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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NFI3
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 2004/06
		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	.	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	.	190	30	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	64	46	4	100	0	.	156	32	0	.	0	.	419	19
	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	.	232	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	76	18	100	0	.	28	78	0	.	0	.	327	27
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2237	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	296	32	0	.	0	.	1402	17	0	.	0	.	3934	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1192	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	13	100	0	.	758	18	0	.	0	.	1965	12
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	51
	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	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	66	0	.	0	.	8	59
	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	.	0	.	0	0
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3875	12	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	411	27	34	100	0	.	2351	14	0	.	0	.	6676	9
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	.	3720	16	0	.	45	100	0	.	535	44	12107	8	0	.	53	84	0	.	214	55	557	37	416	40	0	.	0	.	183	66	0	.	1360	21	294	46	826	33	5322	12	0	.	0	.	25630	5
	fir	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	22	77	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	56	54	0	.	0	.	26	61	0	.	0	.	111	35
	pine	0	.	0	.	0	.	106	45	0	.	0	.	0	.	0	.	325	21	0	.	0	0	0	.	0	.	62	83	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	84	53	0	.	0	.	577	18
	larch	0	.	0	.	0	.	12	85	0	.	0	.	0	.	0	.	2874	12	0	.	0	.	0	.	0	.	0	.	63	78	0	.	0	.	0	.	0	.	1200	18	13	100	56	83	3640	14	0	.	0	.	7857	8
	Arolla pine	0	.	0	.	0	.	20	73	0	.	0	.	0	.	0	.	265	36	0	.	0	.	0	.	0	.	47	100	16	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	193	35	0	.	0	.	546	23
	other conifers	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	34	48	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	5	84	0	.	0	.	61	40
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	93	0	.	0	.	0	.	0	.	0	.	11	93
	maple	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	9	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	15	72	0	.	0	.	0	.	45	45
	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	.	21	57	0	.	0	.	0	.	0	.	25	55	0	.	0	.	0	.	0	.	2	100	5	63	0	.	0	.	0	.	0	.	9	72	2	100	1	100	51	35	0	.	0	.	116	23
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	0	.	0	.	0	.	3908	15	0	.	45	100	0	.	535	44	15661	7	0	.	53	84	0	.	216	55	669	35	502	35	0	.	0	.	186	65	0	.	2652	15	309	44	898	33	9321	10	0	.	0	.	34955	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	.	11	100	0	.	6837	15	0	.	1814	24	0	.	666	47	18507	7	0	.	885	31	0	.	142	55	1616	24	1031	31	0	.	0	.	1131	26	0	.	2841	16	2174	24	2836	18	7426	10	0	0	0	.	47915	4
	fir	0	.	0	.	0	.	781	28	0	.	116	63	0	.	0	.	937	22	0	.	6	100	0	.	2	100	137	45	5	100	0	.	0	.	12	71	0	.	337	40	102	52	492	39	816	27	0	.	0	.	3744	12
	pine	0	.	2	100	0	.	30	66	0	.	0	.	0	.	0	.	1756	18	0	.	50	62	0	.	0	.	33	100	3	100	0	.	0	.	39	100	0	.	188	65	85	74	0	.	1274	18	0	.	0	.	3461	12
	larch	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	2218	14	0	.	0	.	0	.	0	.	0	.	19	81	0	.	0	.	0	.	0	.	1304	20	0	.	57	52	3420	12	0	.	0	.	7029	8
