Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

top

2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

top

3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

top

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

top

5. Two types of changes?

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

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

top

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

top

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

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

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

top

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

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

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

top

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

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

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

top

NFI4
number of stems
development stage · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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	conifers and broadleaves	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
	conifers	0	.	15	78	0	.	0	.	0	.	0	.	323	37	126	71	0	.	0	0	0	.	0	.	0	.	0	.	11	100	0	.	137	69	0	.	0	.	0	.	54	.	0	.	4	100	358	37	37	71	0	.	23	.	43	.	30	100	0	.	0	.	0	.	0	.	0	.	7	.	8	.	31	75	4	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	158	79	34	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	63	14	100	115	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1723	17
	broadleaves	0	.	24	84	0	.	0	.	0	.	0	.	0	0	11	100	4	100	47	71	0	.	0	.	0	.	0	.	42	61	0	.	38	71	0	.	4	.	0	.	0	.	4	.	29	100	0	.	0	.	0	.	5	.	0	.	39	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	30	100	0	.	20	100	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	53	82	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	407	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	39	73	0	.	0	.	0	.	0	.	323	37	136	67	4	100	47	71	0	.	0	.	0	.	0	.	53	60	0	.	175	69	0	.	4	.	0	.	54	.	4	.	33	89	358	37	37	71	0	.	28	.	43	.	69	81	0	.	0	.	0	.	0	.	0	.	7	.	8	.	42	62	34	100	0	.	20	100	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	211	68	42	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	.	0	.	0	.	4	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	63	14	100	115	54	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	2130	15
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	.
	conifers	56	73	55	86	11	100	181	50	0	.	106	62	616	27	380	34	378	42	13	76	13	100	0	.	22	100	0	.	0	.	0	0	145	44	78	68	0	.	205	48	337	36	189	53	126	55	425	32	368	33	110	44	13	78	102	63	177	50	57	.	0	.	0	.	20	100	0	.	11	100	128	54	40	74	54	53	14	100	180	42	43	79	36	67	9	100	8	.	11	100	42	83	0	.	20	100	207	50	19	71	78	58	0	0	9	100	14	78	121	63	42	80	25	81	0	.	0	.	0	.	0	.	29	100	69	67	0	.	154	58	60	59	87	63	13	100	40	55	0	.	32	74	20	71	22	100	0	0	0	0	15	100	36	90	9	.	16	100	0	.	0	0	257	37	201	45	109	49	29	63	11	.	40	72	74	57	54	82	0	.	7	100	23	74	0	.	6701	7
