Selection from the list of topics

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volume of deadwood (stemwood) #24
Volume of stemwood of all dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

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)

higher/lower altitude zone #2635
Areas in higher and lower altitude zones classified according to the system used for altitudinal vegetation belts in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005). The boundary between the higher and lower altitudes runs between the «upper montane» and «lower montane» levels on the Northern Alps and between the «high montane» and «upper/lower montane» levels to the Southern Alps. 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).

forest district (2024) #2777
Regional demarcation with the forest districts as a unit. This variable was implemented in the year 2024 and is based on a survey with the cantonal forest services in the years 2022/2023.

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
volume of deadwood (stemwood)
main tree species · higher/lower altitude zone
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
main tree species	higher/lower altitude zone	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	±%	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	±%	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	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	36	54	64	44	41	67	141	56	0	0	44	52	94	47	138	37	82	38	33	43	10	93	8	78	30	56	3	.	75	51	66	53	63	60	48	53	0	0	47	43	0	0	26	69	0	0	39	54	0	.	106	34	97	57	59	58	42	46	0	0	0	0	1	100	0	0	0	0	13	100	0	0	3	64	256	64	19	94	34	81	24	77	103	95	57	47	48	45	16	63	10	91	25	56	12	100	0	.	23	75	11	77	206	77	12	66	0	0	46	75	0	.	0	.	28	89	7	100	1	100	1	100	14	71	0	0	0	0	0	0	4	100	6	100	0	0	40	54	38	60	3	100	16	100	8	100	0	0	2	100	0	0	17	58	4	100	5	100	0	0	20	60	22	75	4	100	19	77	3	71	35	68	80	49	78	36	52	49	20	55	39	100	25	55	0	.	2901	11
	higher altitude zone	0	.	0	.	0	.	0	.	104	47	160	54	2063	15	289	30	7	76	96	34	0	.	0	.	0	.	0	.	8	60	111	43	446	45	120	51	0	.	334	31	353	24	338	29	539	21	735	19	311	23	39	70	34	68	40	82	401	37	7	100	6	74	0	0	0	0	19	57	102	42	234	36	186	37	0	.	61	47	307	33	94	52	194	47	0	.	22	100	20	61	0	.	10	100	79	48	344	37	107	49	0	.	0	.	0	.	75	58	152	32	67	46	0	.	0	.	0	.	25	71	16	87	12	100	71	63	38	82	227	36	110	47	110	38	168	32	39	65	0	.	117	41	0	.	2	100	34	39	9	67	18	90	82	62	8	100	0	.	30	100	0	.	560	18	276	27	634	19	19	87	3	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	11222	5
	total	36	54	64	44	41	67	141	56	104	47	204	44	2156	14	427	24	89	35	129	28	10	93	8	78	30	56	3	.	84	47	177	33	510	40	168	40	0	0	381	28	353	24	364	27	539	21	774	18	311	23	145	31	132	46	99	48	443	34	7	100	6	74	1	100	0	0	19	57	116	39	234	36	189	36	256	64	79	42	341	31	118	44	298	45	57	47	70	44	36	44	10	91	35	49	90	44	344	37	130	43	11	77	206	77	12	66	75	58	198	30	67	46	0	.	28	89	7	100	25	69	16	84	26	60	71	63	38	82	227	36	114	45	116	36	168	32	79	42	38	60	120	40	16	100	11	80	34	39	11	57	18	90	99	53	11	75	5	100	30	100	20	60	582	18	280	27	653	18	22	75	37	63	80	49	78	36	52	49	20	55	39	100	25	55	0	.	14122	5
