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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total forest.

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
number of stems
diameter at breast height (in 4 cm classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (in 4 cm classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3562	12	6316	12	6246	11	14082	7	1665	17	31871	5
	fir	3607	11	2386	14	3741	12	1307	16	305	33	11346	6
	pine	74	62	159	73	104	85	1994	22	49	100	2380	19
	larch	0	.	349	84	133	65	1566	15	689	28	2737	16
	Arolla pine	0	.	0	.	0	.	588	28	32	100	620	27
	other conifers	328	44	350	39	89	45	564	35	0	.	1330	21
	beech	4709	9	4015	9	3595	12	2290	15	2999	16	17607	5
	maple	1584	17	1268	18	1149	20	1591	17	305	34	5898	9
	ash	881	23	1404	18	840	22	892	22	821	27	4838	10
	oak	687	49	187	37	57	58	554	35	513	34	1997	22
	sweet chestnut	0	.	17	100	0	.	0	.	2813	21	2830	21
	other broadleaves	1760	14	2182	16	1579	15	5333	11	4884	15	15738	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	17192	5	18633	6	17532	6	30761	5	15075	8	99193	3
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2999	14	3887	12	5774	10	10315	7	1175	19	24151	5
	fir	2411	11	1997	17	2520	13	1039	18	160	48	8127	7
	pine	71	62	168	73	121	77	1879	21	16	100	2256	19
	larch	0	.	106	53	126	62	1383	15	578	28	2193	13
	Arolla pine	0	.	0	.	0	.	712	22	0	.	712	22
	other conifers	124	44	210	37	18	100	226	37	0	.	578	22
	beech	3859	10	2920	10	2763	12	2308	15	2444	17	14293	6
	maple	1204	16	1109	17	999	19	1292	16	268	43	4872	8
	ash	736	25	820	20	455	23	851	22	408	34	3270	11
	oak	327	35	467	41	0	.	196	39	436	45	1425	22
	sweet chestnut	0	.	0	.	0	.	0	0	2381	18	2381	18
	other broadleaves	719	20	1143	16	791	18	3638	14	3292	15	9582	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	12451	5	12826	6	13566	6	23839	5	11158	7	73839	3
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2027	15	3301	12	4052	10	8116	7	1193	18	18689	5
	fir	1953	13	1069	19	1762	13	670	19	195	37	5648	7
	pine	123	37	135	49	121	71	1731	19	16	100	2127	16
	larch	17	100	120	51	68	79	1200	17	743	23	2148	13
	Arolla pine	0	.	0	.	0	.	460	25	16	100	477	24
	other conifers	51	58	137	39	74	50	70	61	0	.	332	25
	beech	2701	11	2410	10	2475	11	1367	15	1953	18	10906	6
	maple	810	18	1010	22	783	19	699	19	109	47	3411	10
	ash	386	24	637	20	748	21	615	26	282	40	2668	11
	oak	246	35	258	33	43	71	247	39	279	31	1073	17
	sweet chestnut	0	.	0	.	0	.	0	0	2220	18	2220	18
	other broadleaves	516	19	954	19	574	30	1721	16	2034	15	5799	9
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0
	total	8830	6	10031	6	10701	6	16898	5	9041	7	55500	3
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1893	15	2492	13	4084	10	6772	7	940	18	16180	5
	fir	1392	16	870	18	1499	14	733	19	88	66	4581	8
	pine	273	39	137	39	139	88	1189	18	0	0	1739	16
	larch	0	0	51	74	0	.	807	18	830	20	1688	14
	Arolla pine	0	.	0	.	0	.	632	23	0	.	632	23
	other conifers	52	58	51	58	0	.	35	71	0	.	137	35
	beech	2731	10	2054	11	1790	13	1459	16	1479	19	9513	6
	maple	667	19	631	20	920	18	572	21	39	70	2828	10
	ash	251	39	551	21	778	19	316	32	234	38	2131	12
	oak	395	41	365	27	45	72	110	41	287	32	1201	18
	sweet chestnut	0	.	0	.	0	.	15	100	1293	19	1309	19
	other broadleaves	345	24	542	19	380	31	712	18	1457	16	3435	9
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	7999	6	7744	6	9633	6	13351	5	6647	7	45374	3
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1618	14	1984	14	3590	9	5756	8	946	18	13893	5
	fir	906	20	748	18	954	16	531	22	126	43	3265	9
	pine	298	42	68	62	69	100	849	18	68	60	1352	16
	larch	0	.	103	47	0	.	1061	16	462	25	1626	13
	Arolla pine	0	.	0	.	0	.	444	23	0	.	444	23
