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 (10 classes) · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4705	13	1202	33	1303	34	4592	18	3886	16	3395	20	7242	14	3149	21	4897	14	2235	21	1957	23	7880	11	14308	9	5288	16	66040	4
	fir	6378	11	315	33	684	29	2865	17	1039	23	1097	23	4244	16	1248	24	574	26	948	25	180	43	557	27	139	35	643	29	20912	6
	pine	74	62	56	75	39	100	106	56	59	100	0	.	239	71	0	.	110	82	187	75	44	100	1772	32	2541	26	165	59	5392	17
	larch	0	.	74	80	450	84	33	100	288	52	131	70	50	76	49	100	181	61	0	.	128	61	2359	15	1381	19	2667	19	7792	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	664	32	1586	28	49	74	2298	21
	other conifers	350	47	103	62	0	.	124	38	431	50	0	.	50	58	134	47	156	81	144	72	67	60	156	60	610	35	20	100	2345	17
	beech	6620	10	2917	16	1741	30	2851	16	4042	14	804	32	3449	13	1948	20	1346	25	549	28	1243	28	1281	28	549	27	5998	14	35338	5
	maple	2748	16	960	26	738	43	824	25	1580	15	435	33	1219	28	998	25	1635	20	334	32	417	38	1381	27	373	35	1264	30	14905	7
	ash	871	26	572	29	379	29	396	34	896	24	351	41	418	34	323	30	547	33	488	44	122	62	805	29	410	35	1525	22	8102	8
	oak	918	50	138	55	378	46	59	74	184	43	21	100	0	0	0	.	106	85	18	100	35	100	718	38	15	100	870	41	3459	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	100	18	100	4138	20	4230	20
	other broadleaves	2293	15	698	24	898	27	1588	24	2283	18	1073	29	1414	20	964	23	1739	24	919	40	616	40	3744	15	3301	21	10308	11	31839	6
	indeterminable	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	24957	6	7035	13	6611	16	13439	10	14686	8	7306	14	18325	8	8814	12	11290	11	5824	16	4809	16	21392	8	25231	7	32934	6	202653	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2745	13	467	30	1042	29	2110	19	2711	16	2468	20	4584	12	2946	15	2639	15	1688	20	1253	24	4944	13	9034	9	3179	14	41810	4
	fir	3703	11	328	37	244	39	1474	20	745	24	753	27	2353	14	710	23	451	28	395	29	100	41	305	32	106	48	302	34	11969	6
	pine	234	42	139	49	17	100	151	69	108	48	0	.	278	79	0	.	17	100	146	56	61	77	1872	22	1003	24	16	100	4043	14
	larch	0	.	17	100	140	67	17	100	170	52	0	.	17	100	49	100	121	55	0	.	71	79	1839	17	1091	25	2537	15	6070	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	543	26	1266	22	16	100	1825	17
	other conifers	69	61	69	61	20	100	20	100	138	35	0	.	17	100	36	71	87	82	0	.	0	0	17	100	35	71	0	.	506	23
	beech	4316	11	1212	18	913	21	1179	15	2138	13	673	26	2484	15	1471	19	800	25	687	29	558	34	622	36	490	33	3849	16	21391	5
	maple	1504	13	505	24	282	32	242	33	1326	20	545	24	742	24	871	21	684	28	214	43	477	30	457	28	194	45	287	32	8330	7
	ash	458	27	297	34	255	34	208	33	351	26	251	37	373	40	567	28	174	37	137	43	136	43	383	41	238	74	649	34	4478	10
	oak	523	39	104	52	411	36	161	37	88	45	19	100	91	61	21	100	54	58	0	0	0	.	288	38	15	100	793	23	2570	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	71	15	100	3758	15	3811	15
	other broadleaves	687	25	243	31	296	34	399	31	1008	18	301	40	354	30	629	37	469	24	278	45	216	34	1223	20	1299	26	4268	13	11667	7
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	14241	6	3381	12	3619	14	5961	11	8783	8	5010	14	11293	8	7299	10	5495	11	3545	15	2872	17	12530	8	14787	7	19654	6	118470	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2447	12	264	41	476	25	947	19	1665	15	1864	17	2891	12	2759	13	2147	14	1034	21	683	24	3008	11	5381	9	2015	15	27581	4
	fir	1770	12	183	41	219	38	824	19	395	26	568	29	1282	16	562	23	242	30	293	37	86	45	287	34	107	37	261	33	7081	6
