Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
number of stems
diameter at breast height (in 4 cm classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (in 4 cm classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3598	12	6394	12	7166	10	16617	6	2240	16	36016	4
	fir	3665	11	2386	14	3741	12	1360	16	305	33	11457	6
	pine	74	62	159	73	104	85	2438	19	49	100	2824	17
	larch	0	.	349	84	133	65	2278	13	1433	20	4193	12
	Arolla pine	0	.	0	.	0	.	848	22	32	100	880	22
	other conifers	328	44	350	39	89	45	741	29	0	.	1508	19
	beech	4789	9	4074	9	3612	12	2434	14	3102	15	18011	5
	maple	1584	17	1323	17	1335	19	2016	15	573	33	6831	8
	ash	881	23	1474	17	860	22	1221	19	891	25	5326	9
	oak	687	49	187	37	57	58	588	33	513	34	2032	21
	sweet chestnut	0	.	17	100	0	.	84	100	2813	21	2914	21
	other broadleaves	1831	13	2253	16	2122	15	6922	11	6016	13	19144	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	17437	5	18966	6	19219	6	37547	4	17967	7	111136	2
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3016	14	3907	12	6494	9	12052	6	1551	17	27019	4
	fir	2448	10	1997	17	2520	13	1070	18	160	48	8195	7
	pine	71	62	168	73	121	77	2080	19	16	100	2457	17
	larch	0	.	106	53	126	62	1939	13	1134	19	3305	10
	Arolla pine	0	.	0	.	0	.	1167	21	0	.	1167	21
	other conifers	124	44	210	37	18	100	313	35	0	.	666	22
	beech	3902	10	2990	10	2763	12	2377	14	2605	16	14636	5
	maple	1259	16	1109	17	1144	19	1495	15	422	33	5429	8
	ash	759	25	876	19	538	25	1178	20	443	32	3795	10
	oak	327	35	467	41	0	.	213	37	489	41	1497	21
	sweet chestnut	0	.	0	.	0	.	63	100	2490	18	2553	18
	other broadleaves	809	20	1177	16	1185	24	4310	13	3843	14	11324	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	12716	5	13006	6	14909	6	28258	4	13152	7	82041	2
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2062	14	3340	12	4760	10	9409	7	1485	15	21057	5
	fir	1953	13	1069	19	1762	13	689	18	213	35	5685	7
	pine	123	37	135	49	121	71	1879	18	16	100	2275	16
	larch	17	100	120	51	68	79	1763	16	1111	20	3080	12
	Arolla pine	0	.	0	.	0	.	550	22	16	100	566	22
	other conifers	51	58	137	39	74	50	87	53	0	.	350	24
	beech	2754	11	2410	10	2475	11	1498	14	2040	17	11178	6
	maple	852	18	1010	22	812	19	734	18	160	37	3568	10
	ash	386	24	656	20	852	22	775	23	318	36	2986	11
	oak	246	35	258	33	87	62	287	35	311	30	1188	16
	sweet chestnut	0	.	0	.	0	.	0	0	2474	18	2474	18
	other broadleaves	591	19	1021	18	758	27	2164	15	2496	14	7030	8
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0
	total	9035	6	10157	6	11770	6	19834	5	10640	7	61436	3
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1910	15	2549	13	4477	10	7680	7	1099	17	17715	5
	fir	1411	16	870	18	1532	14	752	19	88	66	4653	8
	pine	273	39	137	39	139	88	1189	18	0	0	1739	16
	larch	0	0	51	74	0	.	1049	16	1100	18	2200	12
	Arolla pine	0	.	0	.	0	.	799	20	0	.	799	20
	other conifers	52	58	51	58	0	.	69	61	0	.	172	35
	beech	2753	10	2072	11	1868	13	1550	16	1570	18	9813	6
	maple	703	19	631	20	1055	18	742	19	39	70	3169	10
	ash	251	39	551	21	902	22	438	28	305	32	2447	12
	oak	395	41	382	26	45	72	110	41	344	29	1275	17
	sweet chestnut	0	.	0	.	0	.	36	72	1331	19	1367	19
	other broadleaves	345	24	560	19	442	29	839	17	1696	15	3883	9
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	8093	6	7855	6	10461	6	15252	5	7572	7	49233	3
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1671	14	2003	14	4057	9	6525	7	1073	16	15330	5
	fir	906	20	748	18	954	16	531	22	126	43	3265	9
	pine	298	42	68	62	69	100	902	18	68	60	1404	16
	larch	0	.	103	47	0	.	1272	14	875	21	2249	12
	Arolla pine	0	.	0	.	0	.	550	22	0	.	550	22
	other conifers	35	71	103	47	0	.	33	71	0	.	170	35
	beech	2376	12	1746	11	1922	12	1156	15	1067	21	8267	6