	broadleaves	199	40	114	47	31	75	180	49	0	.	52	84	207	36	119	40	367	32	106	63	21	70	32	100	43	79	0	.	0	.	19	64	141	49	61	79	0	.	223	47	51	82	137	93	0	.	75	54	10	100	45	55	105	62	38	71	81	63	0	.	0	.	0	.	38	100	0	.	49	76	19	71	116	57	79	58	19	100	96	54	49	61	112	60	21	100	4	.	0	0	69	50	0	.	31	85	43	100	11	100	92	46	0	.	23	60	34	77	0	.	10	100	102	49	12	100	19	.	19	.	29	100	162	75	16	61	0	.	87	67	69	79	10	100	0	.	131	75	25	100	11	100	55	83	11	100	0	.	44	78	0	.	29	73	0	.	43	82	0	.	25	86	41	62	214	45	111	51	15	100	27	.	86	71	43	58	21	70	63	55	65	72	29	66	0	.	4983	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	255	38	169	50	42	62	361	38	0	.	158	64	823	23	499	31	745	28	119	63	34	75	32	100	65	85	0	.	0	.	19	64	286	37	140	54	0	.	428	36	388	36	326	50	126	55	500	29	378	32	155	40	118	56	141	60	258	51	57	.	0	.	0	.	57	100	0	.	60	65	147	52	156	56	132	54	34	100	276	41	91	55	148	49	30	100	12	.	11	100	111	46	0	.	51	65	250	55	30	77	170	49	0	0	31	58	48	59	121	63	52	68	127	44	12	100	19	.	19	.	29	100	190	65	85	55	0	.	241	58	129	57	96	63	13	100	171	69	25	100	43	70	75	74	32	74	0	0	44	78	15	100	65	74	9	.	59	72	0	.	25	86	298	36	415	41	220	37	44	67	37	.	126	66	116	53	75	65	63	55	73	71	52	54	0	.	11684	6
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	.
	conifers	345	62	1033	35	511	49	445	66	160	100	423	66	1997	24	738	46	2868	23	596	52	0	.	319	66	48	64	0	.	238	55	511	56	488	64	520	56	0	.	428	37	738	36	831	48	3281	26	2610	25	5043	18	1410	32	917	41	1286	34	817	38	113	100	312	64	55	84	30	74	456	.	765	58	418	64	821	60	440	57	578	64	410	48	129	86	198	64	787	44	341	58	150	77	181	66	241	71	770	38	727	37	45	83	518	57	588	47	945	53	239	62	859	38	534	51	335	63	0	.	17	100	902	30	414	55	321	68	184	78	89	57	308	58	722	41	286	58	560	58	321	64	190	58	478	58	171	71	216	71	0	.	383	77	368	91	88	89	361	71	205	71	0	.	310	65	3952	21	1289	27	2221	26	492	51	637	60	308	54	614	49	332	75	228	75	307	72	266	53	0	.	58126	5
	broadleaves	989	52	731	33	647	40	671	44	283	93	96	70	752	34	639	34	1792	21	702	52	160	71	259	49	503	51	0	.	325	48	75	72	353	51	506	52	40	100	680	37	390	42	29	100	2147	26	2397	26	800	48	1215	28	305	40	250	59	363	42	431	53	188	58	140	92	119	72	0	.	241	83	105	72	62	85	393	50	273	59	428	55	250	71	173	60	419	59	383	44	165	82	624	46	126	72	959	37	532	50	390	67	617	45	485	39	621	38	777	50	1030	35	880	35	2686	26	1648	30	4282	21	3501	20	1161	34	2251	30	91	100	540	61	114	91	195	64	578	52	730	41	248	71	163	81	395	45	119	67	301	76	0	.	238	96	117	65	355	56	173	88	9	100	0	.	132	72	1373	25	1617	25	2853	20	134	57	128	80	344	46	346	49	307	47	415	48	124	81	560	43	0	.	57134	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	1334	43	1764	30	1157	34	1116	42	443	72	519	58	2749	21	1378	32	4660	17	1299	39	160	71	577	50	551	49	0	.	563	44	586	53	841	48	1026	40	40	100	1108	31	1127	31	860	47	5427	20	5007	18	5843	17	2625	25	1222	36	1536	31	1179	32	544	49	500	54	196	77	149	71	456	.	1007	49	524	62	883	57	833	45	850	48	838	40	378	59	371	59	1206	38	724	41	315	61	805	45	366	59	1729	31	1259	33	435	63	1135	38	1073	36	1566	37	1016	45	1889	28	1414	29	3021	24	1648	30	4299	21	4403	18	1575	30	2572	28	275	84	628	57	422	50	917	40	864	41	1289	36	569	50	353	63	873	45	290	62	517	61	0	.	621	61	485	74	443	53	534	58	214	71	0	.	442	57	5325	17	2906	21	5074	18	626	47	765	52	652	40	960	41	638	49	643	42	431	60	826	38	0	.	115260	3
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	.
	conifers	327	56	160	60	355	51	235	45	226	71	398	71	1967	27	508	35	1087	28	1044	32	84	79	105	88	143	60	0	.	121	73	1097	42	612	50	660	47	58	87	422	52	1315	34	999	33	1246	33	1491	33	2605	23	580	35	510	45	656	42	482	52	172	.	311	62	86	.	0	.	172	76	526	49	280	62	652	43	436	48	579	54	959	41	114	81	249	47	400	42	338	51	256	64	50	100	283	55	426	56	279	55	131	73	202	55	172	100	556	45	53	59	888	45	22	100	113	61	0	.	93	100	401	44	256	53	402	64	409	48	229	60	33	77	49	78	296	61	151	90	188	84	83	89	477	53	0	.	214	46	520	45	302	84	143	69	152	64	14	100	132	63	132	.	114	.	1666	28	1999	32	1914	26	351	57	51	77	282	47	340	63	235	62	103	70	0	.	453	47	0	.	40383	5
