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

NFI1
growing stock (stemwood)
development stage · higher/lower altitude zone
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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	188	57	69	64	63	72	181	71	0	.	0	.	232	46	315	58	97	64	1	.	115	67	108	72	164	66	0	.	0	.	17	72	9	.	1	.	0	.	102	.	11	.	0	.	0	.	4	.	0	.	271	39	81	69	149	71	0	.	142	.	0	.	0	.	0	.	83	.	0	.	0	.	0	.	43	.	152	100	28	75	27	95	0	.	208	59	12	.	183	56	0	.	0	.	73	75	21	.	0	.	245	65	0	.	29	.	0	.	7	.	4	.	31	74	0	.	24	70	4	.	124	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	0	.	15	.	0	.	0	.	0	.	63	71	40	.	144	71	12	87	6	.	30	70	1	.	136	.	0	.	117	.	0	.	101	100	180	71	0	.	0	.	4510	13
	higher altitude zone	0	.	0	.	0	.	0	.	7	.	0	.	761	32	356	49	0	.	114	52	0	.	0	.	0	.	0	.	0	.	121	63	446	53	0	.	0	.	157	58	165	60	71	64	232	80	132	73	176	52	0	.	0	.	0	.	45	80	0	.	0	.	0	.	17	.	45	.	0	.	286	57	31	73	0	.	0	.	98	73	86	97	147	70	0	.	37	.	0	.	0	.	0	.	83	55	7	.	-1	.	0	.	0	.	0	.	0	.	12	.	5	95	8	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	28	.	110	65	0	.	0	.	91	71	0	.	0	.	13	.	0	.	0	.	24	.	38	.	0	.	0	.	0	.	168	50	87	55	236	59	50	.	48	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	4563	13
	total	188	57	69	64	63	72	181	71	7	.	0	.	993	27	671	37	97	64	115	52	115	67	108	72	164	66	0	.	0	.	138	56	455	52	1	.	0	.	259	53	176	56	71	64	232	80	136	71	176	52	271	39	81	69	149	71	45	80	142	.	0	.	0	.	17	100	127	74	0	.	286	57	31	73	43	.	152	100	126	59	112	77	147	70	208	59	49	79	183	56	0	.	0	.	156	46	27	79	-1	.	245	65	0	.	29	.	0	.	20	73	10	69	40	62	0	.	24	70	4	.	124	68	0	.	18	100	0	.	0	.	0	.	28	.	110	65	0	.	0	.	91	71	0	.	46	.	13	.	15	.	0	.	24	.	38	.	63	71	40	.	144	71	180	47	94	52	266	53	50	99	184	79	0	.	125	93	0	.	101	100	180	71	0	.	0	.	9073	9
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	98	68	76	69	64	54	111	71	0	.	0	.	26	100	26	.	204	40	90	100	0	.	120	73	8	.	0	.	0	.	30	85	0	.	64	70	0	.	0	.	1	.	0	.	0	.	9	80	0	.	57	84	4	71	81	63	10	71	34	.	25	90	0	.	0	.	0	.	2	100	0	.	0	.	63	64	30	70	0	.	23	.	0	.	68	61	63	61	0	.	8	91	65	73	12	.	0	.	13	.	53	61	114	92	47	67	0	.	12	64	0	.	77	66	7	100	22	73	9	78	21	90	33	65	0	.	0	.	0	.	14	100	0	.	0	.	0	.	3	100	16	94	0	.	0	.	0	.	1	100	0	.	0	.	0	.	46	.	0	.	0	.	5	86	0	.	37	68	9	100	0	.	47	69	9	75	1	100	43	92	1	.	64	75	0	.	2176	13
