Selection from the list of topics

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number of stems #73
Number of stems of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm.

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

canton #827
Regional demarcation with the cantons as a unit, with the two half-cantons, Basel-Land and Basel-Stadt, combined into one canton for statistical reasons.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

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
number of stems
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: canton
unit: 1000 n
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
altitudinal vegetation belts (NaiS; 6 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	±%	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	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	.
	fir	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	.
	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	.
	larch	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	.
	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	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	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	.
	maple	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	.
	ash	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	.
	oak	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	.
	sweet chestnut	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 broadleaves	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	.
	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	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	.
upper subalpine	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	321	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	100	0	.	0	.	313	46	0	.	0	.	757	32
	fir	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	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1287	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	100	0	.	0	.	466	71	0	.	0	.	1879	33
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1706	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	70	0	.	0	.	1423	28	0	.	0	.	3214	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1831	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	634	30	0	.	0	.	2465	18
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	78
	beech	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	.
	maple	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	.
	ash	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	.
	oak	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	.
	sweet chestnut	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 broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	18	100
	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5214	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	334	57	0	.	0	.	2854	24	0	.	0	.	8402	13
subalpine	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	0	.	0	.	0	.	5535	20	0	.	0	.	0	.	157	61	11832	12	0	.	22	.	0	.	715	85	1059	56	1018	51	0	.	0	.	184	74	0	.	1462	30	589	49	420	46	6311	18	0	.	0	.	29304	8
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100
	pine	0	.	0	.	0	.	248	71	0	.	0	.	0	.	0	.	763	44	0	.	0	.	0	.	0	.	419	91	0	.	0	.	0	.	0	.	0	.	0	.	73	100	0	.	45	62	0	.	0	.	1548	35
	larch	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	2149	20	0	.	0	.	0	.	0	.	0	.	73	100	0	.	0	.	0	.	0	.	1145	25	28	100	14	100	2325	18	0	.	0	.	5770	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	368	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	441	39	0	.	0	.	809	28
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	100	0	.	0	.	87	100
