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	23-35calculated 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).

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 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).

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
growing stock (stemwood)
development stage · higher/lower altitude zone
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 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	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	.	6	.	0	.	9	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-20	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	135
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	0	.	304	60	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	231	71	0	.	0	.	0	.	0	.	24	.	0	.	542	54	91	63	0	.	0	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	1	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	139	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	68	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1543	27
	total	0	.	0	.	0	.	0	.	0	.	0	.	304	60	6	.	0	.	23	72	0	.	0	.	0	.	0	.	5	.	0	.	231	71	0	.	9	.	0	.	0	.	24	.	0	.	542	54	91	63	0	.	0	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	1	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	139	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-20	.	0	.	0	.	13	.	0	.	64	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	68	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1564	27
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	335	61	292	39	1	.	365	55	0	.	35	.	89	88	178	57	379	35	57	71	53	97	0	.	0	.	0	.	0	.	30	71	102	80	82	86	0	.	1	100	0	.	12	.	0	.	0	.	0	.	41	80	229	71	226	80	0	.	0	.	0	.	0	.	52	.	0	.	6	.	0	.	0	.	45	80	0	.	64	71	0	.	83	.	60	100	0	.	4	.	2	100	0	.	2	.	0	.	0	.	40	75	0	.	47	.	3	.	0	.	0	.	27	86	29	100	0	.	3	.	3	.	27	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	.	0	.	2	.	5	.	0	.	45	90	0	.	11	.	56	.	46	.	0	.	3	.	0	.	1	100	29	155	0	.	83	.	240	69	103	94	7	84	57	58	93	88	26	.	0	.	3911	14
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	136	.	732	43	274	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	107	57	150	79	0	.	221	71	315	54	111	59	251	97	391	47	127	90	0	.	4	.	0	.	230	62	157	.	0	.	0	.	0	.	0	.	0	.	35	84	1	.	0	.	142	100	32	100	0	.	78	87	0	.	0	.	0	.	0	.	0	.	10	.	10	.	32	.	0	.	0	.	0	.	6	89	57	100	1	.	85	77	0	.	0	.	0	.	0	.	5	.	162	72	0	.	9	73	84	.	92	85	0	.	103	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	197	59	143	71	152	65	61	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4731	15
	total	335	61	292	39	1	.	365	55	0	.	171	82	821	40	452	46	379	35	57	71	53	97	0	.	0	.	0	.	0	.	56	60	209	48	232	60	0	.	222	71	315	54	123	54	251	97	391	47	127	90	41	80	234	70	226	80	230	62	157	.	0	.	0	.	52	100	0	.	6	.	35	84	1	100	45	80	142	100	95	58	0	.	161	67	60	100	0	.	4	100	2	100	0	.	12	85	10	.	32	.	40	75	0	.	47	.	10	67	57	100	1	.	112	62	29	100	0	.	3	.	3	.	32	85	162	72	0	.	9	73	84	.	92	85	0	.	103	100	96	.	2	.	2	.	5	.	0	.	45	90	0	.	11	.	56	.	46	.	0	.	3	.	197	59	144	70	180	60	61	76	83	.	240	69	103	94	7	84	57	58	93	88	26	.	0	.	8642	10