	higher altitude zone	0	.	0	.	0	.	0	.	0	.	129	97	504	33	55	77	0	.	36	.	0	.	0	.	0	.	0	.	0	.	20	.	18	81	7	.	0	.	46	53	156	47	12	100	163	49	236	50	186	40	0	.	27	.	0	.	50	70	0	.	-2	.	0	.	0	.	0	.	0	.	104	.	3	75	0	.	0	.	77	95	73	.	17	.	0	.	0	.	0	.	0	.	0	.	221	76	127	61	0	.	0	.	0	.	0	.	13	69	27	86	3	100	1	.	0	.	0	.	7	.	0	.	0	.	47	91	1	.	46	92	0	.	31	84	1	100	0	.	0	.	2	100	0	.	0	.	0	.	7	73	0	.	0	.	0	.	0	.	0	.	0	.	202	39	58	44	191	49	42	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	2948	13
	total	98	68	76	69	64	54	111	71	0	.	129	97	530	32	81	61	204	40	126	77	0	.	120	73	8	.	0	.	0	100	50	65	18	81	72	63	0	.	46	53	157	47	12	100	163	49	246	48	186	40	57	84	32	87	81	63	59	60	34	.	23	100	0	.	0	.	0	.	2	100	104	.	3	75	63	64	30	70	77	95	96	80	17	.	68	61	63	61	0	.	8	91	65	73	233	72	127	61	13	.	53	61	114	92	47	67	13	69	39	63	3	100	78	65	7	100	22	73	15	62	21	90	33	65	47	91	1	.	46	92	14	100	31	84	1	100	0	.	3	100	18	82	0	.	0	.	0	.	8	64	0	.	0	.	0	.	46	.	0	.	0	.	208	38	58	44	228	42	51	84	0	.	47	69	16	61	1	100	43	92	1	.	64	75	0	.	5125	9
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	501	31	229	39	797	31	473	35	88	.	89	72	182	44	252	51	1410	21	640	31	0	.	260	43	182	54	73	.	24	.	252	53	169	56	297	49	212	41	312	60	79	76	0	.	0	.	589	25	0	.	560	28	241	64	208	60	51	.	15	.	0	.	74	82	0	.	0	.	79	66	34	.	147	61	404	41	164	54	203	56	200	56	65	66	792	28	32	93	58	62	275	41	376	43	254	43	0	.	0	.	246	41	174	65	60	71	27	100	106	37	24	95	388	28	386	31	1107	18	454	28	584	29	624	27	102	63	10	.	0	.	94	70	36	71	0	.	564	47	24	88	39	75	235	54	166	60	22	.	113	58	484	35	388	37	213	45	170	60	0	.	234	62	199	45	164	38	344	34	86	71	182	62	180	53	359	42	381	40	105	62	105	65	172	46	0	.	20693	5
	higher altitude zone	0	.	0	.	0	.	0	.	56	.	196	63	590	32	182	47	0	.	392	41	0	.	0	.	0	.	0	.	33	.	64	71	334	44	55	.	0	.	254	45	188	45	243	41	228	45	681	30	365	29	130	84	0	.	0	.	237	49	13	.	55	.	0	.	0	.	34	.	199	46	291	53	185	60	0	.	0	.	195	56	0	.	130	54	0	.	0	.	36	.	0	.	61	.	85	54	295	50	124	58	0	.	0	.	0	.	82	49	25	.	69	77	1	.	0	.	0	.	101	54	29	95	13	.	177	53	100	59	260	64	85	62	10	.	19	.	38	.	0	.	47	.	0	.	47	.	171	43	35	.	88	66	0	.	0	.	0	.	0	.	0	.	808	24	434	30	367	39	80	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9015	8
