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
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
grid: 1.4-km grid
state 1983/85
		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	4486	11	7984	10	9433	7	13377	5	1550	15	36829	4
	fir	3389	8	2877	10	3705	8	1574	14	205	30	11750	5
	pine	352	42	304	36	238	40	3515	13	29	100	4438	11
	larch	40	61	225	59	10	100	1849	13	651	19	2776	11
	Arolla pine	0	.	0	.	11	100	759	22	0	.	770	22
	other conifers	209	33	322	37	81	43	333	36	0	.	945	20
	beech	6452	7	5883	7	4879	8	3175	11	3792	12	24181	4
	maple	1243	11	1111	20	1197	12	1532	14	164	29	5246	7
	ash	905	16	1264	14	1232	17	989	16	720	18	5111	7
	oak	745	25	1013	26	97	59	376	21	759	32	2990	14
	sweet chestnut	0	.	0	.	0	.	20	70	3821	13	3841	13
	other broadleaves	1887	14	2569	12	2469	14	4966	10	5186	10	17077	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	19709	4	23552	5	23351	4	32465	4	16876	5	115953	2
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3170	11	4333	9	7510	7	10982	5	1176	15	27170	3
	fir	2562	9	1846	12	2985	9	1193	13	207	32	8793	5
	pine	152	33	134	35	189	54	2695	12	49	66	3220	11
	larch	29	58	149	44	10	100	1540	11	637	18	2366	9
	Arolla pine	0	.	0	.	21	100	555	21	0	.	576	21
	other conifers	178	53	123	35	22	71	140	49	0	.	463	27
	beech	5306	7	4217	7	3432	8	2339	11	2306	13	17600	4
	maple	1093	12	989	17	908	13	808	15	201	26	3999	7
	ash	795	18	1243	13	1151	16	612	20	400	24	4202	8
	oak	541	28	770	24	73	42	312	27	517	23	2212	13
	sweet chestnut	0	.	49	82	0	.	40	61	3085	12	3174	12
	other broadleaves	1113	13	1625	12	1525	16	2249	10	3187	9	9699	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	14939	4	15477	4	17826	4	23467	4	11765	5	83474	2
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2270	10	3124	9	6135	7	9099	5	1076	14	21705	3
	fir	1713	9	1680	12	2110	9	884	14	97	34	6484	5
	pine	166	32	206	26	126	39	1740	12	19	71	2257	10
	larch	39	80	110	39	0	.	1464	11	569	21	2183	10
	Arolla pine	0	.	0	.	52	73	591	18	10	100	653	17
	other conifers	39	50	98	34	21	71	20	71	0	.	177	25
	beech	4165	7	3457	7	2448	10	1597	11	1454	13	13120	4
	maple	864	13	785	14	882	13	665	16	49	62	3246	7
	ash	724	16	1041	13	744	17	355	24	220	34	3084	8
	oak	518	20	658	17	67	52	189	35	282	25	1714	11
	sweet chestnut	0	.	29	100	0	.	20	71	1713	13	1761	13
	other broadleaves	782	13	998	14	560	18	1189	12	1524	12	5053	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	11280	4	12185	4	13145	4	17812	3	7014	6	61437	2
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2063	9	2950	9	5487	6	8058	5	884	15	19441	3
	fir	1763	10	1443	11	1863	9	622	15	110	37	5802	5
	pine	332	25	271	26	106	49	1440	12	67	48	2217	9
	larch	29	58	88	40	0	.	1281	12	433	17	1832	9
	Arolla pine	0	.	0	.	11	100	441	22	0	.	451	22
	other conifers	10	100	20	71	0	.	20	71	0	.	50	45
	beech	3439	7	2756	8	2143	9	1189	13	980	13	10506	4
	maple	693	13	528	16	532	16	478	17	98	39	2329	7
	ash	408	19	653	15	555	17	371	25	106	40	2094	9
	oak	423	25	520	18	53	53	81	38	221	29	1298	12
	sweet chestnut	0	.	39	79	0	.	20	71	778	16	837	16
	other broadleaves	556	17	674	15	235	26	674	17	1203	13	3341	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	9717	4	9942	4	10984	4	14674	4	4880	6	50197	2
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2069	10	3274	8	4773	7	7206	5	947	17	18268	3
	fir	1546	11	1089	13	1690	10	441	17	28	74	4794	6
	pine	352	22	290	24	99	37	1109	14	144	54	1994	10
	larch	67	47	61	53	0	.	1033	13	494	21	1654	10
	Arolla pine	0	.	0	.	11	100	425	20	0	.	436	19
	other conifers	0	.	29	58	0	.	0	.	0	.	29	58