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	221	51	597	45	443	49	480	63	0	.	28	.	4	.	403	57	1603	23	203	58	139	76	91	61	498	50	0	.	54	.	191	80	40	.	250	67	18	100	16	.	33	.	152	.	0	.	649	44	0	.	605	52	384	53	559	48	174	86	191	45	86	71	35	74	37	73	0	.	102	.	0	.	63	.	175	73	0	.	51	.	0	.	90	.	614	41	130	71	36	.	138	75	98	104	217	78	0	.	0	.	478	56	283	51	409	49	0	.	211	51	205	65	774	34	255	49	1245	28	981	32	218	64	703	37	76	.	0	.	0	.	139	.	70	92	42	.	133	71	225	78	235	82	75	70	169	63	0	.	263	70	41	.	7	.	249	76	37	.	0	.	29	.	108	67	19	.	389	40	60	73	109	90	162	77	292	68	219	88	403	62	40	.	653	41	0	.	19903	7
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	224	77	800	34	51	.	0	.	39	.	0	.	0	.	0	.	0	.	173	.	193	82	282	61	0	.	0	.	300	59	203	58	120	74	702	28	860	31	1110	26	0	.	214	75	0	.	354	48	0	.	78	.	0	.	0	.	0	.	257	84	0	.	109	72	0	.	116	73	85	71	2	.	102	.	0	.	65	85	0	.	0	.	0	.	128	58	484	56	77	.	0	.	0	.	0	.	144	58	172	50	21	109	23	.	0	.	0	.	251	48	104	66	87	.	-9	.	322	58	164	68	267	58	182	71	143	55	0	.	0	.	130	71	0	.	0	.	0	.	96	.	328	74	0	.	55	.	0	.	0	.	0	.	1103	32	695	33	783	42	147	.	102	.	0	.	166	.	0	.	0	.	0	.	0	.	0	.	12605	8
	total	221	51	597	45	443	49	480	63	0	.	252	69	804	34	454	52	1603	23	242	51	139	76	91	61	498	50	0	.	227	80	384	57	322	54	250	67	18	100	316	56	236	52	272	65	702	28	1509	26	1110	26	605	52	598	44	559	48	528	42	191	45	163	60	35	74	37	73	0	.	358	67	0	.	172	59	175	73	116	73	137	58	2	.	192	71	614	41	196	55	36	.	138	75	98	104	345	54	484	56	77	.	478	56	283	51	409	49	144	58	382	36	226	60	797	33	255	49	1245	28	1232	27	322	48	790	34	66	115	322	58	164	68	406	51	252	57	185	48	133	71	225	78	365	58	75	70	169	63	0	.	359	58	369	67	7	.	304	65	37	.	0	.	29	.	1210	29	714	32	1172	31	207	74	211	67	162	77	459	57	219	88	403	62	40	.	653	41	0	.	32509	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	546	45	247	59	120	.	99	.	0	.	188	.	422	53	410	51	2006	27	1394	37	0	.	266	58	415	52	0	.	212	83	497	71	715	46	0	.	353	58	467	75	67	.	87	96	0	.	319	58	0	.	851	42	300	61	812	49	311	.	0	.	0	.	0	.	0	.	60	.	453	71	0	.	247	71	377	62	455	66	109	72	587	57	203	.	1344	33	299	67	184	79	138	76	831	44	110	.	0	.	0	.	340	77	192	.	663	52	0	.	193	75	0	.	279	55	652	50	741	38	474	58	1015	37	568	46	274	.	0	.	132	.	71	.	231	76	0	.	249	.	259	73	330	61	0	.	0	.	0	.	251	58	474	61	0	.	180	71	370	66	236	.	0	.	204	71	55	81	306	53	323	.	101	.	494	51	693	45	380	59	153	.	287	74	421	59	0	.	28092	7
	higher altitude zone	0	.	0	.	0	.	0	.	161	.	291	.	666	40	354	71	0	.	658	50	0	.	0	.	0	.	0	.	0	.	870	53	311	77	434	71	0	.	180	72	678	51	422	58	628	42	912	37	1502	29	161	.	0	.	0	.	171	71	218	.	224	.	0	.	0	.	0	.	152	71	65	.	251	72	0	.	375	76	633	52	0	.	736	66	0	.	0	.	348	58	0	.	0	.	64	.	354	58	254	82	0	.	0	.	0	.	52	.	220	63	0	.	39	.	0	.	0	.	68	.	73	.	232	98	562	54	0	.	30	.	0	.	57	.	39	.	0	.	0	.	329	72	0	.	0	.	130	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	382	42	1236	38	506	43	336	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16367	10
