Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2
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 1993/95
		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	5073	10	9134	9	9129	7	14117	5	1707	14	39161	4
	fir	3507	9	2151	12	3907	8	1505	13	300	27	11369	5
	pine	222	33	238	29	213	42	2844	14	38	100	3556	12
	larch	38	78	179	36	42	100	2100	12	876	17	3236	9
	Arolla pine	0	.	0	.	0	.	552	20	10	100	562	19
	other conifers	282	36	379	27	83	35	515	25	0	.	1259	16
	beech	4919	7	4915	7	4165	9	3271	12	3643	12	20913	4
	maple	1398	10	1291	15	1151	16	1392	14	306	27	5538	7
	ash	895	16	1223	17	960	19	919	16	887	18	4885	8
	oak	653	26	699	26	22	72	381	24	688	28	2442	13
	sweet chestnut	0	.	20	71	0	.	40	77	3435	13	3495	13
	other broadleaves	2146	11	2304	12	2428	11	5991	9	5668	10	18538	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	19133	4	22534	5	22100	4	33628	3	17559	5	114954	2
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2988	11	6321	9	7340	7	10749	5	1324	13	28722	3
	fir	2211	10	1878	11	2520	9	1011	14	261	31	7881	5
	pine	270	36	311	35	126	46	2543	12	39	79	3289	11
	larch	20	71	149	37	14	100	1684	11	694	19	2561	9
	Arolla pine	0	.	0	.	21	100	646	19	0	.	667	19
	other conifers	155	47	204	33	62	47	252	43	0	.	673	22
	beech	4120	7	3777	7	3097	9	2107	11	2721	12	15821	4
	maple	963	13	874	17	1128	13	1139	13	118	36	4220	7
	ash	806	19	724	15	906	18	731	17	525	23	3692	8
	oak	446	28	653	24	60	55	387	25	480	25	2027	13
	sweet chestnut	0	.	20	71	0	.	33	76	2758	13	2811	12
	other broadleaves	1049	14	1514	12	1142	15	3198	11	3606	8	10510	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	13029	4	16424	4	16415	4	24482	3	12525	5	82875	2
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2460	11	3810	9	6088	7	8697	5	1220	13	22275	3
	fir	1447	10	1274	13	1849	10	876	15	125	33	5571	6
	pine	227	29	134	32	147	61	1902	13	9	100	2420	11
	larch	49	60	127	53	0	.	1407	11	738	17	2321	9
	Arolla pine	0	.	0	.	21	71	645	18	0	.	665	17
	other conifers	87	37	126	40	10	100	82	39	0	.	304	22
	beech	3835	7	2914	7	2399	9	1661	12	1627	13	12436	4
	maple	745	13	635	20	933	13	658	17	129	35	3101	7
	ash	611	20	910	16	741	17	525	23	234	29	3020	9
	oak	496	25	581	25	72	55	222	29	311	25	1683	13
	sweet chestnut	0	.	10	100	0	.	44	80	1760	13	1814	12
	other broadleaves	717	15	793	14	733	19	1322	12	2031	10	5596	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	10677	4	11314	4	12992	4	18040	4	8184	5	61206	2
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2054	10	2733	9	5218	7	7678	6	978	14	18661	4
	fir	1335	10	1000	13	1435	10	586	16	93	37	4449	6
	pine	220	32	192	27	83	50	1132	13	29	58	1655	11
	larch	38	50	177	31	0	.	1549	11	509	19	2274	9
	Arolla pine	0	.	0	.	42	80	604	18	0	.	646	18
	other conifers	49	60	89	54	10	100	20	71	0	.	168	35
	beech	3224	7	2398	8	2169	10	1409	14	1017	14	10217	4
	maple	774	13	711	14	655	14	522	17	147	28	2809	7
	ash	499	18	745	14	581	19	349	26	148	33	2321	9
	oak	483	23	384	19	24	71	126	45	228	28	1245	13
	sweet chestnut	0	.	0	.	0	.	60	47	1238	15	1299	14
	other broadleaves	412	16	597	16	410	20	816	14	1122	15	3356	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	9087	4	9026	4	10627	5	14852	4	5509	6	49101	2
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1662	11	2271	9	4553	7	6650	5	764	15	15899	3
	fir	1148	11	1076	13	1397	10	363	19	91	47	4076	6
	pine	297	24	141	30	88	40	1070	13	125	54	1721	11
	larch	10	100	80	39	0	.	1121	13	541	18	1752	10
	Arolla pine	0	.	0	.	11	100	443	21	0	.	453	21
	other conifers	40	100	80	50	0	.	10	100	0	.	130	44
	beech	3153	7	2141	9	1746	11	803	14	745	14	8589	5
	maple	494	16	469	16	483	18	389	19	54	45	1889	8
	ash	352	24	744	15	419	20	198	25	99	38	1812	10
