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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NFI1
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		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	1170	17	265	35	367	26	862	21	960	16	839	20	1136	13	778	19	402	15	307	29	313	30	1005	14	1480	11	330	17	10212	5
	fir	950	13	64	37	165	36	588	22	244	21	184	30	558	14	245	21	89	29	99	40	55	53	62	40	40	37	61	43	3404	7
	pine	58	43	39	67	27	92	19	86	31	46	0	.	43	68	0	.	8	100	27	60	0	.	407	24	258	23	5	73	922	14
	larch	2	100	3	109	0	0	3	100	27	55	0	.	0	.	0	.	9	74	0	.	10	71	282	26	93	25	164	22	594	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	39	78	25	0	.	120	21
	other conifers	11	41	33	74	8	100	26	47	30	53	0	.	0	.	12	51	2	100	4	72	4	78	4	100	3	84	0	.	137	25
	beech	1536	10	625	17	334	21	699	15	842	14	149	37	703	15	446	19	261	33	108	36	150	28	152	39	119	30	824	18	6947	5
	maple	326	17	85	28	23	60	57	35	209	22	91	30	139	23	135	26	122	29	19	43	97	40	50	41	16	55	39	38	1407	8
	ash	235	28	87	33	130	37	89	37	289	19	136	35	206	36	243	29	55	42	27	40	79	61	42	38	55	68	93	30	1765	10
	oak	165	40	25	45	239	39	54	44	45	34	10	67	8	71	8	73	11	54	0	100	0	.	40	39	1	100	144	37	750	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	73	0	.	1069	15	1084	15
	other broadleaves	271	20	92	28	135	37	139	31	331	18	90	28	162	28	261	30	124	28	85	46	65	36	246	21	187	25	1096	12	3284	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4725	7	1317	13	1428	14	2534	11	3006	8	1499	15	2955	8	2128	11	1084	13	674	20	773	20	2348	10	2329	9	3825	7	30625	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2289	12	581	34	1250	23	1696	16	1998	14	1695	17	2695	11	2671	16	1330	16	846	24	924	29	2265	13	3971	11	1085	19	25297	4
	fir	2191	13	159	37	287	46	1449	17	693	20	489	36	1281	14	742	21	155	29	196	34	101	42	131	46	77	42	68	46	8022	6
	pine	265	32	123	53	124	57	52	56	132	38	0	.	61	63	0	.	34	75	116	43	0	.	583	20	593	22	59	53	2143	11
	larch	13	100	27	71	49	85	48	90	49	50	0	.	0	.	0	.	19	77	0	.	20	82	671	19	422	20	417	26	1735	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	30	234	31	0	.	341	23
	other conifers	0	.	13	71	17	100	17	100	51	59	0	.	0	.	0	.	0	.	0	.	-4	100	0	.	0	.	0	.	95	42
	beech	3580	10	1318	16	784	22	1372	15	1584	13	292	32	1203	16	1494	18	454	22	170	31	344	31	156	46	198	35	890	17	13840	5
	maple	663	15	203	30	24	81	150	29	545	20	73	49	268	26	376	22	235	29	92	36	233	29	19	79	56	50	20	58	2960	8
	ash	496	23	217	37	295	32	237	27	465	20	93	61	188	32	443	25	69	47	77	71	64	78	70	56	71	62	16	76	2803	9
	oak	259	28	109	37	195	28	147	27	323	27	22	100	4	100	30	100	5	100	10	100	0	.	29	60	0	.	176	36	1309	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	3	100	891	18	901	18
	other broadleaves	318	25	126	33	216	49	154	32	494	21	33	77	90	43	189	32	73	40	25	49	72	47	96	32	76	49	804	19	2766	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10074	6	2877	13	3243	13	5323	9	6335	7	2697	15	5791	8	5946	10	2375	12	1533	18	1754	19	4135	9	5701	9	4427	7	62212	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3823	10	875	23	2244	21	3711	13	4698	11	2615	17	4907	11	3882	13	3140	16	1141	25	1078	21	2725	13	5815	10	1601	16	42257	3
	fir	2944	11	334	42	436	41	2148	15	1467	16	585	28	1926	15	1436	17	304	30	332	37	180	42	192	42	172	33	223	34	12677	5
	pine	538	25	401	31	392	40	257	41	916	22	0	.	74	53	22	71	23	59	66	66	0	.	393	22	799	22	131	71	4013	10
