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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NFI3
number of stems
diameter at breast height (in 4 cm classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		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	4703	11	6748	9	7691	7	16568	5	2226	12	37936	3
	fir	3592	8	2234	12	3694	9	1378	13	359	25	11256	5
	pine	169	48	155	70	156	53	2742	13	19	100	3242	12
	larch	10	100	99	41	59	71	2764	12	1481	14	4413	9
	Arolla pine	0	.	0	.	0	.	787	16	10	100	797	16
	other conifers	266	36	381	34	41	50	737	24	10	100	1435	17
	beech	4514	7	4614	8	3814	9	2717	13	3375	12	19035	4
	maple	1437	14	1553	12	1295	18	1703	12	532	21	6520	6
	ash	917	17	1432	15	895	18	1107	16	1031	18	5381	7
	oak	513	32	415	25	9	100	480	28	528	24	1945	14
	sweet chestnut	0	.	49	72	0	.	55	84	2761	16	2865	16
	other broadleaves	1952	11	2359	11	2010	12	6646	9	6333	9	19299	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	18074	4	20038	4	19664	4	37683	3	18664	5	114123	2
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3385	11	5312	8	6104	7	12062	5	1526	13	28389	3
	fir	2372	9	1384	12	2134	9	1052	13	262	28	7204	5
	pine	157	34	138	38	249	45	2155	13	19	71	2720	11
	larch	9	100	142	33	35	58	2418	10	873	16	3477	8
	Arolla pine	0	.	0	.	11	100	615	16	0	.	626	16
	other conifers	118	37	262	35	62	47	301	28	0	.	743	18
	beech	3367	8	3245	8	2814	9	2198	11	2839	11	14463	4
	maple	1053	12	1328	15	1369	14	1297	12	381	24	5428	6
	ash	620	19	1292	15	711	19	701	16	627	21	3951	8
	oak	327	31	494	25	23	71	433	22	487	33	1764	14
	sweet chestnut	0	.	30	75	0	.	29	100	2424	13	2483	13
	other broadleaves	1108	15	1431	12	1043	16	3854	10	3807	9	11243	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	12514	4	15059	4	14554	4	27116	3	13245	5	82488	2
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2497	11	4020	9	4711	8	9624	5	1182	12	22033	4
	fir	1746	10	1173	13	2044	9	863	13	202	29	6028	5
	pine	156	31	182	30	84	59	1813	13	19	71	2255	11
	larch	0	0	102	43	28	100	1667	13	952	16	2748	10
	Arolla pine	0	.	0	.	21	100	657	18	10	100	688	18
	other conifers	117	49	198	32	42	50	132	38	0	.	489	21
	beech	3168	8	2858	8	2318	9	1516	12	2048	13	11908	4
	maple	891	13	993	16	915	14	833	15	113	44	3746	7
	ash	567	16	721	15	744	18	813	26	495	25	3340	9
	oak	335	26	332	24	70	54	253	29	440	21	1430	12
	sweet chestnut	0	.	10	100	0	.	11	100	1890	12	1911	12
	other broadleaves	668	15	833	15	525	20	1997	12	2569	10	6592	6
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	10145	4	11421	5	11502	4	20180	4	9919	5	63167	2
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1808	11	2939	9	4582	7	7508	5	1117	13	17955	4
	fir	1028	12	884	13	1313	12	677	15	135	35	4038	6
	pine	269	29	134	33	74	65	1180	14	19	71	1676	11
	larch	48	67	116	43	0	.	1480	11	843	14	2487	8
	Arolla pine	0	.	0	.	30	74	738	16	0	.	768	16
	other conifers	95	48	97	35	0	.	51	45	0	.	244	25
	beech	2931	8	2307	8	1873	9	1467	12	1346	13	9925	4
	maple	692	13	508	18	982	13	642	16	167	30	2992	7
	ash	561	21	519	15	717	18	372	24	244	31	2412	9
	oak	526	21	413	21	50	60	130	35	330	27	1450	12
	sweet chestnut	0	.	10	100	0	.	9	100	1221	14	1240	14
	other broadleaves	503	16	696	15	466	20	1023	12	1708	10	4396	6
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	8461	5	8623	5	10088	4	15280	4	7131	5	49584	2
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1772	10	2262	9	4113	8	6576	5	1036	13	15759	4
	fir	1215	11	962	13	1241	11	353	19	116	33	3887	6
	pine	264	29	166	27	81	50	996	14	96	47	1602	11
	larch	10	100	146	32	0	.	1206	11	540	15	1901	9
	Arolla pine	0	.	0	.	21	100	482	18	10	100	514	17
	other conifers	57	47	60	47	10	100	22	72	0	.	150	29
	beech	2668	8	1656	10	1778	11	1173	13	814	16	8089	5
	maple	518	17	543	16	519	15	462	19	104	42	2146	8