	beech	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	0	.	0	.	0	.	0	.	0	.	75	77
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100	0	.	0	.	0	.	106	74
	ash	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	.
	oak	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	.
	sweet chestnut	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 broadleaves	0	.	0	.	0	.	88	67	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	44	100	40	71	0	.	0	.	0	.	0	.	21	100	0	.	0	.	97	56	0	.	0	.	289	34
	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	0	.	0	.	0	.	5928	19	0	.	0	.	0	.	157	61	15147	11	0	.	22	.	0	.	737	86	1522	49	1130	48	0	.	0	.	184	74	0	.	2682	22	690	49	505	46	9305	15	0	.	0	.	38009	7
high-montane	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	0	.	0	.	0	.	6222	16	0	.	2493	38	0	.	611	58	18431	10	0	.	1549	48	76	.	134	.	2070	33	1210	43	0	.	0	.	1760	50	0	.	2635	23	2843	31	2003	30	10170	14	96	.	0	.	52304	6
	fir	0	.	0	.	0	.	779	41	0	.	79	100	0	.	0	.	611	34	0	.	7	100	0	.	0	.	186	70	17	100	0	.	0	.	22	100	0	.	390	57	160	62	552	48	877	37	44	.	0	.	3725	17
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2315	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	100	0	.	148	64	236	75	0	.	3496	27	0	.	0	.	6298	18
	larch	0	.	0	.	0	.	36	71	0	.	0	.	0	.	0	.	1358	24	0	.	0	.	0	.	0	.	0	.	88	100	0	.	0	.	0	.	0	.	1237	30	0	.	95	81	2883	20	0	.	0	.	5696	13
	Arolla pine	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	44	60
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	75	0	.	17	100	0	.	0	.	94	64
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	204	62	0	.	0	.	0	.	0	.	0	.	254	51
	maple	0	.	0	.	0	.	377	51	0	.	22	100	0	.	0	.	169	51	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	136	51	274	65	0	.	0	.	985	29
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	605	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	100	0	.	0	.	170	59	0	.	0	.	856	48
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	18	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	72	0	.	0	.	0	.	0	.	0	.	43	72
	other broadleaves	0	.	0	.	0	.	182	38	0	.	15	100	0	.	0	.	2413	24	0	.	37	72	0	.	0	.	0	.	15	100	0	.	0	.	103	87	0	.	503	42	35	71	149	60	1446	28	0	.	0	.	4899	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	0	.	0	.	0	.	7604	16	0	.	2609	37	0	.	611	58	25968	9	0	.	1593	47	76	.	134	.	2263	33	1331	40	0	.	0	.	1988	50	0	.	5242	17	3352	29	2935	30	19370	11	140	.	0	.	75217	5
lower and upper montane	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	181	66	2049	30	3208	29	15558	9	217	50	6868	18	0	.	2563	31	2337	24	2111	37	6799	16	4378	21	814	44	1623	32	6557	17	1129	49	1336	32	3739	19	1148	45	554	46	759	39	8231	13	744	36	707	46	2759	19	76368	4
	fir	372	58	529	42	772	36	10557	10	291	54	2131	27	0	.	349	46	446	36	2835	23	3425	19	3445	19	223	50	807	37	2395	17	38	84	1223	28	1558	24	189	46	464	42	881	39	4139	15	360	46	219	43	1068	23	38716	5
	pine	75	61	0	.	8	100	860	33	39	100	7	100	0	.	0	.	138	60	347	48	0	.	101	77	0	.	0	.	49	87	101	69	246	32	6	100	55	43	0	.	0	.	131	77	653	57	0	.	350	35	3166	17
	larch	70	79	0	.	0	.	81	46	24	77	0	.	0	.	7	100	222	46	0	.	17	100	7	100	0	.	0	.	37	70	17	100	0	.	15	100	0	.	539	34	0	.	83	61	89	58	14	100	7	100	1231	19
	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	53	58	0	.	18	100	85	55	0	.	0	.	0	.	18	100	0	.	17	100	70	100	17	100	0	.	0	.	135	61	0	.	103	52	18	100	0	.	0	.	0	.	151	52	0	.	0	.	221	72	907	25
	beech	750	40	423	44	620	32	13000	10	1136	36	2544	23	0	.	1492	28	2248	24	3188	20	2386	24	2143	19	731	40	2238	33	4947	19	1146	42	3425	20	1427	23	341	39	7893	17	638	43	6060	12	1349	36	196	63	2211	22	62533	4