	larch	0	.	32	70	66	74	57	54	169	41	0	.	0	.	0	.	74	54	0	.	0	.	1110	15	1197	16	687	23	3392	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	137	36	354	27	0	.	499	21
	other conifers	0	.	0	.	10	100	85	72	42	78	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	148	48
	beech	4391	9	1281	16	1049	23	2188	15	1665	14	365	35	1841	17	1577	19	438	28	140	37	452	28	58	65	270	39	534	18	16248	5
	maple	439	20	279	30	31	80	66	49	229	29	81	61	218	41	340	25	86	40	50	56	119	38	24	100	15	71	12	89	1989	10
	ash	304	31	204	32	198	34	279	32	926	17	24	77	177	36	176	34	108	45	50	71	45	61	24	100	17	100	-10	74	2521	10
	oak	241	33	146	37	255	34	155	32	343	26	0	.	10	100	5	100	0	.	12	100	0	.	0	.	34	71	246	39	1448	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	72	4	100	377	22	404	21
	other broadleaves	243	30	225	35	196	38	91	41	329	27	13	100	35	69	79	47	157	44	12	100	90	46	61	46	98	61	353	29	1983	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12923	5	3778	13	4877	13	9037	8	10785	7	3684	15	9186	8	7517	9	4337	13	1803	19	1964	17	4758	9	8776	8	4153	9	87578	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4854	8	1185	28	1680	19	4166	14	4690	12	2542	16	5265	10	4275	12	3295	14	1137	23	1478	21	2559	13	5799	10	1793	16	44717	3
	fir	2283	11	194	36	357	42	1788	15	1674	17	510	24	2507	12	1594	19	500	29	190	42	209	42	266	33	293	42	400	31	12767	5
	pine	407	23	223	38	329	43	463	27	1141	22	0	.	252	63	110	69	98	67	11	100	0	.	305	38	509	24	15	100	3863	11
	larch	17	100	48	85	15	100	31	100	235	37	0	.	15	100	40	100	54	51	13	100	33	72	1312	15	1169	17	729	17	3711	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	49	235	34	0	.	313	29
	other conifers	0	.	0	.	123	100	62	64	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	266	57
	beech	2881	11	1226	19	541	25	1846	18	1898	14	181	43	1632	20	980	21	350	34	163	45	471	30	67	59	332	38	346	22	12914	6
	maple	174	31	88	38	25	71	25	71	220	34	39	54	39	59	105	45	77	40	17	71	58	42	0	.	29	59	0	.	897	14
	ash	190	33	115	39	143	41	102	52	491	22	0	.	73	48	162	39	9	100	21	100	55	55	0	.	0	.	40	75	1401	13
	oak	153	39	129	45	186	51	192	36	380	27	0	.	0	.	0	.	0	.	11	100	0	.	0	.	32	72	59	51	1141	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	7	100	327	23	342	22
	other broadleaves	50	64	68	65	57	72	56	46	115	51	0	.	30	59	25	72	91	56	58	63	21	73	46	63	8	100	48	47	674	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11008	6	3275	15	3457	13	8731	9	10925	8	3272	14	9813	8	7293	9	4474	12	1621	19	2325	17	4640	10	8413	8	3758	9	83004	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3295	12	414	30	1051	23	2541	15	2878	15	1437	21	3547	12	3295	15	2304	17	790	28	1051	26	1825	15	4145	12	1440	18	30013	4
	fir	1300	19	43	72	189	39	1183	20	627	24	453	36	1679	19	618	23	416	33	353	43	209	48	175	44	209	43	96	50	7549	8
	pine	182	37	37	100	168	52	195	52	345	28	0	.	94	74	0	.	0	.	0	.	0	.	81	46	392	34	32	71	1527	16
	larch	21	100	23	100	24	100	0	.	147	41	0	.	51	73	20	100	174	51	50	100	120	71	1033	17	1080	20	637	23	3379	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	71	172	39	0	.	192	36
	other conifers	0	.	0	.	26	100	61	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	76
	beech	922	21	1033	23	194	41	941	23	1065	19	97	50	1117	26	430	33	334	33	98	52	177	63	40	100	186	54	363	36	6997	8
	maple	51	121	20	100	0	.	19	100	174	46	13	100	0	.	53	71	26	72	0	.	0	.	0	.	13	100	0	.	368	31
	ash	61	58	0	.	74	58	58	78	260	33	23	100	20	100	30	100	22	100	0	.	0	.	0	.	25	100	0	.	571	21
