Selection from the list of topics

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

development stage #20
Stage of stand development, defined by the dominant diameter at breast height (dbhdom = dbh of the 100 largest [thickest] trees per hectare). Young growth/thicket: dbhdom <12 cm, pole timber: dbhdom 12-30 cm, young timber: dbhdom 31-40 cm, medium timber : dbhdom 41-50 cm, old timber: dbhdom >50 cm, mixed: trees of different development stages, no development stage predominant or groups of different development stages covering < 500 m². Reference: Field Survey (MID 261: Entwicklungsstufe)

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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NFI3
growing stock (stemwood)
development stage · higher/lower altitude zone
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
development stage	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	.	51	100	0	.	0	.	0	.	0	.	0	.	5	.	46	77	0	.	0	.	0	.	0	.	0	.	0	.	146	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	55	.	0	.	132	.	42	.	130	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	4	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	731	36
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	337	56	82	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	82	116	.	0	.	150	95	0	.	18	.	0	.	328	65	32	.	0	.	0	.	0	.	121	67	0	.	0	.	0	.	0	.	0	.	0	.	83	100	0	.	0	.	0	.	0	0	0	.	38	100	0	.	0	.	0	.	0	.	0	.	0	.	192	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	164	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	-4	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1900	24
	total	0	.	51	100	0	.	0	.	0	.	0	.	337	56	87	74	46	77	0	.	0	.	0	.	0	.	0	.	0	.	146	.	89	82	125	93	0	.	150	95	0	.	18	.	0	.	383	58	32	.	132	100	42	.	130	.	121	67	0	.	0	.	0	.	0	.	0	.	0	.	83	100	0	.	0	.	0	.	0	0	0	.	38	100	11	.	0	.	0	.	0	.	0	.	0	.	192	71	0	.	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	164	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	4	.	0	.	0	0	0	0	-4	.	0	.	24	100	0	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	2631	20
young growth/ thicket	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	564	45	202	47	0	.	144	.	0	.	13	.	118	74	108	58	321	54	136	66	253	61	15	100	153	.	0	.	94	.	5	.	65	100	38	100	0	.	6	100	0	.	4	.	0	.	0	.	0	.	18	100	217	71	3	.	0	.	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	30	.	0	.	0	.	208	63	0	.	107	.	0	.	47	.	0	.	0	.	0	.	0	.	62	.	0	.	0	.	0	.	51	82	52	76	53	.	66	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	2	.	57	.	0	.	0	.	19	100	0	.	0	.	0	.	187	83	0	.	0	.	0	.	0	.	50	73	0	.	8	100	16	.	0	.	333	63	3	.	210	71	26	86	65	100	209	71	0	.	4384	14
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	464	66	128	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	292	71	193	74	72	71	233	60	120	97	89	71	0	.	0	.	0	.	82	103	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	177	83	0	.	1	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	81	0	.	22	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	.	0	.	0	.	0	.	0	.	0	.	301	50	50	62	144	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2680	20
