Selection from the list of topics

back

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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is accessible on foot.

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

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

top

2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

top

3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

top

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

top

5. Two types of changes?

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

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

top

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

top

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

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

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

top

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

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

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

top

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

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

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

top

NFI5
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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	29	59	36	60	22	81	85	59	0	.	45	100	371	45	258	34	214	36	315	47	0	0	31	59	28	68	0	.	22	100	17	71	49	84	32	66	0	0	29	72	0	0	35	71	0	.	9	100	0	.	322	41	117	64	150	49	81	61	8	71	0	0	14	87	0	0	134	90	0	0	0	.	15	100	12	100	106	70	89	58	29	100	5	71	272	66	5	100	20	100	13	100	26	79	27	100	0	.	47	100	17	79	33	69	169	57	0	0	144	77	0	0	0	.	0	0	0	0	0	0	1	100	17	76	16	100	0	0	0	0	4	100	0	0	0	.	77	71	0	0	29	72	0	0	16	100	16	100	13	100	2	100	0	0	10	73	27	74	16	100	39	64	24	72	38	82	27	62	17	74	213	69	76	74	91	71	19	79	42	51	7	100	4	100	0	.	4321	11
	higher altitude zone	0	.	0	.	0	.	0	.	91	40	238	54	2090	15	267	46	0	.	214	69	0	.	0	.	0	.	0	.	11	60	115	46	679	41	79	51	0	.	338	35	433	25	300	33	1086	29	1172	21	534	26	18	81	118	67	10	100	711	31	0	0	23	92	0	0	43	97	45	61	113	47	129	54	332	43	0	.	42	64	285	30	114	67	217	46	0	.	36	100	19	100	0	.	0	.	81	43	366	44	152	55	0	.	0	.	0	.	56	51	114	41	354	51	0	0	0	.	0	.	98	48	24	100	0	0	66	54	133	59	263	38	64	49	210	37	256	42	35	73	0	.	183	83	0	.	22	84	46	49	6	100	66	52	157	70	0	0	0	.	0	.	0	.	768	21	745	25	794	23	58	68	75	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	15095	6
	total	29	59	36	60	22	81	85	59	91	40	283	48	2461	15	525	29	214	36	528	39	0	0	31	59	28	68	0	.	33	70	132	41	728	38	111	41	0	0	368	33	433	25	335	30	1086	29	1181	20	534	26	340	39	235	47	160	46	792	28	8	71	23	92	14	87	43	97	179	69	113	47	129	54	348	41	12	100	148	53	373	27	143	57	222	46	272	66	40	89	39	71	13	100	26	79	108	41	366	44	199	48	17	79	33	69	169	57	56	51	258	47	354	51	0	0	0	0	0	0	98	48	25	95	17	76	81	48	133	59	263	38	68	47	210	37	256	42	112	54	0	0	212	72	0	0	38	64	62	44	19	75	68	51	157	70	10	73	27	74	16	100	39	64	793	21	783	24	821	22	75	55	288	57	76	74	92	70	19	79	42	51	7	100	4	100	0	.	19416	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	86	59	0	0	0	0	115	46	0	.	73	74	162	64	209	41	256	40	179	49	23	89	0	0	44	68	0	.	0	0	0	0	39	89	88	100	0	0	13	80	0	0	0	0	0	.	31	98	0	.	243	36	24	72	176	45	230	58	21	100	0	0	16	82	0	0	10	100	0	0	0	.	0	0	116	56	0	.	2	100	0	0	92	100	0	0	38	87	16	93	63	71	28	59	17	100	0	.	0	0	8	100	0	0	0	0	0	.	14	100	0	.	0	.	0	.	0	0	8	100	32	97	3	100	0	0	0	0	0	0	20	82	0	0	0	0	2	100	0	0	99	96	2	100	9	100	0	.	0	0	36	77	0	0	0	0	53	73	11	100	0	0	0	0	0	0	38	100	22	100	0	0	29	71	55	60	34	100	3	100	0	0	0	0	0	.	2886	12
