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
growing stock (stemwood)
development stage · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
development stage	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	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	.
	spruce	324	65	14	100	0	.	572	31	0	.	486	39	0	.	75	100	355	44	0	.	83	71	98	59	0	.	131	79	230	82	170	75	16	100	0	.	361	73	0	.	33	100	294	52	248	54	0	.	322	79	3812	16
	fir	71	100	0	.	0	.	474	47	0	.	336	80	0	.	22	100	17	100	0	.	47	85	143	75	0	.	45	100	0	.	10	100	0	.	0	.	21	100	0	.	0	.	54	62	100	75	0	.	0	.	1340	29
	pine	63	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	100	0	.	0	.	0	.	0	.	0	.	16	100	0	.	15	100	0	.	0	.	59	100	0	.	0	.	0	.	227	47
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	66	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	0	.	28	75	0	.	0	.	99	100	0	.	0	.	280	47
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100
	other conifers	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	.
	beech	69	77	0	.	0	.	153	41	127	62	59	87	0	.	38	100	15	67	0	.	95	72	120	89	0	.	0	.	85	100	39	77	31	100	0	.	42	83	0	.	0	.	34	64	0	.	0	.	91	82	996	22
	maple	0	.	0	.	0	.	24	78	61	82	2	100	0	.	0	.	0	.	0	.	2	100	11	100	0	.	14	71	59	77	7	72	5	100	11	100	0	.	0	.	0	.	-2	100	2	100	0	.	14	100	210	35
	ash	43	74	0	.	0	.	38	67	69	84	9	100	0	.	93	100	0	.	4	100	73	100	96	100	0	.	0	.	19	75	0	.	0	.	0	.	0	.	0	.	0	.	9	100	22	88	0	.	27	100	501	34
	oak	7	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	25	71	40	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	26	100	0	.	25	100	5	100	0	.	0	.	137	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	71	0	.	0	.	43	71
	other broadleaves	41	100	0	.	0	.	27	52	122	67	8	82	0	.	37	100	8	58	11	100	14	65	20	100	0	.	0	.	45	78	3	100	0	.	0	.	0	.	7	100	0	.	1	100	27	66	0	.	13	100	384	29
	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	618	45	14	100	0	.	1287	28	387	51	899	45	0	.	265	73	536	37	15	100	413	51	528	60	0	.	189	79	437	69	262	76	69	100	11	100	438	66	61	56	33	100	474	41	546	43	0	.	467	61	7950	13
young growth/ thicket	n/a	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	.
	spruce	67	84	0	.	7	100	791	35	0	.	78	55	0	.	1	100	641	40	0	.	169	49	56	78	3	100	0	.	220	66	0	.	39	100	180	58	45	81	56	70	82	95	137	68	158	60	74	100	84	62	2889	16
	fir	5	100	0	.	0	.	288	47	0	.	-4	100	0	.	0	.	49	71	0	.	2	100	43	100	0	.	0	.	31	85	0	.	21	100	15	100	0	.	12	100	0	.	26	101	19	100	2	100	3	100	512	30
	pine	25	90	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	55	58	0	.	0	.	0	.	6	79	0	.	75	71	195	36
	larch	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	80	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	76	0	.	2	100	145	46	0	.	28	100	309	37
	Arolla pine	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	.
