Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

top

2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

top

3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

top

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

top

5. Two types of changes?

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

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

top

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

top

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

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

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

top

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

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

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

top

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

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

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

top

NFI1
number of stems
development stage · main tree species
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
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 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	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	86	58	15	.	0	.	1019	25	13	100	254	35	0	.	83	76	594	47	40	91	111	64	55	80	0	.	227	51	263	40	75	56	31	71	98	43	107	60	30	71	42	100	220	33	244	40	79	78	135	60	3820	12
	fir	31	100	0	.	0	.	447	40	26	100	139	67	0	.	14	77	10	71	42	72	87	60	32	71	0	.	29	60	8	71	4	100	41	72	11	100	24	100	0	.	0	.	113	44	33	79	11	100	9	100	1109	20
	pine	41	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	48	100	0	.	0	.	0	.	20	100	9	100	21	74	0	.	4	100	0	.	0	.	53	100	10	100	0	.	11	100	221	38
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	157	63	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	33	59	0	.	0	.	170	80	0	.	0	.	380	45
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	88	0	.	0	.	58	66
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	beech	171	43	0	.	0	.	320	39	184	52	87	96	0	.	107	77	24	67	125	74	96	60	53	74	0	.	0	.	60	73	80	57	160	48	36	72	59	74	147	93	0	.	81	43	0	.	0	.	120	58	1910	16
	maple	20	71	0	.	0	.	188	44	68	59	27	74	0	.	32	100	0	.	50	64	10	100	11	100	0	.	8	71	39	70	29	74	29	100	77	68	28	100	51	73	0	.	93	58	43	80	0	.	58	72	861	18
	ash	17	64	0	.	0	.	130	44	81	91	82	100	0	.	181	96	70	71	74	53	63	75	34	100	0	.	0	.	57	84	0	.	0	.	72	59	0	.	10	100	0	.	40	79	115	45	0	.	48	100	1074	23
	oak	22	71	0	.	0	.	0	.	7	71	0	.	0	.	0	.	0	.	14	100	18	64	22	100	0	.	0	.	0	.	37	79	11	100	0	.	0	.	14	100	0	.	110	69	34	89	0	.	0	.	290	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	64	0	.	0	.	32	79	0	.	0	.	62	51
	other broadleaves	58	85	0	.	0	.	241	33	129	62	41	61	0	.	87	71	166	44	23	72	59	50	22	100	0	.	0	.	127	65	10	100	0	.	104	55	0	.	119	43	0	.	90	53	150	40	0	.	42	84	1469	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	446	34	15	.	0	.	2345	20	510	44	630	36	0	.	504	63	1047	36	372	39	492	42	229	57	0	.	264	47	574	40	267	53	294	46	397	40	222	51	434	38	42	100	801	29	862	28	90	74	421	47	11258	9