	other conifers	35	71	103	47	0	.	15	100	0	.	153	37
	beech	2340	12	1728	11	1922	12	1119	15	1049	21	8158	6
	maple	463	24	526	20	839	17	546	21	100	53	2474	10
	ash	320	27	558	19	479	25	484	30	80	81	1921	13
	oak	369	29	242	28	21	100	71	61	225	39	927	17
	sweet chestnut	0	.	0	.	0	.	37	100	1153	21	1190	20
	other broadleaves	179	34	327	28	232	31	362	27	813	17	1913	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	6528	7	6386	6	8105	6	11276	5	5021	8	37315	3
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1394	15	1792	14	3025	10	5035	7	809	22	12054	5
	fir	1102	15	672	18	1136	14	265	32	91	53	3265	9
	pine	244	35	223	29	35	70	682	21	16	100	1200	15
	larch	34	71	86	44	0	.	1012	17	442	25	1574	13
	Arolla pine	0	.	0	.	0	.	334	28	0	.	334	28
	other conifers	17	100	51	75	0	.	0	.	0	.	68	61
	beech	2100	11	1306	14	1229	17	545	20	550	24	5729	7
	maple	369	24	339	22	389	23	291	30	36	70	1424	12
	ash	234	32	556	21	405	30	291	41	38	71	1523	15
	oak	209	31	223	32	21	100	0	.	99	45	552	19
	sweet chestnut	0	.	0	.	0	.	18	100	618	24	637	23
	other broadleaves	199	38	324	26	116	41	166	34	539	21	1344	13
	indeterminable	0	0	0	0	0	.	0	0	0	.	0	0
	total	5901	7	5572	7	6354	6	8639	6	3238	9	29704	3
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1122	13	1415	11	2509	7	4214	6	715	17	9975	4
	fir	762	13	496	15	748	11	393	17	81	41	2479	7
	pine	325	27	206	22	77	47	472	15	27	60	1106	12
	larch	21	58	41	47	14	71	704	16	392	21	1173	12
	Arolla pine	0	.	0	.	0	.	186	26	0	.	186	26
	other conifers	21	75	13	71	0	.	0	.	0	.	34	53
	beech	1869	8	1092	10	1056	12	508	16	390	19	4915	5
	maple	296	16	205	21	280	20	207	25	23	58	1011	10
	ash	231	22	504	19	249	24	156	27	39	44	1178	11
	oak	123	29	194	25	8	100	52	43	67	35	445	15
	sweet chestnut	0	.	7	100	0	.	10	100	357	21	374	20
	other broadleaves	64	40	257	20	40	45	167	26	341	18	868	11
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0
	total	4833	5	4430	5	4980	5	7070	4	2431	8	23744	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1105	10	1138	10	2132	8	3656	6	644	16	8675	4
	fir	870	12	543	15	730	12	301	24	73	37	2517	7
	pine	257	22	162	25	59	60	352	20	21	75	851	13
	larch	7	100	34	53	0	.	817	14	305	19	1163	11
	Arolla pine	0	.	0	.	0	.	194	23	0	.	194	23
	other conifers	14	71	28	79	0	.	0	.	0	.	41	58
	beech	1516	9	967	10	938	13	521	16	284	21	4227	5
	maple	204	20	92	30	164	23	185	23	22	58	667	11
	ash	102	28	408	17	183	25	78	38	27	70	798	12
	oak	144	30	180	25	23	58	29	61	70	43	448	16
	sweet chestnut	0	.	0	.	0	.	14	71	351	22	364	22
	other broadleaves	144	45	141	23	35	50	116	38	173	24	609	16
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	4363	5	3693	5	4265	5	6264	4	1970	8	20555	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	827	11	957	12	1925	8	2855	6	468	17	7032	4
	fir	776	12	423	14	688	12	358	17	43	40	2288	7
	pine	120	29	196	22	27	59	250	23	7	100	599	14
	larch	7	100	55	39	0	.	704	13	207	23	972	11
	Arolla pine	0	.	0	.	0	.	90	34	0	.	90	34
	other conifers	0	.	34	53	0	.	0	.	8	100	42	47
	beech	1157	10	893	10	795	12	378	18	143	27	3366	6
	maple	166	23	86	31	94	31	89	27	0	.	436	14
	ash	158	26	240	21	101	32	102	32	8	100	609	13
	oak	69	34	179	23	21	71	20	73	57	51	345	17
	sweet chestnut	0	.	0	.	0	.	0	.	193	25	193	25
	other broadleaves	28	50	111	34	33	67	49	38	139	27	361	17
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0
	total	3308	5	3173	5	3684	5	4895	4	1274	9	16333	2
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	716	12	878	12	1659	9	2668	7	385	17	6305	4
	fir	564	14	519	14	629	13	242	19	74	33	2029	7
	pine	108	33	134	25	71	64	122	30	0	.	436	18
	larch	7	100	28	50	7	100	636	14	180	24	858	12
