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net increment #80
Increase in the stemwood volume between two inventories of all surviving trees and shrubs with a diameter at breast height (dbh) ≥12 cm with bark, the stemwood volume of all ingrowing trees and shrubs with bark, and the modelled increase in the stemwood volume of the losses with bark during half the inventory period minus the volume of mortality. The net increment thus corresponds to the increment (=gross increment) minus the volume of mortality.

tree species (5 classes) #1157
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh). The three most common species or species groups in Switzerland (spruce - Picea spp.; fir - Abies spp.; beech - Fagus sylvatica) are in individual classes, while the remaining species are classified as «other conifers» or «other broadleaves». 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 NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

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

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2–NFI3
net increment
tree species (5 classes) · higher/lower altitude zone
regional demarcation: forest district (2024)
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 1993/95–2004/06
		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
tree species (5 classes)	higher/lower altitude zone	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
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	21	42	26	42	30	54	24	71	0	.	10	107	47	28	74	26	184	16	21	56	1	65	9	49	11	50	0	.	3	168	24	52	16	44	24	42	0	100	10	43	3	66	7	83	0	.	6	72	0	.	67	26	10	121	56	30	67	35	0	71	0	100	7	52	4	77	0	7741	7	93	0	.	4	94	-2	1122	19	60	2	218	4	56	2	862	53	34	28	38	2	174	24	50	4	58	1	277	0	.	2	213	40	33	40	37	49	39	1	.	8	107	0	.	0	0	1	88	0	100	1	100	0	100	0	451	2	72	2	.	0	952	2	66	4	73	0	.	22	54	8	82	8	71	13	70	8	78	5	84	5	125	3	47	8	53	11	78	18	45	1	195	18	54	1	84	4	75	2	62	4	63	24	40	33	40	42	55	63	32	23	47	14	58	32	31	0	.	1409	6
	higher altitude zone	0	.	0	.	0	.	0	.	38	34	42	45	59	67	72	28	0	.	-13	143	0	.	0	.	0	.	0	.	17	78	40	38	-18	153	31	42	0	.	54	42	89	25	54	30	76	27	127	22	83	25	-9	301	7	62	8	59	42	75	6	85	39	32	3	72	11	54	22	46	11	64	17	53	1	1647	0	.	17	56	39	33	14	49	59	37	0	.	2	71	-4	73	0	.	0	.	22	37	39	70	18	39	0	.	0	.	0	.	16	41	26	36	13	56	0	.	0	.	0	.	3	78	0	1913	4	106	2	201	11	45	24	46	11	62	28	37	23	42	7	75	0	.	4	224	0	.	2	71	26	28	5	58	14	46	-1	604	2	.	0	.	0	.	0	.	39	39	72	26	93	21	20	56	4	.	0	.	25	94	0	.	0	.	0	.	0	.	0	.	1574	7
