Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
number of stems
diameter at breast height (in 4 cm classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	2963	16	4744	12	7033	11	16069	6	2939	19	33748	5
	fir	3507	12	2657	13	3619	13	1105	16	428	30	11315	7
	pine	19	100	115	47	135	71	2257	21	116	65	2642	19
	larch	74	80	310	53	108	63	1936	13	1181	22	3610	11
	Arolla pine	0	.	0	.	0	.	1156	30	32	70	1189	29
	other conifers	225	52	314	43	74	50	724	27	20	100	1357	20
	beech	5515	11	5042	12	2924	12	2414	13	2908	15	18804	6
	maple	2141	16	1492	16	1289	18	2434	17	706	33	8062	8
	ash	824	26	854	18	450	28	1208	21	949	27	4285	11
	oak	678	50	280	33	21	100	542	35	472	39	1994	22
	sweet chestnut	0	.	0	.	0	.	19	100	2018	25	2037	25
	other broadleaves	1759	13	2913	14	2152	15	6073	10	5699	12	18595	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	17706	6	18720	6	17805	6	35938	4	17468	7	107637	3
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2396	13	4337	12	5302	11	12439	6	1908	15	26383	4
	fir	2814	12	1518	17	2566	13	950	16	161	36	8009	7
	pine	112	53	70	60	87	82	2006	20	49	100	2323	18
	larch	0	.	363	54	105	68	1580	14	1160	20	3209	12
	Arolla pine	0	.	0	.	0	.	859	21	16	100	876	21
	other conifers	211	37	207	41	111	47	389	29	0	.	918	19
	beech	3411	10	2722	10	2661	12	2154	17	2476	15	13423	6
	maple	1268	16	1443	16	1108	19	1375	14	434	34	5628	8
	ash	397	24	698	22	544	22	975	18	526	30	3139	10
	oak	343	42	239	48	0	0	315	38	325	45	1222	22
	sweet chestnut	0	.	0	.	0	.	56	100	1581	20	1638	20
	other broadleaves	1067	18	1386	16	1079	18	3448	13	3828	14	10808	7
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	12018	5	12984	6	13561	6	26547	4	12466	6	77575	2
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1533	14	2690	13	4643	10	9807	7	1469	18	20143	5
	fir	1939	13	1310	16	1998	13	818	19	178	42	6242	7
	pine	92	45	127	57	104	85	1710	17	0	.	2032	16
	larch	0	.	253	57	17	100	1656	14	1203	19	3130	11
	Arolla pine	0	.	0	.	0	.	808	22	0	.	808	22
	other conifers	86	53	141	39	0	.	71	50	0	.	298	27
	beech	2381	12	2406	11	2133	12	1442	15	1941	17	10302	6
	maple	1206	16	801	17	999	17	1083	18	296	37	4386	8
	ash	558	23	487	23	517	25	564	26	331	43	2457	12
	oak	141	50	353	34	21	100	213	48	348	33	1075	19
	sweet chestnut	0	.	0	.	0	.	18	100	1873	19	1891	19
	other broadleaves	558	23	1186	16	779	32	2650	14	2413	14	7587	8
	indeterminable	0	.	0	0	0	0	0	.	0	.	0	0
	total	8495	6	9754	6	11211	6	20838	4	10052	7	60351	3
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1299	15	2137	14	4039	11	7690	7	1127	16	16293	5
	fir	1536	13	904	17	1207	15	447	22	107	40	4201	8
	pine	105	41	135	55	69	100	1096	19	16	100	1422	16
	larch	0	.	84	66	42	72	1265	18	997	19	2388	13
	Arolla pine	0	.	0	.	0	.	849	22	16	100	865	22
	other conifers	51	74	37	71	53	58	52	74	0	.	193	35
	beech	2245	11	1498	13	1961	13	1161	15	1704	18	8570	6
	maple	667	18	776	22	655	19	956	17	54	58	3109	9
	ash	230	31	257	29	428	24	510	26	207	40	1632	13
	oak	241	46	256	29	66	75	87	45	284	31	934	18
	sweet chestnut	0	.	0	.	0	.	15	100	1424	18	1439	18
	other broadleaves	316	27	685	18	519	24	1106	17	1879	15	4505	9
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	6690	6	6770	7	9040	6	15235	5	7816	7	45551	3
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1325	15	2158	13	3508	10	6343	7	1056	26	14390	5
	fir	1123	15	926	19	1321	15	549	20	71	50	3990	8
	pine	244	39	51	58	139	70	917	18	34	71	1385	16
	larch	17	100	87	52	25	100	1003	16	827	19	1959	12
	Arolla pine	0	.	0	.	0	.	587	23	0	.	587	23
	other conifers	17	100	34	71	0	.	35	71	0	.	86	45
	beech	2007	13	1465	12	1569	12	1181	18	1162	17	7385	6
	maple	556	19	512	20	1026	18	455	22	93	45	2642	10
	ash	225	31	361	22	411	37	349	33	218	38	1563	15
	oak	350	32	189	30	44	71	145	39	310	31	1038	16
