Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2
number of stems
diameter at breast height (in 4 cm classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (in 4 cm classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5771	14	8980	12	9376	10	12958	7	1329	18	38414	5
	fir	3311	11	2169	17	3431	11	1492	16	235	35	10638	6
	pine	279	41	224	38	165	76	2615	20	65	100	3348	17
	larch	52	100	85	66	76	100	2053	17	734	27	2999	14
	Arolla pine	0	.	0	.	0	.	585	25	16	100	601	25
	other conifers	195	38	358	37	89	45	402	36	0	.	1044	21
	beech	5112	9	5048	9	4308	13	3033	15	3116	16	20619	5
	maple	1186	14	1071	20	1049	19	1227	19	274	37	4807	9
	ash	754	22	1010	26	903	25	797	25	571	32	4035	12
	oak	798	33	872	33	37	72	264	33	885	35	2855	18
	sweet chestnut	0	.	17	100	0	.	0	.	3815	17	3831	17
	other broadleaves	1975	16	1918	17	2276	14	5369	12	5385	15	16924	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	19434	6	21753	6	21710	6	30795	5	16424	8	110116	3
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3383	16	6226	12	7663	9	9841	7	990	18	28103	5
	fir	2316	14	1683	14	2608	12	982	18	320	43	7908	7
	pine	371	45	292	43	73	77	2306	18	16	100	3057	15
	larch	17	100	124	55	25	100	1200	16	770	25	2136	13
	Arolla pine	0	.	0	.	0	.	633	25	0	.	633	25
	other conifers	51	74	172	49	39	71	155	70	0	.	416	35
	beech	4401	9	3506	9	3184	11	2134	14	2717	16	15942	5
	maple	1013	17	753	22	1087	17	1083	19	55	58	3992	9
	ash	794	23	703	20	1130	21	671	25	429	30	3727	11
	oak	471	35	742	31	103	54	354	31	433	36	2102	17
	sweet chestnut	0	.	0	.	0	.	19	100	2682	18	2700	17
	other broadleaves	911	21	1270	18	990	21	2750	14	3301	12	9222	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	13727	6	15470	6	16902	6	22126	5	11713	7	79939	3
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2693	15	3546	12	6303	9	8354	7	1145	18	22041	5
	fir	1741	13	1229	18	1746	13	711	20	124	51	5551	8
	pine	290	34	155	43	139	100	1810	19	0	.	2394	16
	larch	51	74	71	50	0	.	1231	16	625	22	1979	12
	Arolla pine	0	.	0	.	0	.	544	20	0	.	544	20
	other conifers	86	53	34	71	0	.	52	58	0	.	172	35
	beech	3990	9	2780	9	2398	12	1749	16	1695	19	12612	5
	maple	731	18	419	25	898	18	629	22	57	58	2734	10
	ash	520	31	752	20	846	21	502	34	168	37	2787	12
	oak	437	37	663	33	75	59	122	52	197	51	1495	20
	sweet chestnut	0	.	0	.	0	.	19	100	1911	17	1930	17
	other broadleaves	546	24	903	17	717	27	1038	17	1956	13	5160	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	11085	6	10551	6	13121	6	16761	5	7879	7	59397	3
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2300	13	2775	12	5225	9	7475	8	1005	19	18780	5
	fir	1363	14	1168	16	1210	15	491	24	35	70	4268	8
	pine	239	44	176	34	52	100	979	19	34	71	1480	15
	larch	17	100	138	46	0	.	1348	18	450	24	1953	14
	Arolla pine	0	.	0	.	0	.	459	25	0	.	459	25
	other conifers	52	75	51	58	0	.	15	100	0	.	119	43
	beech	3046	11	2272	10	2402	14	1221	16	1010	20	9950	6
	maple	837	16	701	18	585	20	564	23	113	40	2800	9
	ash	432	23	914	16	619	27	472	30	55	74	2492	12
	oak	416	31	443	23	18	100	71	61	271	36	1220	16
	sweet chestnut	0	.	0	.	0	.	71	62	1385	20	1456	19
	other broadleaves	301	25	447	23	365	29	607	22	968	20	2688	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	9003	6	9086	6	10476	6	13772	5	5326	8	47664	3
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1597	14	2371	12	4352	9	5854	7	822	20	14996	5
	fir	1111	15	1065	16	1396	15	358	27	95	66	4025	8