	oak	294	22	387	22	87	43	73	51	225	28	1066	13
	sweet chestnut	0	.	10	100	0	.	10	100	604	17	624	17
	other broadleaves	240	23	379	22	284	29	367	20	663	16	1933	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7690	4	7779	4	9069	5	11496	4	3912	6	39945	2
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1801	10	2539	9	3874	7	5897	6	868	16	14980	4
	fir	1227	12	847	14	1339	11	342	21	68	61	3822	7
	pine	343	22	238	23	53	54	769	16	86	65	1489	11
	larch	68	43	60	47	0	.	1157	11	410	21	1695	9
	Arolla pine	0	.	0	.	11	100	405	18	0	.	416	17
	other conifers	10	100	30	58	0	.	0	.	0	.	40	50
	beech	2561	8	1778	9	1642	9	767	14	421	19	7168	5
	maple	352	19	283	21	238	24	226	22	21	71	1120	11
	ash	338	18	531	18	270	24	107	30	74	47	1320	10
	oak	220	26	341	21	54	100	20	100	94	33	729	15
	sweet chestnut	0	.	10	100	0	.	32	58	384	19	425	18
	other broadleaves	272	32	341	18	121	41	240	22	553	17	1527	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7192	5	6996	5	7601	5	9962	4	2979	8	34730	2
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1375	8	2243	7	3598	5	4304	4	646	12	12166	3
	fir	1035	9	785	9	1186	8	314	14	74	31	3395	5
	pine	394	18	448	16	46	35	610	12	42	47	1540	8
	larch	7	71	64	33	25	41	948	9	276	17	1320	8
	Arolla pine	0	.	0	.	13	100	235	17	0	.	247	17
	other conifers	0	.	12	58	0	.	0	.	4	100	16	50
	beech	1889	6	1466	7	1120	9	474	12	320	14	5270	4
	maple	238	15	178	17	252	15	162	19	20	60	850	8
	ash	189	23	440	13	129	20	91	25	19	50	868	9
	oak	149	21	273	14	38	42	41	44	51	48	553	11
	sweet chestnut	0	.	4	100	0	.	4	100	188	17	196	17
	other broadleaves	150	27	195	18	43	46	145	21	161	18	693	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	5428	4	6108	3	6450	3	7328	3	1801	6	27114	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1240	7	2184	6	2937	5	3743	5	528	13	10633	3
	fir	829	9	815	9	898	9	277	16	55	37	2874	5
	pine	279	17	330	15	75	36	401	14	53	46	1138	8
	larch	19	66	67	33	13	75	949	9	321	15	1368	7
	Arolla pine	0	.	0	.	0	.	160	19	0	.	160	19
	other conifers	4	100	33	55	0	.	0	.	0	.	37	50
	beech	1426	7	1199	7	939	10	451	14	231	15	4245	4
	maple	143	19	96	26	95	23	88	21	4	100	427	11
	ash	179	18	357	14	109	24	71	27	12	58	729	9
	oak	86	23	246	17	8	100	24	41	45	40	409	12
	sweet chestnut	0	.	4	100	0	.	0	.	184	24	188	23
	other broadleaves	91	23	123	20	25	41	54	42	125	25	419	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4295	4	5453	3	5098	4	6219	3	1560	7	22626	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1129	8	1892	6	2640	6	3024	5	357	13	9042	3
	fir	671	9	646	10	909	9	238	15	60	25	2525	5
	pine	174	18	282	15	69	43	265	16	8	100	797	9
	larch	7	100	60	29	17	61	799	9	218	15	1101	7
	Arolla pine	0	.	0	.	0	.	203	20	0	.	203	20
	other conifers	4	100	4	100	0	.	0	.	0	.	8	71
	beech	1086	8	934	8	654	12	321	14	190	19	3186	5
	maple	66	25	52	33	94	28	47	30	9	71	267	14
	ash	63	29	203	16	80	29	38	31	14	59	400	12
	oak	53	30	210	19	0	.	30	51	24	57	317	15
	sweet chestnut	0	.	4	100	0	.	4	100	162	26	170	25
	other broadleaves	14	60	83	32	40	45	70	33	44	33	252	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	3269	4	4370	4	4503	4	5039	4	1086	7	18267	2
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	879	8	1506	7	2077	6	2408	5	353	15	7223	3
	fir	440	11	583	11	656	9	248	16	69	32	1996	6
	pine	110	24	171	17	31	46	188	17	4	100	503	10
	larch	4	100	62	34	8	71	656	10	170	16	899	8
	Arolla pine	0	.	0	.	0	.	88	24	4	100	91	23
	other conifers	0	.	37	40	0	.	0	.	0	.	37	40
	beech	698	12	702	9	425	11	279	18	99	20	2204	6
	maple	62	26	43	35	61	31	36	37	0	.	201	16
	ash	43	32	140	23	33	35	11	58	0	.	227	16
	oak	89	33	166	21	0	.	16	61	21	51	291	17
