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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NFI2
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern 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	26448	6	18556	6	11493	9	7891	9	12679	7	3760	12	80827	3
	fir	11770	6	6119	8	3107	10	965	25	381	23	686	22	23029	4
	pine	1215	20	554	35	297	34	3725	17	3102	18	115	55	9008	10
	larch	495	27	51	45	188	51	3353	12	1454	15	2170	14	7711	8
	Arolla pine	0	.	32	100	11	100	552	24	907	22	10	100	1511	16
	other conifers	1093	21	71	43	195	34	165	82	581	35	0	.	2105	16
	beech	20303	4	6844	9	5362	9	1266	25	912	20	7088	11	41774	3
	maple	5005	8	2636	12	1822	15	587	22	337	35	466	23	10854	6
	ash	4338	9	1351	16	1693	18	561	24	448	39	1519	18	9909	6
	oak	3058	16	339	34	61	41	586	25	59	63	1423	23	5528	11
	sweet chestnut	49	66	0	.	0	.	96	67	0	.	8345	11	8490	11
	other broadleaves	8380	7	4616	11	3308	14	4102	13	3365	18	10835	8	34606	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	82154	3	41168	4	27538	6	23848	6	24227	6	36418	4	235353	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12508	5	13145	6	7712	8	5271	9	8596	7	2677	12	49909	3
	fir	6394	6	3275	9	1692	12	461	25	216	27	357	24	12395	4
	pine	1149	15	328	39	493	29	1769	16	1499	17	154	49	5392	9
	larch	423	25	50	53	86	51	2455	12	1333	14	1592	14	5938	7
	Arolla pine	0	.	43	100	42	71	667	23	814	20	0	.	1565	14
	other conifers	367	31	21	71	31	58	0	.	60	52	0	.	479	25
	beech	14844	4	4286	10	3383	10	536	25	489	26	2719	12	26256	3
	maple	3397	7	1373	12	1444	13	151	32	108	35	287	28	6761	5
	ash	3175	9	822	21	1183	16	226	31	143	46	434	22	5982	7
	oak	2283	13	257	37	78	50	117	37	29	75	625	20	3388	10
	sweet chestnut	20	71	0	.	0	.	83	53	9	100	3032	12	3144	12
	other broadleaves	2834	9	1109	15	1088	15	705	18	560	24	3354	10	9650	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	47395	2	24707	4	17232	6	12440	6	13858	6	15230	4	130861	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9886	5	8959	5	5501	8	3370	9	5555	7	1933	12	35204	3
	fir	4816	6	2378	9	1306	11	270	28	110	28	173	37	9053	4
	pine	1801	10	149	33	264	28	777	15	829	17	166	45	3988	7
	larch	237	26	40	55	99	41	1525	11	983	13	947	14	3831	7
	Arolla pine	0	.	32	76	0	.	346	21	475	20	0	.	853	14
	other conifers	122	38	0	.	0	.	0	.	0	.	4	100	126	37
	beech	9660	4	2301	9	2261	10	172	39	216	28	1040	13	15650	3
	maple	1300	10	474	17	589	13	39	53	32	56	53	46	2487	7
	ash	1910	11	315	20	454	18	37	54	34	48	104	36	2855	8
	oak	1251	11	120	59	13	58	11	100	46	85	221	26	1662	10
	sweet chestnut	17	100	0	.	0	.	32	58	4	100	821	15	874	14
	other broadleaves	1202	12	251	28	209	24	160	31	127	32	885	13	2834	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	32202	2	15017	4	10697	5	6740	6	8412	5	6347	5	79416	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7551	4	6171	5	3348	7	1627	8	3575	6	1086	11	23359	2
	fir	3444	5	1690	8	905	11	118	30	140	22	184	25	6481	4
	pine	1097	9	166	36	87	29	235	23	422	16	50	48	2058	7
	larch	183	20	56	35	44	48	1164	10	855	10	592	12	2894	6
	Arolla pine	0	.	0	.	5	100	150	27	243	18	4	100	402	15
	other conifers	54	35	0	.	0	.	0	.	0	.	0	.	54	35
	beech	5059	4	1284	11	1122	11	36	54	127	29	437	13	8066	4
	maple	350	13	155	20	209	18	4	100	8	71	13	75	739	9
	ash	808	10	103	25	147	20	12	58	14	59	27	41	1111	8
	oak	716	12	9	71	8	70	8	71	44	55	77	40	863	11
	sweet chestnut	8	100	0	.	0	.	4	100	0	.	388	17	400	17
	other broadleaves	313	15	74	45	65	33	52	46	14	59	162	18	680	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	19582	2	9709	4	5940	5	3411	6	5444	5	3019	6	47105	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3712	5	3126	6	1944	8	833	12	1817	7	614	11	12047	3
	fir	1789	7	1037	9	434	13	80	37	73	27	85	27	3497	5
	pine	446	12	28	68	42	53	113	37	156	23	23	47	809	10
	larch	94	26	55	33	47	35	700	11	471	13	365	12	1732	7
	Arolla pine	0	.	4	100	0	.	35	36	107	24	0	.	146	20
	other conifers	45	48	0	.	0	.	0	.	0	.	0	.	45	48
	beech	1992	6	641	14	488	14	12	58	54	36	174	20	3360	5
	maple	122	21	31	38	35	39	0	.	8	71	0	.	196	16
	ash	267	15	26	46	31	42	0	.	0	.	6	100	330	13
	oak	419	14	25	45	12	58	10	72	4	100	18	59	488	12
	sweet chestnut	0	.	0	.	0	.	25	85	4	100	242	19	271	19