	Arolla pine	0	.	0	.	0	.	113	28	0	.	113	28
	other conifers	35	72	20	74	0	.	0	.	0	.	55	53
	beech	855	10	879	10	531	16	247	21	231	23	2741	6
	maple	56	35	56	35	125	26	114	45	0	.	350	19
	ash	36	45	339	18	61	35	67	31	41	62	544	13
	oak	79	37	157	31	0	.	13	71	18	71	267	22
	sweet chestnut	0	.	0	.	0	.	7	100	104	28	111	27
	other broadleaves	7	100	121	35	23	59	46	46	24	58	221	24
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	2462	6	3130	6	3105	6	4276	5	1057	9	14030	3
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	481	14	593	13	1420	9	1857	7	331	21	4682	5
	fir	392	17	434	15	575	12	154	24	74	39	1629	8
	pine	42	41	123	25	19	73	132	31	14	100	331	17
	larch	0	.	36	45	0	.	604	14	204	21	843	12
	Arolla pine	0	.	0	.	0	.	43	41	0	.	43	41
	other conifers	7	100	34	53	0	.	0	.	0	.	41	47
	beech	509	14	563	12	391	16	200	21	124	29	1787	7
	maple	35	44	21	58	55	38	28	50	8	100	147	22
	ash	49	37	186	20	44	41	30	51	0	.	309	15
	oak	23	58	101	27	0	.	22	74	18	72	163	22
	sweet chestnut	0	.	0	.	0	.	6	100	113	33	119	32
	other broadleaves	22	58	27	71	8	100	66	39	24	58	147	26
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	1559	7	2118	6	2512	6	3142	5	909	11	10240	3
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	615	12	649	14	1092	10	1504	8	325	20	4185	5
	fir	315	16	369	16	383	14	215	20	51	47	1334	8
	pine	34	53	67	37	19	71	74	32	0	.	195	21
	larch	7	100	40	47	21	74	543	14	121	25	732	12
	Arolla pine	0	.	0	.	0	.	43	41	0	.	43	41
	other conifers	0	.	7	100	0	.	0	.	8	100	15	71
	beech	420	15	462	14	262	19	196	24	45	41	1386	8
	maple	35	45	27	50	24	58	15	72	0	.	100	27
	ash	48	41	163	28	29	61	6	100	0	.	246	21
	oak	7	100	34	45	0	.	6	100	0	.	47	38
	sweet chestnut	0	.	0	.	0	.	0	.	106	32	106	32
	other broadleaves	7	100	27	80	16	71	33	52	15	71	98	33
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	1488	8	1847	7	1847	7	2636	6	671	14	8489	3
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	326	17	381	16	716	12	889	10	142	24	2454	6
	fir	248	21	230	17	286	18	124	27	8	100	895	10
	pine	15	71	20	58	13	71	43	41	0	.	91	28
	larch	0	.	7	100	0	.	280	19	86	35	373	17
	Arolla pine	0	.	0	.	0	.	29	61	0	.	29	61
	other conifers	0	.	20	58	0	.	0	.	0	.	20	58
	beech	190	21	276	17	92	29	61	33	74	34	693	11
	maple	8	100	28	61	24	79	24	59	0	.	83	36
	ash	38	45	75	30	7	100	20	72	0	.	141	23
	oak	7	100	83	33	0	.	6	100	8	100	104	29
	sweet chestnut	0	.	0	.	0	.	7	100	30	50	38	45
	other broadleaves	14	70	7	100	9	100	33	52	8	100	71	34
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	845	11	1126	9	1147	9	1516	7	356	15	4991	4
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	233	21	210	22	595	14	647	12	128	23	1812	7
	fir	150	26	242	20	287	22	104	29	14	70	797	12
	pine	7	100	27	50	11	100	36	53	0	.	81	33
	larch	0	.	14	71	0	.	287	24	64	36	365	20
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	20	58	0	.	0	.	0	.	20	58
	beech	106	27	177	20	125	25	79	34	52	42	539	12
	maple	0	.	21	58	0	.	7	100	0	.	28	50
	ash	7	100	25	78	23	58	0	.	10	100	65	41
	oak	19	73	29	50	0	.	0	.	7	100	56	39
	sweet chestnut	0	.	0	.	0	.	0	.	16	71	16	71
	other broadleaves	7	100	7	100	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	530	13	771	10	1040	10	1174	9	292	17	3806	5
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	175	22	166	22	354	14	523	13	133	30	1351	8
	fir	110	25	218	19	128	25	94	35	15	71	565	12
	pine	0	.	14	71	7	100	14	71	0	.	35	45
	larch	0	.	7	100	7	100	162	22	73	34	249	18
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	21	58	0	.	0	.	0	.	21	58
	beech	58	50	139	24	70	32	6	100	32	50	305	17
	maple	7	100	0	.	6	100	7	100	0	.	20	58
	ash	8	100	32	51	7	100	12	70	0	.	60	36
	oak	18	70	37	45	0	.	7	100	0	.	62	36
