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	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

NFI2
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		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	8264	14	1687	31	1932	25	5710	19	8581	16	5544	18	7216	13	6026	18	3840	15	1848	26	1873	24	7015	14	10302	10	2677	16	72514	4
	fir	5457	10	637	38	841	30	2125	18	1142	28	1034	27	3989	12	1504	20	622	30	931	28	269	33	500	34	287	32	589	35	19927	6
	pine	306	43	432	59	190	75	152	71	209	44	0	.	272	78	0	.	17	100	89	72	0	.	3255	25	2127	25	81	100	7131	15
	larch	17	100	52	100	52	100	0	.	157	45	0	.	0	.	101	100	54	58	0	.	126	66	2542	18	848	31	1680	23	5628	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	480	29	862	29	16	100	1358	21
	other conifers	194	40	52	58	121	100	137	50	273	55	15	100	38	71	74	50	0	.	70	78	49	73	277	82	177	55	0	.	1477	23
	beech	8144	10	2609	16	1681	18	3429	13	4060	11	1152	31	4245	16	2781	16	1573	29	904	29	1254	27	1144	33	841	29	6316	15	40132	5
	maple	1716	14	587	26	279	41	313	31	1305	24	612	28	948	28	825	24	876	32	216	45	433	37	599	30	289	48	348	32	9347	8
	ash	1203	22	500	36	700	41	312	28	1021	23	438	41	718	28	1078	30	212	40	258	37	264	55	508	35	399	69	1000	28	8611	9
	oak	1103	36	290	62	1280	37	179	53	275	45	126	59	37	72	21	100	125	62	18	100	0	.	493	32	15	100	1376	33	5339	15
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	7127	16	7162	16
	other broadleaves	2378	16	685	29	743	31	1129	37	1486	18	906	23	1214	24	1370	26	1204	23	657	43	778	35	3118	18	2666	23	9220	12	27555	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	28781	6	7530	13	7835	14	13486	11	18507	9	9826	14	18678	8	13780	11	8523	12	4993	17	5045	17	19950	9	18815	8	30431	6	206181	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4679	12	803	30	1539	22	2202	17	3290	16	3460	17	5780	13	3957	14	2949	14	1257	25	1741	33	4539	12	7566	9	2454	15	46216	4
	fir	3328	12	243	35	408	37	1838	20	826	21	909	28	1937	13	694	21	355	30	317	36	195	37	288	39	144	51	219	39	11702	6
	pine	459	38	140	46	119	63	96	61	154	37	0	.	156	100	0	.	89	82	245	53	0	.	1402	24	1237	23	86	60	4182	13
	larch	34	100	35	70	71	61	34	71	171	53	0	.	0	.	0	.	55	74	0	.	89	72	1893	18	930	21	1271	18	4584	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	439	35	708	24	0	.	1147	20
	other conifers	17	100	156	62	0	.	101	57	50	58	0	.	0	.	0	.	17	100	0	.	15	100	0	.	17	100	0	.	375	32
	beech	6289	10	1835	18	1043	20	2223	15	2669	12	775	37	2674	18	2081	18	748	22	452	33	677	25	640	33	597	31	2692	17	25396	5
	maple	1383	15	345	26	39	100	319	28	1057	19	328	32	598	21	815	23	502	33	115	43	567	32	149	47	88	45	150	40	6454	7
	ash	723	25	315	32	493	28	442	23	895	18	332	52	430	41	881	22	198	53	123	79	343	52	164	48	185	56	260	33	5783	9
	oak	718	30	139	50	718	33	318	30	266	27	21	100	32	100	75	77	54	58	18	100	0	.	108	57	15	100	507	31	2989	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	72	15	100	3104	16	3212	16
	other broadleaves	641	21	223	40	432	36	405	31	727	23	83	54	335	35	633	32	278	30	151	40	180	36	458	30	453	36	2884	13	7882	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18270	6	4233	13	4863	13	7977	9	10106	8	5908	14	11943	9	9138	10	5244	11	2678	18	3807	19	10172	9	11957	8	13626	6	119923	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3028	11	545	26	1647	22	2042	14	2599	12	1941	15	3764	10	2795	13	2252	15	1175	23	869	30	2728	13	5231	10	1776	16	32392	3