fir	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	43	51	0	0	3	100	83	50	0	0	47	63	31	87	108	35	85	29	54	57	13	89	0	0	40	49	0	.	0	0	3	100	46	58	25	67	0	.	7	60	0	0	26	80	0	.	36	63	0	.	164	30	33	69	60	43	86	65	0	0	0	0	6	72	0	0	1	100	0	0	0	.	11	100	34	96	0	0	0	0	16	100	15	64	0	0	1	100	12	85	0	0	113	66	6	78	0	.	0	0	14	90	0	0	0	0	0	.	10	100	0	.	0	.	0	.	0	0	31	71	38	100	1	100	0	0	21	100	0	0	42	66	0	0	0	0	21	100	0	0	31	79	1	100	2	82	0	.	2	100	18	80	0	0	0	0	13	78	7	100	0	0	0	0	0	.	69	66	0	0	0	0	16	93	57	59	12	91	0	0	0	0	0	0	0	.	1613	11
	higher altitude zone	0	.	0	.	0	.	0	.	0	0	21	68	60	40	182	35	5	100	56	43	0	.	0	.	0	.	0	.	3	72	3	78	54	65	24	62	0	.	26	80	41	62	32	52	29	99	26	53	4	100	32	48	1	100	1	100	65	59	10	80	5	87	0	0	0	0	1	100	31	74	38	54	47	76	0	.	8	90	14	63	9	100	17	73	0	.	29	96	1	100	0	.	0	.	22	65	23	72	29	68	0	.	0	.	0	.	0	.	5	100	8	100	0	.	0	.	0	.	0	0	0	0	0	0	21	56	0	0	23	71	1	100	54	85	103	55	34	100	0	.	2	100	0	.	15	100	5	100	4	100	6	63	0	0	0	0	0	.	0	0	0	.	3	64	4	100	78	48	21	92	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	1336	11
	total	43	51	0	0	3	100	83	50	0	0	67	49	92	40	290	25	91	28	110	35	13	89	0	0	40	49	0	.	3	72	6	62	100	44	49	46	0	.	33	64	41	62	58	46	29	99	62	43	4	100	196	26	34	67	62	42	151	45	10	80	5	87	6	72	0	0	2	73	31	74	38	54	58	64	34	96	8	90	14	63	25	74	32	49	0	0	30	93	13	78	0	0	113	66	29	53	23	72	29	68	14	90	0	0	0	0	0	.	15	74	8	100	0	.	0	.	0	0	31	71	38	100	1	100	21	56	21	100	23	71	43	64	54	85	103	55	54	73	0	0	33	75	1	100	18	88	5	100	6	73	24	63	0	0	0	0	13	78	7	100	0	0	3	64	4	100	147	40	21	92	0	0	16	93	57	59	12	91	0	0	0	0	0	0	0	.	2949	8
pine	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	88	70	2	71	7	100	0	0	0	0	0	.	9	100	5	100	52	59	21	64	14	100	0	0	18	52	0	.	0	0	41	71	0	.	23	78	15	100	0	.	15	61	46	63	0	.	0	.	0	.	33	45	0	0	1	100	0	.	9	100	0	0	3	100	0	.	8	100	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	3	100	0	0	4	100	16	100	17	59	29	76	0	.	0	.	4	100	18	100	3	100	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	0	0	.	0	0	5	100	7	100	0	.	0	.	0	.	6	100	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	56	44	10	76	33	44	0	.	7	100	12	71	16	62	2	100	0	0	12	100	17	100	0	.	689	15
	higher altitude zone	0	.	0	.	0	.	0	.	0	0	0	0	43	44	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	27	73	0	0	44	36	148	37	0	0	0	.	0	.	11	71	0	.	0	0	0	.	0	.	0	.	0	.	4	100	8	100	0	.	0	.	8	100	0	0	2	100	0	.	0	.	7	72	0	.	1	100	15	70	0	0	2	100	0	.	0	.	0	.	0	.	7	71	11	93	0	.	0	.	0	.	0	.	0	.	0	0	2	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	33	24	40	77	34	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	577	15
	total	88	70	2	71	7	100	0	0	0	0	0	0	52	41	5	100	52	59	21	64	14	100	0	0	18	52	0	.	0	0	41	71	0	.	23	78	15	100	0	.	15	61	74	48	0	0	44	36	148	37	33	45	0	0	1	100	11	71	9	100	0	0	3	100	0	.	8	100	0	.	4	100	8	100	0	0	0	.	8	100	0	0	2	100	3	100	0	0	11	58	16	100	18	55	44	56	0	0	2	100	4	100	18	100	3	100	0	.	7	71	11	93	0	.	0	.	3	100	0	.	0	.	0	0	2	100	0	0	5	100	7	100	0	.	0	.	0	.	6	100	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	187	27	34	36	109	27	0	.	7	100	12	71	21	53	2	100	0	0	12	100	17	100	0	.	1267	10
larch	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	71	0	.	0	.	0	.	0	0	0	.	13	100	14	100	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	1	100	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	39	73	0	0	2	100	7	70	1	100	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	6	100	0	.	1	100	0	.	0	0	0	.	0	0	2	100	0	0	10	92	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	97	38