	pine	256	31	106	47	43	71	928	18	85	60	1419	14
	larch	0	.	85	53	0	.	853	18	507	26	1445	14
	Arolla pine	0	.	0	.	0	.	320	33	0	.	320	33
	other conifers	70	100	83	66	0	.	0	.	0	.	153	58
	beech	3164	10	2037	11	1793	16	881	17	678	20	8553	6
	maple	389	24	416	21	633	21	458	23	0	.	1896	11
	ash	300	30	780	20	551	23	245	30	37	71	1913	12
	oak	248	29	386	23	78	60	37	70	138	47	887	16
	sweet chestnut	0	.	0	.	0	.	19	100	554	25	572	25
	other broadleaves	247	26	454	27	271	31	218	33	590	22	1781	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7382	6	7782	6	9119	6	10171	5	3506	9	37959	3
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1650	14	2587	11	3548	9	5609	8	794	22	14188	5
	fir	1289	16	1018	17	1202	15	369	28	55	58	3933	9
	pine	375	28	256	32	74	64	729	19	0	.	1434	14
	larch	70	61	68	61	0	.	933	16	319	35	1390	14
	Arolla pine	0	.	0	.	0	.	316	26	0	.	316	26
	other conifers	17	100	33	71	0	.	0	.	0	.	51	58
	beech	2439	10	1571	13	1680	13	671	19	404	27	6764	6
	maple	360	23	167	31	295	29	296	25	18	100	1136	13
	ash	381	24	609	23	182	37	123	37	0	.	1294	14
	oak	215	33	289	27	96	100	0	.	64	58	665	22
	sweet chestnut	0	.	0	.	0	.	42	70	364	26	405	24
	other broadleaves	143	44	372	24	74	50	273	29	441	27	1303	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	6938	6	6971	6	7151	7	9361	6	2459	12	32880	3
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1376	10	2421	9	3359	7	4185	6	688	15	12028	4
	fir	952	12	871	12	1021	11	308	20	73	39	3225	6
	pine	311	22	449	20	30	52	507	17	48	66	1345	11
	larch	0	.	84	39	7	100	818	14	302	22	1210	11
	Arolla pine	0	.	0	.	0	.	246	23	0	.	246	23
	other conifers	0	.	7	100	0	.	0	.	8	100	15	71
	beech	1830	9	1339	9	1081	12	484	16	293	21	5027	5
	maple	236	19	146	23	281	19	182	25	7	100	851	11
	ash	205	30	483	17	112	27	120	29	19	72	938	12
	oak	127	31	234	20	35	68	32	72	59	71	486	17
	sweet chestnut	0	.	0	.	0	.	6	100	158	27	164	26
	other broadleaves	167	29	179	24	10	100	177	26	154	25	686	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	5202	5	6212	5	5936	5	7064	4	1809	9	26222	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1173	10	2339	8	2657	8	3537	6	538	17	10244	4
	fir	791	12	869	12	905	12	303	22	52	42	2921	6
	pine	292	23	390	19	93	45	358	21	35	67	1168	12
	larch	7	100	56	44	0	.	893	13	235	23	1192	11
	Arolla pine	0	.	0	.	0	.	162	25	0	.	162	25
	other conifers	7	100	40	71	0	.	0	.	0	.	47	62
	beech	1301	9	1189	10	902	13	417	18	222	20	4030	6
	maple	170	23	68	31	115	28	109	25	0	.	462	13
	ash	136	28	414	18	99	34	91	32	14	70	754	13
	oak	83	31	231	21	14	100	21	58	46	56	394	16
	sweet chestnut	0	.	0	.	0	.	0	.	227	31	227	31
	other broadleaves	105	28	127	28	24	58	56	59	107	26	419	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4065	5	5724	5	4808	5	5948	5	1476	9	22021	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1084	12	1813	8	2521	8	3074	7	397	17	8890	4
	fir	580	14	686	13	860	13	279	19	66	33	2471	7
	pine	152	25	306	21	67	68	232	21	0	0	757	13
	larch	0	.	29	50	7	100	678	13	225	20	939	10
	Arolla pine	0	.	0	.	0	.	175	32	0	.	175	32
	other conifers	7	100	7	100	0	.	0	.	0	.	14	71
	beech	1135	10	1031	10	663	16	319	18	166	29	3314	6
	maple	87	29	35	45	119	33	51	38	8	100	300	18
	ash	75	34	245	19	51	42	45	38	11	100	428	14
	oak	53	42	191	28	0	.	7	100	37	66	288	22
	sweet chestnut	0	.	0	.	0	.	7	100	132	32	139	30
	other broadleaves	7	100	71	56	55	53	60	43	36	45	229	26