	ash	361	26	673	15	448	19	458	25	137	29	2077	10
	oak	365	23	263	29	44	61	95	48	202	30	969	14
	sweet chestnut	0	.	10	100	0	.	43	71	842	17	895	17
	other broadleaves	273	25	394	21	324	26	481	18	973	15	2446	9
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0
	total	7504	5	7135	5	8580	5	12347	4	4870	6	40436	2
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1359	12	1817	10	3366	8	5743	5	644	17	12928	4
	fir	1014	13	659	15	949	12	359	19	155	34	3135	7
	pine	298	29	226	22	98	43	818	14	106	65	1545	11
	larch	29	58	79	39	0	.	1164	13	462	19	1734	10
	Arolla pine	0	.	0	.	21	100	369	20	0	.	391	20
	other conifers	39	62	20	71	0	.	10	100	0	.	69	43
	beech	2388	8	1689	9	1414	13	491	16	553	16	6535	5
	maple	461	17	379	18	305	23	245	22	57	45	1447	10
	ash	273	23	582	15	389	24	245	32	83	35	1572	11
	oak	193	23	324	25	35	58	52	52	224	26	827	14
	sweet chestnut	0	.	0	.	0	.	10	100	564	16	574	16
	other broadleaves	265	30	236	24	53	53	333	23	649	15	1536	10
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	6319	5	6010	5	6630	5	9840	4	3496	6	32294	2
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1204	8	1417	8	2761	6	4450	4	710	12	10542	3
	fir	871	9	626	10	975	8	328	14	67	40	2868	5
	pine	348	18	263	17	47	32	534	13	34	59	1226	9
	larch	27	43	41	38	8	71	911	9	429	14	1416	7
	Arolla pine	0	.	0	.	4	100	269	17	0	.	273	16
	other conifers	8	100	27	63	0	.	0	.	0	.	35	54
	beech	1907	6	1279	7	1126	8	654	12	427	15	5392	4
	maple	224	14	165	16	208	16	186	17	51	33	835	8
	ash	229	17	462	17	203	18	90	25	68	32	1052	10
	oak	185	26	234	18	9	71	49	42	81	25	558	13
	sweet chestnut	0	.	12	100	0	.	10	72	330	17	351	17
	other broadleaves	184	24	213	19	86	34	155	21	334	14	972	9
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	5186	4	4740	4	5427	4	7636	3	2532	6	25521	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1139	8	1394	8	2635	6	3754	5	535	13	9457	3
	fir	773	9	468	11	926	8	348	14	73	31	2589	5
	pine	249	19	257	18	52	43	440	13	57	48	1055	9
	larch	15	79	51	33	17	50	982	9	309	14	1373	8
	Arolla pine	0	.	0	.	13	100	196	18	0	.	208	18
	other conifers	0	.	24	57	0	.	0	.	4	100	29	51
	beech	1616	6	1111	7	931	10	512	13	318	15	4488	4
	maple	220	15	142	20	112	21	143	19	13	76	630	9
	ash	152	22	345	12	134	20	137	37	30	43	797	10
	oak	121	25	241	17	25	57	44	41	41	51	472	13
	sweet chestnut	0	.	4	100	0	.	8	71	245	16	256	16
	other broadleaves	125	27	124	24	21	45	96	26	219	17	586	11
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	4410	4	4161	4	4866	4	6660	3	1844	6	21940	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	997	8	1196	8	2285	6	3119	4	517	13	8115	3
	fir	769	9	504	11	720	9	256	15	54	33	2304	5
	pine	188	17	230	17	58	32	284	16	31	68	791	9
	larch	11	74	69	35	12	77	793	10	278	15	1163	8
	Arolla pine	0	.	0	.	0	.	144	18	0	.	144	18
	other conifers	23	75	33	39	0	.	0	.	0	.	56	39
	beech	1147	7	1023	8	752	10	354	15	217	17	3493	4
	maple	144	18	109	24	121	20	70	25	0	.	444	11
	ash	132	20	271	16	103	25	86	33	19	60	611	11
	oak	69	25	156	20	23	63	38	37	62	34	349	13
	sweet chestnut	0	.	8	100	0	.	0	0	161	18	168	18
	other broadleaves	53	43	120	24	25	41	53	38	64	26	316	15
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	3534	4	3719	4	4101	4	5196	3	1403	7	17954	2
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	841	9	1020	8	1915	6	2450	5	340	14	6566	3
	fir	580	10	564	11	708	10	198	16	52	34	2101	5
	pine	118	24	184	18	39	52	199	17	4	100	543	11
	larch	11	58	53	34	17	61	752	9	271	14	1104	7
	Arolla pine	0	.	0	.	0	.	151	19	0	.	151	19
	other conifers	4	100	8	71	0	.	0	.	0	.	12	58
	beech	895	9	741	9	562	12	255	17	178	18	2631	5
	maple	59	27	53	39	87	28	39	34	9	71	246	16
	ash	63	27	260	15	86	28	35	33	14	59	458	11
	oak	91	28	205	18	0	.	21	45	16	78	333	14
	sweet chestnut	0	.	0	0	0	.	4	100	154	26	158	26