	total	564	45	202	47	0	.	144	.	0	.	13	100	582	55	236	60	321	54	136	66	253	61	15	100	153	.	0	.	94	.	5	.	75	87	38	100	0	.	298	70	193	74	76	67	233	60	120	97	89	71	18	100	217	71	3	.	82	103	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	177	83	30	.	1	.	0	.	208	63	10	.	107	100	0	.	47	.	0	.	7	.	0	.	0	.	62	.	0	.	0	.	52	.	51	82	52	76	53	.	66	.	0	.	0	.	0	.	147	81	0	.	22	.	0	.	1	.	4	.	2	.	57	.	0	.	0	.	19	100	0	.	0	.	82	.	187	83	0	.	0	.	0	.	0	.	351	44	50	62	151	67	16	.	0	.	333	63	3	.	210	71	26	86	65	100	209	71	0	.	7064	12
pole timber	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	237	58	428	49	243	55	302	63	0	.	0	.	247	104	312	54	1135	27	249	73	0	.	27	.	104	.	0	.	28	.	120	.	127	79	90	84	0	.	0	.	8	.	128	91	0	.	555	45	0	.	623	51	320	54	603	44	26	.	69	72	49	.	24	100	17	71	0	.	143	88	10	.	28	.	157	71	432	63	28	.	0	.	138	.	386	49	159	62	9	.	124	63	47	.	188	79	0	.	0	.	655	53	247	64	726	52	0	.	188	42	0	.	684	37	459	49	305	40	687	36	299	58	565	39	73	.	0	.	0	.	154	.	53	.	0	.	162	77	161	75	165	86	19	.	71	77	0	.	93	.	0	.	0	.	280	60	14	.	0	.	0	.	85	61	114	82	320	42	37	.	87	.	460	60	478	53	160	98	254	61	11	72	293	53	0	.	16282	8
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	193	75	532	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	.	415	54	99	72	304	71	0	.	208	58	241	68	274	66	401	40	986	29	499	40	0	.	52	.	0	.	453	62	0	.	219	82	0	.	0	.	0	.	0	.	110	.	0	.	0	.	0	.	51	65	41	.	103	.	0	.	39	.	139	.	0	.	0	.	98	62	327	67	68	.	0	.	0	.	0	.	112	65	80	58	23	.	0	.	0	.	0	.	256	74	19	100	0	.	0	.	75	.	33	.	85	88	42	77	16	.	0	.	0	.	0	.	0	.	0	.	0	.	38	.	169	.	0	.	0	.	0	.	0	.	0	.	754	38	767	47	396	48	120	.	38	.	0	.	198	.	0	.	0	.	0	.	0	.	0	.	9195	11
	total	237	58	428	49	243	55	302	63	0	.	193	75	779	43	312	54	1135	27	249	73	0	.	27	.	104	.	0	.	150	83	536	48	226	54	393	58	0	.	208	58	249	66	402	54	401	40	1541	25	499	40	623	51	372	49	603	44	479	59	69	72	268	69	24	100	17	71	0	.	143	88	120	92	28	.	157	71	432	63	79	55	41	.	240	71	386	49	199	53	148	94	124	63	47	.	286	56	327	67	68	.	655	53	247	64	726	52	112	65	268	34	23	.	684	37	459	49	305	40	942	33	318	55	565	39	73	.	75	.	33	.	239	71	94	65	16	.	162	77	161	75	165	86	19	.	71	77	0	.	131	76	169	.	0	.	280	60	14	.	0	.	0	.	839	34	881	42	716	33	157	80	125	76	460	60	676	48	160	98	254	61	11	72	293	53	0	.	25476	6
young timber	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	232	71	266	59	102	.	98	.	0	.	246	.	294	71	528	47	2384	27	1761	32	210	.	303	50	605	57	0	.	58	.	705	52	519	54	211	.	355	59	1233	71	104	88	262	72	0	.	437	47	0	.	1647	31	372	58	819	52	320	.	123	.	0	.	0	.	38	.	0	.	345	75	0	.	176	.	1334	37	113	.	134	.	536	60	622	64	1788	33	378	57	293	59	286	50	491	58	42	.	0	.	0	.	169	73	435	61	295	60	0	.	107	.	78	.	114	79	298	66	1436	28	134	.	722	51	1031	34	0	.	0	.	108	.	0	.	206	82	0	.	0	.	34	.	19	.	29	.	0	.	0	.	255	62	674	46	136	.	183	.	270	.	124	.	0	.	335	58	74	71	388	55	0	.	447	58	299	65	84	72	398	55	101	.	111	.	433	59	0	.	30297	7