	total	546	45	247	59	120	.	99	.	161	.	479	72	1088	32	765	43	2006	27	2053	30	0	.	266	58	415	52	0	.	212	83	1367	43	1027	40	434	71	353	58	647	58	745	47	508	51	628	42	1231	31	1502	29	1011	39	300	61	812	49	482	69	218	.	224	.	0	.	0	.	60	.	605	56	65	.	498	51	377	62	829	50	741	46	587	57	939	56	1344	33	299	67	532	47	138	76	831	44	174	73	354	58	254	82	340	77	192	.	663	52	52	.	413	48	0	.	317	50	652	50	741	38	541	52	1089	35	800	43	836	49	0	.	163	83	71	.	289	64	39	.	249	.	259	73	659	47	0	.	0	.	130	72	251	58	474	61	0	.	180	71	370	66	236	.	0	.	586	37	1291	36	812	33	659	61	101	.	494	51	693	45	380	59	153	.	287	74	421	59	0	.	44459	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	1048	36	456	45	587	50	434	65	304	.	819	54	1711	39	1123	38	2441	26	1095	43	148	.	937	41	762	54	0	.	0	.	896	43	379	61	1634	37	0	.	1000	48	87	.	386	59	0	.	160	.	0	.	3285	23	1264	54	890	48	558	51	222	71	0	.	242	70	0	.	294	77	509	58	0	.	543	71	469	58	558	62	348	70	190	.	1166	46	1738	32	556	61	733	45	556	51	706	42	191	74	0	.	584	60	1228	41	1059	47	605	46	0	.	0	.	0	.	197	74	222	71	597	50	0	.	437	58	485	65	0	.	214	.	0	.	415	71	84	.	0	.	178	.	88	.	259	78	240	.	311	60	188	.	103	.	641	52	137	.	508	51	0	.	303	83	126	.	127	.	0	.	121	.	508	59	1096	46	665	45	584	49	1777	31	506	60	1026	46	182	.	0	.	47223	6
	higher altitude zone	0	.	0	.	0	.	0	.	1235	43	828	59	2079	32	818	51	0	.	1460	38	0	.	0	.	0	.	0	.	432	71	588	68	630	58	310	78	0	.	912	54	1796	31	968	43	2082	32	2309	30	4275	20	1525	43	116	.	0	.	462	77	0	.	199	71	109	.	0	.	422	53	82	.	0	.	184	.	0	.	191	.	413	61	160	.	835	46	0	.	152	.	274	71	0	.	0	.	397	71	639	55	749	53	0	.	0	.	0	.	0	.	482	53	813	42	0	.	0	.	0	.	240	75	67	.	0	.	302	.	797	59	1174	46	73	.	256	58	1263	50	76	.	0	.	296	75	0	.	32	.	580	48	0	.	196	.	0	.	402	.	0	.	0	.	0	.	1030	43	1031	40	1940	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38685	7
	total	1048	36	456	45	587	50	434	65	1539	40	1647	40	3790	25	1941	31	2441	26	2555	28	148	.	937	41	762	54	0	.	432	71	1484	37	1010	43	1945	34	0	.	1913	36	1883	30	1354	35	2082	32	2469	29	4275	20	4810	21	1380	50	890	48	1020	44	222	71	199	71	351	58	0	.	716	44	591	52	0	.	727	59	469	58	749	53	760	46	350	71	2002	33	1738	32	708	52	1006	38	556	51	706	42	588	54	639	55	1333	40	1228	41	1059	47	605	46	0	.	482	53	813	42	197	74	222	71	597	50	240	75	505	52	485	65	302	.	1011	51	1174	46	488	63	340	50	1263	50	253	76	88	.	555	54	240	.	344	56	768	43	103	.	837	46	137	.	910	52	0	.	303	83	126	.	1157	40	1031	40	2061	31	508	59	1096	46	665	45	584	49	1777	31	506	60	1026	46	182	.	0	.	85908	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	1037	38	593	56	598	56	4057	24	0	.	795	71	3345	31	4452	24	10064	15	1996	30	320	70	321	89	1514	41	0	.	497	71	1140	49	805	63	721	57	252	.	693	69	195	71	154	75	0	.	123	89	0	.	2574	29	1040	42	3190	27	2024	43	189	.	0	.	274	.	181	.	152	.	0	.	0	.	0	.	1673	42	763	55	891	53	563	72	996	46	1250	43	1433	40	313	75	997	46	248	73	393	71	0	.	560	72	305	81	1382	46	1929	36	0	.	402	77	0	.	215	77	231	80	71	.	546	52	0	.	95	.	212	.	105	.	0	.	334	76	269	.	165	.	803	61	0	.	398	72	567	58	352	71	0	.	402	75	256	71	868	51	183	.	592	65	0	.	941	50	133	.	0	.	220	73	190	.	899	53	354	62	1095	46	1245	42	1041	48	347	72	526	59	0	.	71047	5
