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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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
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 1993/95
		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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	.	0	.	0	.	57	.	67	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	24	.	0	.	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	300	40
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	0	.	29	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	163	48
	total	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	36	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	44	.	29	.	0	.	57	.	67	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	24	.	0	.	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	463	31
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	0	.	11	100	65	81	1	.	0	.	1	.	0	.	30	100	334	57	4	.	0	.	3	72	121	.	0	.	108	81	0	.	0	.	17	100	0	.	5	.	51	.	0	.	0	.	27	97	0	.	1	100	2	.	158	92	0	.	23	100	0	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	59	.	3	.	0	.	0	.	28	100	3	100	0	.	29	.	0	.	0	.	0	.	0	.	11	78	0	.	62	100	0	.	0	.	0	.	26	100	0	.	60	.	43	76	0	.	26	100	0	.	60	.	0	.	97	71	0	.	0	.	61	100	0	.	1	.	0	.	25	89	0	.	0	.	0	.	154	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	21	71	0	.	41	.	0	.	0	.	5	77	0	.	73	100	0	.	1860	20
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	56	.	261	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	126	82	277	58	0	0	239	60	372	56	120	61	0	.	9	.	0	.	351	64	0	.	-1	.	0	.	0	.	0	.	0	.	107	71	45	81	0	.	14	100	12	100	34	100	79	.	0	.	8	.	14	.	0	.	0	.	27	100	96	71	0	.	0	.	0	.	0	.	67	92	45	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	36	99	0	.	37	71	2	.	61	100	0	.	50	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	124	58	70	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2798	17
	total	0	.	11	100	65	81	1	.	0	.	57	98	261	65	30	100	334	57	4	.	0	.	3	72	121	.	0	.	108	81	0	.	21	100	17	100	0	.	131	79	329	51	0	0	239	60	399	53	120	61	1	100	11	87	158	92	351	64	23	100	-1	.	0	.	0	.	0	.	0	.	114	67	45	81	0	.	73	83	16	82	34	100	79	.	28	100	11	80	14	.	29	.	0	.	27	100	96	71	0	.	11	78	0	.	62	100	67	92	45	.	0	.	26	100	0	.	60	.	43	76	30	100	26	100	36	99	60	.	37	71	99	69	61	100	0	.	111	71	0	.	1	100	0	.	25	89	0	.	0	.	0	.	154	.	0	.	0	.	0	.	0	.	9	100	124	58	73	81	21	71	0	.	41	.	0	.	0	.	5	77	0	.	73	100	0	.	4658	13
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	516	39	295	51	429	56	295	64	0	.	0	.	315	55	635	54	2177	26	627	39	0	.	234	65	286	55	0	.	0	.	504	52	269	68	0	.	392	58	247	.	129	83	7	.	0	.	833	33	0	.	1538	31	345	53	519	46	215	93	47	.	17	.	241	66	0	.	0	.	256	56	0	.	206	76	494	53	258	63	76	.	431	57	363	58	852	41	106	73	149	72	208	63	0	.	132	79	0	.	0	.	624	45	435	50	623	57	0	.	200	46	29	87	703	33	555	36	1390	24	666	38	594	37	823	29	149	75	0	.	0	.	54	.	51	.	0	.	40	.	88	72	169	64	23	.	7	.	80	.	318	54	357	71	99	.	243	52	300	74	0	.	0	.	391	47	98	61	270	43	73	.	80	73	367	59	442	49	454	47	254	61	42	.	181	60	0	.	25913	6