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	150	58	0	.	105	74	651	28	0	.	293	49	0	.	34	59	626	27	32	88	363	40	28	87	23	100	57	100	108	65	4	100	14	79	232	46	268	56	156	43	190	66	171	39	238	32	23	100	190	53	3955	11
	fir	40	67	0	.	0	.	232	39	0	.	77	62	0	.	0	.	35	87	22	100	20	71	57	100	0	.	0	.	23	71	0	.	44	66	32	58	4	100	37	74	0	.	42	61	70	57	12	100	10	100	757	19
	pine	34	63	0	.	0	.	45	92	21	100	4	100	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	11	58	0	.	0	.	0	.	62	62	0	.	41	83	266	31
	larch	9	100	0	.	0	.	39	100	0	.	0	.	0	.	0	.	157	35	0	.	10	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	116	43	0	.	20	100	144	31	0	.	4	100	506	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100
	other conifers	9	100	0	.	0	.	10	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	10	100	51	100	0	.	0	.	101	55
	beech	128	56	0	.	8	100	141	42	12	100	43	60	0	.	44	91	0	.	0	.	29	74	29	74	0	.	0	.	61	45	39	71	27	63	39	78	4	100	115	44	4	100	40	100	30	75	0	.	50	50	843	17
	maple	55	68	0	.	0	.	61	62	22	100	9	100	0	.	36	63	55	71	0	.	20	71	0	.	0	.	33	100	9	100	0	.	23	85	71	83	0	.	19	71	0	.	21	71	10	100	0	.	33	52	476	22
	ash	25	54	0	.	0	.	39	81	76	100	19	71	0	.	10	100	22	100	0	.	21	100	0	.	0	.	0	.	7	100	0	.	23	85	12	100	10	100	85	90	0	.	32	100	38	100	4	100	15	73	436	30
	oak	4	100	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	4	100	4	100	0	.	27	78	0	.	0	.	0	.	118	56	0	.	0	.	182	40
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	56	0	.	0	.	0	.	0	.	0	.	105	47
	other broadleaves	23	61	0	.	0	.	113	61	69	76	0	.	0	.	40	100	338	44	37	100	75	55	28	100	0	.	10	100	15	77	4	100	88	65	0	.	107	67	286	29	21	71	40	80	265	36	0	.	66	47	1626	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	478	33	0	.	113	73	1331	21	199	76	458	39	0	.	164	59	1329	19	92	72	551	34	142	67	23	100	100	66	229	43	58	59	223	46	385	41	430	44	893	21	226	57	375	32	1027	21	39	91	409	40	9273	8
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	2059	29	140	75	675	52	5411	17	262	59	1949	30	15	78	883	48	3807	21	876	52	1524	36	584	70	534	62	1104	42	2144	27	673	51	838	42	1091	44	1288	39	272	47	1001	41	2695	22	2347	24	265	67	1894	24	34332	7
	fir	286	50	129	71	18	100	1629	25	51	76	625	45	0	.	117	88	52	93	292	39	532	63	184	74	125	69	60	73	207	39	10	100	38	71	341	41	51	66	79	100	323	54	906	34	503	52	259	83	137	47	6955	12
	pine	199	51	0	.	0	.	140	62	99	78	24	61	134	94	0	.	1207	34	0	.	106	100	0	.	0	.	252	91	4	100	166	69	214	69	112	71	54	100	0	.	128	100	52	50	2779	24	0	.	74	53	5743	15
	larch	273	66	0	.	0	.	138	57	21	71	4	100	0	.	0	0	875	28	0	.	4	100	0	.	0	.	0	.	98	64	36	100	20	71	0	.	48	100	746	33	0	.	54	79	1452	24	0	.	40	79	3808	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	271	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	357	50	0	.	0	.	628	40
	other conifers	30	100	0	.	0	.	70	55	119	100	0	.	0	.	0	.	10	100	10	100	31	100	0	.	0	.	0	.	40	76	19	100	20	100	0	.	10	100	0	.	31	100	90	100	0	.	0	.	30	74	510	33
	beech	1792	24	87	72	5	100	2803	21	600	43	994	32	0	.	374	73	548	40	628	40	255	76	64	78	204	61	409	71	665	43	1274	30	1199	31	422	42	43	61	3952	19	385	45	1802	22	832	39	20	71	756	39	20111	8
	maple	290	28	40	71	12	100	371	30	83	70	75	66	0	.	206	63	143	43	48	60	156	63	0	.	47	56	42	71	315	37	57	84	191	35	107	45	180	85	254	41	210	41	935	31	192	32	46	100	521	32	4519	11