	sweet chestnut	0	.	0	.	0	.	37	71	1154	20	1192	19
	other broadleaves	256	26	421	26	258	32	572	22	1211	22	2718	12
	indeterminable	0	0	0	.	0	.	0	0	0	0	0	0
	total	6121	7	6203	7	8300	7	12173	5	6138	9	38935	3
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1224	15	1281	14	3294	10	5596	7	1021	19	12415	5
	fir	816	17	529	22	1158	16	496	23	143	43	3142	9
	pine	227	38	84	45	88	71	611	21	32	100	1043	17
	larch	0	.	34	71	0	.	1260	16	790	21	2085	12
	Arolla pine	0	.	0	.	0	.	408	25	0	.	408	25
	other conifers	17	100	51	75	0	.	15	100	0	.	84	53
	beech	1794	12	1314	13	1358	14	835	17	798	22	6099	7
	maple	484	23	370	23	559	20	473	24	132	47	2019	11
	ash	142	43	495	22	288	30	281	27	49	73	1255	14
	oak	158	33	192	33	20	100	18	100	123	41	511	19
	sweet chestnut	0	.	0	.	0	.	75	71	814	24	888	22
	other broadleaves	160	37	292	25	140	38	135	35	660	18	1386	12
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0
	total	5022	7	4643	7	6905	7	10204	5	4561	8	31335	3
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	856	13	1111	12	2726	8	4138	6	809	15	9640	4
	fir	682	14	437	16	770	12	329	20	89	37	2307	7
	pine	310	26	147	29	81	53	522	16	14	71	1074	12
	larch	14	71	41	47	9	100	887	14	491	19	1442	11
	Arolla pine	0	.	0	.	0	.	284	20	0	.	284	20
	other conifers	7	100	55	39	0	.	7	100	0	.	69	35
	beech	1816	9	1031	10	1009	12	595	15	427	17	4877	5
	maple	247	19	283	17	251	20	255	22	22	74	1058	10
	ash	143	23	346	17	264	29	220	23	46	40	1020	11
	oak	132	33	89	33	16	71	29	61	67	43	333	19
	sweet chestnut	0	.	0	.	0	.	12	100	367	19	380	18
	other broadleaves	82	32	197	23	30	50	202	21	428	19	939	11
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	4288	5	3738	6	5154	5	7482	4	2761	7	23422	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	860	12	1055	12	2450	8	3836	6	611	17	8813	4
	fir	646	14	595	14	711	12	367	18	81	44	2401	7
	pine	235	23	139	25	48	59	443	17	48	54	914	12
	larch	14	71	41	41	7	100	908	12	475	19	1445	10
	Arolla pine	0	.	0	.	0	.	237	23	0	.	237	23
	other conifers	21	100	27	50	0	.	0	.	8	100	56	47
	beech	1235	9	899	10	961	12	413	16	384	19	3893	5
	maple	223	17	159	24	244	19	177	22	29	50	832	10
	ash	147	23	356	20	143	31	91	30	23	57	760	13
	oak	99	30	134	24	28	76	56	42	76	31	393	15
	sweet chestnut	0	.	0	.	0	.	6	100	276	22	282	21
	other broadleaves	131	35	152	26	33	50	115	33	226	21	657	13
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	3611	5	3559	6	4625	5	6650	4	2238	7	20682	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	749	11	932	11	1857	8	3357	6	578	17	7473	4
	fir	667	13	459	15	549	13	256	21	51	43	1982	7
	pine	99	28	149	22	34	72	263	22	7	100	551	14
	larch	7	100	34	45	0	.	886	13	312	18	1239	11
	Arolla pine	0	.	0	.	0	.	134	25	0	.	134	25
	other conifers	14	70	14	71	0	.	0	.	0	.	27	50
	beech	1133	9	813	10	822	15	514	17	219	21	3501	6
	maple	215	21	74	32	146	23	131	28	0	.	566	13
	ash	114	26	246	18	115	27	37	47	27	70	540	12
	oak	155	34	216	21	22	58	28	50	70	46	490	16
	sweet chestnut	0	.	0	.	0	.	7	100	272	26	280	25
	other broadleaves	102	46	114	26	8	100	77	44	180	25	482	17
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	3255	6	3052	6	3553	6	5689	4	1717	8	17266	3
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	721	13	599	13	1673	8	2598	7	486	22	6076	5
	fir	656	12	413	14	616	13	330	19	52	37	2068	7
	pine	85	33	112	30	50	66	177	27	0	.	425	17
	larch	0	.	29	61	0	.	709	12	171	21	909	11
	Arolla pine	0	.	0	.	0	.	98	30	0	.	98	30
	other conifers	0	.	20	58	0	.	0	.	0	.	20	58
	beech	791	10	741	13	627	13	276	18	216	22	2651	6
	maple	106	27	95	29	62	39	107	26	14	71	385	14
	ash	75	36	151	25	113	37	46	42	10	100	395	17
	oak	64	36	110	27	20	71	14	71	47	50	255	19
	sweet chestnut	0	.	0	.	0	.	7	100	120	29	127	28
	other broadleaves	21	58	70	34	35	45	26	50	154	26	306	17
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0
