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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI5
number of stems
diameter at breast height (in 4 cm classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (in 4 cm classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2893	16	4727	12	6269	12	13990	7	1909	23	29787	5
	fir	3431	12	2657	13	3574	13	1051	17	394	32	11106	7
	pine	19	100	115	47	135	71	1894	24	16	100	2179	21
	larch	74	80	310	53	108	63	1141	17	506	33	2140	15
	Arolla pine	0	.	0	.	0	.	869	37	16	100	885	36
	other conifers	225	52	314	43	56	58	547	33	20	100	1162	22
	beech	5490	11	4950	12	2924	12	2305	13	2810	15	18478	6
	maple	2123	16	1473	16	1142	18	1767	16	405	31	6910	8
	ash	806	27	748	19	450	28	807	24	888	28	3699	12
	oak	678	50	280	33	21	100	508	37	440	42	1927	23
	sweet chestnut	0	.	0	.	0	.	0	0	1985	26	1985	26
	other broadleaves	1688	14	2709	14	1495	16	4571	12	4661	14	15123	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	17427	6	18281	6	16174	7	29448	5	14050	9	95381	3
16-19 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2340	13	4337	12	4595	11	10835	6	1451	17	23560	5
	fir	2794	12	1518	17	2544	13	897	16	161	36	7915	7
	pine	112	53	70	60	87	82	1703	22	49	100	2020	20
	larch	0	.	363	54	105	68	1074	17	540	27	2082	15
	Arolla pine	0	.	0	.	0	.	511	26	16	100	528	25
	other conifers	211	37	207	41	111	47	245	32	0	.	773	19
	beech	3385	10	2657	10	2643	12	2013	18	2303	16	12999	6
	maple	1242	16	1389	16	971	19	1120	16	241	47	4963	8
	ash	397	24	634	23	544	22	746	21	468	32	2788	11
	oak	343	42	239	48	0	0	315	38	325	45	1222	22
	sweet chestnut	0	.	0	.	0	.	0	0	1581	20	1581	20
	other broadleaves	940	17	1370	16	789	18	2525	14	3239	15	8862	8
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	11764	6	12784	6	12388	6	21984	5	10374	7	69294	3
20-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1533	14	2690	13	4331	10	8322	7	1273	19	18148	5
	fir	1939	13	1310	16	1998	13	802	19	178	42	6227	7
	pine	92	45	127	57	104	85	1551	18	0	.	1873	17
	larch	0	.	253	57	17	100	1125	15	578	25	1973	14
	Arolla pine	0	.	0	.	0	.	479	24	0	.	479	24
	other conifers	86	53	141	39	0	.	35	71	0	.	261	29
	beech	2381	12	2380	11	2133	12	1387	16	1801	18	10082	6
	maple	1187	16	766	17	931	17	889	20	213	41	3986	9
	ash	558	23	427	25	446	24	431	30	313	45	2175	13
	oak	141	50	353	34	21	100	194	52	282	38	991	20
	sweet chestnut	0	.	0	.	0	.	18	100	1839	19	1857	19
	other broadleaves	485	22	1186	16	524	29	2176	15	2036	16	6407	9
	indeterminable	0	.	0	0	0	0	0	.	0	.	0	0
	total	8403	6	9631	6	10504	6	17408	5	8514	8	54460	3
24-27 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1280	15	2098	14	3186	11	6588	7	886	19	14038	5
	fir	1462	13	904	17	1207	15	447	22	107	40	4127	8
	pine	105	41	135	55	69	100	1078	19	16	100	1404	17
	larch	0	.	84	66	42	72	867	19	765	23	1758	14
	Arolla pine	0	.	0	.	0	.	632	25	16	100	648	24
	other conifers	51	74	37	71	53	58	17	100	0	.	158	37
	beech	2203	11	1475	13	1961	13	1068	16	1600	19	8307	6
	maple	623	19	758	22	593	19	903	18	54	58	2931	10
	ash	230	31	257	29	405	25	388	25	207	40	1487	13
	oak	241	46	256	29	22	100	87	45	259	33	864	19
	sweet chestnut	0	.	0	.	0	.	15	100	1424	18	1439	18
	other broadleaves	254	28	641	18	414	27	816	20	1531	16	3656	9
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	6449	6	6644	7	7952	6	12908	5	6865	7	40818	3
28-31 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1269	16	2139	13	3140	10	5452	8	840	30	12840	5
	fir	1123	15	926	19	1321	15	531	20	53	57	3954	8
	pine	244	39	51	58	139	70	878	19	34	71	1346	16
	larch	17	100	87	52	25	100	748	18	571	22	1448	13
	Arolla pine	0	.	0	.	0	.	438	25	0	.	438	25
	other conifers	17	100	34	71	0	.	17	100	0	.	69	50
	beech	2007	13	1447	12	1491	12	1037	18	1106	17	7089	6
	maple	504	19	512	20	937	18	417	23	61	58	2432	10