	oak	103	60	107	54	81	65	44	78	336	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	691	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	168	28	179	27
	other broadleaves	17	100	62	58	44	72	19	100	84	63	0	.	36	100	0	.	79	60	0	.	23	100	50	84	0	.	20	67	434	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5951	9	1740	17	1850	16	5060	11	5916	10	2022	19	6545	10	4446	13	3353	14	1290	24	1581	22	3236	11	6221	9	2777	12	51988	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1021	20	177	53	640	29	773	27	920	21	604	28	1386	18	1294	21	703	27	597	34	652	35	649	30	1572	18	881	28	11868	7
	fir	596	23	28	100	57	71	304	34	251	39	73	100	1097	23	192	41	90	58	0	.	0	.	60	71	75	100	94	59	2917	12
	pine	28	100	44	100	0	.	56	100	124	50	0	.	40	100	31	100	0	.	0	.	0	.	0	.	90	51	0	.	412	29
	larch	0	.	0	.	0	.	31	100	28	100	0	.	0	.	71	71	35	100	0	.	38	100	607	25	568	25	290	33	1668	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	72	65	0	.	84	58
	other conifers	0	.	0	.	0	.	38	100	0	.	0	.	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	71
	beech	276	37	342	45	149	61	372	38	723	29	75	71	207	48	89	71	141	57	31	100	13	100	9	100	162	51	104	40	2693	14
	maple	0	.	146	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	146	50
	ash	23	100	60	100	23	100	0	.	66	71	0	.	0	.	42	100	0	.	0	.	0	.	0	.	0	.	0	.	215	43
	oak	0	.	35	100	0	.	32	100	80	58	0	.	0	.	50	100	0	.	0	.	0	.	0	.	0	.	0	.	197	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	44	107	44
	other broadleaves	0	.	0	.	0	.	0	.	29	100	0	.	0	.	33	100	31	100	0	.	0	.	29	100	0	.	15	100	138	46
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1944	15	832	26	869	25	1607	18	2221	15	752	28	2772	14	1802	19	1000	22	628	33	702	33	1366	19	2538	14	1492	19	20526	5
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	551	32	46	100	231	53	250	41	260	42	150	52	351	37	991	30	341	35	59	71	271	46	335	35	855	22	461	41	5151	11
	fir	511	33	36	100	84	100	137	58	186	50	0	.	779	28	76	71	47	100	92	100	47	100	0	.	124	70	190	61	2308	16
	pine	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	73	71
	larch	34	100	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	500	30	218	39	172	48	963	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	52	36	72	0	.	114	42
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	59	100	52	100	97	72	179	50	0	.	97	75	33	100	27	100	0	.	0	.	35	100	0	.	37	100	617	27
	maple	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	32	71	0	.	0	.	0	.	47	58
	ash	0	.	0	.	35	100	78	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	113	62
	oak	0	.	0	.	36	100	76	71	154	50	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	285	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	254	47	273	45
	other broadleaves	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1096	23	140	59	478	35	670	26	814	23	164	48	1227	22	1100	28	415	33	170	61	350	38	968	21	1271	18	1114	24	9975	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	79	72	57	100	110	71	152	59	0	.	33	100	28	100	174	58	79	74	33	100	172	46	76	71	198	46	62	75	1253	19
	fir	49	100	0	.	0	.	0	.	0	.	0	.	392	42	58	100	39	100	0	.	0	.	50	100	0	.	123	73	712	30
	pine	0	.	0	.	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	73	99	58	0	.	173	46
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	24	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	81	100	64	100	161	59	43	100	0	.	0	.	0	.	0	.	34	100	0	.	0	.	47	78	0	.	431	36
