NFI Results

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

NFI3
growing stock (stemwood)
main tree species · diameter at breast height (main diameter classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
main tree species	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	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	.
	12-15 cm	140	31	32	49	21	47	448	10	23	36	144	21	0	.	35	23	736	11	67	27	151	16	99	28	20	53	111	31	103	25	42	43	51	32	80	35	117	33	98	16	36	38	278	15	322	12	26	38	156	26	3338	4
	16-23 cm	381	21	77	30	141	32	1646	11	48	43	560	18	0	.	245	32	2262	7	237	24	529	17	211	24	125	44	317	24	486	18	214	39	214	30	323	24	394	24	308	17	219	23	800	13	1286	10	54	71	647	20	11725	3
	24-35 cm	654	26	507	31	492	29	5524	8	98	36	1697	16	0	.	750	38	6329	7	706	24	1382	17	702	21	217	39	831	21	1542	14	258	38	391	24	863	19	695	28	754	18	755	23	2427	10	3355	10	144	40	1484	15	32554	3
	36-51 cm	1201	18	687	28	1126	23	11843	6	297	47	3087	13	0	.	1159	21	11866	6	1177	18	3274	14	1530	14	251	30	1339	16	3457	12	665	27	800	21	2228	14	1083	23	1413	14	1073	20	5473	9	4682	8	387	34	2213	14	62310	2
	=52 cm	1183	25	592	31	1328	25	14065	7	372	39	2381	15	15	100	1286	25	12297	7	1505	24	2960	15	2200	15	292	40	1474	19	5029	13	407	33	1061	29	2615	17	950	23	2488	15	1229	25	6262	10	4439	12	534	43	2514	14	69478	3
	total	3560	15	1894	25	3108	19	33524	5	837	31	7869	10	15	100	3476	20	33490	5	3692	16	8297	11	4742	12	905	28	4072	15	10617	10	1585	22	2517	17	6110	12	3240	16	5061	11	3312	17	15240	7	14085	7	1144	29	7014	11	179406	2
fir	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	.
	12-15 cm	40	34	-5	224	12	73	294	12	10	34	44	33	0	.	6	47	11	26	62	19	114	23	61	21	7	49	12	48	15	132	2	82	36	29	42	35	32	89	16	31	24	51	106	20	52	61	20	43	10	45	1022	8
	16-23 cm	105	36	17	47	49	46	710	12	49	44	186	28	0	.	30	61	58	22	202	19	344	16	240	22	12	44	56	36	105	33	7	78	141	27	117	23	124	37	116	47	76	37	422	16	146	26	75	38	27	160	3413	6
	24-35 cm	318	31	99	52	93	41	2165	11	82	32	473	26	0	.	32	59	215	23	639	20	956	18	494	21	48	61	141	37	644	18	38	58	288	25	144	35	239	32	181	38	185	35	935	17	240	31	74	57	272	28	8994	5
	36-51 cm	696	22	221	37	609	29	5550	8	201	34	873	20	0	.	173	40	485	22	1413	16	1705	15	1304	15	42	66	163	33	1530	15	19	71	549	21	532	21	315	28	301	27	135	41	2079	12	453	23	179	36	824	18	20350	4
	=52 cm	1013	23	152	39	614	30	7860	9	263	45	951	26	0	.	306	47	1034	20	1104	22	2033	15	1731	16	82	67	321	41	1694	17	42	58	624	27	772	23	464	43	687	27	131	48	2797	14	447	29	191	48	1205	19	26516	4
	total	2170	18	484	34	1378	25	16579	7	605	32	2528	18	0	.	546	36	1804	16	3419	15	5152	12	3829	13	191	49	694	30	3987	13	108	47	1638	17	1607	18	1172	27	1301	21	550	28	6339	10	1338	20	539	30	2338	16	60295	3
pine	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	.