	higher altitude zone	0	.	0	.	0	.	0	.	0	0	0	.	21	79	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	6	70	6	79	23	59	166	29	158	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	11	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	54	53	42	39	75	31	68	0	.	0	.	52	46	0	100	10	90	0	.	0	.	0	0	11	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	334	24	91	30	106	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1133	11
	total	0	0	0	0	0	0	0	0	0	0	0	0	21	79	0	0	1	71	5	100	0	.	0	.	0	0	0	.	13	100	14	100	0	.	0	0	0	.	0	0	6	70	6	79	23	59	166	29	158	25	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	12	82	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	10	54	53	42	39	75	31	68	39	73	0	0	53	45	8	68	11	85	0	.	0	.	0	0	11	100	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	6	100	0	.	1	100	0	.	0	0	0	.	0	0	336	24	91	30	116	29	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	1230	10
Arolla pine	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	18	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	1	100	52	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	94	39	73	3	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	31
	total	0	.	0	.	0	.	0	.	0	.	0	.	18	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	1	100	52	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	94	39	73	3	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	31
other conifers	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	0	0	0	0	0	0	3	100	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	1	100	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	8	88	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	13	58
	higher altitude zone	0	.	0	.	0	.	0	.	0	0	0	.	0	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	1	100	5	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	37
	total	0	0	0	0	0	0	3	100	0	0	0	0	0	100	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	2	100	0	.	0	.	1	100	5	50	0	0	0	0	1	100	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	1	100	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	1	100	8	88	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	23	37
beech	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	14	87	50	71	45	100	47	67	0	0	13	73	48	42	53	51	97	49	53	90	46	100	26	48	82	47	0	.	14	95	27	62	5	77	15	60	0	.	17	47	0	0	11	71	0	.	0	0	0	.	44	42	19	62	1	100	31	63	0	0	0	0	1	100	0	0	0	0	4	100	0	0	20	61	0	0	22	75	12	72	43	58	7	100	12	62	10	91	11	48	4	100	9	71	32	92	0	.	0	0	0	0	0	0	9	71	0	.	1	100	0	.	18	57	29	52	16	62	25	54	15	62	44	59	0	0	0	0	0	0	9	67	2	100	0	0	5	79	0	0	18	59	2	100	0	0	0	0	0	0	3	78	0	0	0	0	0	0	0	0	10	71	0	0	0	0	13	58	2	100	13	100	0	0	15	76	20	58	12	76	0	0	11	64	0	.	1235	11
	higher altitude zone	0	.	0	.	0	.	0	.	5	58	1	100	11	71	28	51	0	0	25	57	0	.	0	.	0	0	0	.	0	0	0	0	9	100	0	0	0	.	4	65	7	63	0	0	9	100	8	100	0	.	21	72	0	0	1	100	65	98	0	0	0	0	0	0	0	0	2	100	19	60	0	0	5	75	0	.	10	100	5	72	6	64	15	66	0	.	6	100	4	100	0	.	3	100	8	100	4	100	10	80	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	100	0	.	0	0	0	0	0	0	7	87	12	100	0	.	1	100	0	.	0	0	3	72	4	84	2	100	0	0	0	0	0	.	0	0	0	.	0	.	0	0	9	83	8	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	337	23