	higher altitude zone	0	.	0	.	0	.	0	.	2	100	22	77	67	46	209	38	2	.	125	50	0	.	0	.	0	.	0	.	7	71	3	100	48	93	64	95	0	.	0	0	43	63	70	100	24	71	64	68	46	82	82	42	3	72	0	0	192	48	3	100	25	72	0	0	0	0	15	71	43	79	72	86	21	73	0	.	18	100	22	71	0	0	51	81	0	.	2	71	1	100	0	.	0	.	22	100	49	59	61	68	0	.	0	.	0	.	0	0	0	0	27	100	0	.	0	.	0	.	0	0	0	0	0	0	72	58	0	.	88	54	5	100	78	85	117	63	0	0	0	.	3	100	0	.	0	0	8	100	29	74	2	100	2	100	0	0	0	.	0	.	0	.	6	81	30	76	81	56	37	72	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	2062	12
	total	86	59	0	0	0	0	115	46	2	100	95	59	229	47	418	28	257	40	304	35	23	89	0	0	44	68	0	.	7	71	3	100	87	65	152	70	0	0	13	80	43	63	70	100	24	71	95	55	46	82	325	29	27	64	176	45	422	38	24	87	25	72	16	82	0	0	25	59	43	79	72	86	21	73	116	56	18	100	24	66	0	0	143	70	0	0	40	83	17	88	63	71	28	59	39	71	49	59	61	68	8	100	0	0	0	0	0	0	14	100	27	100	0	.	0	.	0	0	8	100	32	97	3	100	72	58	0	0	88	54	25	69	78	85	117	63	2	100	0	0	102	93	2	100	9	100	8	100	29	74	38	73	2	100	0	0	53	73	11	100	0	0	6	81	30	76	120	50	59	58	0	0	29	71	55	60	34	100	3	100	0	0	0	0	0	.	4947	9
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	.	42	71	4	100	80	59	59	47	11	100	0	0	43	59	0	.	0	0	0	0	0	.	16	100	14	100	0	.	0	0	44	89	0	.	0	.	0	.	58	55	0	0	0	0	0	.	0	0	2	100	0	.	0	.	23	100	0	.	0	.	0	.	0	0	0	.	15	100	0	.	0	0	29	58	0	0	0	0	43	94	35	77	0	.	0	.	0	.	16	100	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	5	100	11	100	0	.	0	.	0	.	0	0	0	0	0	.	9	100	0	.	8	100	0	.	0	0	0	.	0	.	26	100	0	.	32	50	29	80	75	54	0	.	46	100	43	49	10	100	12	100	0	0	0	0	0	0	0	.	837	16
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	40	59	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	81	53	66	0	0	66	47	203	35	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	1	100	13	100	0	.	0	.	9	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	23	69	0	0	0	.	0	.	0	.	0	.	0	.	33	71	12	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	.	60	48	63	37	155	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	740	17
	total	0	0	0	0	0	0	0	0	0	.	0	.	81	46	4	100	80	59	59	47	11	100	0	0	43	59	0	.	0	0	0	0	0	.	16	100	14	100	0	.	4	81	97	54	0	0	66	47	203	35	58	55	0	0	0	0	0	.	0	0	2	100	0	.	0	.	23	100	0	0	1	100	13	100	0	0	0	.	23	73	0	.	0	0	29	58	0	0	5	100	43	94	35	77	23	69	0	0	0	.	16	100	0	0	0	0	0	.	33	71	12	88	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0	5	100	11	100	0	.	0	.	0	.	0	0	0	0	0	.	9	100	0	.	8	100	0	.	0	0	0	.	0	.	26	100	0	.	93	36	92	36	229	35	0	.	46	100	43	49	10	100	12	100	0	0	0	0	0	0	0	.	1577	11
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	.	4	100	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	8	100	0	.	0	.	4	100	27	100	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	2	100	0	0	5	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	.	0	0	0	.	0	0	0	0	2	72	2	73	0	.	0	.	0	.	17	79	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	12	100	0	.	0	0	0	.	1	100	18	100	0	0	47	65	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	157	32