	pine	500	31	178	32	89	57	84	51	109	32	0	.	189	57	6	100	60	89	145	53	53	100	678	22	561	26	94	71	2744	12
	larch	0	.	14	71	38	84	0	.	45	60	9	100	0	.	0	.	82	61	0	.	74	73	1468	17	1060	16	1527	16	4316	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	310	31	642	24	0	.	961	19
	other conifers	0	.	31	81	7	100	59	51	41	85	0	.	0	.	0	.	7	100	0	.	15	100	0	0	0	.	0	.	160	35
	beech	3409	10	989	18	605	20	1043	16	1231	14	419	34	1272	14	1373	16	508	25	390	33	276	31	273	32	310	26	1695	16	13793	5
	maple	765	19	173	33	41	41	249	27	473	19	147	45	496	22	531	23	296	33	142	55	258	31	64	48	118	42	187	42	3939	8
	ash	236	26	142	39	390	35	215	25	491	22	182	51	226	67	261	29	176	41	110	66	103	44	190	38	124	46	151	34	2996	10
	oak	246	30	128	37	120	43	92	63	139	34	29	77	0	.	16	71	9	100	7	100	0	.	107	43	7	100	297	27	1197	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	62	0	.	1382	20	1469	19
	other broadleaves	218	31	80	46	122	48	208	30	316	24	9	100	69	60	162	34	79	43	51	48	40	57	110	33	135	36	1599	17	3198	10
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	9590	6	2183	13	2106	14	3720	10	4904	9	3226	13	6425	8	5670	9	3615	11	2173	16	1587	16	6582	8	8444	7	9209	7	69435	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1764	10	180	30	404	23	677	20	1096	12	1272	15	2051	11	1588	12	1639	13	699	24	563	21	1761	12	3602	8	1430	14	18726	3
	fir	1460	10	131	41	159	32	560	16	444	18	240	26	813	13	467	16	184	32	269	33	113	33	173	34	88	35	148	32	5250	5
	pine	225	25	94	35	54	47	61	37	211	24	0	.	117	59	7	100	21	58	37	54	14	100	273	25	363	23	41	52	1519	11
	larch	7	100	14	70	29	61	21	74	47	55	0	.	0	.	7	100	30	79	0	.	28	100	1171	16	901	15	835	14	3089	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	133	33	255	23	0	.	388	19
	other conifers	0	.	28	61	21	100	27	50	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	90	35
	beech	1955	9	762	16	471	21	737	14	738	13	136	36	861	18	994	16	341	26	141	32	294	24	77	55	201	33	649	15	8357	4
	maple	347	16	83	37	23	75	48	43	138	26	63	41	112	29	168	24	106	31	48	42	124	29	56	60	41	55	44	47	1400	8
	ash	196	24	75	39	175	33	157	38	300	19	40	60	74	37	175	30	34	54	45	61	33	54	20	73	0	0	51	54	1374	10
	oak	179	32	119	40	131	32	95	33	155	29	19	100	34	61	7	100	8	100	25	72	0	.	21	75	12	100	155	31	960	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	6	100	600	20	620	20
	other broadleaves	176	42	49	42	42	47	30	60	215	25	6	100	45	41	0	.	54	62	6	100	25	61	52	43	42	72	484	18	1227	12
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	6309	5	1535	12	1510	13	2414	10	3349	7	1778	12	4106	8	3413	9	2424	11	1271	17	1193	15	3744	9	5512	7	4443	6	43001	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1058	11	132	31	268	29	335	18	497	16	687	15	1162	11	1255	12	868	15	374	21	368	20	975	13	1885	9	890	19	10754	4
	fir	947	11	35	44	145	34	498	18	283	21	278	23	756	14	478	20	167	27	99	38	69	47	152	39	92	34	190	29	4189	6
	pine	105	29	31	50	47	62	54	39	128	30	0	.	43	54	17	100	7	100	22	74	7	100	68	41	198	28	14	100	742	13
	larch	7	100	7	100	8	100	14	71	63	36	0	.	0	.	14	100	35	53	0	.	12	100	807	14	621	16	538	15	2127	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	56	135	27	0	.	191	25
	other conifers	0	.	42	62	0	.	47	62	28	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	124	36
	beech	870	12	370	21	263	25	566	14	686	12	111	35	531	19	343	20	150	31	102	38	164	35	34	63	59	44	302	20	4551	5
	maple	116	26	35	44	18	72	28	50	70	35	38	45	40	45	105	34	31	60	12	70	27	50	0	.	52	41	0	.	571	12