	total	2520	6	2341	6	3195	6	4387	5	1271	10	13715	3
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	557	13	538	13	1487	8	2077	7	294	20	4953	5
	fir	406	15	328	17	706	13	225	21	87	44	1752	8
	pine	80	32	107	28	36	51	138	27	14	100	375	16
	larch	7	100	49	38	0	.	706	14	231	22	994	11
	Arolla pine	0	.	0	.	0	.	113	29	0	.	113	29
	other conifers	21	75	34	52	0	.	0	.	8	100	63	40
	beech	652	12	669	10	429	16	228	21	146	26	2124	7
	maple	59	36	43	41	127	30	75	35	0	.	304	17
	ash	23	58	175	22	21	57	57	36	23	71	299	17
	oak	63	39	129	28	0	.	20	76	36	50	248	20
	sweet chestnut	0	.	0	.	0	.	6	100	81	40	87	38
	other broadleaves	7	100	74	38	29	61	48	47	14	100	172	25
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0
	total	1875	7	2147	6	2835	6	3693	5	935	11	11484	3
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	336	17	329	18	1156	9	1550	8	457	31	3827	6
	fir	420	15	384	15	462	14	273	19	95	31	1635	7
	pine	27	50	81	33	24	59	100	30	0	.	233	19
	larch	7	100	28	50	14	100	575	13	236	18	860	10
	Arolla pine	0	.	0	.	0	.	57	35	0	.	57	35
	other conifers	21	74	27	50	0	.	0	.	0	.	48	43
	beech	372	19	541	13	315	18	219	22	83	32	1530	8
	maple	63	33	28	50	29	50	14	71	0	.	135	23
	ash	44	40	180	27	29	61	22	58	6	100	281	20
	oak	23	58	107	26	0	.	13	71	13	100	156	22
	sweet chestnut	0	.	0	.	0	.	8	100	171	35	180	33
	other broadleaves	29	50	57	46	8	100	31	51	24	58	150	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1342	9	1762	7	2038	7	2863	5	1085	18	9090	4
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	397	16	257	16	685	12	1142	9	158	23	2639	6
	fir	203	20	292	17	396	15	108	29	8	100	1006	9
	pine	0	.	34	45	0	.	43	41	0	.	76	30
	larch	0	.	14	71	0	.	307	16	93	29	413	14
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	7	100	20	58	0	.	0	.	0	.	27	50
	beech	206	19	305	16	184	23	45	41	80	30	820	10
	maple	7	100	28	50	16	71	20	58	0	.	71	32
	ash	24	58	97	26	0	.	16	72	0	.	136	23
	oak	0	.	53	42	0	0	8	100	0	.	61	39
	sweet chestnut	0	.	0	.	0	.	0	.	28	60	28	60
	other broadleaves	0	.	27	50	8	100	49	42	0	.	84	31
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	843	10	1126	8	1289	8	1751	7	367	15	5377	4
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	253	20	285	19	527	13	724	11	96	29	1884	7
	fir	164	24	207	22	246	21	91	29	0	0	708	12
	pine	15	71	20	58	10	100	37	45	7	100	89	29
	larch	0	.	14	71	0	.	347	20	73	34	433	17
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	7	100	7	100	0	.	0	.	8	100	22	58
	beech	136	27	174	21	101	31	101	29	59	39	571	12
	maple	8	100	7	100	6	100	22	71	0	.	43	46
	ash	22	58	42	41	28	61	12	100	0	.	104	29
	oak	14	70	61	36	0	.	6	100	0	0	81	31
	sweet chestnut	0	.	0	.	0	.	7	100	52	38	60	35
	other broadleaves	7	100	16	71	0	.	29	60	15	71	67	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	625	12	832	10	917	10	1383	8	311	15	4068	5
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	189	22	130	22	571	15	542	12	176	21	1607	8
	fir	115	26	163	22	232	25	92	31	22	58	625	13
	pine	0	.	14	71	14	71	14	71	0	.	42	41
	larch	0	.	14	71	0	.	213	21	86	33	313	17
	Arolla pine	0	.	0	.	0	.	21	58	0	.	21	58
	other conifers	0	.	40	41	0	.	0	.	0	.	40	41
	beech	79	30	220	19	92	30	48	42	48	41	487	13
	maple	7	100	14	71	27	76	7	100	0	.	55	45
	ash	15	71	15	71	7	100	7	100	10	100	55	38
	oak	12	100	40	54	0	.	0	.	8	100	60	43
	sweet chestnut	0	.	0	.	0	.	0	0	23	57	23	57
	other broadleaves	7	100	7	100	0	.	12	70	0	.	26	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	425	14	656	10	944	11	957	9	373	15	3355	5
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	111	28	126	25	252	18	445	13	89	35	1022	9
	fir	123	24	137	25	108	26	119	30	7	100	495	13
	pine	7	100	14	71	0	.	7	100	0	.	27	50
	larch	7	100	0	.	15	71	172	22	58	35	252	18
	Arolla pine	0	.	0	.	0	.	21	58	0	.	21	58