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	55	90	7	100	44	67	396	28	322	36	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	80	57	45	10	71	39	100	0	.	0	.	46	43	1	100	40	67	0	.	0	.	0	.	0	0	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	25	108	30	171	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1809	12
	total	0	0	0	0	0	0	0	0	0	.	0	.	33	88	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	8	100	0	.	0	0	59	85	34	82	44	67	396	28	322	36	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	2	100	0	0	5	100	13	86	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	7	100	0	0	18	80	57	45	10	71	39	100	0	.	0	0	46	43	2	60	42	64	0	.	0	.	0	.	17	79	0	.	4	100	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	12	100	0	.	0	0	0	.	1	100	467	24	108	30	218	34	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	1966	12
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	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	61	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	48	49	82	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	188	32
	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	61	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	48	49	82	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	188	32
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	0	0	.	0	0	0	.	0	.	4	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	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	74	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	7	65
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	2	71	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	5	55	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	5	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	.	3	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	11	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	39
	total	0	0	0	.	0	0	0	0	0	.	0	0	2	71	0	.	4	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	4	100	0	.	0	.	4	100	5	55	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	5	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	.	3	100	0	.	0	0	0	.	0	.	0	0	3	74	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	11	100	1	100	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	41	35
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	211	53	46	61	0	0	72	58	0	.	24	71	76	83	54	67	130	47	52	71	93	71	93	75	89	47	0	.	40	93	13	72	25	76	39	64	0	.	38	54	1	100	16	100	0	.	21	87	0	.	97	41	29	58	4	81	63	64	54	67	0	0	0	0	0	0	0	0	9	80	0	.	47	54	6	100	88	61	32	54	32	73	72	72	56	44	11	100	64	56	63	66	108	46	14	93	0	.	12	100	0	0	0	0	2	100	0	.	0	0	0	.	23	63	34	76	24	62	30	61	71	65	34	81	0	0	0	0	0	0	24	65	0	0	2	100	5	100	1	100	129	53	2	74	3	100	0	0	10	100	3	100	3	100	17	94	0	0	5	100	0	0	0	.	0	.	79	54	0	0	14	100	0	0	49	49	9	84	1	100	6	100	6	100	0	.	2580	10
	higher altitude zone	0	.	0	.	0	.	0	.	2	78	16	100	24	73	47	54	0	.	71	64	0	.	0	.	0	.	0	.	1	100	0	0	26	71	0	.	0	.	34	67	56	68	1	100	2	100	3	100	0	.	8	100	0	0	0	0	73	74	0	0	5	100	0	0	0	0	0	0	8	100	8	100	3	100	0	.	0	0	41	62	7	60	42	48	0	.	1	100	0	0	0	.	0	.	0	0	48	87	7	75	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	6	100	0	.	9	74	0	.	0	0	0	0	0	0	3	59	0	0	0	.	26	62	0	.	0	0	2	100	28	73	2	100	12	100	0	0	0	.	0	.	0	.	0	.	0	100	2	100	12	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	635	18