	ash	28	50	45	49	88	36	124	38	224	20	0	.	31	75	52	42	28	50	18	74	38	46	7	100	0	.	40	63	724	12
	oak	63	37	30	60	56	56	99	30	163	24	0	.	0	.	0	.	0	.	6	100	0	.	8	100	27	81	57	41	509	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	6	100	297	26	312	25
	other broadleaves	15	71	28	61	62	51	45	54	45	41	0	.	6	100	39	52	38	69	6	100	12	70	23	57	25	71	57	42	401	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	3209	6	756	14	955	14	1808	10	2185	8	1114	13	2570	8	2302	9	1324	12	640	17	699	16	2141	9	3100	7	2392	10	25195	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	558	15	90	29	106	36	213	24	250	19	370	20	501	16	673	16	417	17	217	24	165	30	397	18	750	13	364	16	5071	5
	fir	395	18	21	58	77	35	249	22	189	25	108	42	359	18	197	23	120	34	30	50	6	100	20	73	53	39	30	50	1853	8
	pine	15	71	0	.	14	71	27	50	20	75	0	.	17	72	7	100	0	.	0	.	0	.	7	100	58	35	7	100	172	21
	larch	0	.	0	.	7	100	7	100	20	58	0	.	0	.	0	.	7	100	0	.	6	100	429	19	336	19	209	21	1021	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	22	58	0	.	36	45
	other conifers	0	.	14	100	0	.	40	57	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	69	42
	beech	206	22	138	27	102	27	191	21	306	20	14	71	135	32	92	31	28	50	42	57	46	54	20	73	41	41	151	28	1511	8
	maple	15	71	0	.	7	100	0	.	41	47	0	.	6	100	27	76	35	52	8	100	0	.	0	.	0	.	0	.	140	27
	ash	39	53	14	70	14	71	36	45	63	33	0	.	7	100	28	61	17	71	0	.	12	100	0	.	0	.	10	100	241	19
	oak	19	73	7	100	30	50	32	51	53	53	0	.	0	0	0	.	0	.	0	.	0	.	0	.	6	100	8	100	156	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	75	31	82	30
	other broadleaves	0	.	14	100	27	50	7	100	9	100	0	.	0	0	8	100	31	82	8	100	6	100	7	100	18	72	15	71	151	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	1248	10	298	18	383	17	801	13	959	11	492	17	1025	12	1033	13	654	14	305	21	242	24	901	13	1283	10	877	10	10501	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	183	22	49	47	37	46	77	33	107	31	99	32	228	18	217	24	232	21	109	33	146	32	178	27	309	17	188	27	2158	7
	fir	190	20	7	100	42	49	90	29	125	27	35	52	155	24	98	27	87	35	56	62	13	71	37	60	34	45	30	49	999	10
	pine	7	100	0	.	0	.	14	71	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	48	38
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	15	71	14	71	0	.	7	100	180	26	117	26	93	27	433	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	0	.	0	.	0	.	20	58	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	58
	beech	35	53	38	45	20	58	88	36	113	28	22	58	70	37	38	65	18	57	6	100	9	100	6	100	18	74	44	41	525	13
	maple	0	.	16	71	0	.	0	.	7	100	6	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	36	45
	ash	15	71	0	.	0	.	10	100	7	100	8	100	0	.	7	100	0	.	6	100	0	.	0	.	6	100	6	100	65	34
	oak	0	.	18	70	14	71	0	.	37	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	76	32
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	67	40	75	37
	other broadleaves	0	.	16	71	21	58	0	.	7	100	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	437	14	143	25	135	25	297	18	435	15	169	25	453	14	375	18	364	16	177	28	175	30	438	17	527	13	435	15	4562	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	0	.	17	72	20	58	53	39	32	52	55	39	73	34	43	41	23	58	36	45	43	41	141	26	75	37	659	11
	fir	51	42	0	.	33	53	49	42	50	42	9	100	72	37	39	51	8	100	0	.	12	70	0	.	15	71	7	100	345	16
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	37	56	43	80	40	218	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	75	15	71	0	.	36	53