	maple	523	23	526	20	859	17	651	20	116	48	2675	10
	ash	320	27	558	19	479	25	536	28	80	81	1973	12
	oak	369	29	259	27	21	100	88	53	225	39	961	17
	sweet chestnut	0	.	0	.	0	.	37	100	1153	21	1190	20
	other broadleaves	179	34	327	28	232	31	401	26	995	15	2135	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	6677	7	6440	6	8593	6	12682	5	5778	7	40170	3
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1394	15	1792	14	3278	9	5447	7	845	21	12755	5
	fir	1102	15	672	18	1136	14	298	29	109	47	3317	9
	pine	244	35	223	29	35	70	752	21	16	100	1270	15
	larch	34	71	86	44	0	.	1173	16	510	23	1803	12
	Arolla pine	0	.	0	.	0	.	371	26	0	.	371	26
	other conifers	17	100	51	75	0	.	0	.	0	.	68	61
	beech	2100	11	1306	14	1328	17	563	19	550	24	5846	7
	maple	369	24	339	22	389	23	308	29	36	70	1441	12
	ash	257	30	556	21	405	30	360	35	38	71	1615	14
	oak	209	31	223	32	21	100	0	.	125	41	577	19
	sweet chestnut	0	.	0	.	0	.	61	76	618	24	679	22
	other broadleaves	216	36	324	26	116	41	184	32	684	22	1524	13
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0
	total	5942	7	5572	7	6706	6	9516	5	3531	9	31267	3
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1166	12	1415	11	2820	7	4601	6	750	16	10752	4
	fir	769	13	496	15	748	11	393	17	81	41	2487	7
	pine	325	27	206	22	77	47	479	15	27	60	1113	12
	larch	21	58	41	47	14	71	895	14	489	18	1460	10
	Arolla pine	0	.	0	.	0	.	240	25	0	.	240	25
	other conifers	21	75	13	71	0	.	7	100	0	.	41	47
	beech	1880	8	1092	10	1084	12	530	16	390	19	4975	5
	maple	303	16	205	21	323	21	293	26	23	58	1146	11
	ash	245	22	512	19	249	24	156	27	39	44	1200	11
	oak	123	29	194	25	18	71	59	40	74	33	468	15
	sweet chestnut	0	.	7	100	0	.	10	100	357	21	374	20
	other broadleaves	64	40	257	20	40	45	233	23	415	19	1008	11
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0
	total	4916	5	4438	5	5371	5	7895	4	2644	7	25264	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1150	10	1138	10	2374	8	4002	6	680	16	9343	4
	fir	877	12	543	15	778	12	335	22	73	37	2607	7
	pine	257	22	162	25	59	60	407	18	28	62	913	12
	larch	7	100	34	53	0	.	940	13	396	17	1376	10
	Arolla pine	0	.	0	.	0	.	209	22	0	.	209	22
	other conifers	14	71	28	79	0	.	0	.	0	.	41	58
	beech	1527	9	967	10	1017	13	529	16	298	20	4338	5
	maple	211	19	92	30	164	23	185	23	22	58	674	11
	ash	115	27	416	17	183	25	86	36	27	70	827	12
	oak	144	30	180	25	23	58	29	61	70	43	448	16
	sweet chestnut	0	.	0	.	0	.	14	71	351	22	364	22
	other broadleaves	155	42	148	22	35	50	133	34	196	25	667	15
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	4456	5	3708	5	4634	5	6868	4	2141	8	21808	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	835	11	957	12	2075	8	3091	6	481	17	7439	4
	fir	776	12	431	14	694	12	358	17	51	37	2310	7
	pine	120	29	196	22	27	59	258	23	7	100	607	13
	larch	7	100	55	39	0	.	803	13	221	22	1085	11
	Arolla pine	0	.	0	.	0	.	123	28	0	.	123	28
	other conifers	0	.	34	53	0	.	0	.	8	100	42	47
	beech	1164	10	893	10	813	12	393	18	143	27	3406	6
	maple	173	22	86	31	94	31	89	27	0	.	443	14
	ash	158	26	255	20	101	32	102	32	8	100	624	13
	oak	69	34	179	23	21	71	20	73	72	46	360	17
	sweet chestnut	0	.	0	.	0	.	0	.	193	25	193	25
	other broadleaves	28	50	111	34	33	67	73	42	146	26	392	17
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0
	total	3330	5	3196	5	3859	5	5309	4	1329	9	17022	2
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	744	12	888	12	1823	8	2756	7	391	16	6602	4
	fir	564	14	519	14	640	13	248	19	74	33	2046	7
	pine	108	33	134	25	71	64	151	26	0	.	465	17
	larch	7	100	28	50	7	100	682	13	233	21	957	11
	Arolla pine	0	.	0	.	0	.	127	26	0	.	127	26
	other conifers	35	72	20	74	0	.	0	.	0	.	55	53
	beech	884	10	879	10	531	16	287	19	238	22	2818	6