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	324	.	483	42	586	51	0	.	1177	37	0	.	0	.	0	.	0	.	0	.	589	63	0	.	177	.	0	.	1005	74	351	60	437	51	126	71	1119	37	415	37	386	81	0	.	0	.	399	59	143	.	142	.	0	.	0	.	0	.	280	67	0	.	278	.	0	.	0	.	398	66	0	.	802	56	0	.	0	.	104	.	0	.	0	.	0	.	115	91	178	.	0	.	0	.	0	.	0	.	242	71	0	.	26	.	0	.	0	.	45	.	0	.	0	.	476	55	0	.	0	.	0	.	0	.	277	79	101	75	0	.	257	71	0	.	0	.	771	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1148	33	1096	33	655	47	147	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15254	11
	total	232	71	266	59	102	.	98	.	0	.	570	71	778	38	1114	35	2384	27	2938	24	210	.	303	50	605	57	0	.	58	.	1294	40	519	54	388	71	355	59	2238	51	455	51	699	42	126	71	1555	30	415	37	2033	30	372	58	819	52	719	55	266	71	142	.	0	.	38	.	0	.	625	51	0	.	453	72	1334	37	113	.	532	56	536	60	1424	42	1788	33	378	57	397	51	286	50	491	58	42	.	115	91	178	.	169	73	435	61	295	60	0	.	349	58	78	.	140	67	298	66	1436	28	179	79	722	51	1031	34	476	55	0	.	108	.	0	.	206	82	277	79	101	75	34	.	276	67	29	.	0	.	771	40	255	62	674	46	136	.	183	.	270	.	124	.	0	.	1483	28	1170	32	1043	36	147	.	447	58	299	65	84	72	398	55	101	.	111	.	433	59	0	.	45551	6
medium timber	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	1093	37	689	50	529	52	1247	39	0	.	326	73	1557	39	1540	38	4082	23	2922	27	256	71	895	47	1446	44	0	.	32	.	475	63	291	73	1602	35	148	.	1002	47	166	.	0	.	0	.	421	60	0	.	3200	23	1412	53	1033	45	1116	41	182	77	44	.	249	71	0	.	108	.	452	59	0	.	124	.	1251	46	750	58	473	51	299	75	111	.	1669	32	399	78	1016	36	887	45	1821	30	101	.	0	.	347	75	606	57	494	58	1078	44	0	.	160	81	0	.	0	.	58	.	730	43	0	.	605	41	310	64	0	.	0	.	0	.	653	52	129	.	144	.	223	78	87	.	668	52	436	71	379	64	247	71	89	.	677	50	308	73	708	45	852	52	210	.	71	.	0	.	0	.	381	55	267	60	995	47	799	45	929	46	1267	37	286	71	928	52	339	71	0	.	53876	5
	higher altitude zone	0	.	0	.	0	.	0	.	649	45	640	61	3348	22	947	47	85	.	1792	30	0	.	0	.	0	.	0	.	366	71	617	64	684	50	364	59	0	.	260	66	1312	35	1255	37	1566	34	2358	29	4756	19	1530	39	0	.	0	.	637	62	0	.	812	41	141	.	0	.	219	71	853	49	218	.	1	.	0	.	336	71	548	46	149	.	1686	48	0	.	0	.	344	59	0	.	0	.	221	71	660	61	768	50	0	.	0	.	0	.	33	.	509	51	1031	43	0	.	0	.	0	.	91	.	140	74	185	.	390	65	378	.	1238	44	388	.	770	55	652	58	137	.	0	.	379	52	0	.	195	82	736	36	68	.	400	59	135	.	332	.	0	.	0	.	0	.	1025	40	1845	31	2719	26	0	.	0	.	0	.	282	92	0	.	0	.	0	.	0	.	0	.	44181	6
	total	1093	37	689	50	529	52	1247	39	649	45	966	47	4906	19	2487	29	4167	22	4714	20	256	71	895	47	1446	44	0	.	398	65	1092	45	975	41	1966	31	148	.	1261	40	1478	33	1255	37	1566	34	2779	26	4756	19	4730	20	1412	53	1033	45	1754	34	182	77	856	39	391	58	0	.	327	58	1305	38	218	.	126	99	1251	46	1086	45	1021	34	448	60	1797	46	1669	32	399	78	1360	31	887	45	1821	30	322	58	660	61	1114	41	606	57	494	58	1078	44	33	.	670	43	1031	43	0	.	58	.	730	43	91	.	745	36	495	55	390	65	378	.	1238	44	1041	49	899	50	796	51	360	62	87	.	1047	38	436	71	574	51	983	32	157	71	1076	38	443	59	1040	44	852	52	210	.	71	.	1025	40	1845	31	3100	24	267	60	995	47	799	45	1211	41	1267	37	286	71	928	52	339	71	0	.	98057	4