	other broadleaves	24	41	73	35	27	61	48	40	35	44	208	19
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	2686	5	3161	5	3441	5	4151	4	1074	8	14513	2
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	690	9	895	9	1514	7	1901	5	284	15	5284	3
	fir	395	12	475	11	548	10	203	17	60	31	1680	6
	pine	78	29	104	21	21	51	156	20	12	58	370	12
	larch	0	.	51	31	17	63	640	10	181	16	888	8
	Arolla pine	0	.	0	.	0	.	40	32	4	100	44	30
	other conifers	0	.	12	58	0	.	0	.	4	100	16	50
	beech	513	10	616	9	310	13	232	17	122	20	1793	6
	maple	33	36	44	37	34	36	19	45	4	100	136	19
	ash	46	32	107	27	32	44	32	39	8	71	226	17
	oak	32	52	129	22	5	100	12	58	37	38	214	17
	sweet chestnut	0	.	4	100	0	.	4	100	84	23	91	22
	other broadleaves	19	51	19	50	10	71	73	32	25	41	147	21
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	1806	6	2455	5	2490	5	3311	4	827	8	10889	2
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	574	11	613	10	1273	8	1488	6	253	16	4200	4
	fir	357	18	379	12	544	11	178	17	37	36	1496	7
	pine	67	35	97	22	14	59	94	24	4	100	276	14
	larch	8	71	44	37	13	58	531	10	147	17	742	8
	Arolla pine	0	.	0	.	0	.	44	33	0	.	44	33
	other conifers	0	.	32	39	0	.	0	.	0	.	32	39
	beech	346	12	489	11	304	13	171	21	80	28	1389	6
	maple	16	50	25	41	25	46	14	60	0	.	80	24
	ash	26	41	129	20	30	43	12	73	4	100	200	16
	oak	22	46	114	22	13	58	11	58	10	72	171	17
	sweet chestnut	0	.	0	.	0	.	5	100	130	24	135	23
	other broadleaves	20	45	40	51	9	71	65	40	13	58	147	24
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	1436	7	1961	5	2224	6	2614	5	678	9	8913	3
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	357	13	428	12	842	9	895	8	147	18	2670	5
	fir	206	16	297	13	323	13	113	21	11	58	951	7
	pine	29	38	71	27	8	71	40	32	0	0	148	18
	larch	4	100	17	50	10	72	313	15	95	23	438	12
	Arolla pine	0	.	0	.	0	.	48	29	0	.	48	29
	other conifers	4	100	8	71	0	.	0	.	0	.	12	58
	beech	214	15	245	13	171	18	66	25	50	31	746	8
	maple	9	71	17	50	13	60	13	71	0	.	53	31
	ash	27	42	44	36	5	100	0	.	10	72	86	25
	oak	50	42	66	33	6	100	0	.	4	100	126	25
	sweet chestnut	0	.	0	.	0	.	9	71	51	33	60	30
	other broadleaves	22	46	4	100	5	100	30	76	0	0	61	42
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	922	8	1197	7	1384	7	1528	6	369	11	5399	3
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	231	19	275	14	501	11	640	9	95	21	1743	6
	fir	147	17	150	18	235	14	84	28	8	71	625	9
	pine	14	59	12	58	10	72	24	41	4	100	64	26
	larch	0	.	15	60	4	100	208	15	71	26	297	12
	Arolla pine	0	.	0	.	4	100	27	38	0	.	31	35
	other conifers	0	.	25	59	0	.	0	.	0	.	25	59
	beech	99	21	147	18	95	24	60	41	47	32	448	11
	maple	4	100	4	100	0	.	4	100	0	.	12	58
	ash	14	59	39	37	4	100	8	71	0	.	65	28
	oak	28	63	43	36	0	.	10	72	6	100	87	29
	sweet chestnut	0	.	0	.	0	.	0	.	29	37	29	37
	other broadleaves	5	100	12	58	0	.	4	100	12	58	33	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	541	10	723	8	854	8	1068	7	272	13	3458	4
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	143	20	216	16	387	11	610	10	127	22	1482	6
	fir	155	19	105	22	147	18	67	25	32	43	506	10
	pine	4	100	12	58	4	100	22	56	0	.	42	36
	larch	0	.	4	100	18	50	175	17	46	30	243	14
	Arolla pine	0	.	0	.	0	.	27	38	0	.	27	38
	other conifers	0	.	12	58	0	.	0	.	0	.	12	58
	beech	107	32	103	26	100	24	23	41	24	41	357	14
	maple	6	100	0	.	4	100	4	100	0	.	13	59
	ash	16	61	29	41	10	72	4	100	0	.	59	30
	oak	12	58	24	63	5	100	4	100	4	100	49	38
	sweet chestnut	0	.	0	.	0	.	5	100	41	34	47	32
	other broadleaves	0	.	4	100	0	.	8	71	0	.	12	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	442	13	509	10	673	9	950	8	275	14	2849	4
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	103	23	103	21	258	14	346	12	61	31	871	8
	fir	67	28	76	23	116	20	50	30	4	100	313	12
	pine	0	.	8	71	0	.	0	.	0	.	8	71