	ash	225	31	361	22	387	39	260	36	182	42	1415	16
	oak	350	32	172	31	44	71	110	47	279	33	955	17
	sweet chestnut	0	.	0	.	0	.	18	100	1096	20	1115	20
	other broadleaves	256	26	421	26	241	34	518	23	1074	25	2509	13
	indeterminable	0	0	0	.	0	.	0	0	0	0	0	0
	total	6014	7	6150	7	7724	7	10424	5	5297	11	35609	3
32-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1187	16	1281	14	3102	10	5020	8	947	20	11537	5
	fir	816	17	529	22	1132	17	496	23	143	43	3116	9
	pine	227	38	84	45	88	71	589	22	32	100	1021	17
	larch	0	.	34	71	0	.	1043	18	431	26	1508	14
	Arolla pine	0	.	0	.	0	.	337	29	0	.	337	29
	other conifers	17	100	51	75	0	.	15	100	0	.	84	53
	beech	1769	12	1314	13	1358	14	817	18	779	22	6037	7
	maple	484	23	370	23	559	20	385	23	82	61	1880	11
	ash	142	43	495	22	288	30	239	30	49	73	1213	14
	oak	158	33	192	33	20	100	18	100	97	44	485	20
	sweet chestnut	0	.	0	.	0	.	37	100	814	24	851	23
	other broadleaves	123	38	275	26	140	38	135	35	530	20	1203	13
	indeterminable	0	0	0	0	0	.	0	0	0	.	0	0
	total	4923	7	4627	7	6688	7	9130	6	3904	9	29272	3
36-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	822	13	1096	12	2442	8	3637	6	745	16	8741	4
	fir	667	14	437	16	746	12	315	21	74	42	2240	7
	pine	310	26	147	29	81	53	506	17	14	71	1058	13
	larch	14	71	41	47	9	100	754	15	409	21	1227	12
	Arolla pine	0	.	0	.	0	.	241	21	0	.	241	21
	other conifers	7	100	55	39	0	.	0	.	0	.	62	37
	beech	1816	9	1013	10	945	12	572	15	418	17	4764	5
	maple	247	19	283	17	236	21	199	23	22	74	988	10
	ash	143	23	338	18	264	29	200	24	39	44	984	12
	oak	132	33	89	33	16	71	29	61	67	43	333	19
	sweet chestnut	0	.	0	.	0	.	12	100	358	19	371	19
	other broadleaves	82	32	183	24	30	50	146	22	348	20	789	12
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	4240	5	3682	6	4768	5	6613	4	2496	7	21799	2
40-43 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	825	13	1048	12	2080	8	3461	6	590	17	8003	4
	fir	646	14	595	14	711	12	367	18	81	44	2401	7
	pine	235	23	139	25	48	59	413	18	41	62	877	12
	larch	14	71	41	41	7	100	735	14	387	21	1184	11
	Arolla pine	0	.	0	.	0	.	194	24	0	.	194	24
	other conifers	21	100	27	50	0	.	0	.	8	100	56	47
	beech	1235	9	899	10	924	12	406	16	377	19	3841	5
	maple	216	18	159	24	238	19	151	22	29	50	793	10
	ash	137	23	356	20	143	31	83	31	23	57	743	13
	oak	99	30	134	24	28	76	48	47	69	33	378	15
	sweet chestnut	0	.	0	.	0	.	6	100	276	22	282	21
	other broadleaves	131	35	152	26	33	50	105	34	196	23	617	14
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	3559	5	3551	6	4212	5	5970	4	2077	8	19369	2
44-47 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	727	11	932	11	1702	8	3062	6	524	18	6947	4
	fir	652	13	459	15	542	13	220	22	51	43	1925	7
	pine	99	28	149	22	34	72	230	24	7	100	518	14
	larch	7	100	34	45	0	.	723	15	235	21	999	12
	Arolla pine	0	.	0	.	0	.	98	28	0	.	98	28
	other conifers	14	70	14	71	0	.	0	.	0	.	27	50
	beech	1133	9	813	10	771	15	499	17	206	21	3422	6
	maple	208	21	74	32	146	23	131	28	0	.	559	13
	ash	105	27	229	18	115	27	37	47	27	70	514	12
	oak	155	34	209	21	14	72	28	50	55	51	461	17
	sweet chestnut	0	.	0	.	0	.	7	100	272	26	280	25
	other broadleaves	102	46	114	26	8	100	41	41	174	26	440	17
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	3201	6	3029	6	3333	6	5077	5	1551	9	16191	3
48-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	703	13	599	13	1471	9	2440	7	473	23	5686	5
	fir	656	12	413	14	605	13	330	19	52	37	2057	7
	pine	85	33	112	30	50	66	162	29	0	.	410	18
	larch	0	.	29	61	0	.	610	13	137	24	775	11
	Arolla pine	0	.	0	.	0	.	91	32	0	.	91	32
	other conifers	0	.	20	58	0	.	0	.	0	.	20	58
	beech	784	11	741	13	608	13	255	19	216	22	2604	6
	maple	106	27	95	29	62	39	107	26	14	71	385	14
	ash	75	36	139	26	113	37	46	42	10	100	383	17
	oak	64	36	100	28	20	71	14	71	47	50	245	19
	sweet chestnut	0	.	0	.	0	.	7	100	120	29	127	28