	higher altitude zone	0	.	0	.	0	.	0	.	1664	42	2226	38	4566	23	2649	28	78	.	1038	37	0	.	0	.	0	.	0	.	203	.	98	.	997	45	327	71	0	.	1490	42	3430	24	1529	36	3242	27	3784	24	4056	22	490	68	914	59	445	.	1374	47	459	73	966	53	209	.	186	.	818	57	698	58	310	.	2365	33	0	.	318	72	1014	54	1821	39	2189	40	0	.	280	77	0	.	0	.	0	.	2042	33	1537	38	744	54	0	.	0	.	0	.	635	50	403	73	929	54	99	.	0	.	0	.	276	71	80	73	316	.	235	.	217	.	280	.	852	56	846	56	867	54	185	.	0	.	931	45	0	.	0	.	413	55	801	50	155	.	682	51	0	.	0	.	344	.	0	.	3841	22	1000	45	3097	27	0	.	0	.	0	.	237	.	0	.	0	.	0	.	0	.	0	.	68276	5
	total	1037	38	593	56	598	56	4057	24	1664	42	3020	34	7910	19	7101	18	10142	15	3034	23	320	70	321	89	1514	41	0	.	700	58	1238	46	1802	37	1048	45	252	.	2183	36	3625	23	1682	33	3242	27	3908	23	4056	22	3064	26	1954	35	3635	27	3398	32	648	60	966	53	484	71	367	70	970	51	698	58	310	.	2365	33	1673	42	1081	44	1904	38	2384	34	3185	31	1250	43	1713	36	313	75	997	46	248	73	2435	30	1537	38	1304	43	305	81	1382	46	1929	36	635	50	805	53	929	54	314	61	231	80	71	.	822	42	80	73	412	80	447	71	321	75	280	.	1186	45	1114	49	1032	48	989	53	0	.	1329	38	567	58	352	71	413	55	1204	41	411	58	1549	37	183	.	592	65	344	.	941	50	3974	21	1000	45	3317	25	190	.	899	53	354	62	1332	41	1245	42	1041	48	347	72	526	59	0	.	139323	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lower altitude zone	274	71	56	.	383	60	414	60	0	.	0	.	852	50	3610	24	1387	39	1433	43	386	59	0	.	223	68	177	.	87	.	209	.	0	.	493	58	0	.	0	.	154	77	384	64	0	.	937	43	160	72	2304	26	700	46	441	65	1160	45	86	.	0	.	469	51	577	54	300	64	-3	.	15	.	345	59	645	50	115	.	172	52	889	55	409	79	164	.	102	78	477	59	336	72	724	50	0	.	0	.	0	.	664	53	103	100	232	58	90	.	656	52	144	.	130	67	1158	33	837	38	683	35	429	41	1097	37	231	.	0	.	154	.	333	62	0	.	0	.	69	.	10	.	333	73	0	.	0	.	133	.	121	.	403	61	457	58	0	.	352	69	0	.	0	.	49	.	209	64	730	41	88	.	155	.	0	.	713	51	273	57	123	.	165	86	151	84	0	.	33520	7
	higher altitude zone	0	.	0	.	0	.	0	.	-1	.	72	.	8261	16	2624	25	0	.	1826	29	0	.	0	.	69	.	0	.	0	.	266	71	1028	47	269	72	0	.	1170	36	2080	29	1634	29	1501	31	5003	19	2859	21	449	58	274	72	195	62	1373	36	132	.	1362	32	166	.	480	57	1606	33	634	57	1447	33	718	44	0	.	81	.	1083	37	229	73	1138	46	0	.	94	.	0	.	0	.	0	.	617	52	156	74	403	51	0	.	0	.	0	.	843	41	846	45	1081	36	112	83	0	.	0	.	592	38	202	93	100	81	426	52	193	72	647	48	345	75	782	47	496	52	0	.	0	.	296	62	0	.	75	.	1353	29	100	71	415	49	571	47	0	.	0	.	0	.	0	.	3994	22	4080	19	2574	24	113	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61538	5