	beech	3273	7	2061	9	1627	10	858	14	524	17	8342	4
	maple	496	15	350	20	369	19	321	21	32	58	1568	9
	ash	420	18	577	16	331	22	88	33	54	52	1470	10
	oak	332	21	334	20	10	100	49	60	145	42	870	14
	sweet chestnut	0	.	10	100	0	.	20	71	550	17	580	16
	other broadleaves	295	21	473	19	152	30	268	23	538	18	1727	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8850	4	8546	4	9061	4	11819	4	3456	7	41733	2
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1701	10	3067	8	4254	7	6039	5	633	17	15695	3
	fir	1263	11	1199	11	1315	11	396	17	109	49	4282	6
	pine	445	19	456	21	53	45	772	15	57	71	1783	10
	larch	0	.	100	37	0	.	1191	12	321	20	1611	10
	Arolla pine	0	.	0	.	0	.	328	18	0	.	328	18
	other conifers	0	.	50	53	0	.	10	100	0	.	60	47
	beech	2295	8	1836	9	1724	10	614	16	345	18	6814	5
	maple	276	20	205	24	245	22	178	25	10	100	915	11
	ash	194	23	502	18	248	24	128	29	33	58	1106	11
	oak	253	22	291	19	12	100	22	71	131	31	708	13
	sweet chestnut	0	.	0	.	0	.	12	100	311	20	324	20
	other broadleaves	244	22	259	22	102	47	266	23	330	21	1203	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	6673	5	7965	4	7953	4	9958	4	2279	8	34828	2
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1498	7	2942	6	3606	5	4692	4	554	13	13293	3
	fir	971	8	1035	9	1205	8	356	15	57	32	3624	5
	pine	377	15	530	13	60	29	624	12	65	48	1655	7
	larch	19	66	100	26	17	50	892	9	317	16	1344	7
	Arolla pine	0	.	0	.	9	100	236	18	0	.	244	17
	other conifers	0	.	16	50	0	.	0	.	0	.	16	50
	beech	1686	7	1362	8	1013	9	454	12	326	15	4841	4
	maple	184	17	122	20	125	22	99	21	8	71	537	10
	ash	171	22	370	13	118	23	65	34	19	51	743	10
	oak	90	25	241	16	20	52	25	46	44	48	420	12
	sweet chestnut	0	.	0	.	0	.	4	100	163	24	166	24
	other broadleaves	115	22	191	17	35	36	73	32	131	20	545	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	5110	4	6910	3	6207	4	7520	3	1684	7	27430	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1358	7	2448	6	3265	5	3897	5	430	13	11398	3
	fir	817	9	863	10	1104	9	318	16	89	34	3191	5
	pine	235	16	389	14	82	34	377	14	19	67	1103	8
	larch	11	100	48	29	17	71	838	9	211	18	1126	8
	Arolla pine	0	.	0	.	0	.	160	20	5	100	165	19
	other conifers	0	.	33	43	0	.	0	.	0	.	33	43
	beech	1235	7	1069	8	666	11	407	15	177	19	3553	4
	maple	80	22	70	27	86	22	36	33	8	71	279	12
	ash	87	26	248	15	70	28	27	51	0	.	432	11
	oak	64	29	204	16	5	100	34	56	32	78	339	14
	sweet chestnut	0	.	0	.	0	.	0	.	167	23	167	23
	other broadleaves	52	29	96	23	42	34	40	34	55	27	286	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	3939	4	5468	4	5338	4	6133	4	1193	8	22071	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1058	8	1832	7	2388	6	3166	5	378	15	8821	3
	fir	554	11	623	10	797	9	236	15	69	30	2280	5
	pine	225	19	273	16	68	43	227	17	4	100	797	10
	larch	8	71	60	30	12	58	704	9	197	17	980	8
	Arolla pine	0	.	0	.	0	.	120	23	4	100	124	22
	other conifers	0	.	29	67	0	.	0	.	0	.	29	67
	beech	789	8	842	9	555	11	262	16	127	20	2575	5
	maple	62	26	76	26	60	30	60	27	4	100	261	14
	ash	42	32	107	24	29	43	15	61	4	100	197	17
	oak	87	27	181	19	0	.	12	58	17	50	296	15
	sweet chestnut	0	.	4	100	0	.	4	100	128	20	136	20
	other broadleaves	50	31	56	28	13	58	40	34	19	50	178	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	2874	4	4083	4	3921	4	4847	4	951	8	16675	2
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	841	9	1312	7	1843	6	2253	6	365	14	6613	3
	fir	395	12	466	11	584	10	180	17	37	33	1662	6
	pine	109	23	234	17	22	51	216	18	15	50	598	11
	larch	8	100	32	35	21	53	584	10	186	17	831	9
	Arolla pine	0	.	0	.	0	.	75	26	0	.	75	26
	other conifers	0	.	25	63	0	.	0	.	0	.	25	63