	other broadleaves	21	58	64	37	35	45	26	50	119	29	265	18
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0
	total	2496	6	2313	6	2964	6	4087	5	1188	11	13049	3
52-55 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	527	14	538	13	1353	9	1967	7	288	20	4672	5
	fir	406	15	320	17	684	13	219	22	87	44	1716	8
	pine	80	32	107	28	36	51	124	29	14	100	361	16
	larch	7	100	49	38	0	.	663	14	217	23	936	12
	Arolla pine	0	.	0	.	0	.	99	32	0	.	99	32
	other conifers	21	75	34	52	0	.	0	.	8	100	63	40
	beech	632	12	669	10	420	16	220	21	138	26	2080	7
	maple	59	36	43	41	114	31	66	37	0	.	282	18
	ash	23	58	175	22	21	57	57	36	23	71	299	17
	oak	63	39	129	28	0	.	20	76	36	50	248	20
	sweet chestnut	0	.	0	.	0	.	6	100	81	40	87	38
	other broadleaves	7	100	67	41	29	61	48	47	14	100	165	26
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0
	total	1826	7	2132	6	2658	6	3487	5	907	11	11009	3
56-59 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	336	17	319	19	1082	9	1436	8	442	32	3615	6
	fir	404	15	376	15	462	14	256	20	88	33	1586	8
	pine	27	50	81	33	24	59	93	31	0	.	226	20
	larch	7	100	28	50	14	100	526	14	168	21	743	11
	Arolla pine	0	.	0	.	0	.	57	35	0	.	57	35
	other conifers	21	74	27	50	0	.	0	.	0	.	48	43
	beech	332	20	541	13	287	18	203	23	83	32	1445	8
	maple	63	33	28	50	29	50	14	71	0	.	135	23
	ash	44	40	180	27	29	61	22	58	0	.	275	20
	oak	23	58	96	26	0	.	13	71	13	100	145	23
	sweet chestnut	0	.	0	.	0	.	0	.	171	35	171	35
	other broadleaves	22	58	57	46	8	100	21	58	17	71	124	28
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1278	9	1733	7	1937	7	2640	6	982	20	8570	4
60-63 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	383	17	257	16	609	12	1092	9	144	24	2485	6
	fir	203	20	292	17	396	15	108	29	8	100	1006	9
	pine	0	.	34	45	0	.	43	41	0	.	76	30
	larch	0	.	14	71	0	.	282	16	78	29	374	14
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	7	100	20	58	0	.	0	.	0	.	27	50
	beech	197	19	294	16	184	23	45	41	80	30	800	10
	maple	7	100	28	50	16	71	12	70	0	.	63	33
	ash	24	58	97	26	0	.	16	72	0	.	136	23
	oak	0	.	53	42	0	0	8	100	0	.	61	39
	sweet chestnut	0	.	0	.	0	.	0	.	28	60	28	60
	other broadleaves	0	.	27	50	8	100	41	46	0	.	76	32
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	821	10	1114	8	1213	9	1660	7	339	15	5147	4
64-67 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	253	20	285	19	469	13	678	12	82	32	1766	7
	fir	164	24	207	22	246	21	84	31	0	0	701	12
	pine	15	71	20	58	10	100	37	45	0	.	81	30
	larch	0	.	14	71	0	.	325	21	65	37	404	18
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	7	100	7	100	0	.	0	.	8	100	22	58
	beech	126	28	163	21	101	31	101	29	59	39	549	13
	maple	0	.	7	100	6	100	10	100	0	.	23	59
	ash	22	58	42	41	28	61	12	100	0	.	104	29
	oak	14	70	53	38	0	.	6	100	0	0	74	32
	sweet chestnut	0	.	0	.	0	.	7	100	52	38	60	35
	other broadleaves	7	100	9	100	0	.	18	74	15	71	49	42
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	607	12	806	10	860	10	1286	9	281	16	3839	5
68-71 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	189	22	130	22	529	16	511	13	176	21	1535	8
	fir	115	26	155	23	226	25	92	31	22	58	611	13
	pine	0	.	14	71	14	71	14	71	0	.	42	41
	larch	0	.	14	71	0	.	213	21	79	35	306	17
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	40	41	0	.	0	.	0	.	40	41
	beech	72	31	208	19	92	30	42	46	40	45	453	13
	maple	7	100	14	71	27	76	7	100	0	.	55	45
	ash	15	71	15	71	7	100	0	.	10	100	48	41
	oak	12	100	40	54	0	.	0	.	8	100	60	43
	sweet chestnut	0	.	0	.	0	.	0	.	23	57	23	57
	other broadleaves	7	100	7	100	0	.	12	70	0	.	26	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	418	14	636	11	896	12	906	9	358	15	3213	5
72-75 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	111	28	126	25	238	19	410	13	82	37	966	9
	fir	123	24	137	25	108	26	119	30	7	100	495	13
	pine	7	100	14	71	0	.	7	100	0	.	27	50