	total	211	53	46	61	0	0	72	58	2	78	39	58	100	66	101	44	130	47	122	48	93	71	93	75	89	47	0	.	40	91	13	72	50	52	39	64	0	.	71	42	57	67	17	96	2	100	24	78	0	.	106	39	29	58	4	81	136	50	54	67	5	100	0	0	0	0	0	0	17	63	8	100	51	51	6	100	88	61	72	42	39	61	114	49	56	44	12	90	64	56	63	66	108	46	14	93	48	87	19	68	0	0	0	0	2	100	0	.	0	0	0	.	23	63	34	76	24	62	30	61	77	60	34	81	9	74	0	0	0	0	24	65	0	0	4	51	5	100	1	100	155	45	2	74	3	100	2	100	38	59	4	72	15	85	17	94	0	0	5	100	0	0	0	.	0	100	81	53	12	100	14	100	0	0	49	49	9	84	1	100	6	100	6	100	0	.	3215	9
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	12	100	0	0	0	0	2	100	0	.	0	0	18	86	0	0	12	100	4	74	9	74	10	76	2	100	0	.	0	0	2	100	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	35	58	0	0	0	0	0	0	8	100	0	0	3	100	0	0	0	0	13	100	0	.	0	0	0	0	0	0	12	100	0	0	6	100	2	100	0	0	1	100	0	0	0	0	3	100	0	.	0	0	0	0	17	100	0	0	0	.	0	0	0	0	0	0	16	100	3	71	2	100	0	0	0	0	1	100	0	0	0	.	0	0	1	100	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	100	0	0	0	0	0	.	0	0	0	.	0	.	7	58	0	0	0	0	0	0	14	91	0	0	0	0	0	0	6	100	0	.	232	21
	higher altitude zone	0	.	0	.	0	.	0	.	0	0	21	80	20	71	0	0	4	.	10	100	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	25	56	0	0	0	0	0	0	0	0	0	0	6	100	3	100	13	77	2	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	11	100	0	0	0	0	0	.	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	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	12	72	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	131	26
	total	12	100	0	0	0	0	2	100	0	0	21	80	38	55	0	0	16	80	14	73	9	74	10	76	2	100	0	.	0	0	2	100	0	0	0	0	0	0	25	56	0	0	0	0	0	0	0	0	0	0	41	52	3	100	13	77	2	100	8	100	0	0	3	100	0	0	0	0	13	100	0	0	0	0	0	0	0	0	23	71	0	0	6	100	2	100	0	0	1	100	0	0	0	0	4	78	0	0	0	0	0	0	17	100	0	0	0	0	0	0	0	0	0	0	16	100	3	71	2	100	0	0	0	0	1	100	0	0	0	0	0	0	4	75	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	12	100	0	0	0	0	0	.	0	0	0	0	0	0	19	51	0	0	0	0	0	0	14	91	0	0	0	0	0	0	6	100	0	.	363	16
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	24	65	87	80	2	100	5	73	0	.	46	73	55	81	39	67	47	65	4	71	23	85	12	100	0	0	0	.	1	100	30	80	5	77	28	100	1	100	67	71	0	0	0	0	0	.	6	100	0	.	251	49	6	87	34	64	44	74	0	0	0	0	0	0	0	0	0	.	49	97	0	.	6	100	58	46	10	100	5	100	20	100	35	62	11	68	0	0	0	0	20	61	9	70	27	100	0	.	5	100	4	83	38	100	18	100	0	.	0	0	0	0	0	0	15	100	5	100	28	75	30	100	0	0	0	0	0	0	0	.	10	100	7	76	0	.	11	88	0	0	0	0	0	0	0	0	0	0	0	0	7	100	12	71	0	0	0	0	0	.	0	0	2	100	0	0	26	58	0	0	0	0	18	74	78	57	22	95	0	0	2	71	16	100	0	.	1425	14
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	16	100	0	0	11	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	31	82	0	.	0	.	36	67	0	0	0	.	4	100	0	.	0	0	6	73	0	0	0	0	0	.	0	.	0	0	9	100	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	.	21	65	2	100	0	0	0	.	0	.	0	.	8	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	0	0	.	1	100	1	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	4	100	10	100	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	170	27
	total	24	65	87	80	2	100	5	73	0	.	62	60	55	81	49	56	47	65	4	71	23	85	12	100	0	0	0	.	1	100	30	80	5	77	28	100	1	100	98	55	0	0	0	0	36	67	6	100	0	.	255	48	6	87	34	64	50	65	0	0	0	0	0	0	0	0	0	0	58	83	0	.	6	100	58	46	10	100	5	100	20	100	35	62	11	68	0	0	0	0	20	61	9	70	48	63	2	100	5	100	4	83	38	100	18	100	8	100	0	0	0	0	0	0	15	100	5	100	28	75	30	100	0	0	3	100	0	0	0	.	11	89	8	66	6	100	11	88	0	0	0	0	0	0	0	0	0	0	0	0	7	100	12	71	0	0	0	0	0	.	0	0	3	74	4	100	36	50	0	0	0	0	18	74	78	57	22	95	0	0	2	71	16	100	0	.	1594	13