	beech	496	11	469	11	417	13	170	20	79	25	1631	6
	maple	17	51	43	32	19	45	19	52	0	.	99	21
	ash	27	47	115	22	46	35	9	72	6	100	203	16
	oak	49	35	125	21	13	58	20	45	16	59	224	16
	sweet chestnut	0	.	0	.	0	.	4	100	130	21	134	21
	other broadleaves	24	47	45	39	13	58	51	35	17	50	150	20
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1967	5	2866	4	2979	5	3582	4	852	8	12245	2
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	562	10	856	9	1255	7	1590	6	207	16	4470	4
	fir	266	13	276	14	378	12	164	18	23	41	1107	7
	pine	57	33	125	22	11	73	109	32	8	71	309	16
	larch	16	50	35	33	14	59	469	11	121	18	655	9
	Arolla pine	0	.	0	.	0	.	39	32	0	.	39	32
	other conifers	0	.	17	50	0	.	0	.	0	.	17	50
	beech	249	15	326	13	286	16	144	19	40	34	1044	8
	maple	8	71	25	41	26	46	0	.	0	.	59	28
	ash	28	43	51	30	13	58	4	100	0	.	96	22
	oak	35	39	84	22	5	100	0	.	5	100	129	19
	sweet chestnut	0	.	0	.	0	.	4	100	59	33	63	31
	other broadleaves	4	100	20	45	0	.	28	47	8	71	60	29
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1223	6	1815	6	1988	5	2551	5	470	10	8049	3
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	402	12	633	9	963	8	1305	7	179	18	3482	4
	fir	178	18	173	17	312	14	116	23	26	46	805	8
	pine	49	38	84	27	18	59	37	37	0	.	188	18
	larch	0	.	23	41	17	61	322	14	92	23	454	11
	Arolla pine	0	.	0	.	0	.	59	27	0	.	59	27
	other conifers	0	.	12	58	0	.	0	.	0	.	12	58
	beech	209	16	227	16	155	18	81	25	53	27	725	8
	maple	12	58	8	71	0	.	12	58	0	.	31	35
	ash	7	71	55	31	4	100	8	71	0	.	75	26
	oak	61	34	59	29	4	100	4	100	12	100	140	21
	sweet chestnut	0	.	0	.	0	.	0	.	63	28	63	28
	other broadleaves	4	100	21	45	0	.	5	100	18	51	48	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	923	8	1295	6	1473	7	1949	5	442	11	6083	3
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	177	16	244	14	489	11	630	9	80	25	1620	6
	fir	104	21	90	24	197	16	46	29	21	44	457	10
	pine	23	47	31	40	6	100	28	38	0	.	88	23
	larch	0	.	4	100	0	.	191	16	44	30	239	14
	Arolla pine	0	.	0	.	4	100	12	58	0	.	16	50
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	66	24	123	20	42	35	41	32	30	38	303	12
	maple	12	58	4	100	4	100	0	.	0	.	20	45
	ash	17	50	17	51	0	.	4	100	0	.	38	34
	oak	17	50	39	32	19	59	0	.	5	100	80	24
	sweet chestnut	0	.	0	.	0	.	0	.	29	38	29	38
	other broadleaves	17	58	13	59	0	.	8	71	0	.	38	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	433	10	570	9	761	9	960	7	208	14	2932	4
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	164	16	224	15	354	12	462	12	79	30	1283	7
	fir	70	24	71	24	141	20	34	37	16	50	333	12
	pine	0	.	19	53	8	71	13	58	0	.	41	35
	larch	0	.	19	45	4	100	122	19	35	40	180	16
	Arolla pine	0	.	0	.	0	.	27	38	0	.	27	38
	other conifers	4	100	0	.	0	.	0	.	0	.	4	100
	beech	73	26	100	21	29	47	24	41	20	45	247	14
	maple	13	59	0	.	0	.	4	100	0	.	17	51
	ash	4	100	4	100	4	100	0	.	0	.	12	58
	oak	0	.	27	41	4	100	0	.	0	.	31	38
	sweet chestnut	0	.	0	.	0	.	0	.	21	44	21	44
	other broadleaves	4	100	4	100	5	100	0	.	4	100	17	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	332	12	469	10	550	10	687	9	174	18	2213	5
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	107	22	100	20	222	15	390	11	62	32	882	7
	fir	37	33	44	38	74	26	23	41	5	100	183	17
	pine	0	.	4	100	0	.	9	72	0	.	13	58
	larch	0	.	0	.	6	100	128	20	27	38	161	17
	Arolla pine	0	.	0	.	0	.	20	45	0	.	20	45
	other conifers	0	.	4	100	4	100	0	.	0	.	8	71
	beech	19	45	96	24	14	58	22	45	16	50	168	17
	maple	0	.	0	.	4	100	0	.	0	.	4	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	29	46	6	100	0	.	0	.	35	42
	sweet chestnut	0	.	0	.	0	.	0	.	19	50	19	50