old timber	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	1299	36	765	44	649	58	3732	25	324	.	909	58	2253	35	6495	21	9714	15	667	53	341	71	412	68	232	73	0	.	710	58	1064	49	738	62	847	58	258	.	387	91	223	58	575	53	0	.	353	74	0	.	3774	23	1102	37	3673	26	2571	39	0	.	0	.	518	59	259	88	357	71	0	.	0	.	629	50	1059	52	987	52	851	53	1176	50	1403	45	955	48	1714	40	187	.	492	59	525	78	451	64	0	.	667	59	858	48	1913	39	1706	32	0	.	153	.	0	.	252	70	0	.	79	.	358	65	55	.	512	52	349	70	89	.	0	.	319	74	245	.	0	.	1276	52	0	.	341	73	402	71	467	74	0	.	542	55	170	.	584	51	125	.	614	76	129	.	785	59	121	.	0	.	199	73	482	71	914	48	134	.	1215	41	1903	31	1421	39	922	49	554	58	0	.	76489	5
	higher altitude zone	0	.	0	.	0	.	0	.	2141	35	2153	36	4427	25	2892	28	0	.	258	67	0	.	0	.	47	.	0	.	196	.	0	.	892	49	446	58	0	.	1726	40	4282	24	957	46	3474	24	5623	20	4257	21	545	53	917	59	679	67	1772	42	100	.	1188	42	456	71	589	59	510	72	334	73	269	.	2759	32	0	.	229	74	1161	45	1784	37	1685	48	0	.	317	75	0	.	0	.	0	.	2207	30	2227	33	875	57	0	.	0	.	0	.	671	47	363	76	487	60	88	.	0	.	0	.	284	72	240	85	321	.	313	81	344	71	602	59	836	47	490	63	589	63	0	.	0	.	1022	43	0	.	0	.	229	80	764	51	0	.	956	39	0	.	0	.	0	.	0	.	3808	21	1221	40	2210	30	140	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70351	5
	total	1299	36	765	44	649	58	3732	25	2465	33	3061	30	6679	20	9387	17	9714	15	925	42	341	71	412	68	279	63	0	.	907	51	1064	49	1629	39	1293	43	258	.	2113	37	4505	23	1531	35	3474	24	5975	19	4257	21	4318	22	2020	33	4353	24	4343	29	100	.	1188	42	974	46	849	49	868	51	334	73	269	.	3388	28	1059	52	1216	44	2013	34	2960	30	3088	33	955	48	2031	36	187	.	492	59	525	78	2659	27	2227	33	1542	41	858	48	1913	39	1706	32	671	47	517	61	487	60	340	58	0	.	79	.	643	48	295	71	833	50	662	53	433	60	602	59	1156	40	735	54	589	63	1276	52	0	.	1363	37	402	71	467	74	229	80	1306	38	170	.	1540	31	125	.	614	76	129	.	785	59	3929	20	1221	40	2409	28	623	59	914	48	134	.	1215	41	1903	31	1421	39	922	49	554	58	0	.	146841	3
mixed	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	864	85	242	.	344	61	333	63	0	.	486	71	1078	53	1854	29	1403	39	388	63	236	71	0	.	338	56	153	.	0	.	238	68	109	.	339	71	0	.	226	.	0	.	388	57	0	.	0	.	123	73	446	58	217	86	311	.	441	61	98	.	0	.	221	73	370	60	459	64	0	.	0	.	56	.	224	75	0	.	38	.	0	.	441	62	94	.	112	.	0	.	128	.	0	.	0	.	0	.	0	.	674	52	0	.	0	.	92	.	703	56	165	.	209	52	1261	30	589	47	977	35	108	71	111	.	204	.	0	.	104	.	137	73	0	.	0	.	134	.	13	.	195	71	0	.	0	.	0	.	132	.	111	83	0	.	0	.	0	.	0	.	177	.	0	.	208	78	136	68	0	.	0	.	196	.	611	50	45	.	217	74	0	.	80	.	0	.	20385	9
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	66	.	4767	18	2238	26	0	.	1186	36	0	.	0	.	0	.	0	.	0	.	120	.	1083	44	0	.	0	.	502	48	1022	33	927	37	1156	31	2294	25	2889	19	3	.	290	59	0	.	847	42	507	60	350	70	0	.	110	100	1922	30	0	.	943	37	513	51	0	.	80	.	490	54	0	.	179	80	0	.	226	73	0	.	0	.	0	.	0	.	228	75	212	61	0	.	0	.	0	.	403	55	747	46	817	43	107	98	0	.	0	.	410	55	105	71	53	.	99	82	290	72	307	63	342	61	436	55	585	68	0	.	0	.	187	73	0	.	0	.	492	41	0	.	261	63	32	.	0	.	0	.	358	.	0	.	2150	31	2402	23	1672	29	175	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37579	6