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	3	100	0	0	0	.	0	0	0	.	0	.	1	100	21	100	6	66	0	.	0	0	0	0	13	100	0	.	8	100	0	0	2	100	0	0	16	75	0	.	0	.	0	0	0	.	1	100	0	0	25	55	5	100	0	0	0	0	11	89	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	22	100	0	0	0	0	0	.	3	100	0	0	14	84	0	0	18	61	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	16	69	5	100	52	62	22	76	23	61	0	0	9	100	0	.	0	0	0	0	1	100	0	.	0	0	0	0	0	0	0	0	0	0	0	.	11	72	0	0	10	100	30	70	0	.	0	0	0	0	1	100	2	72	9	79	0	.	0	0	0	0	0	0	0	0	2	100	0	0	11	100	0	.	370	18
	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	.	0	.	0	.	0	0
	total	3	100	0	0	0	.	0	0	0	.	0	.	1	100	21	100	6	66	0	.	0	0	0	0	13	100	0	.	8	100	0	0	2	100	0	0	16	75	0	.	0	.	0	0	0	.	1	100	0	0	25	55	5	100	0	0	0	0	11	89	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	22	100	0	0	0	0	0	.	3	100	0	0	14	84	0	0	18	61	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	16	69	5	100	52	62	22	76	23	61	0	0	9	100	0	.	0	0	0	0	1	100	0	.	0	0	0	0	0	0	0	0	0	0	0	.	11	72	0	0	10	100	30	70	0	.	0	0	0	0	1	100	2	72	9	79	0	0	0	0	0	0	0	0	0	0	2	100	0	0	11	100	0	.	370	18
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	.	0	.	55	56	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	66	48	45	82	103	46	201	46	284	29	38	47	62	59	313	30	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1207	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	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	.	16	100
	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	.	55	56	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	66	48	62	66	103	46	201	46	284	29	38	47	62	59	313	30	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1223	13
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	21	59	24	60	0	0	7	100	0	.	4	100	63	67	2	100	16	100	5	100	28	88	8	100	4	74	0	.	0	0	60	100	2	100	0	0	5	100	36	96	10	88	6	100	0	.	100	40	0	0	91	50	3	71	11	82	0	0	0	.	0	0	3	100	0	0	0	0	0	0	0	.	0	0	3	100	0	0	36	55	41	73	0	0	0	0	0	0	20	100	23	62	1	100	7	77	0	.	13	71	17	86	2	100	0	0	0	0	18	64	2	100	27	45	75	40	167	50	108	37	48	66	40	57	2	100	0	0	0	0	51	58	43	86	1	100	5	100	10	84	11	100	13	87	10	89	0	0	0	0	19	94	22	85	5	100	2	100	2	100	0	0	18	63	7	73	36	44	4	100	5	100	2	100	12	72	2	100	0	0	5	71	0	0	0	.	1444	12
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	4	100	38	38	43	58	0	.	2	100	0	.	0	.	0	.	0	.	9	80	8	60	1	100	29	73	0	.	25	60	19	72	13	100	72	39	93	38	100	68	0	0	0	0	0	0	13	58	0	.	9	72	0	.	0	.	17	100	0	0	8	65	10	100	0	.	0	0	14	55	0	.	21	92	0	.	0	0	0	0	0	.	0	.	2	100	0	0	42	65	0	.	0	.	0	.	33	55	0	0	9	76	0	0	0	.	0	.	4	100	0	.	17	87	10	67	34	71	2	100	0	0	15	91	1	100	11	83	0	.	0	0	0	.	0	.	1	100	0	.	1	100	0	.	0	0	0	.	0	.	0	.	41	37	34	47	51	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	860	13
	total	21	59	24	60	0	0	7	100	0	.	7	71	101	44	46	55	16	100	6	79	28	88	8	100	4	74	0	.	9	80	68	88	3	71	29	73	5	100	61	62	29	56	19	75	72	39	193	28	100	68	91	50	3	71	11	82	13	58	0	.	9	72	3	100	0	0	17	100	0	0	8	65	10	100	3	100	0	0	51	43	41	73	21	92	0	0	0	0	20	100	23	62	1	100	9	64	0	0	55	52	17	86	2	100	0	0	33	55	18	64	11	67	27	45	75	40	167	50	112	36	48	66	57	48	12	57	34	71	2	100	51	58	58	68	3	71	16	65	10	84	11	100	13	87	10	89	1	100	0	0	20	88	22	85	5	100	2	100	2	100	0	0	59	32	42	41	88	32	4	100	5	100	2	100	12	72	2	100	0	0	5	71	0	0	0	.	2303	9
