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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NFI5
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 2018/26
		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	4578	14	1202	33	1303	34	4592	18	3869	16	2360	21	6947	15	2898	22	4196	15	1781	25	1790	25	7285	11	12176	10	3766	18	58743	4
	fir	6283	11	315	33	684	29	2865	17	1039	23	1075	23	4244	16	1203	24	574	26	847	27	180	43	537	28	139	35	608	31	20594	6
	pine	74	62	56	75	39	100	106	56	59	100	0	.	239	71	0	.	110	82	35	100	0	.	1772	32	2032	29	65	100	4588	19
	larch	0	.	74	80	450	84	33	100	288	52	131	70	50	76	49	100	181	61	0	.	36	71	1796	18	579	24	1165	28	4833	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	628	34	877	43	32	100	1538	28
	other conifers	350	47	103	62	0	.	124	38	431	50	0	.	33	71	134	47	0	.	87	100	67	60	156	60	483	41	20	100	1987	19
	beech	6620	10	2865	17	1741	30	2851	16	3858	14	786	33	3449	13	1948	20	1329	25	460	31	1171	30	1193	29	549	27	5708	14	34528	5
	maple	2730	17	935	26	719	44	824	25	1525	15	435	33	997	27	892	27	1545	21	239	38	295	40	788	27	333	38	736	33	12992	7
	ash	853	27	572	29	362	30	396	34	742	25	351	41	418	34	299	31	430	36	419	50	35	71	527	31	294	38	1407	23	7104	9
	oak	918	50	138	55	378	46	59	74	184	43	21	100	0	0	0	.	106	85	18	100	0	.	718	38	15	100	838	42	3392	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	18	100	4105	20	4123	20
	other broadleaves	2095	15	698	24	767	29	1572	24	2209	18	604	29	1085	23	722	22	1201	24	420	32	528	44	3113	16	2513	25	8559	13	26087	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
	total	24502	6	6958	13	6444	16	13422	10	14204	8	5763	14	17461	9	8145	12	9672	11	4306	19	4102	18	18513	8	20008	8	27009	7	180509	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2671	13	467	30	1003	30	2110	19	2711	16	1856	20	4112	13	2642	16	2314	16	1297	23	1236	24	4484	14	7370	10	2558	16	36831	4
	fir	3630	11	328	37	244	39	1474	20	745	24	753	27	2353	14	710	23	451	28	377	30	100	41	305	32	106	48	284	36	11859	6
	pine	234	42	139	49	17	100	151	69	108	48	0	.	278	79	0	.	0	0	146	56	0	.	1834	22	965	24	16	100	3887	14
	larch	0	.	17	100	140	67	17	100	170	52	0	.	17	100	49	100	104	62	0	.	53	100	1407	18	601	27	1665	19	4240	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	490	28	811	27	16	100	1317	19
	other conifers	69	61	69	61	20	100	20	100	138	35	0	.	17	100	36	71	17	100	0	.	0	.	17	100	35	71	0	.	437	21
	beech	4299	11	1187	19	913	21	1179	15	2096	13	673	26	2405	15	1471	19	765	26	608	31	485	36	622	36	437	35	3606	17	20745	5
	maple	1465	14	454	25	282	32	242	33	1272	21	545	24	625	24	830	22	666	29	175	50	423	33	321	32	175	49	238	33	7713	7
	ash	458	27	297	34	255	34	208	33	291	28	251	37	373	40	472	27	147	39	122	47	136	43	198	45	186	90	614	35	4008	11
	oak	523	39	104	52	411	36	145	40	88	45	19	100	46	71	21	100	54	58	0	0	0	.	252	42	15	100	671	25	2350	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	15	100	3666	16	3699	16
	other broadleaves	578	23	218	33	296	34	399	31	963	18	214	39	354	30	431	47	398	25	118	60	198	36	1037	20	1033	30	3543	14	9779	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
	total	13927	6	3280	12	3580	14	5944	11	8582	8	4310	14	10581	8	6661	11	4915	12	2842	17	2631	18	10985	9	11749	8	16877	6	106866	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2369	12	264	41	468	25	947	19	1639	16	1521	17	2801	12	2560	13	1839	15	827	22	644	25	2714	12	4770	9	1878	16	25241	4
	fir	1755	12	183	41	219	38	824	19	395	26	560	29	1241	16	562	23	242	30	279	39	86	45	287	34	107	37	247	34	6989	6