	total	501	31	229	39	797	31	473	35	144	72	285	49	772	26	433	36	1410	21	1032	25	0	.	260	43	182	54	73	.	57	72	316	45	503	35	352	44	212	41	566	39	268	39	243	41	228	45	1270	20	365	29	690	27	241	64	208	60	289	44	29	71	55	.	74	82	0	.	34	.	278	38	325	48	332	43	404	41	164	54	398	40	200	56	195	42	792	28	32	93	94	54	275	41	437	39	338	35	295	50	124	58	246	41	174	65	60	71	110	45	131	35	93	62	390	28	386	31	1107	18	554	25	613	28	636	26	278	41	110	55	260	64	180	47	46	59	19	.	601	45	24	88	85	64	235	54	213	52	193	39	148	50	572	31	388	37	213	45	170	60	0	.	234	62	1008	21	598	24	711	26	166	51	182	62	180	53	359	42	381	40	105	62	105	65	172	46	0	.	29707	4
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	1639	23	649	37	320	46	1237	27	14	.	612	50	604	46	1275	28	1530	24	989	32	143	59	145	72	668	34	0	.	262	71	357	60	694	40	1005	37	54	.	495	47	118	.	65	.	0	.	124	.	0	.	2382	19	360	47	630	35	1033	33	151	51	0	.	403	58	66	87	136	73	340	45	0	.	369	51	549	39	213	71	63	.	427	50	440	58	1494	27	552	47	486	45	221	67	1595	23	170	70	0	.	207	71	291	59	247	51	507	44	0	.	47	71	82	75	311	36	497	31	374	39	94	58	149	62	343	48	0	.	0	.	196	58	383	39	213	66	153	58	560	44	318	65	221	49	329	53	246	52	0	.	109	87	594	38	504	40	358	48	175	71	58	.	207	63	0	.	0	.	291	43	114	.	406	51	230	71	823	38	432	41	333	53	540	46	102	.	0	.	36124	5
	higher altitude zone	0	.	0	.	0	.	0	.	575	49	461	50	2705	19	471	39	0	.	733	36	0	.	0	.	0	.	0	.	0	.	282	64	1060	34	278	59	0	.	633	45	189	54	540	40	273	55	974	32	1574	25	534	45	94	.	0	.	821	35	0	.	179	75	197	71	0	.	452	61	132	.	229	73	396	45	0	.	192	.	707	39	71	.	527	49	0	.	0	.	195	71	0	.	0	.	133	61	486	51	208	75	0	.	0	.	0	.	17	.	237	52	193	75	0	.	0	.	0	.	71	73	0	.	88	.	306	50	190	91	483	46	12	.	176	.	442	43	90	72	0	.	176	64	0	.	0	.	691	33	139	59	151	77	307	40	0	.	0	.	70	.	0	.	1150	28	674	33	1294	27	90	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23349	6
	total	1639	23	649	37	320	46	1237	27	589	47	1073	36	3309	18	1747	23	1530	24	1722	24	143	59	145	72	668	34	0	.	262	71	639	44	1754	26	1284	31	54	.	1127	33	307	51	605	37	273	55	1098	31	1574	25	2916	18	455	43	630	35	1854	24	151	51	179	75	600	46	66	87	588	50	472	43	229	73	765	34	549	39	405	60	770	37	497	45	967	38	1494	27	552	47	681	38	221	67	1595	23	303	48	486	51	416	51	291	59	247	51	507	44	17	.	284	45	275	57	311	36	497	31	374	39	165	45	149	62	430	43	306	50	190	91	679	37	395	38	389	58	595	35	650	39	318	65	397	39	329	53	246	52	691	33	248	50	745	34	811	29	358	48	175	71	129	71	207	63	1150	28	674	33	1585	24	205	71	406	51	230	71	823	38	432	41	333	53	540	46	102	.	0	.	59473	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	.