	broadleaves	600	34	250	56	96	61	554	53	62	71	206	85	1160	31	573	39	1273	24	1566	24	82	96	304	61	325	56	0	.	113	67	279	69	707	46	476	61	322	51	462	51	187	66	310	44	393	44	945	32	461	55	881	31	89	55	172	51	42	100	0	.	192	79	75	.	0	.	79	71	294	71	31	100	822	49	292	56	372	52	483	46	465	76	369	45	1068	30	361	59	573	38	123	74	836	35	267	56	80	74	267	59	144	65	48	100	68	57	201	62	488	48	133	75	1267	33	1208	37	1823	26	684	50	1940	28	1398	33	735	44	29	100	99	100	195	81	358	59	265	91	184	66	67	74	312	46	0	.	401	55	215	69	479	41	409	49	308	51	214	79	29	74	15	.	9	.	470	43	580	44	820	37	37	62	179	71	272	50	519	42	223	57	242	67	183	72	80	61	0	.	37272	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	927	36	411	50	452	47	789	43	288	71	604	58	3127	22	1081	32	2360	22	2610	24	166	72	409	53	468	51	0	.	234	64	1376	38	1319	38	1136	42	380	50	884	40	1502	33	1309	30	1639	28	2436	24	3065	21	1461	28	599	43	828	40	523	52	172	.	503	58	161	.	0	.	251	73	820	44	312	59	1473	37	728	43	951	42	1442	39	579	67	618	43	1468	26	699	47	829	36	173	79	1119	35	693	46	359	52	398	60	346	52	220	81	625	43	254	57	1376	35	155	78	1381	31	1208	37	1916	25	1085	38	2196	28	1800	30	1144	42	258	60	132	84	244	75	654	59	416	69	372	65	150	74	788	42	0	.	614	50	735	47	780	44	552	45	460	51	228	76	161	59	146	.	123	.	2136	24	2579	27	2734	24	388	55	231	71	554	40	860	44	458	47	345	58	183	72	533	44	0	.	77655	4
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	.
	conifers	189	42	67	61	294	40	164	53	804	34	614	39	2562	21	954	26	1050	28	1318	24	53	93	76	44	254	35	0	.	247	58	772	35	1368	30	396	50	0	.	813	38	1221	28	1481	25	1559	33	2683	21	4354	15	2303	18	150	66	248	45	653	37	108	63	157	65	274	56	15	.	228	46	86	49	71	72	589	48	207	60	361	45	680	39	296	56	690	33	485	27	120	62	385	35	154	54	264	38	419	45	591	39	460	41	508	38	361	48	128	45	0	.	634	41	687	38	34	66	0	.	0	.	371	44	95	52	248	58	302	73	569	49	616	47	175	54	127	54	1194	42	72	90	35	.	453	41	137	74	217	61	716	34	46	81	173	63	109	64	216	83	28	.	40	100	60	72	1432	27	1148	31	1816	24	279	67	188	57	272	40	171	45	556	35	129	52	350	45	28	.	0	.	48007	4
	broadleaves	745	28	403	43	359	42	456	40	313	47	363	44	1877	31	574	27	1177	27	1602	25	45	92	580	38	922	31	0	.	52	86	398	48	276	39	593	37	105	.	674	34	276	48	359	42	28	74	320	60	38	71	1911	19	333	51	338	53	128	51	270	52	66	75	162	53	40	.	142	61	465	44	151	74	449	47	169	54	586	46	408	33	136	54	575	34	973	27	351	47	694	35	318	41	740	28	310	44	97	57	285	43	268	39	209	43	469	35	0	.	37	71	149	67	301	47	473	56	483	44	252	71	328	54	459	45	4	100	58	74	25	84	336	58	111	62	540	43	315	69	0	.	438	48	157	58	182	47	164	59	107	71	293	44	98	72	439	42	0	.	82	83	12	100	41	60	126	72	464	44	201	62	290	46	206	45	237	47	352	34	270	45	235	43	34	.	0	.	31876	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	934	27	470	39	653	33	619	36	1116	32	977	35	4439	20	1528	24	2228	22	2920	21	98	71	656	37	1177	30	0	.	299	58	1170	32	1644	28	989	32	105	.	1487	30	1497	27	1840	23	1587	32	3002	20	4392	15	4214	15	482	42	586	44	781	35	378	46	223	59	435	47	55	.	370	44	551	43	222	69	1038	37	376	49	946	37	1088	31	432	46	1264	29	1459	24	471	43	1079	28	472	36	1005	27	729	43	689	38	745	36	776	35	570	39	598	34	0	.	671	41	836	35	335	45	473	56	483	44	623	44	423	47	708	39	306	73	627	47	640	47	511	47	238	48	1734	37	387	60	35	.	891	40	293	62	399	47	881	33	153	72	466	43	208	50	655	50	28	.	122	88	72	71	1473	27	1274	30	2280	23	480	50	478	44	478	40	408	40	908	29	400	42	585	37	62	.	0	.	79883	3