	other conifers	6	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	4	100	0	.	0	.	13	59
	beech	57	79	0	.	0	.	174	47	74	100	3	100	0	.	0	.	0	.	0	.	4	100	-2	207	0	.	0	.	30	93	0	.	0	.	13	93	0	.	18	70	7	100	4	100	0	.	0	.	44	85	428	30
	maple	44	100	0	.	0	.	11	87	0	.	0	.	0	.	5	100	32	89	0	.	7	71	0	.	0	.	3	100	0	.	0	.	7	100	28	75	0	.	2	100	0	.	1	100	2	100	0	.	-1	483	142	41
	ash	50	76	0	.	0	.	7	100	0	.	1	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	30	100	0	.	6	100	0	.	0	.	0	.	0	.	26	100	7	100	0	.	0	.	132	43
	oak	0	.	0	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	64	100	0	.	0	.	1	100	0	.	0	.	0	.	4	58	0	.	0	.	102	69
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	64	0	.	0	.	0	.	0	.	0	.	19	64
	other broadleaves	9	106	0	.	0	.	11	62	0	.	0	.	0	.	0	.	32	59	0	.	10	55	3	100	0	.	1	100	9	100	0	.	4	72	0	.	1	100	22	59	0	.	7	78	15	53	0	.	2	100	127	24
	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	263	52	0	.	7	100	1297	27	74	100	105	57	0	.	7	83	863	33	0	.	204	43	100	71	3	100	4	75	320	55	84	80	77	80	237	56	102	59	152	35	90	87	205	49	361	34	75	100	235	50	4866	12
pole timber	n/a	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	.
	spruce	427	37	45	100	254	74	1791	22	114	59	604	39	1	100	284	78	1060	28	390	57	537	42	200	62	139	81	374	57	416	34	264	53	24	69	400	60	204	46	55	50	495	57	1043	30	975	27	37	76	748	29	10883	9
	fir	64	56	30	100	8	100	481	33	41	62	49	60	0	.	0	.	64	75	157	68	390	60	59	68	13	77	36	79	148	54	0	.	44	90	141	50	5	73	41	100	137	57	375	37	30	97	5	100	182	42	2501	15
	pine	44	60	0	.	0	.	52	69	62	73	28	80	39	73	0	.	143	42	0	.	0	.	0	.	0	.	66	100	0	.	114	53	70	67	32	100	0	.	0	.	0	.	77	88	618	31	0	.	76	59	1421	18
	larch	28	75	0	.	0	.	65	92	76	85	15	100	0	.	0	.	198	36	0	.	4	100	0	.	0	.	0	.	30	75	0	.	0	.	0	.	22	100	95	60	0	.	24	100	449	35	0	.	37	71	1045	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	55	0	.	0	.	82	50
	other conifers	10	100	0	.	0	.	51	70	30	100	0	.	0	.	0	.	0	100	1	100	0	.	0	.	0	.	0	.	2	163	2	100	6	100	0	.	2	100	0	.	2	100	0	.	0	.	0	.	11	100	117	42
	beech	619	32	13	100	0	.	1010	26	357	43	238	47	0	.	4	100	97	58	186	47	19	85	39	71	25	77	173	71	265	43	319	46	313	46	77	51	5	74	740	29	111	48	637	33	174	65	1	100	357	38	5780	10
	maple	136	40	5	100	0	.	91	42	70	57	15	79	0	.	48	78	25	70	4	100	9	100	0	.	19	71	23	71	207	43	0	.	91	47	25	59	4	100	17	77	44	60	212	38	17	56	16	100	262	35	1342	14
	ash	138	44	0	.	3	100	140	44	42	88	211	44	0	.	8	100	128	61	75	64	5	100	0	.	97	79	29	100	202	40	94	67	52	86	41	68	26	70	68	37	47	85	339	38	11	71	60	100	322	35	2139	13
	oak	48	80	0	.	0	.	110	42	31	75	42	84	213	54	0	.	1	100	12	100	0	.	27	100	2	100	0	.	3	100	35	77	22	71	0	.	16	66	247	30	0	100	184	45	45	61	0	.	57	74	1094	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1351	19	0	.	0	.	40	73	0	.	0	.	1616	17
	other broadleaves	96	57	1	100	9	100	148	39	10	79	77	59	0	.	8	84	527	29	24	88	15	70	25	100	3	71	58	59	97	63	29	71	30	76	107	65	42	74	1227	19	61	69	291	45	212	32	8	100	55	41	3161	11
	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	1611	24	95	100	274	74	3939	16	832	34	1279	29	253	51	353	69	2507	17	851	35	980	39	350	48	299	50	759	40	1370	27	858	36	653	37	823	37	325	38	3843	12	897	38	3182	20	2615	18	127	72	2106	22	31181	5
young timber	n/a	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	.