	maple	156	68	36	85	376	50	2144	17	233	54	419	33	0	.	533	37	258	43	465	26	553	24	536	42	218	36	309	44	2104	19	41	85	437	26	437	32	283	55	74	62	277	44	1332	21	276	47	39	73	763	29	12300	7
	ash	83	68	0	.	489	50	1101	26	59	89	829	35	0	.	531	57	53	100	614	34	423	48	194	90	665	60	84	69	1497	26	111	94	286	50	411	51	269	63	19	100	160	72	823	28	203	50	0	.	809	30	9712	10
	oak	43	62	0	.	0	.	61	52	0	.	147	64	0	.	0	.	46	72	7	100	34	72	7	100	22	100	0	.	17	100	35	70	47	58	6	100	99	62	71	61	0	.	142	43	90	53	0	.	104	57	978	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	46	0	.	0	.	0	.	0	.	0	.	162	45
	other broadleaves	267	74	30	71	103	71	1298	18	142	61	563	39	0	.	318	45	714	39	643	39	523	32	344	48	155	54	341	58	764	27	17	100	338	37	754	57	66	71	3269	22	397	76	880	21	314	50	18	100	415	34	12674	9
	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	2049	31	3068	29	5593	24	44744	7	2141	30	13508	14	0	.	5811	22	6516	17	10228	15	14229	13	11172	13	2827	29	5403	22	18502	12	2636	35	7439	16	8371	16	2450	31	12991	13	3112	27	21974	9	4080	24	1194	38	8708	15	218745	3
submontane	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	4570	18	0	.	0	.	7142	17	757	46	1626	30	0	.	0	.	186	61	1473	40	2084	24	448	46	0	.	83	100	663	55	1213	42	1139	34	0	.	2688	24	0	.	111	66	2518	28	28	100	0	.	4688	17	31419	7
	fir	1450	28	0	.	0	.	3542	19	164	49	24	77	0	.	0	.	149	67	613	46	2288	28	488	41	0	.	121	100	0	.	17	100	476	48	7	100	579	35	0	.	259	100	1219	39	0	.	33	100	662	31	12091	10
	pine	563	31	0	.	0	.	342	42	367	46	378	47	0	.	0	.	430	50	42	71	307	73	59	100	0	.	0	.	0	.	424	58	515	42	0	.	197	48	0	.	134	80	350	60	0	.	0	.	679	24	4786	13
	larch	228	40	0	.	0	.	167	66	21	100	79	77	0	.	0	.	167	81	7	100	7	100	0	.	0	.	0	.	10	100	35	100	0	.	0	.	86	100	0	.	0	.	13	71	0	.	0	.	201	43	1021	24
	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	50	100	0	.	0	.	135	50	209	100	57	100	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	46	100	35	100	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	7	100	582	41
	beech	5185	15	0	.	0	.	5880	14	2067	26	1871	27	0	.	0	.	550	49	1074	37	1796	24	300	52	0	.	24	100	1211	45	1924	28	2024	25	186	73	1339	24	0	.	564	60	2328	24	465	67	37	71	2507	17	31332	6
	maple	806	23	0	.	0	.	569	28	269	36	0	.	0	.	0	.	29	78	160	49	74	60	17	100	0	.	0	.	128	57	122	87	420	32	68	100	200	42	0	.	0	.	716	38	18	100	83	100	724	32	4402	11
	ash	882	28	0	.	0	.	847	26	304	51	557	44	0	.	0	.	28	100	449	54	269	40	150	71	0	.	0	.	315	47	181	61	326	50	152	60	569	33	0	.	18	100	1254	31	18	100	160	74	659	27	7137	10
	oak	339	32	0	.	0	.	707	28	329	48	179	47	0	.	0	.	0	.	277	52	262	47	235	68	0	.	0	.	143	52	248	39	361	62	0	.	488	29	0	.	55	65	1516	32	74	79	0	.	349	32	5561	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	15	100
	other broadleaves	1146	25	0	.	0	.	1588	29	485	36	351	47	0	.	0	.	65	77	503	46	176	49	486	66	0	.	0	.	933	45	121	55	357	38	178	66	1131	33	0	.	229	66	1639	31	268	64	15	71	850	31	10522	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	15217	11	0	.	0	.	20919	10	4971	21	5121	21	0	.	0	.	1603	37	4598	22	7289	18	2183	30	0	.	229	100	3449	30	4321	25	5618	19	591	50	7294	16	0	.	1371	54	11553	15	886	46	329	74	11326	12	108868	4
hyperinsubric and colline	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	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	100	65	0	.	0	.	310	56	0	.	48	100	492	40
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	8	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	255	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	.	0	.	0	.	0	.	1403	36	0	.	152	100	1830	31
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	78	0	.	0	.	186	53	0	.	0	.	213	47