	ash	400	32	22	100	64	77	539	34	52	71	423	41	0	.	59	71	434	51	237	48	30	74	0	.	292	77	105	76	539	34	162	54	257	57	192	58	139	55	1061	22	167	56	851	27	107	48	168	74	599	28	6900	10
	oak	334	50	0	.	0	.	299	42	264	76	52	45	850	47	0	.	80	58	40	100	10	100	86	100	11	100	5	100	16	78	79	54	54	77	0	.	28	55	1475	25	10	100	1441	29	228	47	0	.	27	63	5387	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	854	43	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	6483	14	0	.	110	100	82	68	0	.	0	.	7550	13
	other broadleaves	527	34	65	87	73	73	709	31	19	71	207	49	21	100	99	53	2306	19	258	54	271	79	178	100	30	58	319	46	745	48	67	59	137	44	473	53	320	49	7387	11	335	54	1180	26	2335	23	15	75	472	41	18546	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6191	16	484	71	846	50	12110	12	1571	31	4353	21	1019	44	1738	37	10588	12	2387	27	2919	29	1096	46	1242	40	2295	31	4794	21	2541	26	2967	23	2737	27	2160	29	21707	8	2590	27	10117	12	11215	12	774	46	4551	18	114989	4
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	1084	27	704	49	649	60	7517	13	80	46	3103	22	0	.	1067	39	4018	18	944	32	2296	24	748	32	277	67	627	42	1682	28	813	38	481	35	896	36	592	40	621	47	1277	31	3678	15	3534	20	81	90	1606	23	38375	6
	fir	920	29	77	85	235	56	1436	23	120	45	485	44	0	.	131	55	163	66	1196	30	1211	28	852	32	24	87	253	47	454	31	12	100	641	27	211	49	47	61	167	82	165	52	1558	19	81	50	28	79	409	38	10875	8
	pine	104	38	0	.	0	.	195	58	204	41	0	.	0	.	0	.	933	32	128	66	136	86	133	60	0	.	0	.	19	78	136	59	143	43	84	100	82	59	226	71	157	53	142	56	816	46	0	.	337	36	3976	15
	larch	86	54	0	.	22	100	26	80	0	.	103	100	0	.	0	.	610	28	0	.	0	.	0	.	0	.	0	.	0	.	10	100	4	100	0	.	0	.	417	41	16	100	22	85	1142	23	0	.	35	54	2494	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	166	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	47	0	.	0	.	575	37
	other conifers	30	58	20	100	10	100	9	100	0	.	0	.	0	.	10	100	0	.	0	.	42	100	0	.	0	.	0	.	30	100	0	.	0	.	0	.	0	.	0	.	14	100	46	56	0	.	0	.	14	77	225	30
	beech	1685	18	141	71	248	53	3509	18	768	30	1182	28	10	.	446	45	447	46	1517	23	1155	30	452	39	200	50	598	44	1497	29	892	33	2210	20	200	51	388	36	1990	22	226	77	2902	15	406	46	61	77	956	28	24088	6
	maple	361	28	35	100	119	71	812	24	72	51	132	44	10	.	52	51	19	71	340	29	149	38	192	46	61	53	48	52	331	41	44	56	448	36	58	59	29	74	44	71	0	.	681	26	82	71	0	.	111	44	4230	10
	ash	305	31	35	100	168	60	245	37	14	100	107	61	10	.	46	63	0	.	328	50	185	56	0	.	77	95	9	100	417	47	43	92	185	46	51	72	95	53	139	45	33	100	619	27	68	62	15	100	134	41	3328	12
	oak	200	33	0	.	0	.	45	64	79	65	83	67	23	.	0	.	0	.	100	70	36	72	152	74	0	.	0	.	136	89	73	52	203	61	10	100	101	58	189	43	0	.	319	33	10	100	0	.	68	57	1826	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1293	26	0	.	20	100	0	.	0	.	0	.	1313	26
	other broadleaves	532	38	20	100	0	.	483	25	113	39	137	50	29	.	20	100	163	60	452	36	66	52	104	41	144	64	10	100	190	45	10	100	136	39	61	72	60	74	946	27	40	77	820	36	204	39	9	100	105	66	4855	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	5307	15	1033	42	1452	40	14278	11	1449	27	5332	18	81	.	1772	32	6521	15	5005	17	5276	18	2633	24	783	42	1546	30	4756	19	2032	28	4450	17	1571	30	1395	29	6032	14	1929	27	10806	11	6752	15	194	71	3775	19	96160	4