	pine	500	31	178	32	89	57	84	51	109	32	0	.	189	57	6	100	53	100	145	53	0	.	678	22	552	27	94	71	2676	12
	larch	0	.	14	71	38	84	0	.	45	60	9	100	0	.	0	.	82	61	0	.	28	100	1280	18	805	18	1025	19	3325	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	262	34	475	26	0	.	747	20
	other conifers	0	.	31	81	7	100	59	51	41	85	0	.	0	.	0	.	0	.	0	.	15	100	0	0	0	.	0	.	153	36
	beech	3409	10	964	19	605	20	1043	16	1213	14	419	34	1235	14	1346	16	491	25	390	33	217	29	273	32	310	26	1630	16	13546	5
	maple	765	19	147	35	41	41	249	27	473	19	147	45	496	22	501	24	296	33	71	45	237	33	40	62	90	46	104	56	3656	8
	ash	236	26	142	39	390	35	215	25	483	22	182	51	226	67	261	29	129	46	110	66	103	44	160	43	103	51	126	38	2865	11
	oak	246	30	128	37	120	43	92	63	139	34	29	77	0	.	16	71	9	100	7	100	0	.	81	51	7	100	272	29	1146	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	79	0	.	1373	20	1422	19
	other broadleaves	181	31	80	46	122	48	191	31	302	25	9	100	69	60	162	34	79	43	51	48	40	57	55	44	124	39	1368	19	2833	11
	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
	total	9461	6	2131	13	2099	14	3704	10	4838	9	2874	14	6257	8	5414	9	3230	11	1880	17	1370	16	5879	9	7343	7	8116	7	64597	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1715	10	170	31	404	23	677	20	1089	12	1179	15	1807	12	1371	13	1483	14	661	25	498	22	1659	12	3279	9	1341	15	17331	3
	fir	1444	10	131	41	159	32	560	16	444	18	240	26	813	13	450	17	184	32	241	36	106	34	173	34	88	35	148	32	5182	5
	pine	225	25	94	35	54	47	61	37	211	24	0	.	117	59	7	100	14	71	22	58	0	.	262	26	356	24	34	60	1457	11
	larch	7	100	14	70	29	61	21	74	47	55	0	.	0	.	7	100	30	79	0	.	0	0	1043	17	711	17	668	16	2576	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	105	36	219	25	0	.	324	21
	other conifers	0	.	28	61	21	100	27	50	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	90	35
	beech	1948	9	762	16	471	21	737	14	738	13	129	37	767	16	987	16	333	27	135	33	264	25	77	55	201	33	628	16	8180	4
	maple	333	16	83	37	23	75	48	43	138	26	63	41	112	29	162	25	106	31	48	42	117	30	56	60	23	57	44	47	1355	8
	ash	196	24	56	39	175	33	157	38	283	19	40	60	74	37	175	30	34	54	45	61	33	54	20	73	0	0	51	54	1339	10
	oak	179	32	119	40	131	32	89	34	144	30	19	100	34	61	0	.	8	100	25	72	0	.	21	75	12	100	133	34	913	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	6	100	600	20	620	20
	other broadleaves	176	42	49	42	42	47	30	60	208	25	6	100	45	41	0	.	54	62	6	100	25	61	45	47	14	71	431	20	1131	12
	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	6223	5	1507	12	1510	13	2407	10	3308	7	1677	13	3769	8	3159	9	2253	12	1183	18	1042	16	3469	9	4909	7	4085	7	40500	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1021	11	132	31	268	29	335	18	486	16	655	16	1097	11	1088	13	787	16	367	22	348	20	966	13	1755	10	862	19	10166	4
	fir	931	11	35	44	145	34	498	18	267	21	269	24	756	14	465	21	160	27	92	40	69	47	142	41	92	34	183	30	4104	6
	pine	105	29	31	50	47	62	54	39	128	30	0	.	43	54	17	100	0	.	15	100	0	.	68	41	184	29	14	100	706	13
	larch	7	100	7	100	8	100	14	71	63	36	0	.	0	.	14	100	29	61	0	.	12	100	744	15	559	17	442	17	1898	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	71	135	27	0	.	177	27
	other conifers	0	.	42	62	0	.	47	62	28	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	124	36
	beech	821	12	360	22	263	25	566	14	686	12	111	35	512	20	325	20	150	31	85	44	164	35	26	77	59	44	295	20	4423	5
	maple	116	26	35	44	18	72	28	50	70	35	38	45	40	45	92	36	31	60	12	70	27	50	0	.	42	45	0	.	549	13