	total	21	42	26	42	30	54	24	71	38	34	52	42	104	40	146	19	184	16	8	262	1	65	9	49	11	50	0	.	20	71	64	31	-1	2463	57	30	0	100	64	36	93	25	62	29	76	27	134	21	83	25	58	54	18	75	64	27	109	36	7	81	40	32	9	43	14	44	22	47	18	53	17	53	5	348	-2	1122	35	41	42	33	18	40	62	48	53	34	30	36	-2	241	24	50	4	58	23	37	39	70	20	39	40	33	40	37	49	39	18	39	34	37	13	56	0	0	1	88	0	100	4	66	0	16317	4	103	4	93	13	41	24	46	13	54	32	34	23	42	28	44	8	82	13	90	13	70	9	65	31	27	10	71	17	38	7	139	13	69	18	45	1	163	18	54	39	38	76	25	93	21	24	47	28	37	33	40	67	50	63	32	23	47	14	58	32	31	0	.	2992	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	15	59	5	55	4	59	18	52	3	.	8	119	0	895	121	19	137	18	25	45	0	74	0	723	3	113	0	.	1	79	7	83	11	63	5	89	0	.	3	67	0	100	3	68	0	.	4	84	0	.	62	23	25	43	80	24	10	87	6	67	1	100	11	45	9	63	15	56	2	79	0	.	6	58	5	277	0	.	5	54	13	52	15	56	4	69	13	48	8	57	3	41	-3	372	0	100	0	.	2	55	25	44	14	38	13	59	0	.	1	100	0	.	0	.	0	.	1	100	4	70	1	259	5	74	4	95	0	.	0	100	7	47	0	100	1	.	3	58	0	100	2	180	7	89	12	46	0	.	3	71	4	43	2	88	5	63	21	52	0	1213	5	70	0	100	0	.	-2	129	3	80	5	54	10	60	15	73	13	45	3	92	0	100	4	70	0	.	840	7
	higher altitude zone	0	.	0	.	0	.	0	.	10	53	20	43	30	31	31	44	0	.	5	92	0	.	0	.	0	.	0	.	5	78	3	72	10	107	8	63	0	.	4	60	1	242	7	43	-2	252	4	57	0	100	17	48	8	55	15	61	13	82	9	55	24	36	2	75	1	160	21	49	1	863	-4	155	10	52	0	.	3	58	3	70	4	77	5	100	0	.	5	67	4	96	0	.	0	.	6	64	23	40	9	78	0	.	0	.	0	.	0	0	2	60	4	100	0	.	0	.	0	.	-2	100	1	100	0	.	6	75	0	.	2	69	3	71	2	229	26	34	-1	120	0	.	17	48	0	.	1	79	6	55	5	78	5	62	3	85	6	.	0	.	3	.	0	.	5	86	6	50	14	45	4	45	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	413	9
	total	15	59	5	55	4	59	18	52	13	46	27	45	30	33	151	17	136	18	31	40	0	74	0	723	3	113	0	.	6	62	10	61	21	61	14	51	0	.	7	45	1	316	9	37	-2	252	7	50	0	100	80	21	33	35	95	23	23	60	15	42	25	34	13	41	10	60	36	37	2	240	-4	155	15	39	5	277	3	58	7	43	17	44	20	49	4	69	18	40	12	51	3	41	-3	372	6	63	23	40	11	64	25	44	14	38	13	59	0	0	3	52	4	100	0	.	0	.	1	100	2	153	2	121	5	74	10	59	0	.	2	67	10	40	2	194	26	33	2	122	0	100	18	46	7	89	13	43	6	55	8	57	9	38	5	61	11	63	21	52	3	108	5	70	5	83	6	50	12	59	7	41	5	54	10	60	17	66	13	45	3	92	0	100	4	70	0	.	1240	5
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	6	51	15	79	8	56	7	55	0	.	0	71	1	294	3	47	20	53	1	370	0	100	4	68	2	85	8	.	0	100	-2	228	0	.	1	214	1	253	0	100	4	67	4	75	0	.	1	100	0	.	1	169	3	78	10	60	0	.	3	100	2	100	0	100	0	100	1	100	0	.	0	.	1	85	9	94	1	100	0	1045	-1	112	-1	100	19	96	-1	88	0	906	2	44	-2	76	0	.	0	.	1	100	2	61	1	477	4	53	0	.	1	100	0	.	1	77	0	.	1	65	8	48	0	283	-3	107	0	.	0	.	0	100	3	70	0	.	0	.	0	.	4	73	0	79	0	100	0	72	0	.	0	1305	0	.	7	74	0	0	0	.	-2	100	0	.	13	49	2	53	3	62	0	100	2	90	4	84	-3	76	5	40	3	68	1	132	4	51	0	.	200	17
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	100	-2	180	0	0	0	.	0	61	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	1	100	11	48	2	256	7	61	38	31	99	19	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-2	100	0	100	0	.	0	100	-1	117	0	.	0	.	0	.	0	.	0	0	0	.	0	.	3	100	-2	73	0	.	0	.	0	.	0	.	1	86	8	59	9	37	4	68	0	.	0	.	13	42	5	62	2	224	0	100	0	.	3	74	6	52	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	25	51	19	16	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	379	10