	Arolla pine	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	5	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	66
	other conifers	0	.	3	100	0	.	8	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	5	72	0	.	0	100	0	.	0	.	17	44
	beech	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	52	45	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	101	51	0	.	0	.	2	100	0	.	0	.	163	35
	maple	0	.	0	.	0	.	196	38	0	.	13	72	0	.	3	100	144	31	0	.	2	100	0	.	16	72	5	83	0	.	0	.	0	.	2	71	0	.	43	78	0	.	189	38	74	32	0	.	0	.	687	18
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	203	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	48	0	.	0	.	138	40	0	.	0	.	410	26
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	16	55	0	.	0	.	123	79
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	73	0	.	0	.	0	.	0	.	0	.	42	73
	other broadleaves	0	.	4	100	0	.	58	31	0	.	6	53	0	.	3	80	758	17	0	.	5	60	0	.	3	71	2	75	2	100	0	.	0	.	15	64	0	.	523	24	22	63	47	45	581	18	0	.	0	.	2031	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	0	.	0	0	0	0	0	.	0	.	0	0
	total	0	.	20	100	0	.	7927	14	0	.	1948	24	0	.	672	47	24686	6	0	.	947	29	0	.	163	55	1795	23	1068	30	0	.	0	.	1200	25	0	.	5450	11	2388	23	3621	17	13748	8	0	0	0	.	65633	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	368	35	1884	25	3108	19	16992	7	284	37	4930	13	0	.	2275	26	2465	17	2972	19	6058	14	4500	12	539	39	1799	24	8255	11	412	43	914	29	4713	15	865	28	697	33	715	30	9808	9	981	23	1126	30	2557	18	79218	3
	fir	563	40	484	34	1378	25	12612	8	428	42	2375	19	0	.	546	36	806	27	2797	16	3813	14	3609	14	184	51	508	38	3869	14	58	67	1029	20	1580	19	504	44	891	27	350	36	5093	12	480	32	484	32	1535	20	45975	4
	pine	59	63	24	100	8	100	665	25	25	65	27	72	0	.	0	.	398	33	333	40	8	100	107	55	0	.	3	100	273	43	50	59	299	30	60	68	96	46	0	.	18	79	29	64	236	46	0	.	302	30	3019	11
	larch	87	96	9	79	74	100	84	52	20	80	0	.	0	.	29	100	278	37	0	.	119	91	24	100	0	.	15	100	302	47	11	100	0	.	30	100	15	100	694	23	0	.	155	48	324	57	32	100	79	59	2380	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	7	77	0	.	3	80	202	50	0	.	0	.	0	.	1	100	0	.	4	63	120	86	4	100	4	100	2	100	169	87	0	.	22	49	2	100	5	100	0	.	0	.	179	56	8	100	0	.	30	47	761	30
	beech	1052	26	245	37	882	29	8830	9	1526	25	2044	19	0	.	1148	24	1362	18	3209	13	1755	18	1532	15	338	34	968	26	3771	13	561	33	2927	14	1435	20	401	39	4555	13	314	41	4289	10	540	25	324	37	1601	16	45608	3
	maple	96	39	101	44	131	42	1234	12	188	36	295	24	0	.	397	26	74	35	526	19	353	26	330	28	90	30	177	35	1056	16	34	73	336	23	264	24	270	42	108	43	120	36	726	16	139	31	87	45	365	21	7498	5
	ash	207	33	51	73	261	39	858	22	81	49	438	29	0	.	446	58	55	54	515	22	221	26	84	45	262	59	46	49	950	19	24	100	224	36	380	36	255	40	86	37	105	55	384	26	113	45	59	69	667	22	6771	8
	oak	61	49	0	.	13	100	46	47	0	.	42	50	0	.	0	.	133	64	35	80	90	47	9	100	2	100	11	100	17	72	4	100	136	52	9	100	89	54	82	70	0	.	163	37	41	56	20	100	101	39	1104	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	245	42	0	.	0	.	14	100	0	.	0	.	261	39