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	65	.	686	38	633	41	0	.	313	67	0	.	0	.	0	.	0	.	220	72	158	59	302	63	320	61	0	.	147	70	288	54	159	85	316	44	881	31	645	27	241	.	87	.	0	.	732	43	96	.	136	.	0	.	0	.	113	.	221	62	430	59	145	81	0	.	156	86	355	56	0	.	184	71	0	.	0	.	98	71	0	.	0	.	20	.	523	55	237	75	0	.	0	.	0	.	40	72	269	57	4	.	15	83	0	.	0	.	287	72	83	89	38	.	327	54	182	76	113	.	28	.	26	71	20	.	0	.	0	.	332	70	0	.	83	.	197	64	20	.	102	.	0	.	0	.	0	.	0	.	0	.	1183	24	707	35	507	44	301	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13769	8
	total	516	39	295	51	429	56	295	64	0	.	65	.	1002	32	1268	34	2177	26	940	34	0	.	234	65	286	55	0	.	220	72	662	42	570	46	320	61	392	58	393	68	417	45	165	81	316	44	1715	22	645	27	1779	30	431	47	519	46	948	39	143	75	153	89	241	66	0	.	113	.	477	42	430	59	351	56	494	53	414	51	432	49	431	57	547	46	852	41	106	73	246	52	208	63	0	.	152	70	523	55	237	75	624	45	435	50	623	57	40	72	469	38	33	77	718	32	555	36	1390	24	954	34	677	34	860	28	477	44	182	76	113	.	82	74	77	70	20	.	40	.	88	72	501	51	23	.	90	92	277	54	338	51	458	60	99	.	243	52	300	74	0	.	0	.	1573	21	806	32	777	32	373	52	80	73	367	59	442	49	454	47	254	61	42	.	181	60	0	.	39682	5
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	1292	31	563	48	334	72	711	46	0	.	793	65	973	54	1355	37	3131	24	1976	31	164	.	833	47	245	71	92	.	161	.	592	66	1208	49	971	46	157	.	667	52	0	.	171	71	0	.	112	.	0	.	2555	27	829	50	775	46	514	53	91	.	9	.	0	.	147	77	83	.	392	58	0	.	550	58	849	38	734	58	84	.	82	.	301	.	1081	39	378	69	632	52	231	80	1942	29	47	.	0	.	410	58	0	.	87	.	199	75	0	.	0	.	0	.	44	.	149	77	547	45	382	53	391	52	417	71	0	.	0	.	58	.	215	74	61	.	0	.	240	71	400	93	539	47	216	.	302	64	121	.	144	68	500	43	534	51	479	52	256	.	105	.	0	.	90	.	13	.	666	41	313	71	289	72	262	71	701	42	956	40	169	.	697	52	102	.	0	.	39858	6
	higher altitude zone	0	.	0	.	0	.	0	.	801	46	624	60	2379	22	1203	41	0	.	1831	33	0	.	0	.	0	.	0	.	365	71	807	52	1444	48	0	.	0	.	702	83	1433	34	638	48	355	61	1987	29	4148	19	825	52	89	.	0	.	0	.	0	.	264	71	197	.	0	.	124	72	251	.	632	58	764	48	0	.	0	.	366	62	0	.	843	66	0	.	357	64	252	72	0	.	0	.	315	55	396	73	207	66	0	.	0	.	0	.	0	.	72	.	0	.	0	.	0	.	0	.	189	71	0	.	207	.	36	.	366	.	1067	47	0	.	29	.	340	73	294	72	0	.	410	58	0	.	0	.	472	43	0	.	91	107	133	.	66	.	0	.	0	.	0	.	701	32	951	34	1186	39	153	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	31390	7
