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 (“±”).

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

The 68%-confidence intervals do not overlap in the public forest (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).

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8286	11	13366	10	25145	6	7748	13	13693	9	3987	15	72225	4
	fir	7687	8	6585	12	7931	9	421	34	405	28	623	32	23653	5
	pine	251	32	415	56	580	45	2888	26	2241	26	134	72	6510	15
	larch	37	71	472	72	617	38	2972	16	885	20	2002	20	6984	11
	Arolla pine	0	.	0	.	0	.	611	31	912	26	69	71	1592	19
	other conifers	541	34	442	24	415	48	139	58	745	43	17	100	2300	19
	beech	9373	8	9327	7	10258	9	946	35	946	25	6243	15	37093	4
	maple	3137	15	2913	14	4820	12	511	34	441	35	628	32	12451	7
	ash	1308	18	3179	15	2473	15	470	34	604	40	1498	26	9532	8
	oak	1169	39	884	30	250	53	734	33	0	.	1357	36	4394	17
	sweet chestnut	17	100	20	100	0	.	37	100	0	.	8405	15	8478	15
	other broadleaves	3032	14	4567	11	6750	10	3762	17	3903	22	10651	12	32665	6
	indeterminable	124	48	67	50	341	26	91	45	124	43	99	66	846	17
	total	34962	5	42235	5	59581	4	21330	9	24900	8	35716	6	218723	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5092	12	7698	10	16944	7	5190	14	8326	10	2838	14	46089	4
	fir	3886	12	2927	12	4133	10	290	34	240	34	444	33	11920	6
	pine	627	28	563	30	463	44	1961	21	1156	26	191	51	4961	12
	larch	17	100	240	49	159	51	1732	18	890	22	1863	16	4902	10
	Arolla pine	0	.	0	.	0	.	581	28	759	27	35	71	1374	19
	other conifers	171	40	207	37	52	58	17	100	53	58	17	100	517	22
	beech	6603	9	6197	7	6896	9	373	40	659	31	3851	16	24580	5
	maple	1577	12	1941	16	3277	11	112	41	319	33	229	38	7455	7
	ash	761	20	1866	15	2135	14	272	39	279	56	549	32	5862	9
	oak	585	28	1302	23	113	41	234	42	36	100	653	24	2924	13
	sweet chestnut	0	.	0	.	0	.	37	100	0	.	4417	14	4454	14
	other broadleaves	751	22	1647	14	1893	17	982	21	919	26	4344	12	10536	7
	indeterminable	19	100	0	.	155	34	0	.	36	71	38	71	248	27
	total	20091	5	24589	5	36220	5	11782	9	13671	8	19469	5	125822	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3313	11	4677	9	12014	6	2853	12	5762	9	2154	15	30773	3
	fir	2402	11	2019	12	2873	10	139	51	129	30	254	37	7815	6
	pine	740	21	646	21	300	42	878	20	777	25	97	48	3438	10
	larch	45	54	199	31	99	52	1389	17	896	18	1138	18	3765	10
	Arolla pine	0	.	0	.	0	.	244	36	418	26	37	100	699	21
	other conifers	14	71	113	45	15	100	0	.	0	.	0	.	142	38
	beech	5070	8	3528	9	3790	10	177	40	567	24	1226	17	14358	5
	maple	807	16	747	15	1388	13	18	100	157	46	74	43	3191	8
	ash	566	19	1534	14	1099	19	241	88	48	60	98	36	3586	11
	oak	338	25	651	19	31	60	40	67	7	100	271	29	1339	13
	sweet chestnut	0	.	7	100	0	.	29	73	0	.	1518	18	1553	17
	other broadleaves	393	22	744	19	486	19	103	45	143	45	1278	15	3146	9
	indeterminable	35	100	24	76	46	54	0	.	7	100	7	100	119	40
	total	13722	5	14890	5	22141	4	6110	9	8911	7	8150	6	73924	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2410	8	3007	8	8193	5	1618	12	3595	8	1378	14	20201	3
	fir	1921	10	1561	10	2076	9	116	43	140	27	154	30	5968	5
	pine	398	21	488	16	173	53	197	32	349	23	28	62	1633	11
	larch	29	61	88	38	43	59	1046	17	837	14	666	16	2708	9
	Arolla pine	0	.	0	.	10	100	112	41	212	23	0	.	334	20
	other conifers	7	100	102	40	0	.	0	.	0	.	8	100	117	36
	beech	2875	7	2702	8	2264	10	79	61	271	28	562	18	8754	4
	maple	369	16	233	21	465	14	30	100	21	77	7	100	1125	9
	ash	254	24	914	13	419	19	15	100	14	71	62	48	1678	9
	oak	175	29	499	17	39	55	21	75	7	100	157	36	897	13
	sweet chestnut	0	.	0	.	15	70	0	.	0	.	607	19	622	18
	other broadleaves	156	38	348	21	157	27	45	41	35	44	306	21	1047	12
	indeterminable	15	71	8	100	71	37	7	100	14	71	0	.	114	28
	total	8608	4	9951	4	13923	4	3286	9	5495	7	3935	7	45198	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1519	10	1891	9	5117	6	867	13	1884	10	798	15	12076	4
	fir	1029	12	1240	10	1399	9	74	47	112	31	163	30	4016	6
	pine	104	29	251	20	72	46	93	45	163	29	14	100	698	13
	larch	22	58	83	35	50	55	789	15	613	16	448	15	2005	8
	Arolla pine	0	.	0	.	0	.	21	58	100	30	0	.	121	27
	other conifers	0	.	68	49	0	.	0	.	0	.	8	100	76	45
	beech	1170	11	1576	9	1068	12	15	100	111	32	275	19	4215	5
	maple	112	25	91	34	159	34	0	.	52	41	8	100	421	17
	ash	111	25	469	18	120	30	7	100	7	100	0	.	714	14
	oak	21	58	298	21	14	71	0	.	14	100	24	59	371	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	265	24	265	24