	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	.	64	100	53	100	0	.	149	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	267	46
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	60	118	60
	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	128	59	203	58	227	50	384	51	193	51	33	100	421	40	232	50	118	59	68	71	172	46	224	42	344	33	302	41	3048	14
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	99	71	0	.	70	100	0	.	72	100	0	.	77	100	35	100	0	.	36	100	131	63	0	.	147	58	0	.	666	29
	fir	128	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	128	71
	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	.	95	72	0	.	58	100	154	59
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	34	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	34	100	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	75
	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	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	53	117	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	227	51	0	.	104	75	0	.	178	60	0	.	77	100	35	100	0	.	36	100	131	63	129	59	147	58	175	48	1239	21
>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	.	65	100	0	.	0	.	37	100	0	.	0	.	0	0	0	.	80	100	182	60
	fir	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	100	172	72
	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	.	50	100	64	100	114	71
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	32	100	0	.	62	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	.	364	58	364	58
	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	68	100	0	.	0	.	0	.	0	.	65	100	0	.	0	.	37	100	0	.	0	.	30	100	82	72	611	42	893	32
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17182	7	3599	19	7643	15	14151	10	16474	9	9979	13	19393	8	17394	10	11629	12	4946	19	6069	17	11439	10	23983	7	7734	12	171616	2
	fir	11020	8	858	29	1576	28	7597	12	5142	13	2294	24	10218	10	4962	13	1640	22	1262	30	801	33	936	28	989	27	1358	27	50654	4
	pine	1478	18	868	23	1041	35	1112	26	2725	16	0	.	564	52	163	56	163	57	219	45	0	.	1769	18	2679	17	243	51	13024	7
	larch	86	52	134	54	194	57	170	54	656	27	0	.	66	71	131	58	365	36	63	100	221	53	5684	11	4895	13	3218	14	15882	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	563	23	1212	21	0	.	1783	16
	other conifers	11	41	46	68	185	83	289	53	204	68	0	.	42	100	12	51	2	100	4	72	0	634	15	77	3	84	0	.	813	32
	beech	13587	7	5965	12	3201	18	7676	12	8067	10	1160	24	6800	13	5050	14	2005	20	744	25	1606	23	518	31	1314	31	3098	13	60791	3
	maple	1653	12	821	21	103	45	317	27	1377	16	311	34	664	21	1010	16	546	22	178	32	540	27	93	41	128	38	71	35	7814	6
	ash	1309	19	684	23	897	24	842	25	2497	13	275	38	664	23	1097	20	263	36	175	52	242	48	136	40	168	53	140	30	9388	7
	oak	921	23	614	28	1045	26	700	21	1848	16	32	72	23	72	93	66	16	63	51	60	0	.	69	42	68	71	645	24	6125	9
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	51	14	100	3791	14	3888	13
	other broadleaves	898	17	574	27	649	29	492	25	1382	17	136	28	353	23	587	23	555	30	180	40	271	30	528	27	370	30	2335	12	9311	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	48144	4	14162	10	16534	10	33345	7	40372	5	14188	12	38787	6	30499	7	17193	10	7823	16	9751	14	21833	7	35822	6	22635	5	351089	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. 1217853. Birmensdorf, Swiss Federal Research Institute WSL

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