	spruce	1039	36	248	72	848	52	4719	17	35	100	2815	27	0	.	775	49	1951	31	575	52	1489	31	260	53	181	100	215	62	920	36	729	42	588	48	787	46	279	67	441	61	978	45	2436	24	1740	31	0	.	1471	29	25519	8
	fir	686	37	113	100	384	54	1113	32	37	70	325	62	0	.	64	58	94	100	734	48	637	36	487	45	38	71	115	74	371	45	0	.	422	41	278	49	24	100	112	84	59	83	1113	27	71	90	0	.	313	51	7589	11
	pine	113	46	0	.	0	.	259	49	150	56	0	.	0	.	0	.	678	41	88	68	97	77	70	100	0	.	0	.	0	0	249	57	98	50	0	.	90	82	0	.	111	66	90	68	87	47	0	.	319	48	2498	17
	larch	112	81	0	.	0	.	37	100	0	.	0	.	0	.	0	.	730	35	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	337	55	63	100	9	100	492	36	0	.	15	100	1810	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	70	0	.	0	.	128	50
	other conifers	16	83	0	.	3	100	0	.	0	.	0	.	0	.	1	100	0	.	0	.	97	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	3	72	0	.	0	.	12	94	132	74
	beech	748	29	109	100	170	45	1439	22	461	44	999	34	0	.	432	53	328	48	818	40	597	43	259	43	85	82	298	68	789	45	452	38	1340	33	219	78	34	80	738	28	80	100	1635	23	85	72	0	.	798	38	12912	8
	maple	115	45	0	.	108	54	445	32	26	94	50	65	0	.	26	50	6	100	199	34	52	62	39	72	7	82	10	100	222	48	0	.	111	52	7	100	13	100	8	100	0	.	241	30	34	78	0	.	75	43	1793	13
	ash	192	53	0	.	142	56	203	40	37	100	84	81	0	.	13	76	0	.	185	77	60	60	0	.	68	100	3	100	256	52	0	.	83	54	25	100	9	100	42	66	0	.	270	39	46	75	0	.	162	44	1880	16
	oak	212	49	0	.	0	.	35	71	67	77	22	59	0	.	0	.	0	.	115	100	39	72	12	100	0	.	0	.	0	.	147	61	53	61	0	.	124	72	130	61	0	.	165	36	0	.	0	.	64	60	1186	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	592	31	0	.	0	.	0	.	0	.	0	.	592	31
	other broadleaves	145	56	0	.	0	.	186	47	18	60	63	65	0	.	0	.	15	100	138	56	5	100	18	64	25	95	0	.	63	72	1	100	35	62	24	77	25	82	345	36	0	.	293	43	65	67	0	.	42	65	1507	16
	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	3376	22	470	76	1655	39	8436	14	831	41	4358	23	0	.	1312	39	3874	22	2853	25	3072	23	1146	33	403	64	642	47	2621	26	1592	33	2730	25	1341	38	597	48	2745	19	1292	40	6256	15	2674	24	0	.	3269	23	57546	5
medium timber	n/a	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	.