	other broadleaves	123	47	13	67	37	72	760	23	63	40	62	36	0	.	226	60	264	33	260	29	124	36	87	39	38	53	68	43	374	23	9	72	177	38	158	29	6	74	1119	16	87	50	289	28	306	33	4	76	155	36	4809	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	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	2623	19	2810	22	5895	17	42285	5	2614	22	10213	11	0	.	5068	19	5838	12	10651	10	12660	10	10285	9	1459	26	3595	19	19036	8	1164	29	6064	12	8630	12	2504	22	8476	9	1708	22	21115	7	3183	16	2136	24	7390	12	197403	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	3192	16	0	.	0	.	5976	12	553	44	1080	27	0	.	0	.	96	49	720	31	1301	23	242	48	10	100	95	59	915	29	1173	26	1603	22	83	69	2363	20	0	.	127	55	1770	22	35	72	19	64	4379	14	25732	5
	fir	1608	20	0	.	0	.	3180	17	178	45	38	53	0	.	0	.	39	69	622	36	1333	22	220	43	2	100	49	100	113	45	50	66	608	30	15	100	666	34	0	.	98	76	754	28	8	86	55	61	804	29	10438	8
	pine	733	27	0	.	0	.	386	45	513	33	317	44	0	.	0	.	179	50	215	41	112	55	91	75	0	.	0	.	85	85	184	38	206	34	63	100	332	34	0	.	36	70	248	40	11	71	0	.	1007	21	4717	10
	larch	292	35	0	.	0	.	175	40	0	.	130	70	0	.	0	.	58	76	25	71	1	100	0	.	0	.	5	100	53	57	69	67	48	60	22	100	47	60	0	.	0	.	265	42	0	.	0	.	208	41	1400	16
	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	97	42	0	.	0	.	123	49	122	77	37	100	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	-6	156	65	71	0	.	1	100	31	87	0	.	0	.	24	74	0	100	0	.	32	73	553	26
	beech	4575	12	0	.	0	.	4323	13	1883	19	1687	23	0	.	8	100	63	55	766	27	1359	26	114	47	26	101	194	83	1356	26	1297	19	2469	16	170	54	1194	20	0	.	105	59	2729	15	165	44	30	74	2761	15	27274	5
	maple	501	17	0	.	0	.	489	23	247	28	87	38	0	.	1	100	8	74	85	36	77	42	22	59	6	80	9	63	139	33	32	51	279	33	14	100	170	28	0	.	17	100	329	23	29	45	23	71	454	21	3018	8
	ash	757	21	0	.	0	.	667	20	439	37	602	31	0	.	1	100	84	71	371	41	535	54	18	134	11	100	7	87	552	27	106	40	217	35	130	74	727	24	0	.	13	77	980	21	43	67	172	56	738	21	7169	8
	oak	976	25	0	.	0	.	650	18	235	40	218	35	0	.	0	.	0	.	206	37	196	59	96	59	0	.	0	.	253	65	318	37	167	32	37	98	482	36	0	.	87	59	1709	20	42	58	0	.	435	25	6107	9
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	78	67	74	0	.	0	.	126	54
	other broadleaves	783	34	0	.	0	.	544	24	234	34	205	50	0	.	0	.	29	57	122	33	96	49	14	54	70	100	17	67	192	37	58	59	236	32	33	69	412	27	0	.	51	57	853	25	126	45	18	70	337	29	4431	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	.	0	0	0	0	0	.	0	.	0	0
	total	13514	9	0	.	0	.	16512	8	4404	15	4402	16	0	.	9	100	556	35	3132	17	5036	17	817	30	126	75	375	54	3652	18	3352	16	5832	13	568	45	6423	13	0	.	534	44	9719	11	527	29	316	52	11156	10	90964	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	.	15	100	0	.	126	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	99	46	0	.	0	.	164	52	0	.	77	89	492	32
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	16	100	0	.	0	.	8	83	0	.	0	.	27	67
	pine	0	.	0	.	0	.	0	.	0	.	0	.	64	81	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	122	74	0	.	0	.	387	25	0	.	33	100	637	23
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	51	0	.	0	.	151	51	0	.	0	.	184	43
	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	.	23	100	0	.	0	.	0	.	0	.	0	.	23	100
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	617	25	0	.	0	.	14	100	0	.	12	100	693	23
	maple	0	.	0	.	0	.	0	.	0	.	0	.	7	81	0	.	23	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	58	0	.	0	.	26	57	0	.	0	.	150	40
	ash	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	113	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	507	23	0	.	10	100	16	58	0	.	0	.	695	18
