Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI1
total number of stems NFI1
diameter at breast height (10 classes) · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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	7346	10	1360	26	2236	19	4303	16	7011	12	5884	12	8616	9	5652	12	4559	11	2336	17	2663	18	7622	9	11990	7	3307	13	74886	3
	fir	6904	7	475	25	1191	21	2719	15	1594	14	1610	19	4063	9	1893	13	647	24	700	25	440	31	1109	25	487	24	509	26	24341	4
	pine	281	29	295	51	164	78	147	55	234	31	10	100	444	46	50	53	124	68	251	40	73	84	3822	17	3234	18	126	47	9255	10
	larch	59	74	39	50	63	63	69	62	283	64	10	100	0	.	10	100	61	53	0	.	71	55	2955	13	1255	16	1677	15	6552	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	43	100	0	.	0	.	10	100	0	.	612	28	850	22	0	.	1515	17
	other conifers	240	44	177	65	99	91	177	35	208	44	0	.	40	62	74	42	10	100	65	56	29	74	51	100	347	51	0	.	1517	19
	beech	9364	7	3816	12	2662	13	3335	11	5201	9	994	23	4576	11	3663	13	1539	18	1086	23	1234	22	1272	25	1060	21	6601	11	46403	3
	maple	1989	11	658	20	593	42	528	26	1194	19	614	21	758	16	1035	17	1101	20	268	30	533	30	456	27	210	35	421	22	10356	6
	ash	1423	17	508	25	895	20	497	28	1559	16	502	30	906	28	1256	19	382	29	254	30	399	40	383	29	335	49	1221	18	10519	6
	oak	1163	28	293	41	1128	34	284	37	587	30	98	59	49	52	59	52	276	45	20	70	10	100	412	28	69	59	1336	25	5782	12
	sweet chestnut	0	.	0	.	49	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	51	72	0	.	8005	11	8125	11
	other broadleaves	2591	12	780	21	1129	27	811	25	2705	13	796	20	1759	20	1866	23	1493	16	786	33	629	28	3324	15	2022	17	9157	8	29849	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	31361	4	8401	10	10210	10	12869	8	20575	6	10518	10	21254	6	15557	8	10190	9	5777	13	6101	13	22070	6	21858	6	32360	5	229101	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4503	8	852	24	2033	15	2215	13	3502	11	3501	12	5681	8	4653	11	3368	12	1686	16	1929	18	5501	9	8584	7	2376	13	50382	3
	fir	3942	9	323	27	523	27	1969	13	1184	13	912	20	2391	10	1506	14	316	25	455	30	233	27	390	26	224	27	208	34	14576	4
	pine	615	24	157	39	166	42	103	49	322	25	10	100	224	36	74	100	78	68	335	35	88	60	1910	16	1635	16	220	46	5939	8
	larch	49	83	67	42	83	59	69	74	88	40	0	.	0	.	0	.	30	58	0	.	76	56	2073	12	1240	14	1275	16	5050	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	43	100	20	100	0	.	21	100	0	.	638	24	636	21	10	100	1367	15
	other conifers	20	100	19	71	18	100	10	100	80	35	0	.	0	.	10	100	0	.	0	.	9	100	0	.	20	71	0	.	187	25
	beech	7008	7	2222	11	1628	14	2215	12	3055	10	724	19	2263	12	2254	12	919	19	502	28	833	20	417	28	457	27	2416	11	26912	3
	maple	1218	11	430	21	175	30	364	24	903	15	203	29	419	19	810	16	450	23	159	31	369	24	110	32	120	36	152	33	5881	5
	ash	937	16	370	24	541	24	303	22	908	15	183	36	339	24	791	18	178	34	125	56	116	55	155	35	110	59	327	27	5383	7
	oak	778	21	330	31	381	21	305	23	644	24	32	74	55	53	42	79	120	59	11	100	0	.	90	45	30	100	583	26	3401	10
	sweet chestnut	0	.	0	.	78	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	71	9	100	2440	13	2547	13
	other broadleaves	977	14	424	24	453	31	342	26	1044	15	89	40	258	25	479	22	297	24	108	35	283	28	603	23	456	29	2690	11	8502	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	20047	4	5193	9	6078	9	7894	7	11730	6	5655	10	11672	6	10639	7	5756	9	3401	12	3936	12	11907	6	13522	6	12698	5	130128	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3490	7	641	20	1928	14	2143	11	3487	8	2495	11	4509	8	3102	10	2723	11	1152	17	1239	16	3114	9	5587	7	1608	13	37218	2