	spruce	2369	28	1263	39	890	44	14639	11	447	72	2960	27	0	.	658	51	8838	14	936	32	4806	20	876	39	249	74	1606	42	2427	26	200	75	1283	36	2299	28	1298	31	1407	27	864	43	4167	21	2117	23	311	54	3458	19	60367	5
	fir	696	44	289	61	475	51	5649	15	121	94	553	64	0	.	305	65	327	44	1023	32	2371	22	726	43	236	71	360	70	1141	32	45	97	422	41	424	54	296	47	293	50	397	60	1023	31	106	82	137	57	1058	28	18472	8
	pine	624	44	0	.	0	.	713	42	84	76	475	58	6	100	0	.	665	34	198	63	0	.	0	.	0	.	0	.	79	100	20	100	383	40	11	100	227	66	97	100	0	.	202	100	250	64	0	.	722	29	4755	14
	larch	140	47	0	.	0	.	225	52	16	100	85	100	0	.	38	100	1346	24	17	100	0	.	0	.	0	.	0	.	153	70	0	.	0	.	0	.	0	.	289	43	0	.	51	72	916	29	52	100	193	60	3521	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	100	0	.	0	.	293	40
	other conifers	0	.	0	.	0	.	46	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	99	0	.	2	100	0	.	0	.	0	.	0	.	149	100	0	.	0	.	0	.	336	62
	beech	1841	25	98	56	139	74	5437	15	562	39	251	60	0	.	272	59	223	37	1521	25	1227	28	181	40	224	85	770	58	1395	31	312	47	1447	24	175	45	530	34	286	56	207	62	1326	25	15	100	78	60	1705	23	20220	7
	maple	213	59	0	.	70	91	288	30	79	48	6	72	0	.	36	61	29	56	138	47	104	49	3	100	66	66	88	81	278	34	14	100	107	52	93	50	64	62	0	.	29	79	55	61	2	100	51	71	94	44	1908	13
	ash	134	55	0	.	0	.	241	53	38	71	56	71	6	100	50	118	42	100	137	48	150	40	21	100	69	69	0	.	118	43	47	94	43	96	179	58	217	49	7	100	1	100	140	68	12	93	91	100	405	35	2202	15
	oak	142	38	0	.	0	.	182	33	92	95	30	73	168	100	0	.	34	100	191	52	117	84	0	.	0	.	0	.	9	100	49	91	46	83	11	100	167	43	46	98	0	.	236	56	0	.	0	.	314	45	1833	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	294	56	0	.	0	.	0	.	0	.	0	.	308	54
	other broadleaves	181	51	0	.	9	100	264	34	53	64	2	71	3	100	43	76	30	50	110	59	26	54	62	100	4	100	0	.	140	47	0	.	175	50	44	61	28	95	62	58	49	100	79	57	4	100	11	86	124	52	1503	15
	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	6338	18	1650	37	1583	41	27685	9	1493	35	4417	24	183	100	1402	40	11773	12	4270	20	8801	16	1870	32	849	51	2823	34	5877	19	686	47	3908	21	3237	25	2826	25	2780	20	1548	35	7428	16	3488	20	731	45	8073	15	115717	4
old timber	n/a	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	.
	spruce	1284	47	410	74	765	58	7197	16	401	69	1028	46	0	.	1483	38	7556	15	644	39	1343	39	594	50	112	100	398	66	4243	24	308	63	910	43	1248	34	1036	35	1413	32	363	69	1951	28	1648	31	63	100	1495	36	37891	7
	fir	240	59	43	100	134	82	3406	18	58	76	123	100	0	.	0	.	576	48	649	33	788	42	406	52	0	.	280	77	720	37	0	.	432	65	553	43	287	62	105	86	0	.	1047	36	63	100	67	100	825	40	10804	10
	pine	22	100	0	.	8	100	353	56	0	.	110	71	0	.	0	.	428	43	97	77	0	.	0	.	0	.	0	.	31	100	0	.	15	100	0	.	68	72	68	82	0	.	17	100	44	72	0	.	325	54	1586	22
	larch	34	73	0	.	0	.	104	60	0	.	63	100	0	.	0	.	1736	26	0	.	0	.	34	100	0	.	0	.	40	100	0	.	0	.	40	100	0	.	600	44	0	.	0	.	1272	30	0	.	0	.	3923	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	272	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	319	49
	other conifers	0	.	0	.	0	.	113	100	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	139	83
	beech	899	41	0	.	138	95	3435	20	689	52	565	49	0	.	141	76	814	47	945	36	815	46	73	59	0	.	124	87	410	52	250	53	699	41	639	49	505	47	76	67	47	100	480	33	68	100	75	100	768	34	12657	10