	total	14	87	50	71	45	100	47	67	5	58	14	66	59	37	81	38	97	49	78	64	46	100	26	48	82	47	0	.	14	95	27	62	13	70	15	60	0	.	21	39	7	63	11	71	9	100	8	100	0	.	65	37	19	62	2	74	96	69	0	0	0	0	1	100	0	0	2	100	23	52	0	0	25	51	0	0	31	60	18	55	49	51	22	55	12	62	17	68	15	45	4	100	13	59	40	76	4	100	10	80	0	0	0	0	9	71	0	.	1	100	0	.	18	57	29	52	16	62	25	54	15	62	44	59	0	100	0	0	0	0	9	67	2	100	7	87	17	75	0	0	19	56	2	100	0	0	3	72	4	84	4	62	0	0	0	0	0	0	0	0	10	71	0	0	0	0	21	48	11	80	13	100	0	0	15	76	20	58	12	76	0	0	11	64	0	.	1572	10
maple	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	14	61	2	71	0	0	1	100	0	0	0	0	2	100	0	0	0	0	1	100	0	0	0	0	6	71	0	.	9	100	7	100	0	0	3	100	0	0	0	0	2	100	0	0	0	0	0	0	0	.	19	56	0	0	0	0	2	100	0	0	0	0	1	100	0	0	0	0	0	0	0	0	0	0	0	0	8	82	3	80	0	0	10	100	1	100	0	0	0	0	1	100	10	74	2	100	0	.	9	100	1	100	0	0	0	0	0	.	0	0	0	0	0	0	1	100	6	85	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	1	100	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	4	100	0	0	2	100	0	.	0	0	0	.	0	0	8	100	0	0	0	0	2	100	18	58	5	100	2	100	0	0	0	0	0	.	162	18
	higher altitude zone	0	.	0	.	0	.	0	.	0	0	0	0	2	73	0	0	4	100	1	100	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	14	53	0	0	0	0	0	0	4	100	0	0	0	0	0	0	0	0	2	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	6	100	10	73	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	3	60	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	3	71	11	65	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	1	100	0	.	0	.	0	0	0	.	0	0	0	0	7	72	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	68	24
	total	14	61	2	71	0	0	1	100	0	0	0	0	4	65	0	0	4	100	3	71	0	0	0	0	6	71	0	.	9	100	7	100	0	0	3	100	0	0	14	53	2	100	0	0	0	0	4	100	0	0	19	56	0	0	0	0	4	71	0	0	0	0	1	100	0	0	0	0	0	0	0	0	0	0	0	0	15	63	13	58	0	0	10	100	1	100	0	0	0	0	1	100	10	74	2	100	3	60	9	100	1	100	0	0	0	0	0	0	0	0	0	0	0	0	1	100	6	85	0	0	0	0	0	0	0	0	0	0	0	.	0	0	3	71	12	59	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	5	77	0	0	2	100	0	0	0	0	0	0	0	0	14	63	0	0	0	0	2	100	18	58	5	100	2	100	0	0	0	0	0	.	230	15
ash	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	4	100	31	100	0	0	1	100	0	0	6	100	10	72	1	100	2	100	7	82	0	0	0	0	0	0	0	.	2	100	7	100	8	76	2	100	0	0	10	100	0	0	4	100	0	0	0	0	0	.	13	60	0	0	8	100	2	100	0	0	0	0	0	0	0	0	0	0	9	100	4	100	6	100	5	93	0	0	0	0	2	100	2	100	0	0	0	0	0	0	0	0	0	0	4	61	0	.	0	0	2	100	15	77	4	100	0	.	0	0	0	.	2	100	0	0	11	59	6	100	0	0	5	100	0	0	0	0	0	0	5	84	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	1	100	10	71	0	0	6	89	0	.	0	0	0	0	0	0	13	79	4	100	0	0	13	100	42	72	1	100	8	96	0	0	0	0	0	.	298	19
	higher altitude zone	0	.	0	.	0	.	0	.	2	100	13	73	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	10	100	0	0	0	.	5	81	0	0	0	0	6	100	0	.	0	.	0	0	0	0	1	100	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	6	100	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	1	100	0	0	6	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	1	100	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	52	34
	total	4	100	31	100	0	0	1	100	2	100	20	59	10	72	1	100	2	100	7	82	0	0	0	0	0	0	0	.	2	100	7	100	8	76	2	100	0	0	20	71	0	0	4	100	5	81	0	0	0	0	20	52	0	0	8	100	2	100	0	0	1	100	0	0	0	0	0	0	9	100	4	100	6	100	5	93	0	0	0	0	2	100	2	100	0	0	0	0	0	0	0	0	0	0	4	61	0	0	0	0	2	100	15	77	4	100	6	100	0	0	0	0	2	100	0	0	11	59	6	100	0	0	5	100	0	0	0	0	0	0	6	74	0	0	6	54	0	0	0	0	0	0	0	0	0	.	0	0	0	0	1	100	10	71	0	0	6	89	0	.	0	0	0	0	1	100	13	79	4	100	0	0	13	100	42	72	1	100	8	96	0	0	0	0	0	.	350	17