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	1087	23	802	34	908	30	9941	9	243	49	2108	18	0	.	599	34	9579	10	1054	29	3314	15	640	27	214	49	1060	32	2490	19	126	56	697	24	1898	21	897	29	1342	21	419	45	4108	15	3040	16	244	46	2632	15	49442	4
	fir	500	30	350	46	422	37	4899	12	237	45	405	38	0	.	192	55	337	30	1125	22	1778	18	651	32	179	46	212	68	1079	22	28	73	496	34	510	35	306	35	333	41	227	62	1410	20	265	50	92	43	1221	20	17252	6
	pine	220	38	8	100	0	.	324	33	163	44	206	49	10	.	0	.	513	25	248	51	0	.	0	.	0	.	0	.	152	62	11	74	150	38	46	93	122	46	110	56	0	.	126	86	287	44	0	.	430	23	3125	11
	larch	58	44	4	100	0	.	57	47	4	100	47	76	0	.	4	100	1260	19	8	71	21	100	0	.	0	.	4	100	181	49	0	.	0	.	6	100	8	100	248	28	0	.	35	65	1263	23	8	100	78	44	3293	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	311	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	169	52	0	.	0	.	480	30
	other conifers	4	100	0	.	0	.	68	51	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	56	83	0	.	20	100	0	.	0	.	0	.	0	.	34	100	0	.	0	.	20	71	237	32
	beech	1710	19	125	43	346	50	4795	11	753	27	702	29	0	.	342	46	747	29	1717	19	1658	19	555	26	308	43	419	57	1739	22	352	35	1199	20	608	34	683	28	317	40	94	82	1652	18	81	63	189	53	2040	17	23131	5
	maple	167	44	28	100	105	64	671	23	214	35	38	55	0	.	45	58	74	44	270	30	173	52	78	46	89	53	138	46	370	30	88	72	147	34	115	48	65	47	0	.	22	72	267	29	10	100	33	59	296	30	3503	9
	ash	146	38	67	86	88	75	272	32	61	53	70	67	19	.	47	82	17	100	215	34	153	31	51	93	46	56	0	.	241	35	24	72	34	59	161	47	123	37	33	74	9	100	352	37	20	71	34	100	399	30	2682	11
	oak	187	32	0	.	4	100	224	25	54	49	15	60	60	.	0	.	40	90	75	45	135	55	0	.	0	.	0	.	10	100	92	52	82	60	23	85	84	47	55	53	0	.	302	36	0	.	0	.	141	40	1583	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	647	37	0	.	0	.	0	.	0	.	0	.	685	35
	other broadleaves	269	33	34	100	33	100	635	28	199	46	43	52	19	.	45	65	133	41	201	40	102	46	101	60	36	64	18	71	148	51	0	.	226	34	146	36	66	52	241	39	22	100	195	33	51	53	14	77	221	33	3200	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4347	14	1420	33	1906	27	21885	7	1929	22	3647	17	108	.	1274	31	13047	9	4912	15	7335	13	2076	20	893	35	1849	29	6466	14	720	35	3050	17	3513	18	2354	20	3325	16	794	36	8481	12	5187	14	615	36	7479	12	108614	3
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	431	32	96	81	283	44	4179	12	66	65	712	36	0	.	415	46	4432	11	431	31	493	31	359	32	67	.	394	43	1787	20	97	57	308	32	718	28	368	33	796	24	310	40	1106	24	1284	21	133	58	582	26	19846	5
	fir	288	34	12	100	48	54	2215	14	66	56	170	50	0	.	19	100	270	34	594	26	520	34	267	34	0	.	75	79	515	39	0	.	273	41	344	31	149	50	166	41	0	.	677	29	113	84	71	58	175	40	7031	8
	pine	38	90	0	.	5	100	106	41	43	100	20	66	0	.	0	.	293	46	47	68	0	.	7	100	0	.	0	.	24	85	0	.	23	62	0	.	47	48	25	86	0	.	9	100	73	79	0	.	178	37	938	20
	larch	17	62	0	.	0	.	16	61	0	.	7	100	0	.	0	.	799	20	0	.	0	.	4	100	0	.	0	.	6	100	20	100	0	.	9	100	0	.	229	30	0	.	27	100	865	24	0	.	15	100	2014	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	381	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	71	0	.	0	.	424	34