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	3180	6	4415	5	4342	6	4928	5	1078	10	17943	3
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	845	11	1591	9	1904	8	2132	7	404	19	6876	4
	fir	428	15	544	15	583	13	271	21	104	34	1930	7
	pine	70	31	198	21	38	51	170	26	7	100	483	14
	larch	7	100	69	37	0	.	654	13	161	23	891	11
	Arolla pine	0	.	0	.	0	.	71	37	0	.	71	37
	other conifers	0	.	62	40	0	.	0	.	0	.	62	40
	beech	554	13	715	12	358	16	276	19	71	31	1974	7
	maple	56	35	23	76	52	46	48	38	0	.	178	22
	ash	54	38	172	29	36	44	12	70	0	.	274	21
	oak	49	43	141	31	0	.	20	76	7	100	217	24
	sweet chestnut	0	.	0	.	0	.	6	100	76	38	83	36
	other broadleaves	21	74	35	66	32	50	55	45	8	100	151	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	2083	7	3549	5	3003	6	3716	5	838	12	13189	3
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	735	12	1105	10	1349	9	1650	8	264	19	5103	4
	fir	240	18	473	13	569	13	179	21	15	71	1476	8
	pine	28	61	156	25	28	59	102	30	14	71	327	17
	larch	7	100	27	61	22	75	492	14	95	29	642	12
	Arolla pine	0	.	0	.	0	.	33	51	0	.	33	51
	other conifers	0	.	27	61	0	.	0	.	0	.	27	61
	beech	429	15	456	14	385	19	178	25	77	34	1525	8
	maple	7	100	49	43	13	71	24	58	0	.	94	30
	ash	19	73	100	28	23	58	12	100	0	.	154	24
	oak	28	50	69	37	0	.	6	100	11	100	115	28
	sweet chestnut	0	.	0	.	0	.	0	.	76	36	76	36
	other broadleaves	22	73	7	100	0	.	47	49	0	.	76	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1516	8	2468	6	2388	7	2725	6	552	12	9649	3
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	509	14	780	12	1007	10	1197	8	227	20	3719	5
	fir	215	21	329	18	356	18	146	27	8	100	1054	10
	pine	49	43	77	32	23	71	85	31	0	.	235	19
	larch	0	.	21	58	7	100	454	17	144	26	626	14
	Arolla pine	0	.	0	.	0	.	56	39	0	.	56	39
	other conifers	0	.	29	78	0	.	0	.	0	.	29	78
	beech	287	18	316	16	186	23	91	30	81	35	961	10
	maple	25	59	23	58	0	.	12	70	0	.	61	36
	ash	22	75	54	51	15	71	6	100	11	100	107	33
	oak	58	42	102	27	0	.	0	.	0	.	160	23
	sweet chestnut	0	.	0	.	0	.	7	100	38	44	45	41
	other broadleaves	9	100	15	71	0	.	43	53	0	.	67	40
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1174	9	1747	7	1594	8	2098	7	508	13	7120	4
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	287	19	427	14	627	13	732	11	143	27	2216	7
	fir	129	25	117	25	190	21	105	28	14	70	555	12
	pine	21	74	14	71	0	.	22	58	0	.	57	39
	larch	0	.	7	100	9	100	211	20	64	32	290	17
	Arolla pine	0	.	0	.	0	.	32	51	0	.	32	51
	other conifers	0	.	20	75	0	.	0	.	0	.	20	75
	beech	130	24	156	23	108	37	70	34	55	38	520	13
	maple	0	.	14	71	18	74	7	100	0	.	38	46
	ash	7	100	29	50	12	100	6	100	0	.	55	39
	oak	14	70	27	50	0	.	0	.	0	.	41	41
	sweet chestnut	0	.	0	.	0	.	0	.	45	45	45	45
	other broadleaves	15	71	15	71	0	.	8	100	8	100	46	41
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	603	12	826	10	964	11	1193	9	329	16	3916	5
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	143	24	294	16	490	14	497	13	124	27	1549	7
	fir	93	29	82	29	168	23	60	36	23	57	426	14
	pine	7	100	27	50	18	72	14	71	0	.	66	34
	larch	0	.	7	100	7	100	95	28	21	58	129	24
	Arolla pine	0	.	0	.	0	.	15	71	0	.	15	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	50	38	72	31	77	37	21	58	7	100	227	19
	maple	8	100	0	.	0	.	0	.	0	.	8	100
	ash	7	100	40	45	7	100	6	100	0	.	60	36
	oak	0	.	7	100	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	0	.	30	50	30	50