	other broadleaves	0	.	0	.	0	.	0	.	4	100	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	164	18	277	13	330	14	593	9	133	19	1497	6
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	61	27	54	30	130	20	238	15	45	33	528	10
	fir	40	38	20	54	66	26	20	45	16	60	162	18
	pine	0	.	4	100	0	.	0	.	0	.	4	100
	larch	4	100	4	100	0	.	60	26	12	58	79	22
	Arolla pine	0	.	0	.	0	.	35	40	0	.	35	40
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	12	58	18	52	18	50	0	.	8	71	56	28
	maple	0	.	6	100	0	.	9	72	4	100	19	51
	ash	0	.	7	71	0	.	0	.	0	.	7	71
	oak	0	.	21	45	0	.	4	100	0	.	25	41
	sweet chestnut	0	.	0	.	0	.	4	100	23	52	27	47
	other broadleaves	0	.	4	100	0	.	0	.	0	.	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	118	20	141	18	213	15	370	11	108	22	950	7
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9	70	37	34	89	26	141	19	41	41	317	13
	fir	16	50	20	45	16	50	4	100	0	.	56	27
	pine	0	.	0	.	0	.	4	100	0	.	4	100
	larch	0	.	0	.	0	.	54	30	12	58	66	26
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	11	58	21	45	8	71	8	71	16	80	65	30
	maple	0	.	0	.	4	100	0	.	0	.	4	100
	ash	0	.	6	100	0	.	0	.	0	.	6	100
	oak	4	100	8	71	0	.	0	.	0	.	12	58
	sweet chestnut	0	.	0	.	0	.	0	.	37	41	37	41
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	40	32	92	23	117	22	220	15	106	25	574	10
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21	45	27	38	29	38	79	22	19	45	175	15
	fir	8	71	4	100	30	38	16	50	0	.	58	27
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	30	38	8	71	38	33
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	46	33	0	.	8	71	12	75	66	28
	maple	0	.	0	.	0	.	4	100	0	.	4	100
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	8	71	16	50	0	.	0	.	0	.	24	41
	sweet chestnut	0	.	0	.	0	.	0	.	28	54	28	54
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	37	33	98	22	59	27	137	17	67	30	397	11
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	8	71	48	29	8	71	77	23
	fir	0	.	0	.	20	45	0	.	10	72	30	38
	pine	0	.	4	100	0	.	0	.	0	.	4	100
	larch	0	.	0	.	0	.	12	58	5	100	17	50
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	5	100	4	100	4	100	0	0	12	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	9	70	9	70
	other broadleaves	0	.	5	100	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4	100	27	41	33	35	68	24	32	38	163	16
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	0	.	8	71	16	50	8	71	36	33
	fir	8	71	8	71	8	71	4	100	0	.	28	38
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	4	100	0	.	12	58	0	.	16	50
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	8	71	0	.	0	.	0	.	12	58
	maple	0	.	0	.	4	100	0	.	0	.	4	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	8	71	0	.	0	.	0	.	8	71
	sweet chestnut	0	.	0	.	0	.	0	.	13	58	13	58
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	16	50	27	38	20	45	36	33	20	45	120	18
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	4	100	8	71	8	71	4	100	27	38
	fir	0	.	0	.	4	100	4	100	0	.	8	71
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	0	.	4	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	4	100	0	.	0	.	0	.	4	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	15	60	15	60
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4	100	12	58	12	58	16	50	19	52	62	26
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	4	100	5	100	37	33	4	100	49	29
	fir	4	100	4	100	4	100	8	71	0	.	20	45
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	9	71	4	100	12	58
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	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	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	4	100	8	100	12	74