	fir	2421	12	198	38	221	36	1334	17	667	19	567	31	1369	14	748	19	224	29	246	36	107	38	212	52	106	40	135	36	8555	6
	pine	583	25	288	33	152	53	172	38	513	24	0	.	126	57	30	62	14	71	89	52	0	.	708	20	707	23	103	59	3485	10
	larch	17	100	52	74	43	62	81	84	67	54	0	.	0	.	7	100	51	75	0	.	18	100	1163	17	843	19	831	21	3173	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	239	29	396	26	0	.	649	19
	other conifers	0	.	59	100	0	.	43	62	40	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	150	49
	beech	4105	9	1393	15	878	23	1425	14	1435	13	375	34	1327	16	1647	16	512	27	174	36	351	30	177	62	261	35	971	18	15032	5
	maple	536	18	237	31	35	53	67	44	280	23	68	78	260	32	395	19	242	31	83	47	226	31	44	71	51	63	25	100	2550	9
	ash	557	26	148	41	277	33	507	50	684	19	72	53	113	45	310	28	120	41	43	100	81	47	32	83	34	64	19	72	2998	12
	oak	290	26	194	35	227	32	111	35	305	30	0	.	138	86	7	100	0	.	0	.	0	.	19	100	12	100	179	41	1483	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	70	6	100	704	21	752	20
	other broadleaves	205	32	193	35	138	45	90	42	436	23	18	100	29	77	131	44	121	44	40	71	62	56	157	43	122	46	713	21	2456	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11742	5	3307	11	3618	13	5872	9	7028	7	3041	13	7127	8	6071	9	3551	12	1851	18	1715	19	5521	9	7769	8	5464	8	73676	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2173	9	472	23	1065	18	1544	13	2153	11	1391	15	2577	10	1956	12	1614	14	588	23	564	20	1455	11	3190	9	1102	15	21845	3
	fir	1423	10	91	33	263	29	898	14	622	17	311	25	1121	13	673	18	230	27	113	39	106	40	134	41	104	32	208	31	6298	5
	pine	261	24	137	32	169	44	143	29	449	20	0	.	149	61	34	62	38	81	7	100	0	.	187	36	393	23	21	57	1989	10
	larch	7	100	7	100	21	58	21	74	77	32	0	.	7	100	0	.	56	51	0	.	12	70	988	16	808	14	504	18	2509	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	40	219	26	0	.	366	22
	other conifers	0	.	7	100	0	.	40	47	27	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	40
	beech	1774	10	719	17	421	24	995	14	1069	12	101	36	847	18	695	19	247	29	114	40	220	24	49	62	180	33	393	19	7823	5
	maple	157	23	90	33	0	.	14	71	85	34	48	38	62	43	153	31	61	40	42	47	44	41	6	100	14	71	8	100	784	11
	ash	110	30	87	28	127	35	158	51	444	18	8	100	55	42	92	32	27	50	25	70	50	43	7	100	6	100	25	59	1222	11
	oak	107	33	50	54	118	48	169	33	188	22	0	.	0	.	0	.	0	.	7	100	0	.	0	.	41	76	74	64	755	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	353	24	360	23
	other broadleaves	54	39	43	57	35	60	72	58	76	30	0	.	29	50	59	51	68	75	6	100	18	74	30	62	14	71	129	25	634	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6067	5	1704	12	2219	12	4054	9	5191	7	1859	13	4847	8	3664	9	2340	12	902	18	1015	15	3012	9	4969	7	2817	8	44659	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1307	10	268	25	416	22	998	15	945	13	684	18	1382	11	1025	13	750	15	352	22	320	25	677	15	1466	10	641	14	11232	4
	fir	528	14	49	42	91	41	484	17	356	19	186	31	655	15	288	19	158	27	85	46	44	47	68	50	72	36	44	46	3109	6
	pine	91	30	14	100	56	43	70	54	147	25	0	.	48	68	8	100	0	.	0	.	0	.	65	37	159	33	14	71	673	14
	larch	0	.	7	100	7	100	7	100	55	46	0	.	7	100	22	75	63	46	7	100	19	75	617	17	507	17	296	19	1612	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	19	73	99	34	0	.	118	30
	other conifers	0	.	0	.	16	100	27	61	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	51
	beech	539	15	339	20	98	28	410	18	493	16	53	54	372	23	281	23	129	32	53	47	117	40	7	100	65	45	158	27	3116	7