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	.
	conifers	260	42	192	57	132	48	518	25	744	36	1071	25	2958	16	3213	14	2501	15	1643	20	78	78	170	51	342	42	0	.	202	54	368	40	606	32	474	34	10	100	627	37	1860	21	783	29	1719	20	2417	17	2096	21	934	23	518	30	1235	24	1235	28	259	43	319	36	188	53	194	49	660	32	153	47	477	50	847	31	465	34	368	51	475	31	598	31	1057	24	180	43	384	32	41	71	87	54	232	42	717	26	816	31	627	34	177	60	145	56	164	37	228	51	521	34	541	36	60	100	64	100	4	100	251	49	55	66	134	60	73	88	187	59	70	59	523	38	383	39	769	37	261	43	90	96	377	43	154	44	103	51	163	42	664	32	257	38	347	33	0	.	204	51	140	71	178	43	2813	15	956	33	1564	23	192	47	264	41	187	51	339	32	407	35	147	52	139	51	139	54	0	.	52485	3
	broadleaves	893	27	404	44	430	37	683	29	104	63	563	34	1466	23	955	24	1809	14	1194	23	239	44	435	36	628	36	0	.	315	50	461	40	346	43	317	37	141	74	289	38	184	43	348	38	48	53	269	55	66	100	910	25	728	33	332	26	681	35	367	37	225	46	141	44	150	47	258	39	193	53	0	.	435	41	335	39	422	44	341	39	680	32	649	30	407	35	480	27	61	58	264	40	350	42	417	37	191	48	243	40	224	68	230	46	430	36	47	72	180	49	81	54	152	85	287	48	280	48	291	56	81	72	206	49	92	87	30	100	54	78	132	51	236	44	177	44	238	48	78	74	161	42	310	41	4	100	173	44	308	33	345	43	302	40	46	.	276	56	83	66	407	42	149	58	58	84	299	42	223	45	310	36	259	36	440	37	392	32	122	50	226	43	280	40	0	.	30549	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	1153	25	597	38	561	33	1201	22	848	37	1635	24	4424	15	4168	14	4310	12	2837	19	317	45	605	35	970	32	0	.	518	39	828	33	952	29	791	31	151	74	916	31	2045	20	1131	26	1767	20	2686	17	2162	20	1844	20	1246	29	1567	23	1917	25	626	36	543	37	329	44	344	44	917	30	346	48	477	50	1282	29	800	30	790	36	816	30	1278	28	1706	23	588	33	864	26	103	60	352	38	582	37	1134	25	1008	29	869	31	401	49	375	42	594	34	275	51	702	33	622	36	212	67	351	43	285	48	541	41	136	56	340	41	164	71	217	54	124	60	655	35	620	33	946	33	499	42	168	84	538	37	464	37	107	51	336	41	972	30	602	34	648	32	46	.	480	51	223	60	586	37	2962	15	1014	32	1863	22	415	44	574	30	446	35	780	33	799	29	268	46	365	42	420	39	0	.	83034	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	.
	conifers	83	71	178	80	141	53	91	53	143	64	154	59	7777	11	4186	14	747	33	1563	24	30	58	0	.	89	48	42	.	10	100	329	55	791	32	368	46	0	.	1142	30	2510	21	1832	20	2273	23	7532	12	5111	14	1813	19	890	33	395	40	1978	21	139	68	1098	28	250	49	627	35	1396	26	252	55	1799	29	1137	34	439	40	257	94	1542	29	299	43	1002	40	0	.	258	59	421	49	42	71	130	56	515	40	205	55	309	44	173	41	29	72	45	76	1020	27	1083	31	1260	30	26	47	34	56	13	75	1073	26	313	53	192	50	570	37	205	74	1290	32	289	44	301	44	773	40	117	62	9	100	543	39	84	89	82	86	1528	22	227	52	489	31	521	39	108	91	47	67	54	93	17	.	5717	13	5201	15	3464	17	452	44	4	.	10	.	152	48	216	40	46	92	15	78	46	100	0	.	82150	3