	total	6	51	15	79	8	56	7	55	0	.	0	89	-1	315	3	47	20	53	2	283	0	100	4	68	2	85	8	.	0	86	-2	228	0	.	1	214	1	253	1	94	15	40	6	95	7	61	39	30	99	19	1	156	3	78	10	60	0	.	3	100	2	100	0	100	0	100	1	100	0	.	-2	100	1	86	9	94	1	84	-1	183	-1	112	-1	100	19	96	-1	88	0	906	2	44	-2	76	3	100	-2	73	1	100	2	61	1	477	4	53	1	86	8	55	9	37	5	55	0	.	1	65	22	32	4	69	-1	606	0	100	0	.	3	71	10	42	0	100	0	.	0	.	4	73	0	79	0	100	0	72	0	.	0	1305	0	.	7	74	0	0	0	.	-2	100	0	.	97	23	53	18	19	28	0	100	2	90	4	84	-3	76	5	40	3	68	1	132	4	51	0	.	586	9
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	43	30	22	39	35	40	56	26	2	.	5	70	58	35	46	32	128	22	34	30	5	92	27	44	19	36	0	.	7	50	10	66	15	36	29	35	0	.	11	67	1	131	8	51	0	.	7	62	0	.	47	30	30	35	30	30	21	49	2	108	4	100	7	67	2	149	5	48	10	62	0	.	0	2892	22	49	14	44	13	78	14	52	7	78	47	27	23	43	8	86	12	42	22	37	9	51	0	.	11	52	13	39	11	48	24	36	0	.	5	67	0	.	12	70	27	43	8	45	19	47	23	38	12	48	1	368	0	.	2	214	7	59	2	93	2	.	-4	130	1	168	7	61	-2	123	3	39	1	100	10	63	27	36	6	64	10	46	9	42	2	58	7	59	0	.	0	.	11	41	5	62	5	51	19	38	7	83	30	33	7	63	15	40	14	38	0	.	1281	5
	higher altitude zone	0	.	0	.	0	.	0	.	3	111	10	49	3	212	23	44	3	.	20	38	0	.	0	.	2	.	0	.	0	100	1	100	2	87	0	.	0	.	8	63	0	1318	8	43	-1	155	1	78	0	.	13	63	0	368	0	100	6	92	5	64	8	36	-2	99	4	71	6	59	1	378	1	95	10	77	0	.	-1	199	-6	89	17	46	3	204	0	.	6	51	10	57	0	.	0	.	0	1335	2	119	5	77	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	2	73	1	100	0	.	8	49	0	.	1	65	2	59	1	67	2	90	0	.	0	.	1	272	0	.	5	92	7	45	1	175	1	149	3	74	1	.	0	.	4	.	0	.	0	.	0	92	3	76	1	100	0	.	0	.	0	373	0	.	0	.	0	.	0	.	0	.	202	13
	total	43	30	22	39	35	40	56	26	4	79	15	40	58	35	69	26	131	22	54	23	5	92	27	44	21	34	0	.	7	48	11	62	17	33	28	35	0	.	19	46	1	245	16	34	-1	155	8	57	0	.	60	27	30	35	30	30	28	43	7	56	12	40	5	113	6	65	11	39	11	65	1	71	10	122	22	49	13	51	7	158	31	34	11	85	47	27	30	35	18	50	12	42	22	37	9	60	2	119	16	43	13	39	11	48	24	36	0	.	5	67	0	.	13	64	27	43	8	45	21	44	24	38	12	48	9	53	0	.	3	147	9	49	4	64	5	67	-4	130	1	168	8	67	-2	123	8	62	8	42	11	59	28	35	9	49	11	42	9	42	6	67	7	59	0	.	0	92	14	36	6	53	5	51	19	38	7	81	30	33	7	63	15	40	14	38	0	.	1445	5
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	19	50	23	44	6	65	9	51	0	.	10	63	8	60	28	37	64	21	15	28	12	50	2	58	1	601	0	.	4	81	22	53	12	47	16	50	16	56	24	77	4	66	2	80	0	.	18	42	1	122	32	37	23	75	13	63	14	59	4	64	3	91	1	84	2	69	1	74	13	58	0	.	1	67	32	34	9	93	-2	159	16	53	6	99	17	41	7	46	5	36	10	67	16	54	12	57	0	.	7	88	46	36	29	41	16	55	1	.	22	52	5	127	21	41	2	479	38	42	-9	134	29	55	18	64	5	64	1	.	1	100	11	67	15	69	0	.	4	109	2	138	5	55	-2	117	3	54	1	100	3	257	6	42	4	51	11	56	11	49	2	97	11	81	0	388	3	87	10	45	7	85	19	38	21	50	0	6835	15	32	6	171	20	42	9	55	0	.	974	7
	higher altitude zone	0	.	0	.	0	.	0	.	2	95	0	544	10	54	5	52	0	.	2	65	0	.	0	.	2	.	0	.	1	137	0	1175	2	83	1	73	0	.	13	45	2	60	-1	100	16	49	11	33	9	62	3	49	0	317	1	69	0	577	1	87	2	108	0	100	2	95	2	76	-1	229	0	5896	9	68	0	.	1	78	0	775	1	66	14	41	0	.	1	74	1	134	0	.	0	.	3	54	2	56	0	282	0	.	0	.	0	.	6	68	1	69	0	836	0	100	0	.	0	.	0	158	0	.	0	100	4	42	2	95	3	111	2	71	2	64	4	41	1	105	0	.	1	62	0	.	0	.	0	92	1	46	1	66	2	79	1	.	0	.	0	.	0	.	13	47	6	41	7	75	8	100	0	.	0	.	1	73	0	.	0	.	0	.	0	.	0	.	179	12