	maple	99	56	14	100	19	100	240	39	34	100	0	.	0	.	41	58	31	95	58	47	19	60	71	100	0	.	0	.	195	45	9	100	28	100	13	89	57	71	0	.	0	.	21	93	0	.	0	.	59	61	1009	18
	ash	20	100	0	.	0	.	215	61	25	100	40	71	0	.	22	100	0	.	39	72	0	.	0	.	0	.	0	.	101	79	37	72	0	.	57	74	40	75	0	.	0	.	42	60	0	.	0	.	45	76	683	26
	oak	120	68	0	.	0	.	110	50	25	100	61	100	0	.	0	.	34	100	0	.	6	100	0	.	0	.	0	.	89	68	6	100	0	.	0	.	241	61	0	.	18	100	92	100	6	100	0	.	79	63	887	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	635	51	0	.	0	.	0	.	0	.	0	.	683	48
	other broadleaves	134	64	0	.	0	.	253	45	16	72	86	100	0	.	0	.	37	62	13	70	14	100	13	100	0	.	0	.	22	54	0	.	0	.	47	73	72	76	29	68	1	100	54	84	133	88	0	.	45	57	969	23
	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	2853	29	467	75	1064	58	15426	12	1248	41	2076	38	0	.	1687	37	11532	12	2445	25	3008	28	1190	39	112	100	802	60	5850	22	611	53	2084	34	2597	29	2306	29	2927	23	430	62	3704	24	3283	21	205	100	3641	24	71549	5
mixed	n/a	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	.
	spruce	95	62	18	100	275	71	4895	14	128	72	1245	32	0	.	247	82	7245	12	124	59	875	32	2501	22	226	67	1297	31	2057	26	0	.	164	76	608	39	258	39	700	30	351	48	2063	18	4553	15	142	100	188	41	30254	6
	fir	49	73	32	100	32	100	3368	19	0	.	552	44	0	.	6	100	375	42	70	56	772	35	1138	23	0	.	254	40	333	40	0	.	184	74	133	61	87	68	281	74	68	77	929	25	546	40	44	100	182	57	9437	10
	pine	0	.	0	.	0	.	253	55	16	100	43	74	0	.	0	.	793	29	50	72	0	.	42	73	0	.	12	100	0	.	0	.	132	59	0	.	62	62	51	96	77	78	11	100	764	26	0	.	37	94	2343	15
	larch	0	.	0	.	0	.	37	92	0	.	0	.	0	.	0	.	1695	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	802	24	0	.	92	68	2310	16	0	.	58	100	4994	10
	Arolla pine	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	585	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	350	27	0	.	0	.	943	21
	other conifers	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	2	100	0	.	0	.	3	100	0	.	0	.	5	100	0	.	2	71	0	.	0	.	0	.	0	.	0	.	10	100	0	.	17	90	74	54
	beech	323	45	12	100	138	74	1150	22	343	57	427	41	0	.	40	76	150	73	216	54	307	54	460	31	72	100	102	64	838	35	79	100	441	38	61	67	138	47	929	25	40	80	1054	25	175	42	0	.	305	37	7799	9
	maple	75	65	0	.	1	100	227	31	23	82	74	53	0	.	39	80	40	52	14	100	36	54	80	44	13	100	0	.	231	35	0	.	65	57	4	73	150	76	9	100	0	.	100	38	37	70	0	.	192	47	1409	15
	ash	324	53	0	.	67	100	166	43	47	100	218	54	0	.	12	100	0	100	173	59	31	90	54	78	0	.	14	100	138	41	0	.	40	62	9	100	210	37	21	83	0	.	170	62	39	88	0	.	118	55	1852	16
	oak	21	100	0	.	0	.	63	59	0	.	-1	100	0	.	0	.	0	.	0	.	32	100	36	78	0	.	0	.	36	100	0	.	18	100	0	.	131	46	194	57	22	100	160	58	8	53	0	.	167	49	886	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	628	36	0	.	0	.	0	.	0	.	0	.	628	36
	other broadleaves	60	72	2	100	0	.	108	29	21	71	76	77	0	.	15	80	78	38	16	71	73	59	35	62	0	.	0	100	169	46	4	100	25	64	6	80	299	48	284	40	15	74	90	45	72	40	0	.	211	43	1659	15
	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	947	39	65	100	514	69	10275	12	577	50	2669	24	0	.	358	71	10963	10	663	43	2126	26	4349	17	311	72	1679	27	3806	21	83	100	1070	34	821	36	1335	31	3899	14	572	43	4668	15	8866	11	186	100	1475	30	62279	4
total	n/a	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	.