	other conifers	0	.	0	.	0	.	19	82	0	.	0	.	0	.	0	.	0	.	0	.	27	72	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	9	100	0	.	0	.	30	100	96	43
	beech	450	28	0	.	151	46	2593	14	399	32	414	34	0	.	190	50	340	36	412	28	356	31	205	37	0	.	33	78	337	35	188	47	437	30	376	34	333	29	634	34	62	100	531	25	132	83	67	100	630	22	9272	7
	maple	53	62	4	100	11	100	218	31	41	53	0	.	0	.	102	72	25	62	129	40	69	65	122	92	0	.	40	100	244	41	4	100	14	100	74	54	62	62	0	.	0	.	39	50	0	.	4	100	56	41	1311	16
	ash	29	53	0	.	42	71	162	35	19	80	46	59	0	.	13	100	0	.	144	52	0	.	40	78	0	.	0	.	76	62	14	77	20	100	28	86	90	76	0	.	0	.	53	49	0	.	4	100	59	41	839	17
	oak	128	53	0	.	0	.	111	34	19	60	67	72	0	.	0	.	15	100	60	79	10	100	0	.	0	.	0	.	36	60	26	72	30	58	0	.	101	51	0	.	4	100	98	52	10	100	0	.	99	46	812	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	533	43	0	.	0	.	0	.	0	.	0	.	571	40
	other broadleaves	144	38	0	.	0	.	318	32	49	58	43	100	0	.	0	.	84	55	129	41	28	100	88	89	0	.	60	100	45	55	0	.	18	65	62	67	100	65	143	46	39	80	95	45	267	52	11	100	213	53	1936	14
	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	1578	21	112	81	539	41	9937	9	702	28	1480	26	0	.	739	36	6677	10	1947	19	1502	24	1093	31	67	.	602	39	3070	18	349	46	1123	25	1621	25	1250	29	2526	18	415	41	2645	19	2788	17	291	53	2037	18	45089	4
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	185	49	108	62	248	53	5661	11	63	48	2134	24	0	.	208	45	11610	9	298	40	1050	27	2085	17	525	44	1731	23	2404	22	0	.	294	64	1269	25	317	34	1326	22	639	32	2726	13	7114	12	220	53	241	38	42454	4
	fir	46	56	47	71	8	100	3324	14	57	94	649	34	0	.	4	100	417	29	210	36	1054	24	1822	19	47	100	209	43	682	25	0	.	287	42	275	32	123	73	113	50	168	54	1451	21	725	30	111	52	115	55	11947	7
	pine	111	56	0	.	0	.	420	35	15	78	38	57	0	.	0	.	2618	19	52	45	22	100	58	78	0	.	69	73	144	78	0	.	271	38	74	100	25	53	84	89	229	54	9	100	2103	20	10	100	90	70	6443	11
	larch	16	71	0	.	0	.	19	71	0	.	0	.	0	.	0	.	1968	13	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	1285	17	0	.	55	43	2765	12	0	.	30	100	6148	8
	Arolla pine	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	1095	21	0	.	0	.	0	.	0	.	94	100	24	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	610	23	0	.	0	.	1862	15
	other conifers	0	.	0	.	0	.	134	83	0	.	30	100	0	.	0	.	337	51	0	.	0	.	9	100	0	.	0	.	11	100	0	.	59	71	0	.	10	100	0	.	0	0	0	.	10	100	0	.	101	87	703	33
	beech	781	39	57	94	143	62	2525	15	510	42	1114	28	0	.	158	61	405	39	305	45	442	36	1015	22	83	100	645	38	1652	23	52	.	992	30	373	38	254	52	2383	19	19	62	1740	18	299	41	21	100	548	33	16515	7
	maple	72	59	0	.	30	74	611	23	39	80	225	38	0	.	136	61	190	40	48	60	142	34	121	32	24	87	21	71	527	27	0	.	155	39	138	64	155	58	61	53	0	.	537	28	252	46	64	84	194	40	3741	10
	ash	182	53	0	.	144	75	499	30	37	100	348	36	0	.	41	80	83	50	195	44	124	51	106	90	0	.	79	57	454	28	0	.	137	47	12	100	257	42	74	46	0	.	290	37	220	53	200	75	138	38	3621	11
	oak	99	53	0	.	0	.	97	44	8	100	76	52	0	.	0	.	291	69	24	72	12	75	23	71	0	.	0	.	46	80	0	.	24	61	0	.	70	51	191	51	16	100	278	60	98	43	5	100	130	45	1487	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1216	30	0	.	0	.	0	.	0	.	0	.	1230	30