oak	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	0	0	2	100	0	0	1	100	0	.	0	0	0	0	0	0	16	53	0	0	19	100	4	100	16	90	0	.	6	100	0	0	4	100	3	100	38	69	0	.	0	0	6	100	0	0	0	0	0	0	32	78	6	100	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	7	100	0	0	0	0	0	0	3	63	0	0	1	100	5	100	1	100	0	0	0	.	0	0	0	0	7	90	16	100	0	.	0	.	0	.	0	0	4	72	17	64	0	0	1	100	1	100	0	0	4	100	0	0	2	71	1	100	0	.	0	0	0	.	0	100	8	77	13	72	0	.	1	100	0	0	15	100	0	0	7	100	0	0	0	0	0	0	1	100	4	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	7	100	0	.	279	20
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	71
	total	0	0	2	100	0	0	1	100	0	.	0	0	0	0	0	0	16	53	0	0	19	100	4	100	16	90	0	.	6	100	0	0	4	100	3	100	38	69	0	.	0	0	6	100	0	0	1	100	0	0	32	78	6	100	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	7	100	0	0	0	0	0	0	3	63	0	0	1	100	5	100	1	100	0	0	0	.	0	0	0	0	7	90	16	100	0	.	0	.	0	.	0	0	4	72	17	64	0	0	1	100	1	100	0	0	4	100	0	0	2	71	1	100	0	.	0	0	0	.	1	72	8	77	13	72	0	.	1	100	0	0	15	100	0	0	7	100	0	0	0	0	0	0	1	100	4	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	7	100	0	.	280	20
sweet chestnut	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	38	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	45	55	26	100	26	51	104	46	186	25	46	65	30	45	151	38	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	0	0	.	0	.	3	100	0	.	22	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	706	14
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	16	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	38	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	45	55	42	67	26	51	104	46	186	25	46	65	30	45	151	38	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	0	0	.	0	.	3	100	0	.	22	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	722	14
other broadleaves	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	14	60	23	53	13	74	0	0	0	0	1	100	13	48	7	100	6	63	13	64	4	84	6	77	5	62	0	.	9	87	0	0	3	100	7	84	9	92	2	100	20	51	1	100	0	.	65	41	0	0	25	37	2	76	11	55	2	72	0	0	0	0	0	0	0	0	2	100	0	0	3	100	1	100	2	100	16	69	21	58	3	71	11	100	1	100	6	100	0	0	13	68	2	100	18	66	0	.	13	73	0	0	5	70	9	91	0	0	10	51	1	100	60	56	80	26	84	40	97	28	9	46	52	41	0	0	6	100	1	100	11	72	17	82	4	80	11	100	10	81	0	0	0	0	1	100	0	0	0	0	8	74	21	73	11	93	7	71	0	0	1	100	30	61	1	100	30	35	2	100	1	100	18	82	10	77	5	100	10	64	0	0	2	100	0	.	999	9
	higher altitude zone	0	.	0	.	0	.	0	.	4	74	5	100	57	34	6	79	0	0	4	59	0	.	0	.	1	100	0	.	5	100	2	73	3	75	9	61	0	.	12	51	15	51	17	78	56	43	22	38	14	44	0	0	0	0	0	0	30	51	0	0	2	100	0	.	3	100	0	0	0	0	10	75	2	73	0	.	2	71	16	67	0	0	15	68	0	.	0	.	0	0	0	.	0	0	0	0	3	58	10	86	0	.	0	.	0	.	12	55	0	0	8	67	0	0	0	.	0	.	20	58	0	.	1	100	6	62	4	100	2	100	2	100	17	67	18	47	8	100	0	.	2	75	0	.	0	.	2	71	0	0	0	0	0	0	0	0	0	.	0	.	0	.	25	37	29	35	15	41	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	502	10
	total	14	60	23	53	13	74	0	0	4	74	6	83	70	29	12	65	6	63	17	51	4	84	6	77	6	53	0	.	14	66	2	73	6	63	16	50	9	92	14	46	36	37	18	74	56	43	88	32	14	44	25	37	2	76	11	55	32	47	0	0	2	100	0	0	3	100	2	100	0	0	13	62	3	59	2	100	18	63	37	44	3	71	26	58	1	100	6	100	0	0	13	68	2	100	18	66	3	58	23	56	0	0	5	70	9	91	12	55	10	51	9	62	60	56	80	26	84	40	116	25	9	46	54	40	6	62	10	73	3	73	13	62	33	53	22	42	19	72	10	81	2	75	0	0	1	100	2	71	0	0	8	74	21	73	11	93	7	71	0	0	1	100	55	37	30	34	45	27	2	100	1	100	18	82	16	61	5	100	10	64	0	0	2	100	0	.	1501	7