	ash	28	50	45	49	88	36	124	38	212	21	0	.	31	75	52	42	28	50	18	74	38	46	7	100	0	.	33	72	706	13
	oak	63	37	30	60	56	56	99	30	152	25	0	.	0	.	0	.	0	.	6	100	0	.	8	100	27	81	57	41	499	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	297	26	303	26
	other broadleaves	7	100	28	61	55	56	45	54	45	41	0	.	6	100	39	52	38	69	6	100	12	70	23	57	14	100	38	52	357	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	3099	6	746	15	948	15	1808	10	2136	8	1074	13	2486	8	2092	10	1222	12	602	18	671	17	2027	10	2873	8	2228	10	24013	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	545	15	90	29	106	36	213	24	250	19	326	20	446	17	609	17	402	17	217	24	165	30	366	19	675	14	344	17	4755	5
	fir	395	18	21	58	77	35	249	22	181	26	108	42	359	18	191	23	120	34	24	58	6	100	20	73	53	39	30	50	1832	8
	pine	15	71	0	.	14	71	27	50	20	75	0	.	17	72	7	100	0	.	0	.	0	.	7	100	58	35	0	.	164	22
	larch	0	.	0	.	7	100	7	100	20	58	0	.	0	.	0	.	7	100	0	.	6	100	401	20	336	19	186	23	970	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	22	58	0	.	29	50
	other conifers	0	.	14	100	0	.	40	57	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	69	42
	beech	190	23	128	28	102	27	191	21	271	18	14	71	135	32	92	31	28	50	42	57	46	54	20	73	41	41	142	29	1441	8
	maple	7	100	0	.	7	100	0	.	41	47	0	.	6	100	27	76	23	59	0	.	0	.	0	.	0	.	0	.	112	30
	ash	39	53	14	70	14	71	36	45	63	33	0	.	7	100	28	61	10	100	0	.	12	100	0	.	0	.	10	100	233	19
	oak	19	73	7	100	30	50	32	51	46	60	0	.	0	0	0	.	0	.	0	.	0	.	0	.	6	100	8	100	148	27
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	75	31	82	30
	other broadleaves	0	.	14	100	21	57	7	100	9	100	0	.	0	0	8	100	31	82	0	.	6	100	7	100	7	100	15	71	125	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	1210	10	288	18	377	17	801	13	908	11	448	18	970	12	963	14	621	15	282	22	242	24	835	13	1198	10	818	10	9961	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	175	23	49	47	37	46	77	33	107	31	99	32	207	19	217	24	199	22	109	33	140	33	163	29	295	18	181	28	2054	7
	fir	182	21	7	100	42	49	90	29	113	28	35	52	155	24	98	27	87	35	56	62	13	71	37	60	34	45	30	49	979	10
	pine	7	100	0	.	0	.	14	71	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	48	38
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	15	71	14	71	0	.	7	100	163	28	106	27	86	28	398	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	0	.	0	.	0	.	20	58	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	58
	beech	28	61	28	50	20	58	88	36	113	28	22	58	70	37	29	79	18	57	0	.	9	100	6	100	18	74	44	41	493	14
	maple	0	.	16	71	0	.	0	.	7	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	50
	ash	15	71	0	.	0	.	10	100	7	100	8	100	0	.	7	100	0	.	6	100	0	.	0	.	6	100	0	.	59	36
	oak	0	.	18	70	14	71	0	.	27	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	66	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	40	67	40
	other broadleaves	0	.	16	71	21	58	0	.	7	100	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	415	15	133	26	135	25	297	18	413	15	169	25	432	14	366	18	324	17	171	29	169	31	398	19	502	13	414	16	4338	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	0	.	17	72	20	58	53	39	32	52	46	42	60	36	36	45	23	58	29	50	43	41	113	30	49	47	568	12
	fir	51	42	0	.	33	53	49	42	50	42	9	100	72	37	28	59	8	100	0	.	12	70	0	.	15	71	7	100	335	16
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	40	56	43	50	51	178	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	15	71	0	.	29	61
	other conifers	0	.	7	100	0	.	13	71	14	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	47
