Selection from the list of topics

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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: canton
unit: 1000 m³
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
altitudinal vegetation belts (NaiS; 6 classes)	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.
	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	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.
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	.	130	41	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	33	73	4	.	0	.	120	38	0	.	0	.	295	25
	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	.	261	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	25	.	0	.	87	69	0	.	0	.	402	28
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1613	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	155	42	0	.	0	.	956	19	0	.	0	.	2724	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1032	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	462	20	0	.	0	.	1502	14
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	58
	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	.	1	73	0	.	0	.	1	73
	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	.	3052	13	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	217	36	38	.	0	.	1627	16	0	.	0	.	4941	10
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	.	3072	18	0	.	0	.	0	.	478	45	9577	9	0	.	36	97	0	.	131	65	438	37	542	39	0	.	0	.	145	64	0	.	1062	22	301	47	604	38	4479	12	0	.	0	.	20865	6
	fir	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	14	85	0	.	0	.	33	66	0	.	0	.	72	42
	pine	0	.	0	.	0	.	109	46	0	.	0	.	0	.	0	.	307	24	0	.	0	.	0	.	0	.	47	86	0	.	0	.	0	.	0	.	0	.	0	.	33	100	0	.	45	70	0	.	0	.	541	20
	larch	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	2294	13	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	586	21	37	100	17	93	2511	14	0	.	0	.	5471	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	216	36	0	.	0	.	0	.	0	.	21	100	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	206	36	0	.	0	.	454	24
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	12	67
	beech	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	6	94
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	94	0	.	0	.	0	.	18	75
	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	.	4	77	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	1	72	0	.	0	.	0	.	0	.	4	72	0	.	0	.	14	53	0	.	0	.	26	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	.	3219	17	0	.	0	.	0	.	478	45	12421	8	0	.	36	97	0	.	133	65	507	35	560	38	0	.	0	.	145	64	0	.	1671	17	372	50	634	37	7289	10	0	.	0	.	27466	5
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	.	6166	13	0	.	1502	25	0	.	531	49	17432	7	0	.	701	33	17	.	192	53	1527	24	1916	26	0	.	0	.	1275	25	0	.	2175	17	1812	23	2173	19	6322	10	49	.	0	.	43790	4
	fir	0	.	0	.	0	.	511	29	0	.	59	76	0	.	0	.	816	24	0	.	3	100	0	.	41	100	87	44	3	100	0	.	0	.	10	71	0	.	283	46	96	50	422	37	632	29	5	.	0	.	2968	13
	pine	0	.	0	.	0	.	22	70	0	.	0	.	0	.	0	.	1434	19	0	.	43	64	0	.	0	.	34	100	1	100	0	.	0	.	19	100	0	.	203	52	57	69	0	.	1282	20	0	.	0	.	3096	13
	larch	0	.	0	.	0	.	2	96	0	.	0	.	0	.	0	.	1622	17	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	781	23	0	.	32	54	2635	13	0	.	0	.	5084	9
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	7	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	13	61
	other conifers	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	63	0	.	6	100	0	.	0	.	10	65
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	43	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	0	0	.	73	54	0	.	0	.	0	.	0	.	0	.	103	40
	maple	0	.	0	.	0	.	136	45	0	.	2	100	0	.	0	.	72	39	0	.	0	.	0	.	11	100	4	100	0	.	0	.	0	.	1	100	0	.	17	100	0	.	87	41	24	62	0	.	0	.	353	23
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	59	0	.	0	.	27	55	0	.	0	.	142	35
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	70	0	.	0	.	101	88
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	73	0	.	0	.	0	.	0	.	0	.	35	60
	other broadleaves	0	.	0	.	0	.	51	36	0	.	3	58	0	.	0	.	335	22	0	.	1	102	0	.	2	75	0	.	1	100	0	.	0	.	11	59	0	.	218	35	9	64	37	47	297	23	0	.	0	.	964	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	.	1	.	0	.	6892	13	0	.	1566	25	0	.	531	49	21945	7	0	.	748	32	17	.	246	56	1651	23	1933	26	0	.	0	.	1316	24	0	.	3794	13	1976	22	2752	17	11236	8	54	.	0	.	56658	4