	total	864	85	242	.	344	61	333	63	0	.	551	64	5845	18	4092	19	1403	39	1574	31	236	71	0	.	338	56	153	.	0	.	357	56	1192	41	339	71	0	.	728	45	1022	33	1315	31	1156	31	2294	25	3012	18	449	58	507	50	311	.	1288	35	605	53	350	70	221	73	480	52	2380	27	0	.	943	37	569	47	224	75	80	.	528	50	0	.	620	50	94	.	337	59	0	.	128	.	0	.	0	.	228	75	212	61	674	52	0	.	0	.	495	48	1450	36	982	40	316	48	1261	30	589	47	1388	29	213	50	164	75	303	72	290	72	411	53	479	48	436	55	585	68	134	.	13	.	382	51	0	.	0	.	492	41	132	.	373	50	32	.	0	.	0	.	358	.	177	.	2150	31	2610	22	1808	27	175	.	0	.	196	.	611	50	45	.	217	74	0	.	80	.	0	.	57964	5
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	4290	23	2644	23	1867	28	5856	19	324	.	1979	36	5547	21	10841	14	19084	10	6123	17	1296	33	1653	32	2878	27	153	.	923	47	2753	27	1849	32	3136	26	760	48	2854	37	501	46	1357	32	0	.	1820	26	123	73	9840	13	3683	25	6573	18	4474	26	514	43	93	71	1012	38	684	47	924	43	940	42	10	.	1013	38	4027	24	2282	32	1555	34	2011	35	2715	30	5111	18	2762	29	1612	28	1917	28	2930	25	782	44	0	.	1014	46	2991	25	3151	27	3806	22	92	.	1311	35	294	64	1310	26	2129	23	3206	17	2156	23	1790	27	2529	21	626	52	89	.	212	71	1263	36	633	52	144	.	1797	39	351	46	1388	34	887	48	937	46	247	71	1116	34	1632	30	1216	35	1297	33	1754	40	463	60	1032	48	592	40	397	49	1432	25	802	47	2444	28	2286	26	3320	23	3984	20	2304	28	2038	33	1908	27	0	.	202443	2
	higher altitude zone	0	.	0	.	0	.	0	.	2789	29	3376	28	14358	11	6873	16	85	.	4413	18	0	.	0	.	47	.	0	.	684	51	1741	34	2857	26	1406	32	0	.	4143	26	7400	16	3939	20	6955	15	12827	12	12937	11	2463	30	1259	45	679	67	4312	23	750	46	2712	24	597	59	700	52	2651	26	1467	35	1622	31	3551	27	0	.	822	41	2648	26	1974	34	4492	27	0	.	592	49	587	45	0	.	0	.	2527	27	3756	24	2101	32	0	.	0	.	0	.	1219	32	1994	27	2359	27	221	63	0	.	0	.	1086	34	504	47	559	67	1425	33	1216	42	2202	31	1652	36	1738	33	2287	31	238	66	0	.	1845	29	0	.	195	82	2229	21	871	46	912	37	1122	35	332	.	0	.	358	.	0	.	9185	13	7378	14	7795	14	607	49	38	.	0	.	481	68	0	.	0	.	0	.	0	.	0	.	181140	2
	total	4290	23	2644	23	1867	28	5856	19	3114	28	5355	22	19906	10	17714	11	19169	10	10536	12	1296	33	1653	32	2924	27	153	.	1607	34	4493	21	4705	20	4542	20	760	48	6997	22	7901	16	5296	17	6955	15	14647	11	13060	10	12303	12	4942	22	7252	18	8786	17	1264	32	2805	24	1609	32	1384	35	3575	22	2407	27	1632	31	4564	23	4027	24	3103	26	4204	21	3985	24	7207	20	5111	18	3354	25	2199	24	1917	28	2930	25	3309	23	3756	24	3114	26	2991	25	3151	27	3806	22	1310	30	3305	21	2652	25	1531	24	2129	23	3206	17	3243	19	2294	23	3088	21	2051	28	1306	40	2414	28	2914	26	2372	28	2430	30	2035	36	351	46	3233	22	887	48	1132	40	2476	20	1987	28	2544	23	2339	25	1629	33	1754	40	821	55	1032	48	9777	12	7774	14	9227	13	1409	34	2481	28	2286	26	3801	22	3984	20	2304	28	2038	33	1908	27	0	.	383583	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2281385. Birmensdorf, Swiss Federal Research Institute WSL

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