	sweet chestnut	0	.	0	.	0	.	0	.	44	46	44	46
	other broadleaves	9	100	21	58	9	100	12	70	0	.	51	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	384	15	655	11	589	11	845	10	296	18	2769	6
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	43	41	154	24	286	18	356	16	98	30	937	10
	fir	79	30	35	45	108	29	66	34	8	100	296	16
	pine	7	100	7	100	0	.	0	.	0	.	14	71
	larch	7	100	0	.	9	100	138	25	34	45	188	21
	Arolla pine	0	.	0	.	0	.	21	58	0	.	21	58
	other conifers	7	100	27	79	0	.	0	.	0	.	34	66
	beech	35	45	63	33	67	39	27	50	7	100	199	20
	maple	9	100	0	.	0	.	0	.	0	.	9	100
	ash	7	100	9	100	11	100	0	.	0	.	27	58
	oak	9	100	7	100	0	.	0	.	0	.	16	71
	sweet chestnut	0	.	0	.	0	.	0	.	39	44	39	44
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	203	19	301	17	481	14	609	12	186	20	1780	7
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	59	43	56	39	160	24	209	22	60	39	544	13
	fir	29	71	74	35	121	27	47	38	15	71	287	18
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	107	29	7	100	114	28
	Arolla pine	0	.	0	.	0	.	28	50	0	.	28	50
	other conifers	0	.	7	100	0	.	0	.	0	.	7	100
	beech	7	100	52	38	0	0	26	50	22	58	107	26
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	15	71	7	100	7	100	0	.	30	50
	oak	0	.	15	71	8	100	0	.	0	.	23	58
	sweet chestnut	0	.	0	.	0	.	0	.	16	71	16	71
	other broadleaves	0	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	95	35	226	19	296	18	424	15	120	26	1162	9
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	80	31	94	28	151	25	30	50	369	15
	fir	0	.	40	41	23	58	6	100	0	.	69	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	51	44	29	50	80	33
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	8	100	0	.	0	.	0	.	8	100
	beech	8	100	14	71	7	100	0	.	22	58	51	38
	maple	0	.	0	.	0	.	8	100	0	.	8	100
	ash	7	100	7	100	7	100	0	.	0	.	21	58
	oak	0	.	22	58	0	.	8	100	0	.	30	50
	sweet chestnut	0	.	0	.	0	.	0	.	0	0	0	0
	other broadleaves	0	.	0	.	0	.	0	.	7	100	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	30	50	170	21	130	24	239	20	88	28	658	11
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	13	71	24	58	109	33	26	59	180	24
	fir	29	50	14	71	29	61	30	50	0	.	101	29
	pine	0	.	7	100	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	36	45	14	71	50	38
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	8	100	7	100	0	.	0	.	15	71
	beech	0	.	42	46	9	100	14	71	7	100	72	34
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	20	58	0	.	0	.	0	.	20	58
	sweet chestnut	0	.	0	.	0	.	0	.	45	46	45	46
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	37	45	104	27	68	36	203	22	92	31	504	13
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	70	7	100	43	70	114	27	15	71	193	24
	fir	22	58	7	100	12	71	7	100	15	71	63	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	22	58	0	0	22	58
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	7	100	7	100	0	.	15	71
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	10	100	0	.	0	.	0	.	10	100
	oak	0	.	7	100	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	8	100	15	71	23	58
	other broadleaves	7	100	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	43	40	30	50	62	51	165	22	45	41	345	16
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	7	100	0	0	6	100	0	.	21	58
	fir	15	71	13	71	8	100	0	.	10	100	47	41
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	15	71	0	.	15	71
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	7	100
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	12	100	0	.	0	.	0	.	12	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	22	58	38	46	8	100	29	50	18	72	115	26
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	14	71	7	100	0	.	21	58