	total	1292	31	563	48	334	72	711	46	801	46	1417	45	3352	22	2558	28	3131	24	3807	22	164	.	833	47	245	71	92	.	526	58	1399	41	2652	34	971	46	157	.	1369	50	1433	34	809	40	355	61	2099	28	4148	19	3380	24	917	46	775	46	514	53	91	.	273	69	197	.	147	77	208	59	643	53	632	58	1314	37	849	38	734	58	450	54	82	.	1143	56	1081	39	735	47	885	43	231	80	1942	29	362	49	396	73	617	44	0	.	87	.	199	75	0	.	72	.	0	.	44	.	149	77	547	45	571	42	391	52	624	58	36	.	366	.	1125	45	215	74	90	75	340	73	534	51	400	93	949	37	216	.	302	64	593	40	144	68	591	40	667	46	546	47	256	.	105	.	0	.	791	31	964	34	1852	29	466	58	289	72	262	71	729	41	956	40	169	.	697	52	102	.	0	.	71248	5
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	1152	43	854	57	505	60	3214	27	321	.	273	.	2582	34	4248	23	7081	17	2646	27	705	49	39	.	1720	36	0	.	621	60	719	61	557	75	2280	34	0	.	1113	49	380	73	415	59	0	.	211	.	0	.	3731	21	1603	45	1952	36	1776	40	366	50	0	.	525	61	0	.	456	56	0	.	0	.	230	.	1246	46	1195	51	619	54	0	.	1219	44	1845	31	589	58	582	62	1643	38	375	59	352	76	0	.	243	.	1549	37	1282	41	923	40	0	.	135	.	0	.	0	.	125	76	138	.	19	.	201	61	292	72	165	.	0	.	0	.	434	59	174	75	133	.	854	61	107	.	363	71	839	50	234	.	0	.	705	54	524	59	684	52	205	.	250	65	296	76	1433	52	122	.	114	.	22	.	217	.	726	46	2041	35	1427	39	1574	37	1055	45	465	73	1044	46	0	.	73054	5
	higher altitude zone	0	.	0	.	0	.	0	.	542	56	1518	39	5620	20	793	46	0	.	873	42	0	.	0	.	0	.	0	.	0	.	292	72	804	48	944	58	0	.	136	.	739	36	924	39	2632	27	3896	23	3220	21	1032	48	0	.	0	.	1407	35	0	.	1141	38	139	.	190	71	1115	45	650	61	708	52	1450	55	0	.	372	60	441	59	176	.	1440	49	0	.	267	78	111	.	0	.	0	.	797	42	2045	38	630	53	0	.	0	.	0	.	360	67	223	71	798	44	0	.	0	.	0	.	0	.	210	83	0	.	522	71	0	.	514	59	432	80	474	76	628	62	0	.	0	.	642	50	0	.	163	.	534	43	308	61	455	65	400	50	0	.	0	.	0	.	0	.	1783	28	1920	32	1665	30	0	.	0	.	0	.	293	.	0	.	0	.	0	.	0	.	0	.	49369	6
	total	1152	43	854	57	505	60	3214	27	864	51	1790	36	8202	17	5040	21	7081	17	3519	23	705	49	39	.	1720	36	0	.	621	60	1011	48	1360	42	3224	30	0	.	1250	45	1119	34	1338	32	2632	27	4107	22	3220	21	4764	19	1603	45	1952	36	3183	27	366	50	1141	38	665	52	190	71	1571	36	650	61	708	52	1679	49	1246	46	1567	41	1060	40	176	.	2659	33	1845	31	856	47	693	54	1643	38	375	59	1149	37	2045	38	873	47	1549	37	1282	41	923	40	360	67	358	58	798	44	0	.	125	76	138	.	19	.	411	52	292	72	688	59	0	.	514	59	866	50	648	59	761	54	854	61	107	.	1004	41	839	50	397	72	534	43	1013	42	979	43	1084	37	205	.	250	65	296	76	1433	52	1904	27	2034	31	1687	30	217	.	726	46	2041	35	1720	37	1574	37	1055	45	465	73	1044	46	0	.	122423	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	1806	35	1877	36	377	72	2358	32	0	.	604	72	1076	46	3998	27	7285	17	204	.	949	51	374	72	122	.	0	.	273	.	650	50	481	.	469	72	159	.	181	.	67	.	604	50	0	.	84	.	27	.	1617	36	1495	41	2816	32	1735	46	0	.	0	.	101	.	229	.	120	.	0	.	0	.	410	.	831	52	0	.	677	61	962	61	591	71	827	47	1165	49	513	59	665	60	586	52	0	.	0	.	248	.	667	61	879	52	1762	34	0	.	448	71	0	.	204	70	0	.	227	67	593	48	0	.	115	.	149	.	0	.	0	.	0	.	286	.	0	.	530	75	0	.	0	.	83	.	420	70	0	.	0	.	135	.	0	.	280	.	604	75	0	.	340	72	0	.	0	.	328	59	0	.	770	51	189	72	1373	42	756	45	859	46	1048	48	626	50	0	.	53291	6
