	0	.	7	100	37	45	0	.	7	100	29	50	36	45	7	100	49	41	231	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	7	100	0	.	0	.	0	.	17	72	8	100	38	45
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	10	100
	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	.	7	100	7	100	14	71
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	14	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	.	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	.	61	46	61	46
	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	.	6	100	0	.	7	100	0	.	0	.	7	100	31	51	86	36	138	27
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20422	8	3949	20	7312	15	12944	12	18366	10	13988	13	22709	9	17729	11	12936	12	5962	19	6384	21	18949	10	30914	7	9710	11	202275	3
	fir	14748	8	1396	26	1937	25	7072	12	3817	14	3155	21	9834	10	4307	13	1846	22	1931	25	887	28	1358	27	892	26	1468	26	54648	4
	pine	1822	21	1137	28	702	39	640	30	1555	16	0	.	808	55	80	46	326	55	473	52	0	.	6639	19	5092	18	312	55	19587	9
	larch	82	67	153	46	240	50	149	61	534	29	0	.	23	72	147	71	293	32	7	100	349	57	8615	13	4558	12	5102	14	20253	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	0	.	1432	25	2483	20	16	100	3954	15
	other conifers	230	39	292	57	144	85	368	35	418	44	15	100	45	62	74	50	17	100	70	78	65	77	277	82	264	51	8	100	2288	18
	beech	21454	7	7228	13	4309	16	8744	10	10143	9	2507	28	9635	12	7788	14	3440	21	1749	25	2769	21	2026	30	2018	25	10888	14	94696	3
	maple	3883	11	1287	19	377	37	719	22	2857	15	1062	24	1890	18	2238	17	1801	23	480	32	1397	27	835	31	455	35	532	29	19814	5
	ash	2770	20	1071	28	1710	24	1480	26	3274	13	858	33	1385	26	2503	20	616	35	455	39	829	49	711	30	655	60	1333	25	19649	7
	oak	2501	27	786	29	2521	30	877	25	1305	18	168	57	228	57	116	59	197	60	58	57	0	.	655	34	84	74	2264	26	11760	11
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	176	61	28	100	13761	13	13982	12
	other broadleaves	3735	14	1260	22	1642	29	1834	27	3112	15	1305	22	1890	21	2410	24	2040	20	916	37	1160	28	4074	17	3890	22	14209	10	43479	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	71648	4	18559	10	20912	10	34827	7	45382	6	23058	11	48447	6	37391	8	23534	9	12101	14	13842	14	45746	7	51332	6	59604	5	506383	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. 1358351. Birmensdorf, Swiss Federal Research Institute WSL

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