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	357	41	1537	26	2801	19	18981	7	375	41	5654	13	0	.	2100	22	1833	17	2265	17	6456	13	4403	12	620	36	2143	22	7963	12	422	37	918	21	4616	14	578	30	542	35	769	31	8944	9	746	27	1045	31	2745	17	78811	3
	fir	473	39	580	33	924	25	11215	8	247	46	2021	19	0	.	501	38	651	26	2400	16	3217	14	2956	13	274	42	657	32	3092	15	36	72	963	22	1443	17	335	46	656	29	342	43	3721	12	334	32	353	31	1649	20	39038	4
	pine	67	61	17	100	5	100	611	26	38	71	27	72	0	.	0	.	435	31	265	40	0	.	72	57	0	.	2	100	258	38	85	55	370	27	104	57	76	40	0	.	10	100	50	68	188	45	1	100	288	29	2970	11
	larch	66	84	7	100	44	100	133	39	16	71	0	.	0	.	22	100	195	33	0	.	76	98	19	100	0	.	6	100	355	43	8	100	0	.	23	100	19	100	666	26	0	.	113	51	275	54	30	100	87	55	2161	15
	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	13	67	0	.	2	100	126	58	0	.	0	.	0	.	1	100	0	.	1	100	61	96	1	100	2	100	0	.	91	91	0	.	10	55	1	100	3	100	0	.	0	.	91	90	0	.	0	.	30	50	431	35
	beech	715	27	222	39	769	27	6812	9	1215	23	1639	19	0	.	842	26	1039	25	2670	13	1394	18	1264	17	452	35	1063	31	2810	14	339	32	2652	15	1223	21	323	40	2437	11	228	39	3326	11	335	28	292	42	1640	18	35702	3
	maple	157	45	36	52	146	37	757	14	121	38	127	29	0	.	234	25	41	33	404	20	221	29	175	31	108	45	99	39	889	15	14	57	232	23	160	26	178	46	61	49	60	40	433	16	58	37	69	51	340	22	5117	6
	ash	147	35	100	86	208	34	516	22	25	57	241	29	0	.	153	47	8	75	360	23	152	29	54	55	152	44	46	50	633	22	48	64	116	34	154	35	78	40	37	57	49	64	251	27	64	48	53	76	460	23	4105	7
	oak	47	50	0	.	12	100	42	46	0	.	33	55	0	.	0	.	84	73	9	100	59	52	7	100	1	100	8	100	14	60	4	88	74	53	8	87	53	58	23	64	0	.	96	37	4	177	21	100	86	44	682	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	213	36	0	.	0	.	7	100	0	.	0	.	225	34
	other broadleaves	117	54	28	64	23	55	467	18	47	55	57	28	0	.	102	35	113	28	196	28	111	35	56	34	35	51	69	36	324	21	1	100	142	38	163	33	6	71	651	17	77	52	228	23	106	36	4	79	134	30	3255	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	2159	20	2527	23	4933	17	39661	5	2083	21	9800	11	0	.	3956	17	4401	13	8568	10	11746	10	9007	10	1645	25	4093	18	16429	8	956	29	5477	12	7894	12	1648	24	5286	9	1534	24	17254	7	2116	18	1868	25	7457	12	172498	2
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	4759	14	0	.	0	.	7155	11	464	43	2203	21	0	.	0	.	77	60	880	30	1528	22	257	45	31	100	137	64	1173	32	961	28	1841	23	65	83	3090	18	0	.	74	62	2179	19	82	71	85	70	5214	13	32254	5
	fir	1729	18	0	.	0	.	3244	15	132	37	54	61	0	.	0	.	14	84	568	36	1385	23	209	41	0	.	14	100	55	46	38	98	690	28	8	100	421	33	0	.	36	100	642	28	7	100	66	66	1286	25	10597	8
	pine	940	22	0	.	0	.	527	37	570	30	469	37	0	.	0	.	303	46	247	38	97	61	70	71	0	.	0	.	64	82	200	41	386	29	52	100	433	29	0	.	43	67	315	44	18	73	0	.	1235	19	5967	9
	larch	268	27	0	.	0	.	161	45	36	100	227	52	0	.	0	.	89	75	23	72	2	100	0	.	0	.	0	.	25	92	63	66	12	77	16	100	12	100	0	.	0	.	47	56	0	.	0	.	274	36	1256	17
	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	24	70	0	.	0	.	39	62	17	100	58	75	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	-1	100	17	93	0	.	0	.	1	100	0	.	0	.	14	100	0	.	0	.	17	72	201	31
	beech	4196	11	0	.	0	.	4514	12	1770	18	1341	23	0	.	125	.	165	58	1026	27	1463	23	131	57	41	98	171	62	887	27	921	19	2040	17	164	63	958	20	0	.	104	57	2098	16	77	52	2	75	2697	14	24891	5
	maple	412	19	0	.	0	.	315	26	209	27	69	54	0	.	0	.	8	81	68	38	44	45	12	73	20	71	3	100	62	40	41	42	202	35	20	100	80	37	0	.	0	.	194	29	9	55	16	71	316	25	2100	9
	ash	758	20	0	.	0	.	557	25	202	37	355	33	0	.	0	.	25	100	262	40	152	36	63	84	10	100	20	71	252	30	98	45	160	37	80	61	404	26	0	.	11	91	818	20	25	59	168	47	505	20	4923	8
	oak	860	19	0	.	0	.	644	17	257	32	191	34	0	.	0	.	0	.	365	38	117	54	88	45	0	.	0	.	177	54	316	33	203	30	22	100	433	27	0	.	23	72	1143	19	21	53	0	.	441	27	5300	8
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	89	10	100	0	.	0	.	51	74
	other broadleaves	732	24	0	.	0	.	398	22	258	33	208	39	0	.	0	.	11	72	177	31	75	43	85	51	58	100	25	65	222	35	26	49	192	29	30	59	367	26	0	.	17	53	399	27	168	57	17	54	422	24	3887	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	14677	8	0	.	0	.	17552	8	3915	14	5175	16	0	.	125	.	692	36	3616	16	4877	15	914	30	160	71	369	47	2916	20	2681	18	5726	13	457	45	6200	13	0	.	308	42	7891	10	415	32	352	46	12408	9	91427	3