	lower altitude zone	1445	28	1163	29	780	35	2439	23	502	58	1098	39	1710	27	4687	18	7902	13	1916	24	402	48	660	39	822	34	0	.	1193	36	942	38	777	47	900	38	103	.	808	42	168	72	602	33	36	.	117	64	29	.	3858	16	2176	25	3029	21	1130	35	287	52	0	.	265	62	117	71	154	73	459	51	0	.	420	62	1961	27	273	72	894	39	599	45	828	42	602	35	1068	33	417	46	926	34	546	39	606	44	0	.	190	75	1359	31	910	40	800	34	0	.	63	85	0	.	41	.	334	48	77	59	142	71	199	53	299	55	72	.	41	.	0	.	166	79	146	87	0	.	924	39	110	.	163	71	587	43	721	42	0	.	185	73	0	.	462	48	182	75	349	70	134	71	1221	34	58	.	0	.	155	61	435	44	1191	34	1896	24	1436	29	2127	24	802	36	747	41	1106	36	0	.	71646	4
	higher altitude zone	0	.	0	.	0	.	0	.	901	38	1086	38	5132	16	2360	23	74	.	1076	33	0	.	0	.	124	74	0	.	91	.	0	.	373	47	569	52	0	.	635	40	1803	27	1071	29	2415	21	3684	17	2155	20	724	40	578	48	543	60	1359	33	259	67	482	46	241	71	59	.	510	42	552	47	583	46	934	46	0	.	315	52	1126	37	78	.	957	37	0	.	197	51	262	47	0	.	81	.	679	35	1545	29	478	42	0	.	0	.	0	.	381	48	350	38	522	32	0	.	0	.	0	.	224	48	93	.	0	.	284	74	200	71	283	60	254	78	146	71	1112	32	157	.	0	.	367	43	0	.	261	60	276	51	142	71	297	59	357	46	63	.	0	.	56	.	0	.	1455	24	1074	28	1701	21	373	62	0	.	0	.	263	76	0	.	0	.	0	.	0	.	0	.	46782	5
	total	1445	28	1163	29	780	35	2439	23	1403	32	2184	27	6842	14	7047	14	7975	12	2992	20	402	48	660	39	946	31	0	.	1284	34	942	38	1150	36	1469	31	103	.	1443	29	1970	25	1673	22	2451	21	3801	16	2184	20	4582	14	2754	22	3571	20	2489	24	546	42	482	46	506	47	176	58	663	37	1011	35	583	46	1354	37	1961	27	588	43	2020	27	678	41	1785	28	602	35	1265	29	680	34	926	34	627	36	1285	28	1545	29	669	37	1359	31	910	40	800	34	381	48	412	34	522	32	41	.	334	48	77	59	365	40	292	48	299	55	356	62	240	61	283	60	420	56	292	56	1112	32	1081	36	110	.	529	37	587	43	982	34	276	51	327	52	297	59	819	34	245	61	349	70	191	58	1221	34	1514	24	1074	28	1856	20	808	37	1191	34	1896	24	1699	27	2127	24	802	36	747	41	1106	36	0	.	118428	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	.
	lower altitude zone	698	39	921	40	335	53	1066	34	0	.	297	72	826	39	2313	26	6691	14	946	37	707	40	402	51	278	60	0	.	141	.	398	59	1008	42	733	48	0	.	125	.	126	76	265	51	0	.	156	71	26	.	2413	23	683	45	630	42	270	71	38	.	0	.	193	71	91	.	180	.	0	.	0	.	0	.	778	43	303	.	248	72	175	.	504	53	426	47	1414	33	166	73	209	78	97	.	0	.	0	.	767	46	199	.	586	45	1065	31	0	.	604	39	0	.	23	.	0	.	154	80	352	49	57	.	362	41	57	.	0	.	0	.	109	.	0	.	186	.	264	73	89	.	87	.	475	46	0	.	0	.	0	.	0	.	272	59	0	.	330	65	0	.	77	.	36	.	0	.	67	.	104	.	313	58	623	52	434	52	1021	36	599	44	686	42	318	59	0	.	37591	6
	higher altitude zone	0	.	0	.	0	.	0	.	273	75	586	52	3917	19	1402	31	0	.	874	31	0	.	0	.	0	.	0	.	0	.	0	.	273	71	255	71	0	.	1094	35	2380	23	1515	30	2049	24	2815	19	2167	21	530	37	416	59	66	.	368	65	105	.	346	62	0	.	376	54	304	59	66	.	286	80	589	42	164	.	353	66	807	44	370	59	1859	31	0	.	206	71	0	.	0	.	0	.	484	51	773	42	715	42	0	.	0	.	0	.	360	40	612	34	373	47	0	.	0	.	0	.	32	.	0	.	249	71	70	.	385	58	189	75	262	62	217	72	172	61	0	.	0	.	647	42	0	.	95	.	47	.	0	.	127	.	120	.	0	.	0	.	127	.	0	.	1968	21	92	77	1868	25	459	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37256	6