	higher altitude zone	0	.	0	.	0	.	0	.	636	59	185	.	3417	27	2341	32	68	.	614	51	0	.	0	.	98	.	0	.	0	.	0	.	237	71	0	.	0	.	1726	36	2829	28	1120	43	1893	30	3400	25	2487	25	210	71	826	59	201	75	847	60	457	80	352	59	208	.	464	71	1192	42	0	.	0	.	507	58	0	.	413	.	652	65	876	50	1272	53	0	.	0	.	0	.	0	.	0	.	670	55	594	58	860	53	0	.	0	.	0	.	527	50	319	74	319	79	0	.	0	.	0	.	145	.	0	.	0	.	214	71	598	59	240	.	494	53	222	.	191	65	0	.	0	.	407	82	0	.	0	.	95	71	516	71	121	.	377	73	0	.	0	.	277	.	0	.	1891	29	608	53	1676	32	184	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41073	7
	total	1806	35	1877	36	377	72	2358	32	636	59	789	60	4493	23	6340	21	7353	17	818	46	949	51	374	72	221	71	0	.	273	.	650	50	719	71	469	72	159	.	1907	34	2896	27	1725	33	1893	30	3484	24	2514	25	1828	33	2321	34	3017	30	2582	36	457	80	352	59	308	75	693	58	1312	39	0	.	0	.	917	55	831	52	413	.	1329	45	1839	40	1864	42	827	47	1165	49	513	59	665	60	586	52	670	55	594	58	1108	47	667	61	879	52	1762	34	527	50	766	52	319	79	204	70	0	.	227	67	738	43	0	.	115	.	363	58	598	59	240	.	494	53	508	71	191	65	530	75	0	.	407	82	83	.	420	70	95	71	516	71	256	71	377	73	280	.	604	75	277	.	340	72	1891	29	608	53	2004	29	184	.	770	51	189	72	1373	42	756	45	859	46	1048	48	626	50	0	.	94364	5
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	0	.	292	.	75	.	126	.	0	.	395	73	360	72	2249	29	1325	37	310	61	0	.	113	.	76	.	0	.	0	.	431	76	306	64	190	.	0	.	60	.	27	.	130	.	0	.	123	.	79	.	724	44	225	.	695	51	0	.	103	.	0	.	234	74	364	51	25	.	0	.	0	.	51	.	215	.	0	.	347	60	119	72	259	.	0	.	113	.	241	72	62	.	0	.	90	.	0	.	0	.	327	72	0	.	98	.	62	.	238	72	192	.	70	67	1028	34	249	47	115	71	27	.	867	42	0	.	0	.	137	.	91	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	207	.	0	.	68	.	0	.	58	96	120	78	64	.	111	.	14	.	416	71	239	56	215	73	0	.	0	.	0	.	15548	10
	higher altitude zone	0	.	0	.	0	.	0	.	335	87	415	87	3143	24	2661	27	0	.	927	38	0	.	0	.	0	.	0	.	0	.	601	75	1006	45	277	60	0	.	500	42	928	34	676	37	1258	37	1632	32	804	32	308	74	104	.	379	84	766	54	185	74	369	57	0	.	0	.	308	71	346	61	153	86	397	52	0	.	0	.	628	43	611	54	460	68	0	.	0	.	0	.	0	.	0	.	213	.	470	52	141	74	0	.	0	.	0	.	267	58	639	44	999	39	149	71	0	.	0	.	298	60	115	71	231	.	152	71	93	.	107	72	371	67	549	48	424	55	0	.	0	.	156	71	0	.	0	.	582	43	24	.	83	.	131	71	0	.	0	.	0	.	0	.	2592	26	1817	26	1625	27	143	.	0	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	32599	7