	fir	2626	8	368	25	307	28	1187	12	1145	12	501	18	1629	11	1072	13	236	25	204	30	157	29	261	26	161	24	215	33	10069	4
	pine	640	18	339	22	247	33	158	33	787	16	0	.	106	37	26	41	31	65	170	39	31	63	607	18	874	16	148	53	4163	7
	larch	0	.	42	54	61	50	30	48	135	29	4	100	4	100	17	80	36	48	0	.	89	55	1425	11	1086	13	833	14	3763	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	5	100	4	100	0	.	275	23	472	20	5	100	768	15
	other conifers	0	.	0	.	13	78	43	62	18	81	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	85	40
	beech	3737	7	1275	11	940	16	1475	12	1547	10	456	23	1331	13	1438	13	394	21	207	28	519	22	79	44	173	31	835	13	14406	3
	maple	433	16	190	25	59	44	102	40	238	23	114	40	147	33	258	23	119	30	37	50	142	29	23	72	13	58	26	54	1900	8
	ash	299	21	162	23	220	27	220	24	653	15	72	51	146	26	283	26	85	42	44	52	60	44	30	72	9	100	62	45	2347	8
	oak	280	24	148	29	280	22	161	26	258	22	4	100	19	72	8	71	21	83	5	100	0	.	0	.	39	61	231	28	1456	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	58	4	100	612	16	647	15
	other broadleaves	249	21	166	30	167	31	67	34	281	20	19	71	75	64	74	38	158	30	33	51	50	44	119	36	75	49	586	16	2119	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11755	4	3331	9	4222	9	5585	7	8550	5	3666	10	7975	6	6280	7	3807	9	1857	13	2286	12	5974	6	8492	6	5162	6	78940	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2413	6	369	22	954	13	1562	10	2092	8	1387	11	2717	7	2090	9	1606	10	605	16	844	14	1592	9	3275	6	1050	10	22557	2
	fir	1369	8	138	22	195	26	724	12	734	13	333	17	1137	10	655	13	187	23	76	31	76	33	129	28	130	27	171	25	6053	4
	pine	345	18	139	28	116	33	156	25	504	15	0	.	97	50	60	45	45	55	20	53	23	62	209	25	393	16	23	71	2132	7
	larch	4	100	19	60	20	53	12	74	93	28	12	100	12	58	13	74	22	46	4	100	36	55	992	10	719	12	498	12	2457	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	27	211	21	4	100	315	16
	other conifers	0	.	0	.	30	100	28	56	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	50
	beech	1412	8	682	12	341	16	816	12	871	10	178	23	658	15	521	13	183	25	134	32	197	23	29	54	125	28	322	15	6469	4
	maple	96	22	31	35	13	58	28	46	120	24	33	36	34	43	52	37	44	35	7	71	40	34	0	.	12	58	4	100	514	10
	ash	89	25	36	37	95	31	55	35	214	18	8	71	39	42	78	28	4	100	7	100	25	46	0	.	4	100	10	72	664	10
	oak	107	27	73	27	153	29	107	26	158	21	5	100	9	71	0	.	4	100	4	100	0	.	16	62	33	50	34	38	702	11
	sweet chestnut	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	383	17	395	17
	other broadleaves	44	32	53	44	30	52	37	33	99	28	0	.	27	41	17	50	39	48	16	50	7	71	39	48	4	100	70	28	483	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5880	4	1540	10	1952	9	3525	7	4906	5	1957	10	4729	6	3486	7	2135	9	874	14	1248	12	3111	7	4911	5	2568	6	42820	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1110	8	104	25	357	16	722	11	865	11	582	14	1246	8	1035	11	751	12	271	18	300	19	806	11	1571	8	473	13	10193	3
	fir	511	11	35	37	75	30	301	16	228	18	148	24	506	13	240	17	109	26	72	37	50	35	67	35	71	29	72	31	2485	5
	pine	116	25	27	48	43	40	52	38	177	20	0	.	20	76	8	100	0	.	0	.	11	100	79	48	122	26	15	50	671	11
	larch	8	71	8	71	11	58	4	100	43	30	0	.	21	66	15	59	39	40	12	100	15	71	567	12	359	14	274	14	1377	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	19	53	99	24	0	.	122	21
	other conifers	0	.	0	.	8	71	12	73	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	42
	beech	311	15	253	17	80	31	242	16	346	14	60	30	287	20	199	18	122	23	30	43	64	37	12	74	38	48	135	24	2179	6