	sweet chestnut	0	.	4	100	0	.	9	72	96	24	109	22
	other broadleaves	20	53	40	41	23	45	38	41	16	50	136	21
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	2344	5	3453	4	3314	5	3977	4	831	8	13919	2
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	634	10	1044	7	1552	7	1718	6	272	14	5219	4
	fir	366	18	475	11	607	10	153	17	37	40	1637	7
	pine	64	32	142	19	20	51	158	22	15	50	400	12
	larch	8	71	25	47	21	53	551	10	138	17	743	9
	Arolla pine	0	.	0	.	0	.	47	31	0	.	47	31
	other conifers	0	.	16	61	0	.	0	.	0	.	16	61
	beech	450	11	503	11	378	14	216	25	72	28	1619	7
	maple	4	100	45	35	17	51	28	41	0	.	94	23
	ash	19	52	120	22	22	45	7	100	0	.	168	18
	oak	47	33	97	27	9	70	22	45	10	72	184	18
	sweet chestnut	0	.	0	.	0	.	0	.	96	23	96	23
	other broadleaves	31	43	21	53	5	100	36	42	12	58	105	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1622	6	2488	5	2631	5	2936	5	652	9	10329	2
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	491	10	771	9	1139	7	1408	6	196	16	4005	4
	fir	284	14	344	13	396	13	131	21	16	50	1171	7
	pine	47	31	97	24	13	71	81	24	4	100	243	14
	larch	4	100	41	41	14	59	398	13	133	19	590	10
	Arolla pine	0	.	0	.	0	.	56	29	0	.	56	29
	other conifers	0	.	21	67	0	.	0	.	0	.	21	67
	beech	298	13	314	13	211	16	115	21	74	26	1011	7
	maple	14	58	26	41	7	100	7	71	0	.	54	29
	ash	25	52	52	36	9	71	8	71	6	100	99	25
	oak	58	34	124	22	15	58	4	100	9	71	210	17
	sweet chestnut	0	.	0	.	0	.	14	77	88	26	102	25
	other broadleaves	5	100	16	50	0	.	25	53	6	100	52	33
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1225	7	1806	6	1805	6	2246	5	531	10	7613	3
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	268	14	417	11	651	10	794	9	118	22	2247	5
	fir	144	18	135	18	226	15	99	21	16	50	621	9
	pine	40	43	37	36	4	100	21	45	4	100	106	23
	larch	0	.	4	100	13	74	258	14	71	25	345	12
	Arolla pine	0	.	0	.	4	100	33	36	0	.	37	34
	other conifers	0	.	12	74	0	.	0	.	0	.	12	74
	beech	133	19	163	18	143	22	70	37	42	32	551	11
	maple	4	100	8	71	14	60	8	71	0	.	34	36
	ash	4	100	29	43	11	72	4	100	0	.	48	33
	oak	19	52	37	37	0	.	4	100	9	71	69	27
	sweet chestnut	0	.	0	.	0	.	0	0	45	33	45	33
	other broadleaves	14	58	8	71	0	.	5	100	14	59	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	626	9	849	8	1066	7	1295	7	317	12	4153	4
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	168	17	286	13	544	10	605	9	96	23	1698	6
	fir	120	20	77	23	177	17	69	25	28	50	471	10
	pine	4	100	27	43	15	59	21	45	0	.	66	27
	larch	0	.	8	71	9	71	121	19	31	35	168	16
	Arolla pine	0	.	0	.	0	.	28	38	0	.	28	38
	other conifers	0	.	5	100	0	.	0	.	0	.	5	100
	beech	82	28	105	24	71	29	12	58	8	71	278	15
	maple	4	100	0	.	0	.	0	.	0	.	4	100
	ash	9	71	43	38	4	100	4	100	0	.	60	31
	oak	17	63	19	52	5	100	0	.	0	.	41	37
	sweet chestnut	0	.	0	.	0	.	5	100	35	40	40	37
	other broadleaves	7	100	4	100	0	.	4	100	5	100	20	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	410	12	574	9	824	8	867	8	203	16	2879	4
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	135	18	183	17	349	12	404	12	86	27	1156	7
	fir	48	31	69	26	145	18	19	45	4	100	284	13
	pine	0	.	4	100	0	.	9	71	0	.	13	58
	larch	0	.	23	58	6	100	139	18	43	32	211	15
	Arolla pine	0	.	0	.	0	.	27	38	0	.	27	38
	other conifers	4	100	9	70	0	.	0	.	0	.	12	58
	beech	62	26	120	20	0	.	11	74	11	57	205	15
	maple	6	100	0	.	4	100	0	.	0	.	9	73
	ash	8	100	0	.	0	.	4	100	0	.	12	74
	oak	5	100	36	37	14	71	0	.	0	.	55	31
	sweet chestnut	0	.	0	.	0	.	0	.	44	32	44	32
	other broadleaves	0	.	12	58	0	.	0	.	4	100	16	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	266	13	456	11	517	10	613	9	192	16	2045	5
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	58	27	53	30	174	18	290	13	56	30	629	9
	fir	43	32	61	28	69	27	23	41	4	100	200	15