	larch	7	100	0	.	15	71	162	22	51	37	234	18
	Arolla pine	0	.	0	.	0	.	21	58	0	.	21	58
	other conifers	0	.	7	100	0	.	0	.	0	.	7	100
	beech	21	58	112	28	51	38	33	45	30	50	246	18
	maple	0	.	7	100	6	100	0	.	0	.	13	71
	ash	0	.	7	100	8	100	12	70	0	.	27	50
	oak	9	100	20	58	0	.	7	100	0	.	36	45
	sweet chestnut	0	.	0	.	0	.	0	.	28	60	28	60
	other broadleaves	9	100	20	58	0	.	0	.	0	.	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	287	17	448	13	425	13	771	10	199	21	2131	6
76-79 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	70	34	80	31	161	28	310	18	55	42	676	12
	fir	37	44	68	31	74	30	64	34	8	100	251	17
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	107	29	20	58	127	26
	Arolla pine	0	.	0	.	0	.	28	50	0	.	28	50
	other conifers	0	.	21	75	0	.	0	.	0	.	21	75
	beech	28	50	47	41	34	60	0	.	7	100	116	28
	maple	9	100	0	.	0	.	0	.	0	.	9	100
	ash	7	100	10	100	0	.	0	.	0	.	17	71
	oak	9	100	21	58	0	.	0	.	0	.	30	50
	sweet chestnut	0	.	0	.	0	.	0	.	30	50	30	50
	other broadleaves	0	.	7	100	0	.	0	.	7	100	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	160	22	259	17	270	20	510	14	127	25	1326	8
80-83 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	58	39	81	29	208	20	38	53	392	15
	fir	22	58	62	33	118	27	27	50	22	57	252	17
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	71	34	14	71	85	31
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	7	100	0	.	0	.	0	.	7	100
	beech	0	.	30	51	8	100	26	50	15	71	79	30
	maple	0	.	0	.	0	.	8	100	0	.	8	100
	ash	7	100	9	100	17	72	0	.	0	.	33	51
	oak	0	.	8	100	8	100	10	100	0	.	26	58
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	7	100	0	.	0	.	6	100	0	.	13	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	43	41	174	21	233	19	364	15	96	29	910	9
84-87 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	24	58	20	58	73	34	34	45	33	51	185	21
	fir	21	74	40	41	36	44	8	100	0	.	105	27
	pine	0	.	7	100	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	43	47	29	50	72	35
	Arolla pine	0	.	0	.	0	.	14	100	0	.	14	100
	other conifers	7	100	14	71	0	.	0	.	0	.	21	58
	beech	8	100	7	100	7	100	0	.	22	58	44	41
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	14	71	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	0	.	30	50	30	50
	other broadleaves	0	.	8	100	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	60	39	109	25	117	26	99	30	115	26	500	13
88-91 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16	100	33	53	25	60	84	29	0	.	158	23
	fir	7	100	43	41	15	71	15	71	0	.	81	30
	pine	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	42	47	7	100	50	43
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	7	100	7	100	0	.	0	.	14	71
	beech	0	.	13	71	0	.	7	100	0	.	21	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	7	100	0	.	0	.	7	100
	oak	0	.	21	57	0	.	0	.	0	.	21	57
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	23	75	117	25	54	38	164	22	14	71	372	15
92-95 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	7	100	15	72	74	32	11	100	114	26
	fir	22	58	0	.	6	100	14	71	25	58	68	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	0	.	14	71
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	19	73	7	100	0	.	0	.	26	60
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	17	71	0	.	0	.	0	.	17	71
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	7	100	22	74	30	61
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	43	46	28	51	116	25	59	42	275	17
96-99 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	0	.	0	.	14	71	7	100	29	50
	fir	0	.	14	71	6	100	0	.	0	.	20	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	22	58	0	.	22	58
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	0	.	0	.	0	.	7	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	15	71	15	71
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	100	20	58	6	100	43	41	22	58	100	27