	maple	19	65	8	71	12	58	8	71	29	38	4	100	4	100	25	47	12	58	0	.	0	.	0	.	4	100	0	.	125	20
	ash	28	38	16	62	31	41	30	43	67	30	5	100	4	100	13	58	4	100	0	.	4	100	0	.	7	100	0	.	209	16
	oak	51	45	65	35	41	38	40	37	107	21	5	100	5	100	9	71	0	.	0	.	0	.	0	.	4	100	26	61	353	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	146	21	150	21
	other broadleaves	4	100	25	46	13	58	18	60	35	39	0	.	4	100	0	.	20	52	0	.	9	100	12	75	0	.	38	34	178	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2157	6	541	12	673	12	1429	8	1906	7	804	12	2097	7	1547	8	1057	10	385	17	453	15	1567	8	2276	6	1179	7	18071	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	317	13	32	39	122	22	155	21	168	17	190	18	372	13	300	15	224	19	126	28	143	27	178	21	509	11	195	20	3031	5
	fir	164	16	8	71	20	45	80	23	39	32	48	35	204	18	65	28	27	38	4	100	11	74	11	58	15	61	33	39	729	8
	pine	4	100	12	74	0	.	8	100	35	37	0	.	11	72	4	100	4	100	0	.	0	.	4	100	30	46	0	.	111	22
	larch	0	.	0	.	4	100	4	100	15	62	0	.	0	.	10	72	4	100	0	.	4	100	209	18	132	21	82	26	463	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	58	32	40	0	.	43	33
	other conifers	0	.	4	100	0	.	8	71	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	50
	beech	68	24	71	29	52	36	82	27	135	22	17	50	39	39	18	50	35	44	5	100	16	61	8	71	19	53	53	28	618	10
	maple	0	.	22	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	26	41
	ash	9	71	12	75	4	100	0	.	8	71	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	37	37
	oak	0	.	9	71	26	55	13	59	31	39	7	100	0	.	18	59	0	.	0	.	0	.	0	.	0	.	5	100	108	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	32	54	32
	other broadleaves	4	100	0	.	0	.	4	100	4	100	0	.	0	.	5	100	4	100	0	.	0	.	4	100	0	.	8	71	33	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	566	10	168	18	228	18	353	13	435	11	261	17	629	10	425	13	298	16	135	26	179	23	425	13	736	9	430	12	5269	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	103	24	4	100	18	52	46	31	61	31	25	41	95	22	143	25	105	24	35	46	44	37	85	27	195	16	104	23	1064	7
	fir	69	27	4	100	12	75	24	41	24	41	0	.	101	23	14	59	4	100	8	100	7	71	4	100	12	58	16	60	301	13
	pine	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	8	71
	larch	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	24	45	30	32	35	182	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	47	28	43	0	.	51	32
	other conifers	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	beech	7	71	16	50	8	71	22	46	30	44	4	100	10	71	13	58	4	100	0	.	0	.	4	100	0	.	37	46	156	19
	maple	0	.	0	.	0	.	6	100	0	.	4	100	0	.	4	100	0	.	0	.	9	72	0	.	0	.	4	100	26	42
	ash	0	.	0	.	4	100	13	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	51
	oak	4	100	0	.	9	71	14	59	21	45	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	51	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	80	37	84	35
	other broadleaves	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	187	19	24	41	59	29	129	20	140	20	32	40	206	17	172	22	113	23	47	39	61	30	218	16	284	13	273	16	1946	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16	50	4	100	8	71	12	58	4	100	4	100	12	58	16	50	13	58	13	58	44	35	24	41	35	33	27	38	233	14
	fir	8	71	0	.	0	.	8	71	0	.	0	.	46	30	4	100	4	100	0	.	0	.	4	100	4	100	10	72	88	22
	pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	larch	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	29	38	25	41	9	71	66	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	4	100	4	100	30	38	13	58	0	.	4	100	0	.	0	.	4	100	0	.	0	.	7	71	4	100	71	24
	maple	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	8	71