	total	274	71	56	.	383	60	414	60	-1	.	72	.	9113	15	6234	18	1387	39	3259	25	386	59	0	.	292	57	177	.	87	.	475	59	1028	47	763	45	0	.	1170	36	2234	28	2017	26	1501	31	5941	17	3019	20	2753	24	974	39	637	49	2533	28	219	72	1362	32	636	46	1057	39	1906	29	631	57	1462	33	1063	35	645	50	195	72	1255	33	1118	47	1547	40	164	.	197	62	477	59	336	72	724	50	617	52	156	74	403	51	664	53	103	100	232	58	932	39	1502	34	1225	33	243	52	1158	33	837	38	1276	25	630	41	1197	35	656	48	193	72	801	44	679	49	782	47	496	52	69	.	10	.	630	48	0	.	75	.	1487	27	220	64	818	39	1028	37	0	.	352	69	0	.	0	.	4042	21	4289	18	3305	21	200	71	155	.	0	.	713	51	273	57	123	.	165	86	151	84	0	.	95058	4
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	3462	19	2241	23	2133	26	5849	19	304	.	1865	40	6422	21	10180	14	17881	10	6188	18	1046	35	1615	31	3413	24	177	.	854	48	2964	27	2042	32	3181	25	632	51	2178	34	537	40	1174	33	0	.	2188	25	160	72	9658	13	3918	24	6119	18	4227	26	689	40	86	71	1020	39	847	43	807	42	1066	42	15	.	1198	39	3385	25	1890	33	1634	33	2229	33	2947	27	5169	18	2521	28	1747	29	2166	28	2607	24	913	41	0	.	1144	47	3056	25	3018	28	3885	22	93	96	1461	34	349	56	1622	23	2546	22	3490	17	2687	20	2102	24	2974	21	792	53	318	74	286	71	1292	36	654	52	207	82	1412	42	679	42	1556	32	896	46	837	39	321	72	1184	35	1814	29	1480	36	1176	33	1397	37	539	64	1099	45	620	40	284	50	1795	23	1169	42	2443	30	1915	26	3480	22	3900	21	2284	29	1958	30	1960	27	0	.	203717	2
	higher altitude zone	0	.	0	.	0	.	0	.	3058	29	3777	28	17407	11	6771	16	78	.	5036	18	0	.	0	.	69	.	0	.	808	50	2041	32	3587	23	1490	34	0	.	4274	22	8502	14	4806	18	8408	15	13803	11	14020	10	2626	30	1523	40	640	72	3988	23	967	46	2829	25	485	59	665	50	2846	25	1823	33	1877	30	3630	24	0	.	1222	37	3259	24	2213	34	5078	23	0	.	592	48	621	45	0	.	0	.	3258	25	3319	23	2259	28	0	.	0	.	0	.	1680	28	2181	26	2846	25	357	44	0	.	0	.	1427	26	526	44	740	55	1677	33	1529	37	2305	30	1621	35	2216	29	2808	29	364	62	0	.	1985	29	0	.	107	76	2542	21	997	42	1094	36	1253	35	457	89	0	.	344	.	0	.	10629	12	8192	13	9052	14	657	47	102	.	0	.	404	72	0	.	0	.	0	.	0	.	0	.	203745	2
	total	3462	19	2241	23	2133	26	5849	19	3362	28	5641	22	23829	9	16952	10	17958	10	11223	12	1046	35	1615	31	3482	24	177	.	1662	34	5005	21	5629	18	4671	20	632	51	6452	18	9039	14	5981	16	8408	15	15991	10	14180	10	12284	12	5441	20	6759	18	8215	17	1656	32	2915	24	1505	33	1512	32	3653	22	2890	26	1892	30	4828	21	3385	25	3112	25	4893	20	4442	23	8026	18	5169	18	3113	24	2368	25	2166	28	2607	24	4170	21	3319	23	3403	24	3056	25	3018	28	3885	22	1773	27	3642	20	3195	23	1980	21	2546	22	3490	17	4113	16	2628	21	3715	20	2469	28	1847	33	2591	28	2913	25	2870	25	3015	28	1776	36	679	42	3540	21	896	46	944	36	2863	20	2181	27	2908	23	2732	25	1633	35	1397	37	883	55	1099	45	11249	12	8475	13	10848	12	1826	31	2545	29	1915	26	3884	21	3900	21	2284	29	1958	30	1960	27	0	.	407462	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. 2280648. Birmensdorf, Swiss Federal Research Institute WSL

back