100-103 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	14	71	7	100	0	.	21	58
	fir	8	100	0	.	0	.	0	.	0	.	8	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	7	100
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	100	16	100	14	71	7	100	14	71	60	40
104-107 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	14	71	7	100	0	.	22	58
	fir	7	100	0	.	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	7	100	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	7	100	14	71	7	100	0	.	36	45
108-111 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	7	100	15	71	0	.	22	58
	fir	0	.	0	.	7	100	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	14	71	15	71	0	.	29	50
112-115 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	7	100	0	.	30	50	11	100	55	38
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	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	7	100	7	100	0	.	37	45	18	72	69	34
116-119 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	7	100	0	.	0	.	0	.	7	100
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	0	.	7	100
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	7	100	0	.	14	71	7	100	28	50
120-123 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
124-127 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
128-131 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
132-135 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
136-139 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	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	.	7	100	7	100
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	7	100	7	100
144-147 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
148-151 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	8	100	8	100
152-155 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
156-159 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
160-163 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	14	71	14	71
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	14	71	14	71
164-167 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
168-171 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
172-175 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
176-179 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
180-183 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
184-187 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
188-191 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	0	0	.	0	.	0	.	0	.	0	0
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	0	0	.	0	.	0	.	0	.	0	0
192-195 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
196-199 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
=200 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	7	100
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	7	100	7	100
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15516	8	22814	7	36987	6	69685	4	11014	13	156015	3
	fir	15727	7	11462	8	16768	8	6463	11	1563	25	51983	4
	pine	1658	19	1411	17	920	55	9327	14	223	72	13541	11
	larch	148	44	1395	35	342	44	11296	8	5302	15	18482	7
	Arolla pine	0	.	0	.	0	.	4183	18	49	100	4231	17
	other conifers	712	31	1083	25	227	33	877	29	44	70	2942	15
	beech	23618	5	19798	6	16948	7	11036	10	12090	13	83489	3
	maple	7086	10	6005	10	6014	11	6192	11	1122	28	26420	5
	ash	2960	13	4535	10	3272	16	3356	16	2240	24	16364	7
	oak	2492	26	2495	16	213	33	1413	27	1978	26	8591	12
	sweet chestnut	0	.	0	.	0	.	143	47	10308	14	10450	13
	other broadleaves	4140	11	7324	10	3755	13	11204	11	13962	11	40383	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	74057	3	78322	3	85446	4	135175	3	59893	5	432892	2

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. 1350115. Birmensdorf, Swiss Federal Research Institute WSL

back