	maple	56	35	56	35	125	26	137	39	0	.	373	18
	ash	36	45	339	18	61	35	67	31	41	62	544	13
	oak	79	37	167	30	0	.	21	58	18	71	286	21
	sweet chestnut	0	.	0	.	0	.	7	100	104	28	111	27
	other broadleaves	32	81	146	32	23	59	46	46	41	53	287	22
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	2545	6	3177	6	3279	6	4531	5	1139	9	14671	3
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	496	14	593	13	1547	9	1913	7	338	20	4886	5
	fir	407	16	443	15	575	12	171	23	74	39	1670	8
	pine	42	41	123	25	19	73	139	30	14	100	338	17
	larch	0	.	36	45	0	.	674	13	244	19	954	11
	Arolla pine	0	.	0	.	0	.	43	41	0	.	43	41
	other conifers	7	100	34	53	0	.	0	.	0	.	41	47
	beech	547	14	570	12	419	16	200	21	124	29	1860	7
	maple	35	44	21	58	67	36	28	50	8	100	159	22
	ash	49	37	186	20	44	41	30	51	0	.	309	15
	oak	23	58	111	26	0	.	22	74	18	72	174	22
	sweet chestnut	0	.	0	.	0	.	6	100	113	33	119	32
	other broadleaves	47	59	27	71	8	100	86	34	24	58	192	25
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	1653	7	2144	6	2680	6	3312	5	956	10	10745	3
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	630	12	649	14	1147	10	1575	8	354	19	4356	5
	fir	315	16	369	16	383	14	215	20	51	47	1334	8
	pine	34	53	67	37	19	71	74	32	0	.	195	21
	larch	7	100	40	47	21	74	584	13	157	23	809	11
	Arolla pine	0	.	0	.	0	.	43	41	0	.	43	41
	other conifers	0	.	7	100	0	.	0	.	8	100	15	71
	beech	464	15	469	14	272	19	202	24	54	38	1461	8
	maple	42	41	27	50	24	58	15	72	0	.	108	26
	ash	48	41	163	28	29	61	6	100	13	100	259	21
	oak	7	100	34	45	0	.	6	100	0	.	47	38
	sweet chestnut	0	.	0	.	0	.	0	.	106	32	106	32
	other broadleaves	7	100	27	80	16	71	81	52	15	71	146	34
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	1554	8	1853	7	1912	7	2802	6	758	13	8879	3
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	333	16	381	16	869	13	926	9	142	24	2650	6
	fir	248	21	238	17	321	19	130	26	8	100	945	10
	pine	15	71	20	58	13	71	43	41	7	100	99	27
	larch	0	.	7	100	0	.	302	19	93	33	401	16
	Arolla pine	0	.	0	.	0	.	29	61	0	.	29	61
	other conifers	0	.	20	58	0	.	0	.	0	.	20	58
	beech	200	21	276	17	103	28	61	33	74	34	714	11
	maple	8	100	28	61	24	79	37	52	0	.	96	34
	ash	38	45	75	30	7	100	28	59	0	.	148	23
	oak	7	100	90	32	0	.	6	100	8	100	111	28
	sweet chestnut	0	.	0	.	0	.	27	77	30	50	57	45
	other broadleaves	14	70	14	71	9	100	57	42	8	100	102	30
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	862	11	1149	8	1346	11	1644	7	370	15	5371	4
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	233	21	210	22	659	13	690	11	135	22	1927	7
	fir	150	26	242	20	287	22	104	29	14	70	797	12
	pine	7	100	27	50	11	100	36	53	0	.	81	33
	larch	0	.	14	71	0	.	287	24	72	34	372	20
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	20	58	0	.	0	.	0	.	20	58
	beech	124	25	184	19	125	25	79	34	52	42	564	12
	maple	0	.	21	58	0	.	7	100	0	.	28	50
	ash	7	100	25	78	23	58	0	.	10	100	65	41
	oak	19	73	40	45	0	.	0	.	7	100	67	36
	sweet chestnut	0	.	0	.	0	.	0	.	16	71	16	71
	other broadleaves	7	100	7	100	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	547	13	789	10	1104	10	1217	9	306	16	3964	5
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	183	21	166	22	374	14	559	13	140	28	1422	8
	fir	118	24	225	18	138	24	94	35	15	71	591	12
	pine	0	.	14	71	7	100	14	71	0	.	35	45
	larch	0	.	7	100	7	100	169	21	80	32	263	17
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	21	58	0	.	0	.	0	.	21	58
	beech	95	45	139	24	70	32	12	70	32	50	349	18
	maple	7	100	0	.	6	100	7	100	0	.	20	58
	ash	8	100	32	51	7	100	12	70	0	.	60	36
	oak	18	70	37	45	0	.	7	100	0	.	62	36
	sweet chestnut	0	.	0	.	0	.	0	.	44	46	44	46
	other broadleaves	9	100	21	58	9	100	12	70	0	.	51	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	437	15	662	11	620	11	902	10	311	17	2931	5