	total	0	.	292	.	75	.	126	.	335	87	810	57	3502	23	4911	20	1325	37	1237	32	0	.	113	.	76	.	0	.	0	.	1032	54	1312	37	467	54	0	.	560	39	955	34	806	35	1258	37	1755	31	883	30	1032	38	328	75	1074	45	766	54	288	59	369	57	234	74	364	51	333	66	346	61	153	86	448	48	215	.	0	.	975	35	730	47	719	57	0	.	113	.	241	72	62	.	0	.	303	76	470	52	141	74	327	72	0	.	98	.	329	51	877	37	1191	36	220	53	1028	34	249	47	413	47	142	60	1098	40	152	71	93	.	243	64	462	56	549	48	424	55	0	.	0	.	156	71	0	.	0	.	582	43	24	.	83	.	131	71	0	.	207	.	0	.	68	.	2592	26	1875	25	1744	25	207	76	111	.	14	.	466	64	239	56	215	73	0	.	0	.	0	.	48147	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	4766	19	3891	23	1787	30	6705	18	321	.	2066	38	5306	22	12516	13	21370	9	5768	17	1817	34	1596	32	2570	27	92	.	1163	43	2896	27	2821	32	3927	25	709	45	2273	31	655	47	1327	32	0	.	1434	27	106	79	10167	12	4554	23	6982	18	4241	26	631	37	26	74	1100	37	741	43	684	43	648	41	6	.	1447	41	3635	23	2246	34	1807	32	1594	40	2733	30	4633	18	2353	31	2117	29	2840	27	2903	23	621	49	0	.	902	46	3179	25	2684	27	3668	22	62	.	1020	39	247	79	1072	26	1857	23	2657	17	1819	24	1213	26	2540	22	464	54	60	.	195	76	891	36	571	57	133	.	1725	39	594	66	1072	35	1160	41	989	43	200	72	1166	37	1516	31	1471	33	1206	37	1616	38	401	62	1841	43	602	40	283	50	1409	25	688	47	1975	29	2912	27	4359	21	3979	20	2557	26	2252	31	2025	29	0	.	209824	2
	higher altitude zone	0	.	0	.	0	.	0	.	2314	29	2863	28	15539	11	7631	16	68	.	4572	19	0	.	0	.	98	.	0	.	586	52	1857	35	3813	25	1541	39	0	.	3336	27	6495	16	3517	21	6754	16	12168	12	11453	11	2617	29	1114	46	580	61	4104	22	739	54	2260	24	544	58	655	54	2852	26	1467	36	2029	29	3306	29	0	.	955	51	2455	26	1698	34	4278	27	0	.	632	49	475	47	0	.	0	.	2057	28	4123	23	2074	30	0	.	0	.	0	.	1261	31	1568	28	2121	27	164	65	0	.	0	.	919	36	437	47	476	65	1287	35	1239	43	2079	31	1327	38	1361	37	1603	33	345	63	0	.	1947	30	0	.	246	74	1880	22	868	47	851	42	1040	36	66	.	0	.	277	.	0	.	8158	13	6140	15	6728	15	781	43	0	.	0	.	371	81	0	.	0	.	0	.	0	.	0	.	171160	2
	total	4766	19	3891	23	1787	30	6705	18	2635	28	4929	22	20845	10	20147	10	21437	9	10339	12	1817	34	1596	32	2668	26	92	.	1749	33	4753	21	6634	19	5468	21	709	45	5610	20	7150	15	4844	17	6754	16	13603	11	11559	11	12783	11	5669	21	7562	17	8345	17	1370	34	2286	24	1644	31	1396	34	3536	22	2115	28	2036	29	4753	23	3635	23	3201	28	4262	20	3292	26	7011	20	4633	18	2985	26	2592	25	2840	27	2903	23	2677	24	4123	23	2975	25	3179	25	2684	27	3668	22	1324	30	2588	22	2367	25	1236	24	1857	23	2657	17	2738	20	1650	23	3016	21	1751	29	1299	41	2273	29	2218	27	1933	31	1736	31	2070	34	594	66	3019	23	1160	41	1234	37	2080	21	2034	29	2367	25	2511	24	1273	35	1616	38	678	55	1841	43	8760	12	6423	14	8138	13	1468	31	1975	29	2912	27	4730	21	3979	20	2557	26	2252	31	2025	29	0	.	380985	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. 2281820. Birmensdorf, Swiss Federal Research Institute WSL

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