	12-15 cm	5	63	0	.	0	.	26	52	0	.	2	71	0	.	0	.	114	19	1	100	0	0	3	71	0	.	2	100	4	62	1	100	2	72	3	66	1	100	2	100	2	100	10	75	86	20	0	.	0	.	262	12
	16-23 cm	17	58	2	100	0	.	40	31	6	73	3	100	4	100	0	.	316	17	19	55	15	79	7	81	0	.	12	80	16	87	17	68	19	66	2	100	30	64	9	71	17	68	12	65	395	20	0	.	15	46	974	11
	24-35 cm	121	50	0	.	0	.	218	34	61	50	8	164	52	90	0	.	754	18	106	48	52	59	44	52	0	.	44	67	64	61	54	50	77	54	94	62	34	58	163	60	75	50	26	61	683	17	0	.	191	28	2922	9
	36-51 cm	342	40	24	100	8	100	565	26	314	29	231	50	8	100	0	.	1118	15	226	39	82	44	82	62	0	.	29	71	126	35	141	41	351	24	52	60	231	31	139	47	45	54	228	40	607	19	0	.	715	18	5663	7
	=52 cm	308	33	0	.	0	.	338	27	157	55	100	42	0	0	0	.	611	22	196	34	21	100	63	71	0	.	12	100	150	48	21	100	56	45	12	100	139	38	47	50	18	71	0	.	249	32	0	.	420	28	2917	10
	total	792	25	26	93	8	100	1186	21	537	31	344	41	64	81	0	.	2913	13	549	29	170	41	198	45	0	.	99	63	361	38	234	32	504	23	163	52	436	28	359	44	157	46	277	37	2021	14	0	.	1342	17	12739	6
larch	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	.
	12-15 cm	0	.	0	.	0	.	-9	109	0	.	0	.	0	.	0	.	78	21	0	.	1	100	0	.	0	.	3	100	6	48	0	.	0	.	0	.	3	76	92	27	0	.	6	43	121	21	0	.	0	.	302	13
	16-23 cm	14	67	2	100	0	.	10	65	0	.	2	100	0	.	0	.	330	16	0	.	1	100	0	.	0	.	2	100	22	54	2	100	2	100	0	.	24	64	201	23	0	.	34	51	500	14	0	.	0	.	1144	9
	24-35 cm	114	58	0	.	0	.	98	43	0	.	46	72	0	.	0	.	1116	13	0	.	11	100	0	.	0	.	0	.	63	46	30	60	0	.	0	.	15	100	658	16	0	.	51	42	1248	12	0	.	26	61	3475	7
	36-51 cm	127	46	7	100	35	100	80	47	20	80	53	84	0	.	0	.	2823	9	11	100	8	100	0	.	0	.	6	100	230	45	35	74	8	91	0	.	5	100	1086	15	0	.	230	36	2840	10	0	.	91	43	7693	6
	=52 cm	124	56	0	.	39	100	103	40	0	.	30	71	0	.	29	100	3320	10	14	100	99	100	24	100	0	.	9	100	116	47	14	100	38	74	52	72	15	100	1490	16	13	100	212	42	4228	10	32	100	171	46	10171	6
	total	379	34	9	79	74	100	281	30	20	80	130	70	0	.	29	100	7666	8	25	71	120	90	24	100	0	.	20	80	438	35	80	59	48	60	52	72	61	52	3526	10	13	100	533	27	8937	8	32	100	287	34	22785	4
Arolla pine	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	1	100	0	.	28	55	0	.	0	.	66	35
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	25	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	2	100	1	100	0	.	91	25	0	.	0	.	226	18
	24-35 cm	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	388	21	0	.	0	.	0	.	0	.	24	100	6	100	0	.	0	.	0	.	0	.	4	100	7	100	0	.	292	21	0	.	0	.	724	14
	36-51 cm	0	.	0	.	0	.	10	73	0	.	0	.	0	.	0	.	464	17	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	309	19	0	.	0	.	802	12
	=52 cm	0	.	0	.	0	.	13	73	0	.	0	.	0	.	0	.	450	20	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	28	0	.	0	.	704	16
	total	0	.	0	.	0	.	27	60	0	.	0	.	0	.	0	.	1462	15	0	.	0	.	0	.	0	.	47	100	16	100	0	.	0	.	0	.	0	.	6	74	13	100	0	.	950	16	0	.	0	.	2522	11
other conifers	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	.