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	43	41	154	24	312	17	390	15	98	30	997	10
	fir	79	30	35	45	108	29	66	34	8	100	296	16
	pine	7	100	7	100	0	.	0	.	0	.	14	71
	larch	7	100	0	.	9	100	145	24	34	45	195	20
	Arolla pine	0	.	0	.	0	.	21	58	0	.	21	58
	other conifers	7	100	27	79	0	.	0	.	0	.	34	66
	beech	54	38	63	33	77	36	27	50	7	100	228	19
	maple	9	100	0	.	0	.	0	.	0	.	9	100
	ash	7	100	9	100	11	100	0	.	0	.	27	58
	oak	9	100	7	100	0	.	0	.	0	.	16	71
	sweet chestnut	0	.	0	.	0	.	10	100	39	44	48	41
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	222	18	301	17	516	14	660	11	186	20	1885	7
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	67	40	56	39	160	24	247	20	68	37	596	13
	fir	29	71	74	35	121	27	47	38	15	71	287	18
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	118	28	23	76	142	26
	Arolla pine	0	.	0	.	0	.	28	50	0	.	28	50
	other conifers	0	.	7	100	0	.	0	.	0	.	7	100
	beech	7	100	52	38	0	0	26	50	22	58	107	26
	maple	0	.	0	.	0	.	7	100	0	.	7	100
	ash	0	.	15	71	7	100	7	100	0	.	30	50
	oak	0	.	15	71	8	100	0	.	0	.	23	58
	sweet chestnut	0	.	0	.	0	.	0	.	16	71	16	71
	other broadleaves	0	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	103	33	226	19	296	18	481	14	144	25	1250	9
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	80	31	106	28	166	24	44	41	410	14
	fir	0	.	40	41	33	50	6	100	0	.	79	30
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	61	40	36	45	96	30
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	8	100	0	.	0	.	0	.	8	100
	beech	19	71	14	71	7	100	0	.	22	58	62	36
	maple	0	.	0	.	0	.	8	100	0	.	8	100
	ash	7	100	7	100	7	100	0	.	0	.	21	58
	oak	0	.	22	58	0	.	8	100	0	.	30	50
	sweet chestnut	0	.	0	.	0	.	0	.	0	0	0	0
	other broadleaves	0	.	7	100	0	.	0	.	7	100	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	40	45	177	21	154	23	264	19	109	25	743	10
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	13	71	33	50	142	29	39	46	234	21
	fir	29	50	14	71	29	61	30	50	0	.	101	29
	pine	0	.	7	100	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	36	45	31	51	66	34
	Arolla pine	0	.	0	.	0	.	15	71	0	.	15	71
	other conifers	0	.	8	100	7	100	0	.	0	.	15	71
	beech	0	.	42	46	9	100	14	71	7	100	72	34
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	20	58	0	.	0	.	0	.	20	58
	sweet chestnut	0	.	0	.	0	.	0	.	45	46	45	46
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	37	45	104	27	78	34	243	20	121	26	582	12
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	70	7	100	52	60	114	27	15	71	202	23
	fir	22	58	7	100	12	71	7	100	15	71	63	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	33	51	7	100	40	46
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	7	100	7	100	0	.	15	71
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	10	100	0	.	0	.	0	.	10	100
	oak	0	.	7	100	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	8	100	15	71	23	58
	other broadleaves	7	100	7	100	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	43	40	37	45	72	46	183	21	52	38	387	15
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	7	100	0	0	6	100	6	100	27	50
	fir	15	71	13	71	8	100	0	.	10	100	47	41
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	15	71	0	.	15	71
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	7	100
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	12	100	0	.	0	.	0	.	12	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	22	58	38	46	8	100	29	50	24	59	122	25
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	28	50	7	100	0	.	35	45
	fir	7	100	0	.	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	0	.	14	71