	maple	32	51	14	71	7	100	0	.	81	35	0	.	6	100	19	73	23	59	0	.	7	100	0	.	14	71	0	.	202	21
	ash	42	52	7	100	57	50	24	58	107	28	8	100	23	73	19	73	0	.	6	100	12	100	0	.	0	.	11	100	316	18
	oak	48	47	53	42	58	58	41	47	136	32	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	11	100	354	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	6	100	155	30	169	28
	other broadleaves	17	71	21	74	15	71	0	.	14	71	0	.	0	0	7	100	70	46	7	100	21	76	15	100	0	.	8	100	194	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2604	7	771	14	821	15	2061	10	2362	8	930	16	2493	9	1670	10	1193	12	517	20	541	20	1476	11	2388	8	1337	9	21165	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	323	18	71	44	116	30	279	20	333	18	198	24	493	15	423	19	232	21	151	32	145	38	228	25	480	15	281	23	3753	6
	fir	191	22	14	71	29	50	146	29	53	46	46	56	250	21	72	34	40	48	34	51	34	52	15	71	29	51	30	49	983	10
	pine	14	71	14	100	0	.	7	100	28	50	0	.	11	100	7	100	0	.	0	.	0	.	0	.	28	50	0	.	109	28
	larch	0	.	0	.	0	.	7	100	7	100	0	.	9	100	17	72	7	100	0	.	13	71	222	21	133	27	113	26	529	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	72	22	58	0	.	39	45
	other conifers	0	.	0	.	8	100	20	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	61
	beech	93	28	86	37	30	62	88	40	138	27	29	50	64	40	24	72	30	51	7	100	20	73	0	.	18	74	84	32	712	12
	maple	0	.	8	100	0	.	7	100	0	.	6	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	27	50
	ash	14	71	14	100	15	71	10	100	30	50	0	.	0	.	7	100	7	100	0	.	0	.	0	.	12	100	0	.	111	29
	oak	7	100	9	100	0	.	0	.	35	45	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	63	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	35	63	35
	other broadleaves	0	.	8	100	14	71	8	100	15	100	0	.	0	.	0	.	15	72	0	.	0	.	0	.	0	.	9	100	69	37
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	642	13	223	23	212	23	572	15	639	13	280	22	827	12	569	16	331	18	193	28	212	29	483	16	723	12	580	13	6485	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	107	29	8	100	36	45	27	50	68	34	46	41	41	41	172	26	82	30	57	35	70	35	78	34	186	21	134	28	1113	9
	fir	81	33	7	100	7	100	52	43	40	40	0	.	116	29	19	58	0	.	0	.	6	100	6	100	21	58	8	100	362	15
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	21	58
	larch	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	127	31	85	31	36	52	269	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	22	58	0	.	50	38
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	beech	14	71	36	54	7	100	36	52	93	33	0	.	30	60	10	100	9	100	0	.	0	.	0	.	0	.	23	58	257	19
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	73	0	.	0	.	0	.	17	73
	ash	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	oak	0	.	0	.	8	100	24	59	14	71	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	54	38
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	77	36	85	34
	other broadleaves	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	15	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	209	20	51	47	64	36	138	25	236	20	46	41	194	22	209	24	98	28	65	33	92	30	247	21	328	16	285	19	2263	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	7	100	13	71	30	50	7	100	16	71	21	57	19	75	22	58	0	.	29	50	28	50	41	41	7	100	255	17
	fir	14	71	0	.	0	.	14	71	16	71	0	.	45	41	0	.	17	72	7	100	0	.	0	.	0	.	18	72	130	24
	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	.	29	50	14	71	7	100	50	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	16	100	13	100	7	100	0	.	0	.	7	100	8	100	7	100	0	.	7	100	6	100	0	.	72	38