	larch	4	100	0	.	0	.	115	23	27	38	146	20
	Arolla pine	0	.	0	.	0	.	16	50	0	.	16	50
	other conifers	0	.	5	100	0	.	0	.	0	.	5	100
	beech	19	45	87	26	9	71	20	45	4	100	139	19
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	8	100	23	45	4	100	4	100	0	.	40	36
	oak	9	71	24	47	5	100	0	.	0	.	38	36
	sweet chestnut	0	.	0	.	0	.	0	.	30	37	30	37
	other broadleaves	0	.	4	100	0	.	0	.	0	.	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	210	16	330	12	392	11	551	10	126	20	1608	6
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	57	27	77	23	165	19	243	14	64	26	607	9
	fir	29	43	37	33	79	25	15	50	12	58	172	16
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	4	100	0	.	69	26	28	38	101	21
	Arolla pine	0	.	0	.	0	.	31	39	0	.	31	39
	other conifers	4	100	4	100	0	.	0	.	0	.	8	71
	beech	29	41	64	27	4	100	7	71	24	41	129	19
	maple	0	.	6	100	4	100	4	100	0	.	14	59
	ash	0	.	0	.	4	100	0	.	0	.	4	100
	oak	0	.	29	42	11	73	4	100	0	.	45	34
	sweet chestnut	0	.	0	.	0	.	0	.	34	44	34	44
	other broadleaves	6	100	4	100	0	.	0	.	4	100	14	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	125	19	225	14	267	15	375	11	166	16	1158	6
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	70	53	12	58	76	26	149	21	58	32	365	15
	fir	21	53	25	47	26	41	12	58	4	100	88	24
	pine	0	.	4	100	0	.	0	.	0	.	4	100
	larch	0	.	0	.	0	.	78	26	29	42	107	22
	Arolla pine	0	.	0	.	0	.	12	58	0	.	12	58
	other conifers	0	.	4	100	4	100	0	.	0	.	8	71
	beech	7	100	22	45	17	60	8	71	4	100	58	30
	maple	0	.	0	.	0	.	9	72	0	.	9	72
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	0	.	5	100	6	100	5	100	0	.	15	58
	sweet chestnut	0	.	0	.	0	.	0	.	12	75	12	75
	other broadleaves	0	.	0	.	0	.	0	.	4	100	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	98	40	75	25	129	20	272	15	111	23	686	10
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	16	50	47	33	86	23	20	45	173	16
	fir	29	38	4	100	29	39	13	58	4	100	79	23
	pine	0	.	0	.	0	.	4	100	0	.	4	100
	larch	0	.	0	.	0	.	37	37	13	58	50	31
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	25	46	8	71	8	71	0	.	41	34
	maple	0	.	0	.	0	.	0	.	4	100	4	100
	ash	0	.	6	100	0	.	0	.	0	.	6	100
	oak	0	.	23	46	0	.	0	.	0	.	23	46
	sweet chestnut	0	.	0	.	0	.	4	100	49	31	54	30
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	33	35	73	25	84	25	161	17	91	22	440	10
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16	50	16	50	33	39	67	25	15	50	148	17
	fir	19	52	12	58	17	50	8	71	15	78	72	28
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	25	41	8	71	33	35
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	6	100	4	100	0	.	0	.	14	59
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	4	100	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	12	58	12	58
	other broadleaves	4	100	6	100	0	.	0	.	0	.	10	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	47	31	44	32	55	32	104	20	50	33	300	13
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	4	100	34	45	29	38	12	58	78	26
	fir	4	100	4	100	8	71	0	.	0	.	16	50
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	0	.	16	50
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	8	71	0	.	0	.	8	71	21	45
	maple	0	.	0	.	4	100	0	.	0	.	4	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	13	76	0	.	0	.	0	.	13	76
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	4	100
	other broadleaves	0	.	11	71	0	.	4	100	0	.	15	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	71	40	37	46	38	53	28	24	41	171	17
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	0	.	20	53	20	45	6	100	51	31
	fir	0	.	0	.	0	.	0	.	4	100	4	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	71	5	100	13	58
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	6	100	0	.	0	.	0	.	6	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	4	100	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	71	6	100	20	53	32	35	27	46	93	23