	other conifers	0	.	7	100	0	.	13	71	14	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	47
	beech	8	100	0	.	0	.	0	.	20	58	0	.	7	100	8	100	0	.	15	71	0	.	0	0	0	.	37	45	95	28
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100
	ash	0	.	7	100	0	.	0	.	9	100	0	.	0	.	17	72	0	.	0	.	0	.	0	.	0	.	0	.	33	51
	oak	0	.	0	.	0	.	0	.	29	50	0	.	0	.	8	100	0	.	10	100	0	.	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	46	45	46
	other broadleaves	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	105	30	14	70	58	39	82	31	182	22	41	46	142	25	145	25	51	38	48	41	56	35	145	26	226	21	244	21	1541	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	0	.	0	.	7	100	13	71	27	58	6	100	0	.	38	45	0	.	29	50	22	58	49	43	25	59	231	19
	fir	22	58	0	.	0	.	7	100	7	100	0	.	6	100	6	100	0	.	7	100	7	100	0	.	0	.	25	58	88	29
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	18	72	7	100	61	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	32	52	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	39	46
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	71
	oak	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	38	60	45	53
	other broadleaves	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	37	45	0	.	7	100	30	50	66	34	27	58	12	71	14	71	38	45	7	100	42	41	80	30	73	35	95	33	529	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	7	100	7	100	0	.	7	100	14	71	21	58	7	100	0	.	14	71	7	100	30	50	18	72	140	23
	fir	15	71	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	14	100	29	61
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	51	50	51
	other broadleaves	0	.	0	.	7	100	7	100	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	22	58	0	.	14	71	14	71	30	71	7	100	21	57	21	58	7	100	0	.	14	71	14	71	51	38	83	39	299	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13529	8	2384	23	3661	21	8983	13	10277	11	10222	14	18735	10	12682	10	12927	11	6381	17	5215	18	19216	9	35488	7	13471	12	173170	3
	fir	14929	8	1020	30	1605	22	6616	13	3277	14	3088	21	10047	11	3805	15	1833	21	2098	22	587	27	1532	24	634	26	1637	24	52707	4
	pine	1160	24	499	28	260	38	497	33	655	22	0	.	883	57	37	55	215	53	538	43	179	80	4670	21	4745	19	338	53	14674	10
	larch	21	58	127	51	671	64	92	48	632	36	140	71	67	81	135	74	470	41	0	.	326	59	8371	11	5596	11	8507	12	25155	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1791	22	3947	20	65	79	5813	15
	other conifers	419	40	293	49	47	72	349	28	686	37	0	.	75	46	170	40	249	81	144	72	82	64	173	56	644	34	44	70	3376	15
	beech	17420	7	6427	12	4115	18	6656	10	9306	9	2180	24	8808	11	6273	13	3191	20	1932	24	2589	23	2313	27	1669	23	12725	12	85602	3
	maple	5494	12	1772	19	1108	34	1391	19	3635	13	1234	24	2615	20	2700	17	2794	18	759	27	1317	24	1958	25	778	29	1781	26	29336	5
	ash	1844	17	1153	22	1300	23	1154	20	2348	14	832	32	1129	34	1430	20	976	28	804	33	444	31	1405	28	778	43	2433	22	18030	6
	oak	1948	32	544	30	1141	31	546	24	870	17	88	56	125	51	52	50	176	65	67	68	35	100	1142	32	91	72	2180	24	9004	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	238	67	45	72	10448	13	10739	13
	other broadleaves	3389	15	1128	19	1489	23	2283	20	3894	14	1388	28	1888	18	1803	23	2416	21	1269	37	915	34	5160	14	4819	21	16738	10	48580	5
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	60153	4	15346	10	15398	11	28567	8	35579	6	19171	11	44373	6	29086	8	25264	9	13991	13	11689	13	47968	6	59235	5	70366	4	476187	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1327033. Birmensdorf, Swiss Federal Research Institute WSL

zurück