	other broadleaves	335	46	9	100	19	100	662	20	178	54	355	36	0	.	82	74	725	24	219	48	242	46	76	45	9	100	104	49	674	30	10	.	247	43	65	48	450	41	1518	18	85	70	589	27	658	29	0	0	500	33	7812	8
	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	1828	27	220	59	592	55	13958	8	906	36	4968	17	0	.	630	39	19754	7	1350	28	3087	19	5315	13	688	44	2950	21	6627	14	61	.	2466	23	2207	23	1661	26	8252	11	1192	30	7677	12	14854	9	630	48	2087	24	103964	3
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	5082	14	1865	25	2867	22	34381	5	728	28	10552	11	15	78	3290	20	34666	5	3674	18	9151	11	4498	14	1640	28	5198	15	10878	10	1788	26	2663	18	6202	13	3836	17	4542	12	3878	17	14704	7	17801	7	1047	26	7280	10	192225	2
	fir	2112	17	616	32	732	28	14181	7	558	26	2550	18	0	.	475	35	1285	17	3482	14	5203	12	3866	13	376	34	838	26	2968	13	53	48	1819	16	1723	16	705	23	896	25	883	29	6157	10	1790	21	583	40	2075	15	55925	3
	pine	748	21	8	100	5	100	1230	19	545	26	292	36	143	88	0	.	5604	13	479	33	313	53	198	47	0	.	320	73	363	41	330	42	821	24	317	45	345	28	444	42	514	38	392	37	6130	14	10	100	1160	16	20712	6
	larch	459	41	4	100	22	100	295	32	25	62	161	68	0	.	4	100	5827	8	8	71	34	68	4	100	0	.	4	100	301	36	85	54	24	61	15	72	57	87	3074	12	16	100	213	31	7801	8	8	100	202	30	18643	5
	Arolla pine	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	2270	16	0	.	0	.	0	.	0	.	94	100	24	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	1620	19	0	.	0	.	4047	12
	other conifers	74	49	20	100	10	100	311	40	119	100	44	76	0	.	10	100	357	48	10	100	100	56	9	100	21	100	0	.	137	47	22	85	99	51	10	100	20	71	0	.	55	64	191	53	62	85	0	.	196	50	1876	17
	beech	6716	10	411	34	901	27	16686	7	3225	15	4535	13	10	100	1662	24	2512	17	4704	12	3992	14	2372	14	796	28	2103	25	6012	12	2875	18	6223	11	2054	17	1763	16	9539	10	790	33	8748	8	1781	23	359	36	5101	11	95870	2
	maple	1017	16	107	50	275	40	2932	11	540	22	505	23	10	100	610	29	506	22	885	16	719	22	523	29	221	30	330	28	1836	15	220	39	1006	19	640	23	519	36	429	28	232	38	2573	15	590	25	148	50	1269	17	18641	4
	ash	1105	18	125	57	506	34	1888	15	340	36	1094	22	29	74	396	47	625	37	1193	20	575	25	231	50	415	57	193	48	1791	17	243	40	657	28	527	28	713	23	1401	18	210	47	2237	15	568	26	425	47	1393	16	18881	5
	oak	973	21	0	.	4	100	776	19	430	48	307	29	933	43	0	.	426	49	311	32	233	35	283	51	11	100	5	100	244	53	312	26	407	35	33	67	410	24	1923	20	30	65	2548	19	498	27	5	100	465	22	11567	8
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	970	38	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	10282	10	0	.	131	86	114	53	0	.	0	.	11517	9
	other broadleaves	1889	18	128	55	126	52	3161	12	757	23	826	22	69	59	373	31	3915	13	1319	20	843	30	598	36	219	44	521	32	1944	22	100	43	852	19	910	30	1103	24	10641	8	543	36	3009	16	3932	15	49	43	1618	18	39444	4
	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	20175	7	3284	22	5449	18	75844	4	7266	12	20868	8	1209	38	6820	15	58963	4	16066	8	21162	8	12583	9	3697	20	9606	12	26516	7	6029	15	14572	9	12431	10	9471	11	43170	4	7187	14	40901	5	42686	5	2633	21	20758	7	489347	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. 1356596. Birmensdorf, Swiss Federal Research Institute WSL

back