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	8	71	4	100	0	.	16	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	38
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	38	31	38
	other broadleaves	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	24	41	16	50	17	50	55	32	41	32	4	100	66	25	21	45	21	45	17	50	44	35	60	26	76	23	81	24	542	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	71	0	.	4	100	0	.	4	100	4	100	8	71	4	100	4	100	4	100	12	58	0	.	25	47	8	71	85	23
	fir	7	71	0	.	0	.	4	100	0	.	8	71	0	.	0	.	0	.	4	100	4	100	0	.	4	100	0	.	32	35
	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	.	12	58	0	.	4	100	16	50
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71
	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	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	36	39	40	37
	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	16	50	0	.	8	71	4	100	16	50	12	58	8	71	4	100	4	100	8	71	16	50	16	50	33	39	48	33	192	15
>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	.	4	100	0	.	4	100	14	77	0	.	4	100	8	71	4	100	12	75	49	34
	fir	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	15	59	23	46
	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	.	4	100	8	71	8	71	20	45
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	8	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	.	4	100	4	100
	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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	34	59	34
	other broadleaves	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	9	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4	100	5	100	0	.	0	.	4	100	4	100	4	100	4	100	14	77	0	.	4	100	16	50	16	50	101	24	175	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19307	6	3366	17	7661	11	11158	9	17194	7	14076	9	23255	6	17000	8	13365	9	6228	14	7221	13	18930	7	31775	5	9161	9	199698	2
	fir	15604	6	1351	18	2324	17	7015	10	4949	10	3561	15	10081	7	5449	10	1529	18	1523	23	979	23	1976	21	1108	20	1249	21	58696	3
	pine	2001	15	969	21	736	29	627	25	2063	12	21	100	903	35	223	43	282	48	777	31	226	63	6632	14	6292	13	533	40	22284	6
	larch	124	66	175	35	245	49	186	41	662	30	27	100	37	62	65	44	193	31	16	100	292	37	8363	8	4869	9	4691	10	19946	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	94	100	24	100	5	100	35	100	0	.	1690	19	2335	16	19	82	4202	12
	other conifers	260	47	200	62	172	59	278	29	335	35	0	.	44	57	84	39	10	100	65	56	38	78	62	85	367	49	0	.	1917	16
	beech	21908	5	8339	9	5720	11	8217	9	11203	7	2433	16	9167	9	8106	10	3197	15	1968	21	2863	18	1820	23	1880	19	10406	10	97226	2
	maple	3756	9	1338	14	852	33	1035	21	2483	12	971	19	1365	12	2183	13	1730	17	471	25	1096	22	590	25	358	27	607	23	18835	4
	ash	2784	14	1103	19	1791	16	1118	18	3408	11	771	26	1435	22	2425	15	652	25	429	30	604	36	568	26	465	47	1621	17	19175	5
	oak	2383	20	925	25	2022	22	925	18	1832	15	151	44	137	35	135	38	421	43	44	50	10	100	518	28	175	59	2215	20	11892	8
	sweet chestnut	0	.	0	.	131	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	114	53	21	83	11845	10	12131	9
	other broadleaves	3869	10	1453	18	1793	23	1283	19	4173	12	904	19	2123	18	2441	19	2011	15	943	29	977	25	4102	15	2557	17	12553	8	41183	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	71996	3	19219	7	23447	7	31843	6	48303	4	22913	8	48641	4	38136	6	23394	7	12501	11	14326	10	45364	5	52202	4	54900	3	507185	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. 1344352. Birmensdorf, Swiss Federal Research Institute WSL

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