	other broadleaves	0	.	8	100	0	.	6	100	9	100	23	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	308	18	537	11	767	11	714	11	214	19	2540	6
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	109	25	192	22	269	17	374	15	72	42	1017	9
	fir	28	50	66	33	119	26	20	58	7	100	239	18
	pine	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	7	100	10	100	168	22	43	41	227	19
	Arolla pine	0	.	0	.	0	.	21	58	0	.	21	58
	other conifers	0	.	8	100	0	.	0	.	0	.	8	100
	beech	78	30	118	28	0	.	18	74	21	57	236	19
	maple	0	.	0	.	6	100	0	.	0	.	6	100
	ash	14	100	0	.	0	.	7	100	0	.	21	74
	oak	9	100	35	45	12	100	0	.	0	.	56	39
	sweet chestnut	0	.	0	.	0	.	0	.	38	44	38	44
	other broadleaves	0	.	15	71	0	.	0	.	0	.	15	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	237	18	441	15	417	13	616	11	181	22	1893	7
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	42	41	20	58	138	24	223	20	60	39	483	13
	fir	43	40	69	35	80	33	14	71	0	.	205	20
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	7	100	7	100	0	.	105	29	14	70	133	25
	Arolla pine	0	.	0	.	0	.	35	44	0	.	35	44
	other conifers	0	.	0	.	7	100	0	.	0	.	7	100
	beech	21	58	69	37	23	73	0	.	15	71	128	27
	maple	0	.	0	.	0	.	10	100	0	.	10	100
	ash	0	.	7	100	7	100	0	.	0	.	14	71
	oak	0	.	15	71	0	.	0	.	0	.	15	71
	sweet chestnut	0	.	0	.	0	.	0	.	12	100	12	100
	other broadleaves	0	.	0	.	0	.	0	.	7	100	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	113	25	186	22	256	18	388	15	108	28	1051	9
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	37	45	34	53	70	35	174	20	50	42	364	15
	fir	30	50	7	100	32	50	14	71	8	100	90	29
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	85	31	22	58	107	27
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	8	100	46	41	17	72	9	100	7	100	87	30
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	17	72	0	.	8	100	0	.	25	59
	sweet chestnut	0	.	0	.	0	.	0	.	24	57	24	57
	other broadleaves	0	.	8	100	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	75	31	118	26	119	26	303	16	111	28	726	10
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	48	38	38	70	56	35	18	72	160	25
	fir	22	58	23	58	26	50	17	72	0	.	88	29
	pine	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	36	45	7	100	43	41
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	14	71	7	100	22	58	0	.	44	41
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	10	100	0	.	0	.	0	.	10	100
	oak	0	.	20	58	0	.	0	.	0	.	20	58
	sweet chestnut	0	.	0	.	0	.	8	100	61	56	69	51
	other broadleaves	0	.	16	71	0	.	0	.	0	.	16	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	22	58	132	24	72	43	146	22	87	43	458	15
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22	58	0	.	16	71	43	41	0	0	82	30
	fir	0	.	7	100	25	57	7	100	7	100	47	41
	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	.	21	58	0	.	7	100	0	.	29	50
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	7	100
	oak	0	.	8	100	0	.	0	.	0	.	8	100
	sweet chestnut	0	.	0	.	0	.	0	.	20	72	20	72
	other broadleaves	0	.	8	100	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	22	58	51	38	41	45	65	33	27	59	206	19
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	23	58	14	71	42	41	0	.	79	30
	fir	0	.	0	.	16	71	0	.	10	100	26	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	0	.	14	71
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	0	.	0	.	0	.	7	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	8	100	0	.	0	.	0	.	8	100
	sweet chestnut	0	.	0	.	0	.	0	.	18	71	18	71