	other conifers	0	.	7	100	0	.	0	.	0	.	7	100
	beech	38	45	112	28	51	38	33	45	30	50	264	17
	maple	0	.	7	100	6	100	0	.	0	.	13	71
	ash	0	.	7	100	8	100	12	70	6	100	33	45
	oak	9	100	30	51	0	.	7	100	0	.	47	42
	sweet chestnut	0	.	0	.	0	.	8	100	28	60	37	51
	other broadleaves	9	100	20	58	0	.	0	.	0	.	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	304	17	459	13	439	13	825	10	220	20	2247	6
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	78	32	80	31	161	28	343	17	55	42	717	12
	fir	45	40	79	31	74	30	64	34	8	100	271	16
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	125	27	20	58	145	24
	Arolla pine	0	.	0	.	0	.	28	50	0	.	28	50
	other conifers	0	.	21	75	0	.	0	.	0	.	21	75
	beech	28	50	47	41	43	52	0	.	7	100	125	27
	maple	9	100	0	.	0	.	7	100	0	.	16	71
	ash	7	100	10	100	0	.	0	.	0	.	17	71
	oak	9	100	21	58	0	.	0	.	0	.	30	50
	sweet chestnut	0	.	0	.	0	.	0	.	30	50	30	50
	other broadleaves	0	.	7	100	0	.	0	.	7	100	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	175	21	271	17	279	20	568	13	127	25	1420	8
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	58	39	81	29	225	20	38	53	409	14
	fir	22	58	62	33	129	26	27	50	22	57	263	17
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	81	33	22	58	103	28
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	7	100	0	.	0	.	0	.	7	100
	beech	0	.	30	51	8	100	26	50	15	71	79	30
	maple	0	.	0	.	0	.	8	100	0	.	8	100
	ash	7	100	9	100	17	72	0	.	0	.	33	51
	oak	0	.	8	100	8	100	10	100	0	.	26	58
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	7	100	0	.	0	.	6	100	0	.	13	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	43	41	174	21	243	19	390	14	104	28	955	9
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	24	58	20	58	73	34	52	38	46	46	216	20
	fir	21	74	40	41	36	44	8	100	0	.	105	27
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	43	47	37	45	80	33
	Arolla pine	0	.	0	.	0	.	21	75	0	.	21	75
	other conifers	7	100	14	71	0	.	0	.	0	.	21	58
	beech	8	100	7	100	7	100	0	.	22	58	44	41
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	14	71	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	0	.	30	50	30	50
	other broadleaves	0	.	8	100	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	60	39	109	25	117	26	124	27	136	24	546	12
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16	100	33	53	46	46	91	28	14	71	201	21
	fir	7	100	43	41	15	71	15	71	0	.	81	30
	pine	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	42	47	28	50	70	35
	Arolla pine	0	.	0	.	0	.	15	71	0	.	15	71
	other conifers	0	.	7	100	7	100	0	.	0	.	14	71
	beech	0	.	13	71	0	.	7	100	0	.	21	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	7	100	0	.	0	.	7	100
	oak	0	.	21	57	0	.	0	.	0	.	21	57
	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	23	75	117	25	76	34	178	21	49	38	443	13
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	7	100	24	59	81	30	11	100	130	25
	fir	22	58	0	.	6	100	14	71	25	58	68	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	25	59	0	.	25	59
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	19	73	7	100	0	.	0	.	26	60
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	17	71	0	.	0	.	0	.	17	71
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	7	100	22	74	30	61
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	43	46	38	45	134	24	59	42	302	17
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	0	.	0	.	14	71	7	100	29	50
	fir	0	.	14	71	6	100	0	.	0	.	20	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	22	58	0	.	22	58
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	0	.	0	.	0	.	7	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	15	71	15	71
	other broadleaves	0	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	100	27	50	6	100	43	41	22	58	107	26