	oak	0	.	0	.	0	.	0	.	0	.	0	.	423	41	0	.	70	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	757	18	0	.	91	62	125	36	0	.	47	64	1515	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	314	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4410	11	0	.	0	.	31	62	0	.	0	.	4755	10
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	32	58	0	.	380	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1852	12	0	.	13	100	142	49	0	.	36	86	2455	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	0	.	0	.	0	0
	total	0	.	0	.	0	.	0	.	0	.	0	.	565	39	0	.	1065	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	100	8530	7	0	.	114	59	1064	20	0	.	206	72	11627	6
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	3560	15	1894	25	3108	19	33524	5	837	31	7869	10	15	100	3476	20	33490	5	3692	16	8297	11	4742	12	905	28	4072	15	10617	10	1585	22	2517	17	6110	12	3240	16	5061	11	3312	17	15240	7	14085	7	1144	29	7014	11	179406	2
	fir	2170	18	484	34	1378	25	16579	7	605	32	2528	18	0	.	546	36	1804	16	3419	15	5152	12	3829	13	191	49	694	30	3987	13	108	47	1638	17	1607	18	1172	27	1301	21	550	28	6339	10	1338	20	539	30	2338	16	60295	3
	pine	792	25	26	93	8	100	1186	21	537	31	344	41	64	81	0	.	2913	13	549	29	170	41	198	45	0	.	99	63	361	38	234	32	504	23	163	52	436	28	359	44	157	46	277	37	2021	14	0	.	1342	17	12739	6
	larch	379	34	9	79	74	100	281	30	20	80	130	70	0	.	29	100	7666	8	25	71	120	90	24	100	0	.	20	80	438	35	80	59	48	60	52	72	61	52	3526	10	13	100	533	27	8937	8	32	100	287	34	22785	4
	Arolla pine	0	.	0	.	0	.	27	60	0	.	0	.	0	.	0	.	1462	15	0	.	0	.	0	.	0	.	47	100	16	100	0	.	0	.	0	.	0	.	6	74	13	100	0	.	950	16	0	.	0	.	2522	11
	other conifers	103	40	3	100	3	80	351	34	122	77	37	100	0	.	1	100	56	36	4	63	146	73	4	100	4	100	2	100	164	89	65	71	22	49	5	64	37	76	23	98	5	72	203	50	14	64	0	.	62	44	1437	19
	beech	5628	11	245	37	882	29	13154	8	3409	15	3731	15	0	.	1156	24	1492	17	3975	12	3114	15	1646	15	364	32	1162	26	5136	12	1858	16	5396	10	1604	19	1628	17	5284	12	419	34	7018	9	722	21	354	34	4374	11	73749	2
	maple	597	16	101	44	131	42	1927	10	435	22	395	20	7	81	401	26	259	22	611	17	432	22	353	26	113	27	191	33	1195	15	66	45	615	19	280	23	439	28	257	31	138	34	1259	12	268	20	110	38	819	15	11398	4
	ash	964	18	51	73	261	39	1524	15	521	32	1040	22	24	100	447	58	455	26	886	22	756	39	102	44	273	57	53	45	1502	15	131	37	441	25	509	32	1006	20	662	19	118	50	1374	16	310	26	230	45	1405	15	15045	5
	oak	1037	24	0	.	13	100	696	17	235	40	261	30	423	41	0	.	309	44	240	33	286	43	105	54	2	100	11	100	271	61	322	37	303	29	46	81	573	31	840	18	87	59	1963	18	224	25	20	100	583	20	8849	7
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	317	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4696	10	0	.	59	78	112	49	0	.	0	.	5184	9
	other broadleaves	906	30	17	57	37	72	1383	16	297	28	273	39	32	58	229	59	1458	13	382	23	225	29	101	34	112	65	90	35	573	19	66	52	413	25	207	25	418	26	3503	8	163	33	1203	19	1213	14	22	58	528	22	13850	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	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	16137	8	2830	22	5895	17	70632	4	7019	12	16608	8	565	39	6284	16	51680	4	13783	8	18697	8	11103	9	1964	21	6439	13	24258	7	4516	14	11896	9	10584	10	9010	11	25519	4	4974	14	35467	5	30193	5	2452	22	18752	7	407258	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. 1372294. Birmensdorf, Swiss Federal Research Institute WSL

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