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	4	100	0	.	12	58	9	71	29	38
	fir	0	.	7	100	0	.	4	100	0	.	10	73
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	4	100	4	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	4	100	0	.	0	.	0	.	4	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4	100	15	59	0	.	16	50	17	50	52	29
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	4	100	0	.	16	50	4	100	25	41
	fir	0	.	0	.	4	100	4	100	0	.	8	71
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	71	4	100	12	58
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	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	.	8	71	4	100	32	35	8	71	52	28
108-111 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	8	71	13	58	0	.	21	45
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	0	.	4	100
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	8	71	21	45	4	100	34	36
112-115 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	0	4	100	4	100	8	71
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	4	100	8	71
	Arolla pine	0	.	0	.	0	.	4	100	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	0	12	58	12	58	24	41
116-119 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	4	100	0	.	4	100
	fir	0	.	0	.	4	100	0	.	0	.	4	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	5	100	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	5	100	0	.	4	100	4	100	0	.	13	58
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	.	9	100	9	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	9	100	9	100
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	.	4	100	4	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
132-135 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	4	100	4	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	12	75	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	12	75	8	71	20	53
136-139 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	4	100	4	100
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
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	.	9	71	9	71
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	9	71	9	71
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	.	14	76	14	76
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	14	76	14	76
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	.	9	100	9	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	9	100	9	100
168-171 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
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	.	19	100	19	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	19	100	19	100
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	.	4	100	4	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	4	100	4	100
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21959	6	30787	5	45322	4	78376	3	10997	8	187441	2
	fir	15388	5	11028	7	16783	5	6565	8	1673	18	51437	3
	pine	2406	13	2242	12	996	34	11502	9	424	43	17570	7
	larch	187	33	1029	18	237	39	16368	6	7100	9	24922	5
	Arolla pine	0	.	0	.	126	83	4684	11	33	66	4843	11
	other conifers	735	27	1214	22	159	32	1254	23	18	71	3381	13
	beech	25943	4	22345	4	18405	6	11931	8	12498	9	91123	3
	maple	5769	7	5869	9	5997	9	5701	8	1434	18	24770	4
	ash	4012	10	6941	8	4519	11	4102	13	2772	15	22346	5
	oak	2882	17	3551	11	340	23	1641	19	2473	18	10889	8
	sweet chestnut	0	.	135	70	0	.	207	53	11145	10	11487	9
	other broadleaves	5238	9	6584	8	4602	10	14872	8	16754	7	48050	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	84521	2	91726	2	97486	2	157203	2	67323	3	498258	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. 1350075. Birmensdorf, Swiss Federal Research Institute WSL

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