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	7	100	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	72
	oak	0	.	0	.	0	.	0	.	29	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	50	73	50
	other broadleaves	0	.	0	.	0	.	15	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	73
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	7	100	36	54	83	35	67	37	16	71	66	33	26	61	47	41	15	71	29	50	72	35	61	33	105	38	658	12
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	8	100	0	.	7	100	0	.	0	.	22	58	0	.	7	100	29	50	7	100	7	100	11	100	106	27
	fir	7	100	0	.	0	.	0	.	0	.	0	.	7	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	23	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	.	14	71	0	.	7	100	21	58
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	8	100	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58
	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	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	50	31	50
	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	14	71	8	100	8	100	8	100	21	58	0	.	7	100	31	50	0	.	7	100	29	50	29	50	7	100	49	41	217	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	7	100	0	.	0	.	0	.	17	72	8	100	38	45
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	10	100
	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	.	7	100	7	100	14	71
	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	.	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	.	61	46	61	46
	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	.	6	100	0	.	7	100	0	.	0	.	7	100	31	51	86	36	138	27
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19903	8	3861	20	6772	15	12833	12	17982	10	13279	13	21282	9	16395	11	11748	12	5437	19	5639	21	16756	11	28487	7	9090	11	189464	3
	fir	13450	8	1239	27	1861	26	6891	12	3722	14	3053	21	9489	10	4007	13	1645	22	1734	26	761	28	1223	28	763	25	1261	27	51100	4
	pine	1713	21	1025	30	686	40	640	30	1507	16	0	.	763	58	80	46	158	57	431	53	0	.	5618	20	4665	18	305	55	17590	9
	larch	82	67	153	46	200	50	149	61	534	29	0	.	23	72	147	71	293	32	7	100	277	53	7596	13	4175	13	4753	14	18391	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	1390	24	2335	20	16	100	3755	15
	other conifers	211	37	274	60	144	85	368	35	418	44	15	100	45	62	74	50	17	100	70	78	65	77	277	82	195	55	8	100	2182	19
	beech	20958	7	7024	13	4174	16	8628	10	9970	9	2486	28	9560	12	7527	14	3256	21	1712	26	2638	21	2026	30	1969	25	10636	14	92563	3
	maple	3824	11	1280	19	360	36	719	22	2807	15	1062	24	1875	18	2214	17	1704	24	456	33	1294	27	798	30	455	35	532	29	19379	5
	ash	2649	19	1071	28	1676	25	1463	26	3189	13	858	33	1339	26	2396	20	565	36	455	39	751	47	711	30	637	62	1315	25	19072	7
	oak	2272	28	734	31	2410	30	843	24	1255	18	146	53	207	63	116	59	179	58	58	57	0	.	620	33	84	74	2147	26	11071	11
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	176	61	28	100	11748	13	11969	13
	other broadleaves	3296	15	1173	23	1376	26	1718	28	2771	16	1007	23	1606	22	2200	25	1755	20	862	37	1059	29	3778	17	3255	22	12971	11	38829	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	68358	4	17834	10	19676	10	34251	7	44157	6	21907	11	46188	6	35155	8	21335	9	11221	14	12484	14	40969	7	47048	6	54781	5	475365	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1326962. Birmensdorf, Swiss Federal Research Institute WSL

back