	12-15 cm	2	61	1	100	1	100	11	33	1	100	0	.	0	.	0	.	22	31	2	59	3	72	0	.	1	100	0	.	3	62	0	.	3	75	2	78	8	92	0	100	3	88	8	56	2	60	0	.	5	49	79	17
	16-23 cm	11	70	3	100	3	100	55	40	8	100	0	.	0	.	1	100	29	40	1	100	5	71	4	100	3	100	2	100	27	62	0	.	14	47	2	75	25	81	0	.	1	100	13	54	4	59	0	.	16	64	228	18
	24-35 cm	43	48	0	.	0	.	43	50	57	71	0	.	0	.	0	.	6	72	0	.	5	100	0	.	0	.	0	.	-12	100	19	100	4	100	0	.	4	100	0	.	0	.	72	64	9	76	0	.	36	50	286	26
	36-51 cm	17	100	0	.	0	.	46	81	38	100	6	100	0	.	0	.	0	.	0	.	46	83	0	.	0	.	0	.	23	100	18	100	0	.	0	.	0	.	9	100	0	.	33	87	0	.	0	.	0	.	237	34
	=52 cm	29	71	0	.	0	.	196	50	18	100	31	100	0	.	0	.	0	.	0	.	87	76	0	.	0	.	0	.	122	100	28	100	0	.	0	.	0	.	14	100	0	.	77	100	0	.	0	.	6	100	608	32
	total	103	40	3	100	3	80	351	34	122	77	37	100	0	.	1	100	56	36	4	63	146	73	4	100	4	100	2	100	164	89	65	71	22	49	5	64	37	76	23	98	5	72	203	50	14	64	0	.	62	44	1437	19
beech	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	.
	12-15 cm	106	17	4	52	8	59	320	22	51	22	48	20	0	.	17	35	45	24	94	23	88	26	42	27	8	60	35	41	104	17	56	28	66	23	26	27	79	38	322	12	9	36	109	18	51	46	6	53	87	27	1781	6
	16-23 cm	421	18	12	64	44	42	1240	9	153	28	326	20	0	.	111	38	167	24	306	20	293	19	143	21	73	50	157	31	395	17	167	24	255	24	145	31	97	22	1052	13	31	61	534	14	155	25	30	52	246	18	6553	4
	24-35 cm	1021	18	52	48	177	33	3375	10	655	19	913	18	0	.	283	32	410	21	875	17	789	19	445	21	132	36	257	30	1156	16	634	23	1221	16	240	21	375	21	1299	13	124	39	1715	12	255	28	63	48	771	16	17238	4
	36-51 cm	2114	12	130	40	363	37	4617	9	1326	17	1535	17	0	.	551	26	425	24	1986	15	972	17	739	18	136	47	418	30	2282	15	693	21	2187	12	697	21	476	21	1303	13	145	50	3156	10	140	34	175	42	1428	12	27994	3
	=52 cm	1966	15	47	59	289	33	3603	13	1224	23	908	22	0	.	194	42	444	27	713	21	972	27	277	32	14	100	295	44	1199	18	309	32	1667	17	497	33	601	29	1309	35	109	55	1504	15	121	40	80	53	1841	16	20183	5
	total	5628	11	245	37	882	29	13154	8	3409	15	3731	15	0	.	1156	24	1492	17	3975	12	3114	15	1646	15	364	32	1162	26	5136	12	1858	16	5396	10	1604	19	1628	17	5284	12	419	34	7018	9	722	21	354	34	4374	11	73749	2
maple	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	.
	12-15 cm	19	62	10	68	4	58	90	30	24	35	24	40	1	100	13	37	22	30	52	33	20	38	18	35	7	49	22	70	30	30	7	56	17	38	24	27	31	32	32	28	9	50	86	16	29	28	3	100	50	29	644	8
	16-23 cm	91	28	34	74	14	100	364	16	55	36	106	24	0	.	37	43	61	39	68	30	81	28	39	40	13	53	43	38	150	25	30	58	96	32	62	32	57	40	59	37	38	47	289	20	107	25	11	68	171	25	2079	6
	24-35 cm	230	25	23	62	83	50	666	13	184	34	143	34	5	100	145	36	70	35	211	26	183	38	181	35	73	33	88	38	463	19	18	58	223	26	113	36	151	34	160	29	55	46	501	17	74	33	22	62	277	23	4342	6
	36-51 cm	232	22	26	75	30	86	608	15	109	36	107	33	0	.	132	30	64	38	251	24	120	32	87	28	10	71	30	57	404	19	12	73	255	23	80	37	124	32	-1	4869	28	51	295	24	59	50	64	37	195	23	3320	6
	=52 cm	24	71	9	100	0	.	200	38	63	64	15	71	0	.	74	49	42	59	29	58	28	100	28	60	10	100	7	100	147	39	0	.	24	74	0	.	76	69	6	100	7	100	88	39	0	.	10	100	126	40	1014	14
	total	597	16	101	44	131	42	1927	10	435	22	395	20	7	81	401	26	259	22	611	17	432	22	353	26	113	27	191	33	1195	15	66	45	615	19	280	23	439	28	257	31	138	34	1259	12	268	20	110	38	819	15	11398	4
ash	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	.