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	0	.	0	.	0	.	0	.	0	.	0	.	63	60	0	.	11	100	1	100	4	100	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	86	0	.	19	100	0	.	14	89	2	100	2	100	1	100	0	.	0	.	0	.	15	100	0	.	0	.	0	.	1	100	4	100	0	.	3	100	3	100	0	.	0	.	0	.	31	86	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	4	71	2	100	0	.	0	.	0	.	0	.	0	.	23	72	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	7	100	21	100	0	.	1	100	0	.	271	24
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	11	93	79	43	9	72	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	7	100	0	.	0	.	29	71	0	.	34	63	20	56	23	70	0	.	6	100	0	.	7	100	0	.	0	.	0	.	0	.	8	100	35	72	9	100	0	.	0	.	3	74	16	100	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	22	100	0	.	0	.	0	.	3	100	3	100	0	.	0	.	9	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	62	49	57	8	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	437	17
	total	0	.	0	.	0	.	0	.	0	.	11	93	143	36	9	72	11	100	1	100	4	100	0	.	13	100	0	.	1	100	0	.	0	.	7	100	0	.	0	.	29	71	18	86	34	63	39	57	23	70	14	89	8	80	2	100	9	85	0	.	0	.	0	.	15	100	8	100	35	72	9	100	1	100	4	100	3	74	18	87	3	100	8	100	0	.	0	.	31	86	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	4	71	3	73	0	.	0	.	22	100	0	.	0	.	23	72	6	71	3	100	0	.	0	.	9	100	0	.	0	.	1	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	37	62	49	57	9	68	0	.	0	.	0	.	0	.	7	100	21	100	0	.	1	100	0	.	708	14
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	387	35	194	48	24	76	286	30	0	.	191	53	857	30	587	25	776	20	618	32	189	53	154	52	236	36	0	.	72	67	121	61	120	58	212	52	36	57	184	46	15	70	146	63	0	.	241	31	0	0	1137	22	185	45	376	33	420	45	102	47	2	100	36	62	15	100	167	81	71	71	0	.	72	43	198	37	232	47	193	37	124	56	209	67	372	50	54	65	166	43	224	44	225	34	95	58	0	.	77	67	63	43	99	53	189	57	8	100	241	58	47	80	169	32	346	33	539	26	239	24	269	32	408	25	28	72	0	0	5	100	161	44	55	69	3	71	100	63	11	78	271	47	18	68	47	48	16	100	43	49	66	51	70	51	61	54	82	58	59	71	40	62	100	46	76	53	372	27	43	59	279	55	168	42	308	38	105	45	69	49	19	52	44	65	0	.	15735	5
	higher altitude zone	0	.	0	.	0	.	0	.	95	39	328	46	2389	14	586	29	6	.	420	41	0	.	0	.	0	.	0	.	30	58	126	44	754	38	179	56	0	.	452	30	643	22	443	29	1297	25	1817	16	1293	20	118	43	130	64	24	85	1003	29	3	100	62	46	0	0	43	97	85	41	208	39	228	46	379	41	0	.	63	52	416	26	122	63	340	35	0	.	39	96	25	96	0	.	0	.	150	32	466	37	262	39	0	.	0	.	0	.	116	33	204	34	429	44	39	100	0	.	0	.	149	42	31	81	57	54	182	37	167	49	355	37	70	46	311	41	390	39	46	71	0	.	221	76	0	.	22	84	58	45	62	65	71	50	172	65	0	0	0	.	0	.	0	.	1428	16	1094	20	1297	18	107	65	75	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	22177	4
	total	387	35	194	48	24	76	286	30	95	39	519	35	3246	13	1173	19	781	20	1038	25	189	53	154	52	236	36	0	.	101	50	247	37	874	33	391	38	36	57	635	25	658	21	589	27	1297	25	2058	14	1293	20	1256	20	315	37	400	31	1423	24	106	45	63	45	36	62	58	77	252	56	279	34	228	46	452	35	198	37	295	39	608	21	246	42	549	33	372	50	93	55	192	40	224	44	225	34	245	30	466	37	339	34	63	43	99	53	189	57	124	31	445	35	476	40	208	32	346	33	539	26	387	22	299	30	466	23	210	33	167	49	361	36	231	34	366	36	393	38	146	48	11	78	492	43	18	68	69	42	74	41	105	43	137	35	242	48	61	54	82	58	59	71	40	62	1528	15	1171	19	1669	15	149	49	354	48	168	42	309	37	105	45	69	49	19	52	44	65	0	.	37911	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. 2277714. Birmensdorf, Swiss Federal Research Institute WSL

back