indeterminable	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	3	100	0	.	0	.	1	100	0	.	1	100	28	54	0	.	9	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	82	0	.	3	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	2	100	15	100	0	.	0	.	0	.	0	.	0	.	0	.	5	77	0	.	0	.	0	.	0	.	1	100	0	.	0	.	5	100	0	.	1	100	3	100	6	57	4	58	0	.	12	78	0	.	0	.	0	.	13	68	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	3	100	2	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	127	23
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	1	74	76	31	5	100	0	.	3	100	0	.	0	.	0	.	0	.	1	100	1	100	0	.	0	.	0	.	10	100	29	49	17	72	87	62	29	50	16	50	0	.	0	.	0	.	13	71	0	.	0	.	0	.	0	.	0	.	0	.	2	84	0	.	0	.	1	100	9	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	1	100	0	.	0	.	1	100	12	72	3	81	0	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	57	16	68	29	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	18
	total	3	100	0	.	0	.	1	100	0	.	2	60	104	27	5	100	9	100	4	86	0	.	0	.	0	.	0	.	1	100	1	100	0	.	3	82	0	.	13	79	29	49	17	72	87	62	29	50	16	50	1	100	0	.	0	.	13	71	0	.	0	.	0	.	0	.	0	.	0	.	2	84	5	100	0	.	4	76	24	73	1	100	0	.	0	.	0	.	0	.	0	.	5	77	1	100	0	.	0	.	0	.	1	100	0	.	0	.	5	100	0	.	1	100	3	100	6	57	5	50	0	.	12	73	0	.	0	.	1	100	25	49	3	81	0	100	2	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	34	57	19	59	31	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	527	15
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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	217	34	173	37	108	50	279	34	0	0	111	43	235	31	312	24	349	21	183	34	106	51	45	36	196	30	3	.	129	40	165	42	129	38	129	32	62	51	86	32	37	40	121	38	0	0	195	28	0	0	437	18	157	41	143	37	165	52	9	100	0	0	11	54	0	0	11	76	27	62	8	100	46	60	297	57	74	48	87	43	88	59	148	68	77	39	65	38	44	37	49	49	182	43	102	49	0	.	45	48	32	49	252	64	53	41	1	100	116	42	26	98	107	36	289	31	335	20	211	25	102	48	280	26	0	0	31	100	6	100	91	40	25	63	5	71	77	48	55	46	61	47	29	76	25	56	0	0	12	74	29	54	77	53	15	74	39	43	7	100	31	47	113	35	19	50	223	27	12	57	55	50	140	35	236	30	97	33	51	40	51	80	62	46	0	.	9119	5
	higher altitude zone	0	.	0	.	0	.	0	.	115	43	202	46	2350	13	509	25	16	76	191	26	0	.	0	.	1	100	0	.	17	62	118	41	512	40	153	44	0	.	410	27	453	21	438	25	748	18	1037	15	709	15	98	37	35	66	42	78	585	32	17	62	14	49	0	0	3	100	23	50	152	35	288	33	247	35	0	.	87	38	380	28	110	46	243	39	0	.	58	63	32	49	0	.	15	75	126	39	377	34	157	37	0	.	0	.	0	.	103	44	217	27	149	32	31	68	0	.	0	.	97	35	16	85	24	63	101	47	42	75	252	34	138	47	188	43	314	30	94	76	0	.	122	39	0	.	18	100	43	36	17	47	25	67	83	61	8	100	0	.	30	100	0	.	1094	14	480	20	956	16	48	67	3	.	0	.	11	72	0	.	0	.	0	.	0	.	0	.	15773	4
	total	217	34	173	37	108	50	279	34	115	43	313	33	2586	13	821	18	366	21	373	21	106	51	45	36	197	30	3	.	146	36	282	30	641	33	282	28	62	51	496	23	490	19	559	21	748	18	1233	13	709	15	535	16	193	36	185	34	750	28	26	54	14	49	11	54	3	100	34	42	179	31	296	32	293	31	297	57	162	30	467	24	198	37	391	35	77	39	123	36	77	30	49	49	197	40	228	30	377	34	203	31	32	49	252	64	53	41	104	43	333	23	176	31	138	31	289	31	335	20	308	20	118	43	304	24	101	47	73	61	259	33	229	32	213	38	319	30	171	47	55	46	183	31	29	76	43	53	43	36	28	41	55	42	160	41	23	59	39	43	38	82	31	47	1206	13	499	19	1179	14	60	55	58	48	140	35	247	29	97	33	51	40	51	80	62	46	0	.	24892	3

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. 2296748. Birmensdorf, Swiss Federal Research Institute WSL

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