	Arolla pine	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	0	.	0	.	0	.	7	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	12	100	0	.	0	.	0	.	12	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	35	61	28	50	22	58	7	100	98	30
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	28	50	14	71	11	100	54	38
	fir	0	.	14	71	0	.	0	.	0	.	14	71
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	7	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	14	71	28	50	14	71	26	59	83	30
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	14	71	0	.	15	71	0	.	35	45
	fir	0	.	0	.	7	100	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	14	71	7	100	15	71	0	.	42	41
108-111 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	30	50	0	.	30	50
	fir	0	.	7	100	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	7	100	0	.	30	50	7	100	44	41
112-115 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	100	7	100	15	71
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	18	100	0	.	0	.	0	.	18	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	18	100	0	.	22	58	14	71	54	45
116-119 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	0	.	7	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	7	100	0	.	7	100
120-123 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
124-127 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
128-131 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	18	100	0	.	0	.	0	.	18	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	18	100	0	.	0	.	0	.	18	100
132-135 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	8	100	8	100
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
136-139 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
140-143 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	6	100	6	100
144-147 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
148-151 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
152-155 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
156-159 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	6	100	6	100
160-163 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
164-167 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
168-171 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
172-175 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
176-179 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
180-183 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
184-187 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
188-191 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
192-195 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
196-199 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
=200 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	6	100	6	100
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19580	7	26713	7	44639	5	78975	4	11990	10	181897	3
	fir	15891	7	11450	9	16528	7	6905	11	1491	25	52265	4
	pine	1999	19	1853	19	892	57	10848	13	249	63	15841	10
	larch	107	34	1075	35	385	51	15247	7	8293	11	25108	6
	Arolla pine	0	.	0	.	0	.	5231	14	49	100	5280	14
	other conifers	691	29	1147	24	188	30	1251	27	16	100	3293	15
	beech	25639	5	20246	5	18502	7	11944	10	12396	13	88727	3
	maple	6132	9	5474	12	6421	11	6766	11	1398	26	26191	5
	ash	3572	14	6723	9	4765	14	5002	14	2211	24	22274	6
	oak	2731	24	2896	16	300	30	1464	25	2274	23	9664	11
	sweet chestnut	0	.	23	76	0	.	363	68	12379	13	12765	13
	other broadleaves	4338	11	6501	10	5028	14	15541	10	16583	10	47992	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	80681	3	84101	3	97649	3	159537	2	69328	4	491296	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. 1350097. Birmensdorf, Swiss Federal Research Institute WSL

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