	spruce	5605	18	1997	30	3039	26	34604	7	1125	39	9215	14	1	100	3523	23	27646	7	2669	20	9303	13	4586	16	911	37	4021	22	10513	13	1671	26	3025	22	5521	16	3480	18	4071	16	3167	22	12091	10	11439	10	627	38	7765	13	171616	2
	fir	1812	24	508	43	1033	33	14779	9	258	49	1933	29	0	.	397	51	1503	24	2634	20	5006	15	3003	17	287	59	1090	33	2744	18	55	81	1525	26	1544	24	721	32	844	34	661	39	4566	14	936	28	255	44	2562	19	50654	4
	pine	891	32	0	.	8	100	1643	25	312	37	656	44	45	65	0	.	2707	17	434	37	171	62	112	68	0	.	78	86	109	77	403	39	714	26	43	79	517	35	216	56	187	50	456	50	1769	18	0	.	1554	21	13024	7
	larch	314	37	0	.	0	.	468	33	92	72	163	66	0	.	38	100	5938	11	17	100	7	72	34	100	0	.	0	.	223	53	44	76	0	.	40	100	22	100	2175	18	63	100	179	43	5684	11	52	100	331	41	15882	6
	Arolla pine	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	1212	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	563	23	0	.	0	.	1783	16
	other conifers	32	55	0	.	3	100	211	60	30	100	35	100	0	.	1	100	3	84	1	100	120	83	3	100	0	.	0	.	145	95	2	100	11	66	1	100	2	100	0	.	4	72	155	96	15	77	0	.	39	55	813	32
	beech	4556	15	232	53	585	36	12798	9	2612	20	2543	20	0	.	927	32	1626	27	3687	17	3065	19	1130	20	406	53	1467	35	3810	18	1452	21	4271	15	1184	31	1254	24	2786	14	493	34	5170	12	518	31	154	57	4068	14	60791	3
	maple	682	25	19	78	198	44	1327	16	293	29	147	36	0	.	196	30	163	32	413	23	228	28	204	41	105	46	137	54	1193	18	31	57	414	24	182	31	288	44	36	49	73	48	629	19	93	41	67	59	694	20	7814	6
	ash	901	25	0	.	212	49	1010	22	259	38	617	28	6	100	199	57	169	53	613	31	323	32	171	62	234	48	46	70	863	22	178	46	225	35	311	38	501	27	139	30	48	83	996	22	136	40	151	72	1078	19	9388	7
	oak	550	27	0	.	0	.	500	21	222	48	182	44	381	53	0	.	69	70	318	48	224	49	115	50	2	100	0	.	136	52	303	40	138	40	11	100	680	29	644	25	40	72	860	25	69	42	0	.	682	26	6125	9
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	286	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3519	14	0	.	0	.	83	51	0	.	0	.	3888	13
	other broadleaves	665	26	3	72	18	71	997	18	239	38	312	39	3	100	103	49	727	22	312	33	157	32	176	42	32	75	60	58	545	24	37	57	269	35	228	37	467	34	1977	14	126	52	815	24	528	27	19	65	492	24	9311	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	.
	total	16007	10	2760	29	5097	23	68345	5	5442	16	15804	11	435	51	5385	19	42049	6	11098	11	18604	10	9533	12	1977	29	6898	18	20282	10	4175	19	10592	12	9067	14	7930	14	16408	6	4862	18	25917	8	21833	7	1324	33	19265	9	351089	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. 1230911. Birmensdorf, Swiss Federal Research Institute WSL

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