	total	19	50	23	44	6	65	9	51	2	95	10	65	17	40	33	33	63	21	17	26	12	50	2	58	2	192	0	.	5	69	23	53	14	42	17	46	16	56	37	52	5	48	1	189	16	49	29	29	10	56	35	35	22	77	13	60	15	59	5	55	4	70	0	199	4	60	3	62	12	63	0	5896	10	61	32	34	10	88	-2	175	17	49	20	40	17	41	8	44	5	36	10	67	16	54	15	46	2	56	7	95	46	36	29	41	16	55	7	62	23	51	5	125	21	41	2	479	38	42	-9	132	29	55	18	63	9	40	2	74	4	82	13	57	17	61	4	43	4	86	2	138	6	47	-2	117	3	54	1	74	4	194	7	37	7	44	12	52	11	49	2	97	11	81	13	47	9	39	17	40	15	67	19	38	21	50	1	669	15	32	6	171	20	42	9	55	0	.	1126	6
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	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	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	.	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	0	0	.	0	.	0	0	0	.	0	.	0	.	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	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	104	23	91	26	83	28	115	25	4	.	33	60	114	24	271	16	532	11	96	24	19	44	42	36	36	34	8	.	15	51	62	36	54	31	76	26	17	58	48	52	13	46	24	39	0	.	37	31	1	122	210	16	91	29	189	20	112	33	16	50	10	72	26	39	17	53	22	44	32	41	0	.	12	97	67	56	43	40	18	69	46	34	30	79	141	22	72	28	22	42	51	33	38	39	22	45	0	.	22	53	126	24	95	27	105	25	2	.	36	42	5	127	34	36	30	48	48	34	23	75	52	35	32	44	13	72	3	.	4	122	31	42	22	58	3	.	24	64	15	57	22	44	16	67	25	39	7	73	21	60	40	31	28	39	37	39	59	37	3	95	41	44	14	49	8	56	24	32	19	50	56	29	87	27	61	47	125	22	42	38	50	31	62	26	0	.	4704	3
	higher altitude zone	0	.	0	.	0	.	0	.	53	32	72	35	100	44	131	25	2	.	15	128	0	.	0	.	4	.	0	.	24	62	44	36	-3	924	41	37	0	.	80	32	103	23	70	28	96	23	181	18	191	16	25	122	15	51	24	58	62	63	21	49	72	26	2	122	18	52	51	32	12	101	12	94	30	76	0	.	19	53	36	36	36	37	81	32	0	.	13	45	11	86	0	.	0	.	35	32	65	48	31	38	0	.	0	.	0	.	24	34	36	33	26	32	5	60	0	.	0	.	16	42	6	58	6	137	20	47	12	42	33	39	24	43	33	37	55	32	7	74	0	.	23	51	0	.	8	82	40	25	12	45	20	43	7	128	10	.	0	.	6	.	0	.	140	21	136	18	132	19	33	52	4	.	0	.	28	87	0	.	0	.	0	.	0	.	0	.	2748	5
	total	104	23	91	26	83	28	115	25	57	30	105	30	208	25	401	13	534	11	111	26	19	44	42	36	39	32	8	.	39	42	106	26	51	66	118	21	17	58	127	28	115	21	94	23	96	23	218	16	193	16	234	19	105	26	213	19	174	31	38	35	82	24	28	37	34	37	73	26	44	41	12	92	42	61	67	56	63	32	54	33	82	25	111	31	141	22	85	25	33	40	51	33	38	39	57	26	65	48	53	31	126	24	95	27	105	25	26	33	72	26	30	33	39	32	30	48	48	34	39	47	59	32	38	43	33	40	15	39	37	37	55	30	55	32	58	31	31	51	15	57	45	34	16	67	33	35	46	24	33	41	61	25	35	40	47	38	59	37	10	74	41	44	155	19	144	17	154	17	52	38	59	28	87	27	89	42	125	22	42	38	50	31	62	26	0	.	7389	2

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. 2287760. Birmensdorf, Swiss Federal Research Institute WSL

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