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4	100	7	71	9	71	61	26	16	61	97	21
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	7	71	0	0	4	100	11	58
	fir	0	.	0	.	0	.	0	.	5	100	5	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	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	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	14	58	14	58
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	4	100	7	71	4	100	23	45	37	34
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	4	100	17	50	4	100	25	41
	fir	4	100	0	.	0	.	0	.	6	100	10	73
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	71	8	71	16	50
	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	5	100	0	.	0	.	0	.	4	100	9	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	9	72	0	.	4	100	25	41	22	46	60	27
108-111 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	5	100	0	.	5	100
	fir	0	.	0	.	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	.	4	100	0	.	4	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	.	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	9	71	0	.	9	71
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	.	4	100	0	.	4	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	.	4	100	0	.	4	100
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	.	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	.	0	.	0	.	0	.	7	100	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	.	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
124-127 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	4	100	4	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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	71	8	71
128-131 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	4	100	4	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	.	4	100	4	100
132-135 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	.
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	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	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	.	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
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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
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	.	4	100	4	100
	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	.	4	100	4	100
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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
=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	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22030	5	35456	4	52263	4	73736	3	8740	9	192225	2
	fir	15709	5	13831	6	18607	5	6637	9	1141	21	55925	3
	pine	2874	12	3358	10	1086	29	12917	9	476	40	20712	6
	larch	269	30	1062	23	130	35	12801	6	4382	10	18643	5
	Arolla pine	0	.	0	.	117	82	3910	12	19	82	4047	12
	other conifers	440	39	785	22	129	32	523	36	0	.	1876	17
	beech	29778	4	24926	4	19460	6	11396	8	10310	10	95870	2
	maple	5053	7	4321	11	4463	8	4226	10	577	23	18641	4
	ash	3832	11	6265	8	4546	11	2676	14	1561	17	18881	5
	oak	3227	16	4638	12	389	24	1128	22	2185	20	11567	8
	sweet chestnut	0	.	131	86	0	.	155	43	11231	10	11517	9
	other broadleaves	5152	9	7053	9	5151	12	9857	8	12230	8	39444	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	88364	2	101827	2	106341	2	139963	2	52852	3	489347	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. 1350037. Birmensdorf, Swiss Federal Research Institute WSL

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