	fir	7	100	0	.	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	0	.	14	71
	Arolla pine	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	0	.	0	.	0	.	7	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	12	100	0	.	0	.	0	.	12	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	35	61	14	71	22	58	7	100	85	34
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	28	50	7	100	11	100	47	41
	fir	0	.	14	71	0	.	0	.	0	.	14	71
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	7	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	14	71	28	50	7	100	26	59	76	32
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	14	71	0	.	15	71	0	.	35	45
	fir	0	.	0	.	7	100	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	14	71	7	100	15	71	0	.	42	41
108-111 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	30	50	0	.	30	50
	fir	0	.	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	.	30	50	7	100	37	45
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	.	7	100	7	100
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	14	71	14	71	28	50
116-119 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	0	.	7	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	7	100	0	.	7	100
120-123 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
124-127 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
128-131 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	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	.	8	100	8	100
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
136-139 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
140-143 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
144-147 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
148-151 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
152-155 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
156-159 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	6	100	6	100
160-163 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
164-167 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
168-171 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
172-175 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
176-179 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
180-183 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
184-187 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
188-191 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
192-195 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
196-199 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
=200 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	6	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	6	100	6	100
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19246	8	26488	7	39819	6	69894	4	10246	11	165693	3
	fir	15738	7	11412	9	16374	7	6687	11	1447	26	51657	4
	pine	1999	19	1853	19	892	57	9828	14	234	66	14807	10
	larch	107	34	1075	35	385	51	12156	8	5475	14	19199	7
	Arolla pine	0	.	0	.	0	.	3981	15	49	100	4030	15
	other conifers	691	29	1147	24	188	30	910	29	16	100	2952	15
	beech	25170	5	20059	5	18123	7	11359	10	11906	13	86616	3
	maple	5912	9	5419	12	5850	11	5681	12	909	28	23771	5
	ash	3498	14	6547	9	4433	13	3930	16	1987	26	20396	6
	oak	2731	24	2823	16	246	32	1340	27	2085	25	9224	11
	sweet chestnut	0	.	23	76	0	.	124	52	11972	13	12120	13
	other broadleaves	4026	12	6220	11	3844	14	12455	11	13749	11	40295	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	79116	3	83067	3	90155	3	138344	3	60076	4	450759	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. 1350087. Birmensdorf, Swiss Federal Research Institute WSL

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