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	4	100	4	100	0	.	80	24	12	58	99	22
	Arolla pine	0	.	0	.	0	.	23	41	0	.	23	41
	other conifers	0	.	8	71	4	100	0	.	0	.	12	58
	beech	24	41	64	31	17	60	7	71	17	50	129	20
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	8	71	4	100	0	.	0	.	12	58
	oak	0	.	29	42	10	72	0	.	0	.	39	36
	sweet chestnut	0	.	0	.	0	.	0	.	23	46	23	46
	other broadleaves	0	.	0	.	0	.	0	.	4	100	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	129	18	226	15	278	15	429	11	115	20	1177	7
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	37	37	29	43	102	22	148	17	51	30	367	11
	fir	33	39	8	71	26	41	12	58	9	70	87	23
	pine	0	.	4	100	0	.	0	.	0	.	4	100
	larch	0	.	0	.	0	.	92	22	16	50	108	20
	Arolla pine	0	.	0	.	0	.	12	58	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	12	58	53	35	10	72	5	100	8	71	88	26
	maple	0	.	6	100	4	100	4	100	4	100	17	51
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	10	73	0	.	5	100	0	.	15	59
	sweet chestnut	0	.	0	.	0	.	0	.	21	45	21	45
	other broadleaves	0	.	10	72	0	.	0	.	0	.	10	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	81	28	120	25	141	19	277	12	108	21	728	9
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	35	33	39	45	78	23	21	45	173	17
	fir	16	50	16	50	43	35	14	59	0	.	89	23
	pine	0	.	0	.	0	.	4	100	0	.	4	100
	larch	0	.	0	.	0	.	25	41	8	71	32	35
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	4	100	19	51	12	58	12	58	17	78	64	31
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	6	100	0	.	0	.	0	.	6	100
	oak	0	.	20	45	0	.	0	.	0	.	20	45
	sweet chestnut	0	.	0	.	0	.	4	100	42	47	46	43
	other broadleaves	0	.	9	71	0	.	0	.	0	.	9	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	20	45	109	20	95	26	141	17	88	30	452	11
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13	58	4	100	42	39	62	26	8	71	127	19
	fir	0	.	8	71	18	50	12	58	4	100	42	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	26	41	9	71	35	36
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	16	50	4	100	4	100	4	100	32	35
	maple	0	.	0	.	4	100	8	100	0	.	12	76
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	4	100	4	100	0	.	0	.	0	.	8	71
	sweet chestnut	0	.	0	.	0	.	0	.	27	42	27	42
	other broadleaves	0	.	4	100	0	.	0	.	0	.	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	20	45	41	32	67	30	116	20	52	29	296	13
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	71	16	50	13	58	35	33	12	58	85	22
	fir	0	.	14	60	26	41	0	.	6	100	46	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	5	100	21	45
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	14	58	0	.	0	.	0	.	18	50
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	8	71	0	.	0	.	0	.	12	58
	sweet chestnut	0	.	0	.	0	.	0	.	14	58	14	58
	other broadleaves	0	.	0	.	0	.	0	.	4	100	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	16	50	53	29	39	33	55	27	41	34	204	14
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	4	100	8	71	4	100	6	100	22	45
	fir	4	100	4	100	0	.	0	.	0	.	8	71
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	4	100	0	.	4	100	5	100	13	59
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	4	100	0	.	0	.	0	.	9	71
	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	.	8	71	8	71
	other broadleaves	0	.	5	100	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	71	25	41	8	71	12	58	20	51	72	24
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	5	100	12	58	16	50	9	71	46	30
	fir	0	.	0	.	8	71	4	100	0	.	12	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	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	14	71	19	60
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4	100	5	100	21	45	36	33	27	46	92	22