	total	698	39	921	40	335	53	1066	34	273	75	883	42	4743	17	3715	20	6691	14	1820	24	707	40	402	51	278	60	0	.	141	.	398	59	1281	37	988	40	0	.	1219	33	2505	23	1780	27	2049	24	2972	19	2194	21	2943	20	1099	36	696	39	638	48	143	78	346	62	193	71	467	48	484	53	66	.	286	80	589	42	942	40	656	58	1054	37	545	51	2363	27	426	47	1621	30	166	73	209	78	97	.	484	51	773	42	1482	31	199	.	586	45	1065	31	360	40	1216	26	373	47	23	.	0	.	154	80	384	46	57	.	611	38	127	71	385	58	189	75	372	53	217	72	358	60	264	73	89	.	734	39	475	46	95	.	47	.	0	.	127	.	392	50	0	.	330	65	127	.	77	.	2004	21	92	77	1935	25	563	52	313	58	623	52	434	52	1021	36	599	44	686	42	318	59	0	.	74847	4
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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.
	lower altitude zone	254	55	813	31	127	68	99	.	0	.	181	71	809	33	2095	21	1525	24	506	34	144	69	160	69	311	51	0	.	372	48	485	45	110	74	513	46	0	.	100	.	63	79	207	62	15	.	268	49	0	.	510	35	193	67	432	39	375	51	102	61	0	.	185	56	243	47	334	46	0	.	191	60	253	55	124	73	258	62	547	38	323	49	383	42	48	.	64	.	457	39	401	40	218	59	15	.	0	.	0	.	499	38	241	58	218	55	0	.	102	64	54	.	83	60	415	37	530	32	800	29	240	46	196	40	0	.	0	.	53	.	0	.	117	74	0	.	0	.	101	.	60	.	0	.	48	.	44	.	337	45	337	51	0	.	0	.	59	.	0	.	150	71	172	41	53	75	364	33	270	64	0	.	195	61	235	58	155	63	113	78	95	72	602	40	0	.	21751	6
	higher altitude zone	0	.	0	.	0	.	0	.	112	62	199	91	2682	16	2518	19	39	.	554	33	0	.	0	.	0	.	0	.	126	71	659	41	642	41	81	62	0	.	329	52	1384	21	932	27	1482	23	3213	15	4238	12	214	47	204	50	268	59	564	37	200	55	1270	22	0	.	158	59	1322	28	357	51	707	34	591	32	0	.	487	37	563	35	717	37	431	51	0	.	35	.	183	58	0	.	0	.	287	48	553	37	202	46	0	.	0	.	0	.	171	45	350	39	338	33	67	70	0	.	0	.	386	39	128	46	43	73	220	61	59	.	382	41	428	38	577	30	757	33	0	.	0	.	246	42	0	.	14	.	870	25	682	35	530	39	0	.	19	.	0	.	8	.	0	.	3205	15	3127	15	1344	21	159	75	0	.	0	.	51	.	0	.	0	.	0	.	0	.	0	.	42665	4
	total	254	55	813	31	127	68	99	.	112	62	380	58	3490	15	4613	14	1565	24	1060	23	144	69	160	69	311	51	0	.	498	40	1144	30	752	36	594	41	0	.	429	46	1447	21	1139	24	1496	23	3491	14	4238	12	724	28	397	42	700	33	939	30	302	41	1270	22	185	56	401	36	1655	24	357	51	898	30	844	28	124	73	746	32	1110	26	1040	30	814	33	48	.	99	74	639	33	401	40	218	59	302	46	553	37	202	46	499	38	241	58	218	55	171	45	452	33	392	32	150	45	415	37	530	32	1186	23	367	34	239	35	220	61	59	.	435	38	428	38	694	28	757	33	0	.	101	.	306	39	0	.	61	81	914	24	1019	28	867	31	0	.	19	.	59	.	8	.	150	71	3377	14	3181	15	1708	18	429	49	0	.	195	61	286	51	155	63	113	78	95	72	602	40	0	.	64425	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.