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	37	45	30	50	64	33	28	58	159	22
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	7	100	7	100	0	0	11	100	25	59
	fir	7	100	0	.	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	8	100	8	100	0	.	0	.	0	.	15	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	7	100	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	0	.	15	71	15	71
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	15	71	22	58	7	100	0	0	26	59	69	34
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	8	100	15	71	22	58	0	.	52	38
	fir	0	.	0	.	7	100	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	7	100	21	58
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	8	100	22	58	43	41	7	100	88	29
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	31	51	0	.	31	51
	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	.	31	51	8	100	39	45
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	6	100	7	100	0	.	13	71
	fir	0	.	0	.	0	.	0	.	10	100	10	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	.	17	72	17	72
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	6	100	7	100	28	58	41	45
108-111 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	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	7	100	7	100	14	71
112-115 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	7	100	0	.	7	100
116-119 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	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	.	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	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	15	71	15	71
128-131 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
132-135 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	7	100	7	100
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	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	7	100	7	100
144-147 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
148-151 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
152-155 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	15	71	15	71
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	15	71	15	71
156-159 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
160-163 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	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	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	7	100	7	100
=200 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	23764	8	37587	6	50956	5	68068	4	9090	11	189464	3
	fir	14689	8	12473	8	16550	7	6127	11	1261	27	51100	4
	pine	2739	17	2833	14	843	52	10871	13	305	55	17590	9
	larch	235	38	884	23	170	62	12349	9	4753	14	18391	7
	Arolla pine	0	.	0	.	0	.	3739	15	16	100	3755	15
	other conifers	485	37	930	27	135	36	624	42	8	100	2182	19
	beech	27982	5	22772	5	19573	8	11601	10	10636	14	92563	3
	maple	5104	9	3887	12	5150	10	4707	13	532	29	19379	5
	ash	3719	16	6328	11	4592	14	3118	20	1315	25	19072	7
	oak	3006	22	4508	17	469	35	941	25	2147	26	11071	11
	sweet chestnut	0	.	17	100	0	.	204	54	11748	13	11969	13
	other broadleaves	4469	12	5866	12	4813	14	10709	10	12971	11	38829	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	86192	3	98084	3	103250	3	133057	3	54781	5	475365	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. 1350046. Birmensdorf, Swiss Federal Research Institute WSL

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