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	5	100	30	38	0	.	34	36
	fir	0	.	0	.	0	.	4	100	0	.	4	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	71	0	.	8	71
	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	.	17	60	17	60
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	4	100	5	100	46	30	17	60	71	26
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	7	71	4	100	4	100	15	50
	fir	0	.	4	100	4	100	0	.	6	100	14	59
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	4	100	8	71
	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	.	10	72	10	72
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	4	100	11	58	8	71	23	45	46	30
108-111 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	8	71	4	100	12	58
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	4	100	8	71
	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	0	0
	other broadleaves	5	100	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	5	100	0	.	0	.	16	50	8	71	29	38
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	.	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	.	9	100	9	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	4	100	9	100	13	77
116-119 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	.
	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	.	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
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	.	4	100	4	100
	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	.	4	100	0	.	4	100
	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	.	4	100	12	58	16	50
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	.	4	100	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	.	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	.	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
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	.	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
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	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
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	.	18	62	18	62
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	18	62	18	62
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	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
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	.	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
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	.	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
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	.	19	100	19	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	.	19	100	19	100
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	.	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	22475	5	37479	5	52090	4	72876	3	9686	8	194606	2
	fir	14907	5	12272	6	17840	5	6300	9	1329	19	52647	3
	pine	2691	13	2796	11	982	32	12019	9	457	42	18945	7
	larch	276	31	1132	20	181	39	14107	6	5239	10	20936	5
	Arolla pine	0	.	0	.	122	83	4236	12	13	77	4371	12
	other conifers	634	32	1068	22	170	30	879	28	4	100	2754	14
	beech	27989	4	23599	4	19202	6	11996	8	11285	10	94072	2
	maple	5268	7	4716	10	5139	8	4723	9	812	21	20658	4
	ash	4040	11	6279	8	4278	11	3068	13	2018	16	19683	5
	oak	3124	16	4332	12	419	25	1354	19	2194	18	11422	8
	sweet chestnut	0	.	84	70	0	.	265	46	11352	10	11702	9
	other broadleaves	5174	9	6460	9	5254	11	12309	8	14039	7	43235	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	86577	2	100216	2	105676	2	144132	2	58430	3	495030	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. 1350057. Birmensdorf, Swiss Federal Research Institute WSL

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