	other broadleaves	28	61	101	37	102	52	23	57	14	71	39	45	308	23
	indeterminable	0	.	0	.	14	71	14	71	0	.	0	.	29	50
	total	4115	6	6069	5	8116	4	1903	10	3071	8	2042	8	25316	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	549	14	753	12	2065	8	250	23	662	13	373	18	4652	5
	fir	426	16	640	13	751	14	15	100	54	35	29	50	1914	8
	pine	15	71	55	35	11	100	0	.	65	40	0	.	145	25
	larch	0	.	34	45	14	71	331	23	248	21	201	23	828	12
	Arolla pine	0	.	0	.	0	.	7	100	35	45	0	.	43	41
	other conifers	8	100	47	47	0	.	0	.	0	.	0	.	54	43
	beech	226	20	543	13	325	18	0	.	27	50	121	31	1242	9
	maple	7	100	28	50	48	49	0	.	0	.	0	.	83	34
	ash	45	41	102	36	36	45	0	.	0	.	10	100	194	23
	oak	20	73	103	27	0	.	0	.	0	.	15	71	138	24
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	83	30	90	28
	other broadleaves	14	70	48	49	26	50	0	.	0	.	8	100	95	31
	indeterminable	0	.	0	.	6	100	7	100	0	.	0	.	13	71
	total	1309	9	2351	7	3289	6	611	17	1091	10	840	11	9491	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	160	24	307	17	968	11	83	36	228	22	203	23	1949	8
	fir	144	24	202	21	292	17	15	71	47	42	30	49	730	11
	pine	7	100	7	100	0	.	0	.	14	71	0	.	28	50
	larch	7	100	16	71	7	100	159	25	100	28	107	32	396	15
	Arolla pine	0	.	0	.	0	.	35	44	14	71	0	.	50	37
	other conifers	8	100	41	58	0	.	0	.	0	.	0	.	49	51
	beech	42	47	197	22	93	30	0	.	18	74	44	41	395	15
	maple	9	100	0	.	6	100	0	.	8	100	0	.	23	58
	ash	7	100	48	41	18	72	0	.	6	100	0	.	79	32
	oak	0	.	54	38	8	100	0	.	7	100	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	92	37	92	37
	other broadleaves	0	.	21	58	6	100	0	.	0	.	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	383	15	893	10	1398	9	291	19	444	15	483	14	3893	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	54	94	28	238	25	61	53	111	26	77	32	617	14
	fir	51	38	77	32	62	37	7	100	7	100	8	100	212	19
	pine	0	.	7	100	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	44	47	49	43	51	47	144	26
	Arolla pine	0	.	0	.	0	.	7	100	7	100	7	100	22	58
	other conifers	8	100	7	100	0	.	0	.	0	.	0	.	15	71
	beech	0	.	64	36	21	58	7	100	0	.	22	58	115	26
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	24	58	0	.	0	.	0	.	0	.	24	58
	oak	0	.	43	41	0	.	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	57	42	65	39
	other broadleaves	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	95	30	322	16	327	20	135	30	182	21	222	20	1284	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	14	71	65	33	15	71	15	72	7	100	123	24
	fir	29	50	22	58	7	100	0	.	0	.	18	72	75	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	8	100	7	100	30	50
	Arolla pine	0	.	0	.	0	.	14	71	0	.	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	7	100	0	.	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	oak	0	.	16	100	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	37	52	37	52
	other broadleaves	0	.	23	100	0	.	0	.	0	.	0	.	23	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	45	89	38	79	30	43	41	23	58	69	36	340	16
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	21	58	36	45	7	100	23	58	19	72	112	26
	fir	0	.	14	71	7	100	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	22	74	22	74
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	51	49	51	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	35	45	43	41	14	71	30	50	91	36	220	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21378	8	31829	6	70784	5	18692	11	34299	7	11834	10	188816	3
	fir	17576	7	15286	8	19531	7	1076	30	1133	21	1722	25	56325	4
	pine	2143	19	2433	17	1598	36	6017	19	4773	19	463	50	17427	10
	larch	156	44	1132	33	988	30	8476	12	4526	12	6504	13	21782	7
	Arolla pine	0	.	0	.	10	100	1639	24	2466	21	148	75	4263	15
	other conifers	756	28	1027	21	483	46	156	62	797	41	51	75	3270	15
	beech	25359	6	24142	5	24723	7	1597	34	2600	22	12345	13	90766	3
	maple	6019	10	5952	11	10169	9	672	34	998	27	946	27	24756	5
	ash	3052	13	8142	9	6301	11	1005	38	959	43	2218	25	21677	6
	oak	2308	26	3850	16	454	36	1030	33	71	71	2477	26	10191	11
	sweet chestnut	17	100	26	78	22	74	111	84	0	.	15531	12	15707	12
	other broadleaves	4374	13	7506	10	9420	10	4915	16	5014	21	16632	10	47861	5
	indeterminable	193	37	99	39	634	19	120	36	181	35	144	53	1370	13
	total	83330	3	101425	3	145117	3	45506	7	57817	6	71016	4	504211	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. 1296676. Birmensdorf, Swiss Federal Research Institute WSL

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