	beech	8	100	0	.	0	.	0	.	20	58	0	.	7	100	8	100	0	.	15	71	0	.	0	0	0	.	37	45	95	28
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100
	ash	0	.	7	100	0	.	0	.	9	100	0	.	0	.	17	72	0	.	0	.	0	.	0	.	0	.	0	.	33	51
	oak	0	.	0	.	0	.	0	.	29	50	0	.	0	.	8	100	0	.	10	100	0	.	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	46	45	46
	other broadleaves	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	105	30	14	70	58	39	82	31	182	22	41	46	133	26	122	26	44	41	48	41	49	38	128	28	199	22	188	24	1393	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	0	.	0	.	7	100	13	71	9	100	6	100	0	.	31	50	0	.	29	50	22	58	49	43	18	72	199	20
	fir	22	58	0	.	0	.	7	100	7	100	0	.	6	100	6	100	0	.	7	100	7	100	0	.	0	.	25	58	88	29
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	7	100	0	.	43	41
	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	.	0	.	0	.	32	52	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	39	46
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	71
	oak	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	38	60	45	53
	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	37	45	0	.	0	.	30	50	66	34	9	100	12	71	14	71	31	50	7	100	42	41	73	31	63	37	81	37	465	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	7	100	7	100	0	.	7	100	14	71	14	71	7	100	0	.	14	71	7	100	23	58	18	72	126	24
	fir	15	71	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	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	.	0	.	7	100	14	100	22	74
	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	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	57	44	57
	other broadleaves	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	22	58	0	.	7	100	14	71	16	100	7	100	21	57	14	71	7	100	0	.	14	71	14	71	37	45	76	41	251	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13142	8	2374	23	3614	21	8983	13	10216	11	8044	14	17484	10	11458	11	11294	11	5283	18	4893	19	17710	10	30504	7	11014	13	156015	3
	fir	14708	8	1020	30	1605	22	6616	13	3241	15	3048	22	10007	11	3714	15	1826	21	1923	24	579	27	1501	24	634	26	1563	25	51983	4
	pine	1160	24	499	28	260	38	497	33	655	22	0	.	883	57	37	55	176	62	361	51	0	.	4621	22	4169	20	223	72	13541	11
	larch	21	58	127	51	671	64	92	48	632	36	140	71	67	81	135	74	446	43	0	.	142	72	6942	12	3766	13	5302	15	18482	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1592	24	2581	24	49	100	4231	17
	other conifers	419	40	293	49	47	72	349	28	686	37	0	.	57	52	170	40	17	100	87	100	82	64	173	56	518	40	44	70	2942	15
	beech	17324	7	6294	12	4115	18	6656	10	9027	9	2154	24	8580	11	6213	13	3113	20	1735	25	2356	24	2216	28	1615	24	12090	13	83489	3
	maple	5416	12	1670	19	1089	35	1391	19	3526	13	1234	24	2276	19	2505	18	2667	19	544	30	1113	25	1205	25	663	31	1122	28	26420	5
	ash	1826	17	1134	22	1284	23	1154	20	2098	14	832	32	1129	34	1311	20	778	29	720	36	357	33	912	29	589	52	2240	24	16364	7
	oak	1948	32	544	30	1141	31	522	24	832	18	88	56	81	56	45	56	176	65	67	68	0	.	1080	34	91	72	1978	26	8591	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	90	65	45	72	10308	14	10450	13
	other broadleaves	3037	14	1102	19	1331	24	2250	20	3743	15	833	25	1559	19	1363	24	1807	21	602	29	809	36	4280	15	3705	25	13962	11	40383	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	59000	4	15057	10	15157	11	28510	8	34655	6	16373	11	42123	6	26950	8	22317	9	11322	15	10333	14	42322	7	48881	6	59893	5	432892	2

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. 1327027. Birmensdorf, Swiss Federal Research Institute WSL

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