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	.	2	75	0	.	134	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	74	59	0	.	0	.	136	42	0	.	78	78	441	31
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	11	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	25	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	.	99	71	0	.	0	.	357	25	0	.	75	100	577	24
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	74	0	.	0	.	105	60	0	.	0	.	136	48
	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	.	3	100	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	352	25	0	.	0	.	9	100	0	.	7	100	375	24
	maple	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	48	0	.	0	.	10	56	0	.	0	.	42	34
	ash	0	.	0	.	0	.	0	.	0	.	0	.	4	80	0	.	7	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	187	24	0	.	6	71	18	62	0	.	0	.	223	21
	oak	0	.	0	.	0	.	0	.	0	.	0	.	306	41	0	.	44	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	499	20	0	.	52	64	40	45	0	.	48	62	990	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	241	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3296	11	0	.	0	.	35	59	0	.	0	.	3572	11
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	21	71	0	.	207	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1254	13	0	.	78	85	105	47	0	.	45	59	1710	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	0	.	0	.	0	.	0	.	0	.	0	.	369	39	0	.	654	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	.	5811	8	0	.	136	57	815	20	0	.	253	63	8079	7
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	5116	13	1537	26	2801	19	35374	5	839	30	9359	10	2	75	3109	18	29193	5	3144	14	8720	11	4677	12	974	27	4252	15	11593	10	1384	22	2759	17	6101	12	3685	16	3886	12	2961	17	13900	7	11884	7	1179	28	8037	10	176466	2
	fir	2201	17	580	33	924	25	14982	7	378	32	2135	18	0	.	501	38	1492	17	2968	15	4605	12	3165	13	316	38	757	28	3151	14	74	61	1653	17	1461	17	756	27	964	24	474	34	4785	10	1005	21	424	28	2935	15	52687	3
	pine	1007	21	17	100	5	100	1269	20	608	28	496	35	25	65	0	.	2740	13	512	27	140	47	142	45	0	.	83	63	323	34	285	33	756	20	175	46	531	25	331	40	169	38	364	39	1977	15	1	100	1597	16	13553	6
	larch	334	27	7	100	44	100	317	29	52	73	227	52	0	.	22	100	5827	8	23	72	78	95	19	100	0	.	6	100	396	39	71	60	12	77	40	72	32	72	2206	13	37	100	210	32	6482	8	30	100	360	31	16833	5
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	1255	16	0	.	0	.	0	.	0	.	21	100	11	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	670	17	0	.	0	.	1969	12
	other conifers	37	52	1	100	2	100	164	47	17	100	58	75	0	.	1	100	26	47	1	100	75	80	1	100	2	100	0	.	91	92	17	93	10	55	1	100	4	81	0	.	3	63	105	79	9	77	0	.	47	41	670	24
	beech	4911	10	222	39	769	27	11327	7	2985	14	2980	15	3	100	967	26	1237	22	3696	12	2857	15	1394	16	494	33	1234	28	3698	13	1260	16	4693	11	1386	20	1281	18	2866	10	333	32	5424	9	420	24	294	42	4344	11	61077	3
	maple	569	18	36	52	146	37	1208	12	330	22	199	27	7	100	234	25	128	25	472	18	265	25	187	29	139	38	106	37	951	14	54	35	433	20	181	25	258	34	101	36	60	40	727	13	101	27	84	44	656	17	7630	4
	ash	905	17	100	86	208	34	1073	17	226	33	596	23	4	80	153	47	129	40	622	21	304	23	117	52	162	42	66	41	884	18	146	37	277	26	234	31	484	23	249	21	59	55	1075	17	134	29	221	40	965	15	9394	5
	oak	907	18	0	.	12	100	685	16	257	32	223	30	306	41	0	.	220	51	374	37	176	40	94	42	1	100	8	100	191	51	320	32	277	26	29	78	487	25	522	19	23	72	1292	17	74	31	21	100	575	22	7073	6
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	259	33	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	3528	11	0	.	41	89	52	46	0	.	0	.	3883	10
	other broadleaves	849	22	28	64	23	55	920	13	305	29	268	31	21	71	102	35	667	16	373	21	186	27	141	34	96	64	95	31	548	19	26	48	334	23	204	28	373	26	2127	10	103	40	743	18	691	19	20	46	601	19	9843	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	16836	7	2528	23	4933	17	67324	4	5997	12	16541	8	369	39	5089	15	43173	4	12184	8	17407	8	9938	9	2184	21	6628	13	21838	7	3638	15	11203	9	9812	10	7891	11	16779	4	4229	15	28667	5	23499	5	2274	22	20118	7	361078	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. 1372254. Birmensdorf, Swiss Federal Research Institute WSL

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