	12-15 cm	21	35	1	100	3	74	54	29	17	38	31	49	14	100	9	57	54	29	36	32	53	54	6	47	2	100	5	67	37	33	11	91	12	33	25	52	48	59	86	24	7	58	58	30	27	34	2	74	61	43	680	10
	16-23 cm	71	25	8	100	33	61	147	21	64	45	92	34	4	100	75	92	160	32	73	39	82	39	9	129	32	61	22	58	169	24	20	41	61	42	93	47	141	35	193	23	47	41	156	20	99	34	12	67	177	32	2041	8
	24-35 cm	177	35	18	76	146	40	475	20	160	36	392	32	7	100	84	57	151	32	307	26	111	33	30	60	145	80	14	72	560	19	38	71	148	34	143	42	200	34	186	26	48	77	380	22	124	38	63	54	240	25	4348	7
	36-51 cm	506	21	24	95	66	45	465	19	129	38	355	26	0	.	217	65	55	37	315	30	351	60	56	48	57	59	12	71	554	21	43	47	191	27	175	41	360	25	143	32	16	76	408	22	52	51	103	49	593	18	5243	7
	=52 cm	189	35	0	.	13	100	383	32	151	57	171	37	0	.	61	52	35	60	155	34	158	54	0	.	38	76	0	.	182	39	19	100	30	71	73	72	258	41	55	56	0	.	372	35	7	100	50	77	334	30	2733	11
	total	964	18	51	73	261	39	1524	15	521	32	1040	22	24	100	447	58	455	26	886	22	756	39	102	44	273	57	53	45	1502	15	131	37	441	25	509	32	1006	20	662	19	118	50	1374	16	310	26	230	45	1405	15	15045	5
oak	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	.
	12-15 cm	10	50	0	.	0	.	1	71	4	59	0	.	3	59	0	.	4	51	1	100	1	100	12	67	0	.	0	.	0	.	1	100	8	54	1	100	2	61	41	24	0	.	38	34	26	29	0	.	8	69	161	13
	16-23 cm	63	36	0	.	0	.	20	42	25	72	16	48	48	55	0	.	32	44	3	100	8	63	24	62	2	100	0	.	3	100	7	73	14	63	0	.	11	54	186	25	0	0	220	27	66	30	0	.	6	59	754	12
	24-35 cm	140	32	0	.	0	.	160	29	74	54	25	59	161	53	0	.	51	91	26	60	30	76	16	75	0	.	0	.	107	73	57	37	89	41	-8	100	83	40	339	22	16	76	541	23	61	43	0	.	61	37	2030	11
	36-51 cm	306	38	0	.	0	.	257	25	69	60	70	55	165	52	0	.	140	52	166	33	127	53	53	57	0	.	0	.	90	65	97	46	81	40	0	.	170	37	194	37	30	66	570	20	44	53	0	.	264	24	2893	9
	=52 cm	517	28	0	.	13	100	258	29	62	58	149	48	47	90	0	.	83	51	43	58	121	66	0	.	0	.	11	100	70	59	159	60	111	52	53	85	306	48	80	42	41	72	594	33	27	81	20	100	244	32	3010	12
	total	1037	24	0	.	13	100	696	17	235	40	261	30	423	41	0	.	309	44	240	33	286	43	105	54	2	100	11	100	271	61	322	37	303	29	46	81	573	31	840	18	87	59	1963	18	224	25	20	100	583	20	8849	7
sweet chestnut	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	26	0	.	5	72	7	81	0	.	0	.	248	24
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	840	13	0	.	9	74	10	75	0	.	0	.	909	12
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	161	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1065	13	0	.	10	71	27	71	0	.	0	.	1263	13
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	912	17	0	.	28	90	22	68	0	.	0	.	1005	16
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1655	19	0	.	6	100	46	65	0	.	0	.	1758	18
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	317	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4696	10	0	.	59	78	112	49	0	.	0	.	5184	9
other broadleaves	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	.