	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	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1442	31	0	.	0	.	10	100	0	.	0	.	1459	31
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	55	0	.	0	.	23	100	0	.	0	.	160	49
	ash	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	790	29	0	.	17	100	125	64	0	.	0	.	1023	24
	oak	0	.	0	.	0	.	0	.	0	.	0	.	1177	52	0	.	58	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	1974	28	0	.	586	60	303	56	0	.	48	71	4163	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1014	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10848	14	0	.	0	.	160	66	0	.	0	.	12023	13
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	914	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6267	15	0	.	152	71	622	40	0	.	211	72	8201	13
	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	0	.	0	.	0	.	0	.	0	.	0	.	1521	47	0	.	2034	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	.	21592	10	0	.	754	60	3142	27	0	.	459	75	29571	8
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	4752	17	2049	30	3208	29	34457	7	974	37	10987	15	20	100	3331	26	33106	7	3584	27	10454	14	4902	19	1663	43	4837	22	9448	15	2342	32	2475	23	5682	20	3850	21	4874	16	4302	22	13173	11	17877	10	804	43	7495	13	190643	3
	fir	1822	25	529	42	772	36	14878	9	455	39	2234	26	0	.	349	46	1206	23	3448	21	5719	16	3933	18	245	47	1115	31	2412	17	56	65	1699	24	1588	23	768	29	862	34	1300	34	5911	14	1237	29	296	37	1730	18	54562	4
	pine	638	28	0	.	8	100	1450	25	406	42	385	46	255	88	0	.	4934	17	389	44	307	73	160	61	0	.	419	91	49	87	525	49	761	30	110	95	272	36	275	58	442	50	480	48	6063	19	0	.	1182	22	19508	9
	larch	298	36	0	.	0	.	321	39	45	62	79	77	0	.	7	100	5602	12	7	100	24	77	7	100	0	.	0	.	208	57	52	74	0	.	15	100	86	100	3033	16	28	100	205	46	6905	12	14	100	208	42	17144	7
	Arolla pine	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	2216	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1093	23	0	.	0	.	3318	15
	other conifers	102	57	0	.	18	100	220	37	209	100	57	100	0	.	18	100	70	78	17	100	96	77	17	100	0	.	0	.	181	52	35	100	103	52	18	100	17	100	0	.	77	75	151	52	104	85	0	.	228	70	1740	20
	beech	5935	14	423	44	620	32	18902	8	3203	21	4415	17	0	.	1492	28	2854	21	4262	17	4182	17	2443	18	731	40	2263	33	6158	17	3070	23	5448	16	1612	22	1681	21	9593	15	1203	36	8388	11	1824	31	234	54	4717	14	95653	3
	maple	962	22	36	85	376	50	3090	14	502	31	441	32	0	.	533	37	492	30	625	23	627	22	553	40	218	36	316	43	2231	18	163	68	856	20	505	31	483	37	211	42	277	44	2255	18	591	37	122	72	1487	21	17953	6
	ash	964	26	0	.	489	50	1948	19	363	45	1386	27	34	100	531	57	725	54	1063	30	692	33	343	60	665	60	84	69	1812	23	292	52	612	35	563	40	856	29	890	27	178	65	2094	22	515	32	160	74	1468	20	18728	7
	oak	381	29	0	.	0	.	768	26	329	48	326	39	1177	52	0	.	105	64	284	51	296	43	242	66	22	100	0	.	160	48	283	35	408	55	6	100	604	26	2045	28	55	65	2244	27	485	38	0	.	501	26	10720	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1067	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10999	14	0	.	0	.	175	61	0	.	0	.	12242	13
	other broadleaves	1413	24	30	71	103	71	3155	17	627	31	929	29	34	100	318	45	4106	19	1146	29	737	26	830	44	155	54	385	53	1752	27	139	50	696	26	1035	44	1197	31	10059	12	662	51	2819	20	2766	19	33	63	1476	23	36602	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17266	10	3068	29	5593	24	79196	5	7112	17	21237	11	1521	47	6579	20	56483	6	14826	12	23133	10	13431	12	3698	28	9418	17	24412	10	6957	20	13057	12	11134	15	9814	14	42841	6	8524	18	37720	8	39636	7	1663	32	20493	9	478813	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. 1325801. Birmensdorf, Swiss Federal Research Institute WSL

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