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	30	50	7	100	6	100	44	41
	fir	8	100	0	.	0	.	0	.	0	.	8	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	7	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	16	100	0	.	0	.	0	.	16	100
	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	8	100	16	100	30	50	7	100	21	58	82	33
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	14	71	14	71	0	.	29	50
	fir	7	100	0	.	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	14	71	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	14	71	14	71	14	71	0	.	50	38
108-111 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	7	100	15	71	0	.	22	58
	fir	0	.	0	.	7	100	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	14	71	15	71	6	100	35	45
112-115 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	7	100	0	.	30	50	11	100	55	38
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	7	100	14	71
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	7	100	0	.	44	41	18	72	76	32
116-119 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	7	100	0	.	0	.	0	.	7	100
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	0	.	7	100
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	7	100	0	.	14	71	7	100	28	50
120-123 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
124-127 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	7	100	0	.	0	.	0	.	7	100
128-131 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
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	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	7	100	0	.	0	.	0	.	7	100
140-143 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	7	100	7	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	.	7	100	7	100
144-147 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
148-151 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
152-155 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
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	.	14	71	14	71
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	14	71	14	71
164-167 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
168-171 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
172-175 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
176-179 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
180-183 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
184-187 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
188-191 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	0	0	.	0	.	0	.	0	.	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	.	0	0
192-195 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
196-199 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
=200 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	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
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15913	7	22921	7	41639	6	79227	4	13471	12	173170	3
	fir	15949	7	11498	8	16939	8	6684	11	1637	24	52707	4
	pine	1658	19	1411	17	920	55	10346	13	338	53	14674	10
	larch	148	44	1395	35	342	44	14763	7	8507	12	25155	6
	Arolla pine	0	.	0	.	0	.	5748	15	65	79	5813	15
	other conifers	712	31	1083	25	245	31	1293	26	44	70	3376	15
	beech	23847	5	20076	6	17261	7	11693	10	12725	12	85602	3
	maple	7266	10	6134	10	6549	11	7605	10	1781	26	29336	5
	ash	2997	13	4802	10	3391	16	4407	14	2433	22	18030	6
	oak	2492	26	2557	15	265	32	1510	26	2180	24	9004	11
	sweet chestnut	0	.	0	.	0	.	291	56	10448	13	10739	13
	other broadleaves	4517	12	7667	10	5079	12	14579	10	16738	10	48580	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	75499	3	79544	3	92630	3	158148	3	70366	4	476187	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. 1350122. Birmensdorf, Swiss Federal Research Institute WSL

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