	12-15 cm	52	25	2	71	6	78	148	14	18	34	34	35	11	51	25	39	242	21	42	29	31	31	20	30	5	81	15	42	59	32	7	58	28	26	30	28	40	43	466	11	33	51	117	24	235	15	1	100	17	53	1684	6
	16-23 cm	195	25	3	100	8	61	224	19	48	32	77	37	7	59	46	63	523	14	72	31	48	37	29	33	19	43	22	38	110	39	11	54	95	31	53	38	81	37	1056	10	65	42	221	20	421	15	6	71	92	21	3532	5
	24-35 cm	194	29	8	71	15	100	482	18	86	31	36	46	14	80	21	65	398	19	118	27	59	50	45	66	40	76	41	62	224	25	32	100	94	33	92	33	178	35	1357	12	38	48	355	26	293	22	3	100	166	30	4389	6
	36-51 cm	249	45	4	100	0	.	217	27	76	41	41	67	0	.	38	74	219	22	150	36	59	70	8	71	48	80	0	.	143	31	12	71	160	41	17	71	105	36	522	15	17	71	255	28	115	36	12	100	225	28	2690	8
	=52 cm	216	51	0	.	9	100	313	42	69	72	85	85	0	.	99	100	76	48	0	.	28	83	0	.	0	.	11	100	37	76	4	100	37	58	15	80	14	100	102	40	10	100	255	54	149	53	0	.	27	72	1556	18
	total	906	30	17	57	37	72	1383	16	297	28	273	39	32	58	229	59	1458	13	382	23	225	29	101	34	112	65	90	35	573	19	66	52	413	25	207	25	418	26	3503	8	163	33	1203	19	1213	14	22	58	528	22	13850	5
indeterminable	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	.
	12-15 cm	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	16-23 cm	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	24-35 cm	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	36-51 cm	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	=52 cm	0	.	0	.	0	.	0	0	0	.	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	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	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	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	.
	12-15 cm	395	15	44	49	54	35	1380	8	148	15	328	14	29	53	105	21	1375	8	357	13	463	13	260	14	50	29	206	21	362	14	127	23	222	15	233	18	361	21	1381	7	123	24	821	9	986	8	58	27	395	15	10264	2
	16-23 cm	1369	11	157	26	291	22	4457	6	456	18	1368	12	62	45	547	25	4112	5	983	12	1408	11	706	13	278	29	643	17	1484	11	475	21	910	14	798	15	985	15	4021	6	497	17	2709	8	3278	7	189	32	1397	12	33579	2
	24-35 cm	3013	11	707	26	1007	22	13209	5	1458	14	3733	11	239	43	1315	25	10049	5	2987	10	3577	11	1956	12	654	28	1440	16	4817	9	1178	19	2534	12	1681	14	1974	15	6165	6	1303	17	7014	7	6662	7	369	27	3524	10	82564	2
	36-51 cm	5790	9	1122	24	2237	18	24256	5	2579	13	6358	10	173	51	2269	17	17701	5	5695	10	6742	10	3859	10	545	24	2011	14	8839	8	1735	18	4583	10	3783	11	2868	14	6020	6	1493	17	12755	6	9323	6	919	24	6548	8	140203	1
	=52 cm	5570	10	800	28	2305	23	27331	5	2378	17	4820	12	61	73	2049	19	18443	6	3760	14	6508	11	4322	12	436	32	2140	17	8757	10	1003	22	3648	15	4089	15	2822	16	7932	9	1558	22	12168	8	9943	7	918	32	6888	10	140648	2
	total	16137	8	2830	22	5895	17	70632	4	7019	12	16608	8	565	39	6284	16	51680	4	13783	8	18697	8	11103	9	1964	21	6439	13	24258	7	4516	14	11896	9	10584	10	9010	11	25519	4	4974	14	35467	5	30193	5	2452	22	18752	7	407258	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. 1360373. Birmensdorf, Swiss Federal Research Institute WSL

zurück