	other broadleaves	85	25	49	40	25	48	12	74	0	.	36	42	206	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8970	3	5022	5	3058	6	1819	8	2695	6	1564	6	23127	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1166	7	1065	8	741	11	282	18	646	11	255	18	4156	4
	fir	468	11	374	13	181	18	32	35	36	33	40	39	1130	7
	pine	88	23	15	59	4	100	4	100	34	45	4	100	149	19
	larch	19	45	21	53	22	45	286	16	119	21	114	20	581	10
	Arolla pine	0	.	0	.	4	100	25	41	43	35	0	.	72	26
	other conifers	29	42	0	.	0	.	0	.	0	.	0	.	29	42
	beech	502	11	119	26	81	30	0	.	11	74	66	26	780	9
	maple	18	51	8	71	4	100	0	.	0	.	0	.	29	38
	ash	77	27	4	100	8	71	0	.	7	100	0	.	97	24
	oak	97	25	7	100	12	73	0	.	4	100	9	71	129	22
	sweet chestnut	0	.	0	.	0	.	5	100	0	.	82	26	87	25
	other broadleaves	41	37	8	71	0	.	0	.	0	.	9	72	59	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2505	5	1621	7	1058	9	635	11	900	9	578	10	7297	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	264	14	298	14	323	16	97	25	211	16	128	22	1322	7
	fir	190	16	119	20	31	35	4	100	20	45	16	50	381	11
	pine	4	100	0	.	0	.	0	.	8	71	0	.	12	58
	larch	19	53	4	100	0	.	131	21	68	27	51	30	274	14
	Arolla pine	0	.	0	.	0	.	19	45	27	38	0	.	47	29
	other conifers	8	71	4	100	0	.	0	.	0	.	0	.	12	58
	beech	184	18	34	39	10	72	0	.	0	.	37	43	265	15
	maple	6	100	4	100	9	72	0	.	0	.	4	100	22	46
	ash	8	71	0	.	4	100	0	.	0	.	0	.	12	58
	oak	51	33	0	.	15	58	0	.	0	.	0	.	66	29
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	76	26	80	26
	other broadleaves	10	72	0	.	0	.	0	.	0	.	8	71	18	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	745	9	463	11	393	14	255	15	334	13	320	13	2510	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	64	26	98	24	77	25	40	32	48	29	35	36	361	11
	fir	50	29	70	26	14	59	4	100	4	100	14	58	156	17
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	33	35	26	49	18	50	77	25
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	beech	51	33	9	71	12	58	4	100	4	100	8	71	88	24
	maple	0	.	8	100	4	100	0	.	0	.	0	.	12	76
	ash	10	72	0	.	0	.	0	.	0	.	0	.	10	72
	oak	36	37	0	.	0	.	0	.	0	.	0	.	36	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	65	36	65	36
	other broadleaves	13	73	0	.	0	.	0	.	0	.	4	100	18	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	229	15	184	18	107	22	84	23	86	23	144	21	834	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	11	58	44	30	8	71	8	71	19	50	103	20
	fir	8	71	13	58	8	71	0	.	4	100	0	.	32	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	4	100	0	.	0	.	16	50	0	.	9	72	29	38
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	18	50	0	.	0	.	0	.	0	.	0	.	18	50
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	4	100	39	49	43	45
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	50	29	24	41	52	28	32	35	20	45	67	33	246	14
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	17	50	7	71	0	.	22	45	12	74	58	29
	fir	4	100	4	100	0	.	0	.	0	.	9	72	18	51
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	12	58	11	58	28	38
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	4	100	0	.	0	.	19	100	23	84
	maple	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	79	35	79	35
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12	58	21	45	15	50	12	58	38	34	130	27	229	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	61613	4	51447	4	31192	6	19418	7	33159	5	10518	9	207347	2
	fir	28933	4	15078	6	7677	8	1932	20	983	18	1566	19	56170	3
	pine	5800	8	1240	28	1188	25	6623	14	6051	13	512	41	21415	6
	larch	1473	17	277	24	486	30	9667	8	5322	9	5869	9	23094	5
	Arolla pine	0	.	110	89	62	73	1813	18	2629	16	13	77	4628	12
	other conifers	1722	18	96	42	226	31	165	82	642	35	4	100	2854	15
	beech	52613	3	15518	8	12723	8	2027	22	1813	19	11587	10	96281	2
	maple	10198	6	4688	9	4120	10	781	21	493	27	822	21	21103	4
	ash	10593	6	2621	13	3521	14	836	21	646	37	2090	16	20306	5
	oak	7918	10	757	28	200	29	733	23	187	61	2373	18	12168	8
	sweet chestnut	94	72	0	.	0	.	249	50	21	83	13169	9	13533	9
	other broadleaves	12889	6	6106	10	4696	13	5031	13	4067	17	15293	7	48081	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	193846	1	97936	3	66091	5	49276	5	56013	4	63817	3	526978	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. 1297040. Birmensdorf, Swiss Federal Research Institute WSL

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