	broadleaves	292	50	336	50	90	67	235	54	47	72	213	60	2108	22	2157	17	556	29	1393	23	235	54	0	.	301	48	0	.	176	71	112	73	663	40	312	45	0	.	760	33	530	39	526	41	534	49	1126	26	169	60	2630	19	609	45	457	53	1089	28	86	66	307	39	146	47	313	44	256	32	170	56	186	41	381	37	334	40	134	100	910	33	887	42	845	42	113	.	315	55	475	47	149	73	379	52	554	43	42	61	285	68	546	39	204	61	136	64	379	56	966	30	187	73	721	34	1693	25	1568	27	2307	22	1510	34	1402	29	231	53	0	.	157	56	665	41	361	65	520	44	230	48	128	76	782	46	230	62	30	100	309	35	94	90	443	39	513	39	48	100	150	63	10	100	87	.	582	39	350	49	1799	21	84	69	72	.	76	.	197	48	284	38	70	71	64	100	76	64	0	.	45184	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	375	48	514	45	230	56	326	48	190	60	366	55	9885	11	6343	13	1303	25	2956	21	265	53	0	.	390	45	42	.	186	71	441	48	1454	29	680	40	0	.	1902	26	3040	19	2358	20	2807	22	8658	12	5280	14	4443	16	1499	33	853	41	3067	21	226	59	1405	27	395	47	940	32	1652	24	422	48	1985	28	1518	30	773	35	391	96	2452	25	1186	36	1847	37	113	.	574	47	896	44	190	72	509	50	1069	38	247	50	594	49	719	36	233	61	180	66	1398	30	2048	23	1447	28	747	33	1727	25	1581	27	3380	18	1824	31	1594	27	801	35	205	74	1447	30	954	34	662	43	1294	33	347	45	137	77	1325	42	313	66	112	89	1837	22	321	51	932	32	1034	31	156	73	197	58	64	81	105	.	6299	13	5551	14	5262	15	535	44	76	.	86	.	350	46	501	36	116	75	79	88	123	70	0	.	127334	3
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	.
	conifers	1260	25	1701	25	1443	24	1634	22	2077	22	2766	20	18200	7	10104	9	8632	10	6177	12	257	41	670	37	899	22	42	.	829	29	3077	21	4147	17	2496	21	68	76	3637	16	8035	11	6116	13	10208	12	17516	8	19613	8	7150	10	3021	18	3965	16	5371	13	849	31	2197	20	853	28	885	27	2911	22	1800	30	3181	21	4118	19	2044	21	2157	28	4246	16	1478	21	3231	17	1862	22	1449	23	1265	25	555	30	1150	24	2867	18	2984	17	1624	20	1656	24	1296	29	1847	31	1581	21	4107	17	3085	18	593	39	98	68	126	75	2998	16	1133	28	1326	29	1606	25	1279	28	2471	22	1818	22	1480	22	3460	21	999	30	407	40	2360	21	569	33	853	28	2952	16	1621	28	1445	28	1253	21	707	46	633	32	366	48	680	36	15974	8	10809	11	11203	10	1795	24	1155	36	1099	23	1691	24	1800	21	653	33	818	34	956	28	0	.	289574	1
	broadleaves	3718	18	2263	18	1653	21	2778	19	810	39	1493	23	7570	12	5029	11	6978	9	6610	11	783	28	1610	22	2722	18	0	.	1024	26	1343	25	2524	20	2266	21	613	36	3087	16	1618	20	1713	20	3178	20	5131	15	1544	31	7592	10	2173	19	1588	22	2422	18	1153	26	978	25	663	29	660	28	735	22	1412	27	494	32	2275	23	1632	20	1805	24	2686	18	2467	24	2734	18	3001	17	1895	20	1968	21	1547	23	2431	18	2539	20	1038	29	1490	26	1891	21	1175	23	1746	20	1438	32	2701	19	1439	25	5229	16	5329	15	8456	13	7053	13	5050	17	5877	16	1168	31	656	51	535	34	1591	25	1654	27	2231	23	1356	26	461	39	2099	23	871	26	929	36	865	25	1269	26	1607	21	1605	22	921	32	507	37	190	47	672	32	2656	18	2944	18	6346	12	693	27	1006	25	1244	21	1787	20	1580	18	1181	25	897	26	1060	26	0	.	207404	2
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	4978	16	3964	17	3096	17	4412	16	2886	21	4259	17	25770	7	15134	8	15610	8	12787	10	1041	26	2280	21	3621	17	42	.	1853	22	4420	18	6671	14	4761	17	681	36	6724	13	9652	10	7828	12	13386	11	22647	7	21158	7	14742	8	5195	16	5554	15	7793	12	2002	22	3175	18	1516	25	1546	23	3646	19	3212	22	3675	20	6393	16	3675	17	3962	21	6932	14	3945	19	5965	16	4863	15	3343	18	3233	19	2103	22	3581	17	5406	16	4022	16	3114	19	3547	18	2471	21	3594	20	3019	21	6808	13	4524	15	5822	15	5427	15	8582	13	10052	11	6184	16	7204	14	2775	23	1935	27	3006	20	3409	18	3134	20	5691	17	2354	22	868	35	4458	19	1440	26	1782	27	3817	16	2890	21	3052	20	2858	18	1628	31	1140	29	556	42	1352	28	18630	8	13753	9	17549	8	2489	21	2161	24	2343	19	3478	19	3379	16	1835	22	1715	23	2016	22	0	.	496978	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. 2297365. Birmensdorf, Swiss Federal Research Institute WSL

back