	lower altitude zone	4822	13	3921	16	2486	18	5607	14	604	51	2277	26	4389	16	10962	11	19359	7	5088	14	1512	25	1855	21	2434	18	73	.	1992	26	2482	22	2767	23	3514	19	369	39	1941	25	565	37	1139	25	50	77	1268	21	55	71	10052	9	3738	18	5159	15	2869	20	769	30	25	90	1120	30	516	34	887	33	880	32	225	53	1188	30	3922	18	1394	33	1982	23	1774	24	2220	24	3638	15	3205	20	1768	21	2040	21	2897	16	1130	28	21	.	1178	34	2891	19	2273	23	2727	18	27	100	940	27	164	51	954	19	1640	18	2287	14	1855	18	1374	19	1855	18	231	48	51	82	250	51	766	30	512	41	339	61	2312	23	645	42	586	33	1625	25	1227	28	66	74	761	31	1416	23	1627	23	753	32	1192	31	233	51	2035	24	483	28	224	33	1287	18	1020	30	2228	23	3172	19	3413	18	4115	16	2095	22	2354	22	2363	22	0	.	194491	2
	higher altitude zone	0	.	0	.	0	.	0	.	1924	25	2656	23	16290	8	7344	12	113	74	3780	15	0	.	0	.	124	74	0	.	251	53	1146	29	3145	18	1246	31	0	.	3148	18	6265	13	4384	15	6842	12	11736	9	10861	8	2132	21	1320	30	877	42	3445	18	577	40	2330	18	438	50	610	38	2666	20	1304	28	2486	21	2729	21	164	.	1347	29	3573	18	1394	27	4067	19	0	.	476	39	676	32	0	.	142	71	1972	21	3785	17	1727	24	0	.	0	.	0	.	1024	24	1613	19	1504	20	78	61	0	.	0	.	821	24	249	45	393	51	1121	28	934	35	1643	24	1041	29	1185	26	2614	19	285	61	0	.	1576	23	0	.	416	46	2068	17	1004	27	1193	27	807	29	120	63	0	.	262	59	0	.	8958	9	5547	11	7000	11	1254	32	48	.	0	.	330	63	0	.	0	.	0	.	0	.	0	.	166578	2
	total	4822	13	3921	16	2486	18	5607	14	2528	23	4933	17	20678	7	18306	8	19472	7	8868	10	1512	25	1855	21	2558	18	73	.	2243	24	3627	18	5912	14	4759	16	369	39	5089	15	6830	12	5523	13	6892	12	13012	8	10916	8	12184	8	5058	15	6036	14	6314	13	1346	24	2355	18	1558	26	1127	26	3553	17	2184	21	2711	20	3917	17	4086	17	2742	22	5555	14	3168	18	6287	15	3638	15	3681	18	2443	18	2040	21	3039	16	3102	17	3805	17	2905	20	2891	19	2273	23	2727	18	1052	24	2553	15	1668	19	1032	18	1640	18	2287	14	2676	14	1623	17	2248	17	1352	25	985	33	1892	22	1808	21	1697	22	2953	18	2597	22	645	42	2161	19	1625	25	1643	24	2134	16	1765	20	2609	18	2434	18	874	28	1192	31	495	39	2035	24	9441	9	5770	11	8287	10	2274	22	2276	23	3172	19	3743	17	4115	16	2095	22	2354	22	2363	22	0	.	361078	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. 2280620. Birmensdorf, Swiss Federal Research Institute WSL

back