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
number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		production region
		Jura		Plateau		Pre-Alps		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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8822	9	16554	8	17942	6	27075	4	3352	12	73745	3
	fir	6099	7	4377	10	6943	7	2735	11	579	24	20734	4
	pine	551	30	569	30	370	42	5973	12	77	73	7539	10
	larch	58	57	377	32	56	100	4145	10	1754	16	6389	8
	Arolla pine	0	.	0	.	32	100	1349	17	10	100	1390	17
	other conifers	446	36	626	25	145	30	788	29	0	.	2005	16
	beech	10049	6	9462	6	7910	7	5872	10	6882	11	40175	3
	maple	2549	9	2308	14	2547	12	2658	11	455	23	10516	6
	ash	1877	14	2236	13	2016	15	1799	15	1469	18	9398	7
	oak	1207	25	1478	23	116	42	836	20	1274	24	4911	12
	sweet chestnut	0	.	39	61	0	.	96	67	6727	12	6863	12
	other broadleaves	3413	10	3983	10	3757	11	9605	8	9897	8	30655	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	35072	4	42008	4	41833	4	62930	3	32477	5	214320	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5059	8	7127	7	13319	6	19067	5	2452	12	47025	3
	fir	3275	8	2823	11	3796	8	1494	13	291	25	11680	5
	pine	574	23	419	20	251	45	3282	11	145	52	4670	9
	larch	97	43	326	30	0	.	3522	9	1474	14	5419	7
	Arolla pine	0	.	0	.	62	75	1423	15	0	.	1485	14
	other conifers	146	42	221	43	21	71	91	40	0	.	479	25
	beech	8495	6	6182	6	5316	8	3221	10	2644	12	25858	3
	maple	1706	10	1642	11	1708	11	1350	13	287	28	6692	5
	ash	1226	15	1909	11	1459	14	884	19	413	22	5891	7
	oak	1059	20	1138	16	149	36	321	30	596	20	3264	10
	sweet chestnut	0	.	20	71	0	.	92	49	2936	12	3047	12
	other broadleaves	1112	12	1524	12	1196	16	1997	11	3084	10	8912	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	22748	3	23331	3	27278	4	36745	3	14321	4	124423	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3793	7	5949	6	9251	5	12923	4	1833	12	33749	3
	fir	2742	8	1964	9	3057	7	846	13	150	40	8760	4
	pine	925	15	825	14	144	33	1730	11	166	45	3790	7
	larch	83	39	154	34	33	40	2517	8	903	14	3691	7
	Arolla pine	0	.	0	.	23	100	794	14	0	.	817	14
	other conifers	34	100	88	36	0	.	0	.	4	100	126	37
	beech	5573	5	4039	6	3354	7	1520	12	1035	13	15522	3
	maple	751	13	538	15	601	13	508	15	53	46	2451	7
	ash	655	16	1245	15	569	15	261	21	104	36	2834	8
	oak	492	16	759	15	100	67	91	53	221	26	1662	10
	sweet chestnut	0	.	17	100	0	.	36	53	751	15	804	14
	other broadleaves	485	22	657	15	217	31	500	16	860	13	2720	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	15533	3	16235	3	17351	3	21726	3	6081	5	76926	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2767	6	4680	5	6431	4	7688	4	1016	11	22582	2
	fir	1638	7	1767	7	2169	7	622	12	168	25	6365	4
	pine	459	15	634	12	157	37	702	13	46	50	1998	7
	larch	23	57	156	22	25	53	2028	7	563	12	2796	6
	Arolla pine	0	.	0	.	0	.	394	15	4	100	398	15
	other conifers	4	100	50	37	0	.	0	.	0	.	54	35
	beech	2658	6	2382	6	1681	9	866	12	429	13	8015	4
	maple	211	15	135	22	233	18	143	19	13	75	735	9
	ash	209	17	599	13	178	19	97	23	27	41	1111	8
	oak	188	20	524	14	0	.	70	38	77	40	859	11
	sweet chestnut	0	.	8	100	0	.	4	100	370	18	382	17
	other broadleaves	101	25	198	19	75	29	126	33	149	18	649	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	8257	3	11132	3	10951	3	12741	3	2861	6	45941	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1448	7	2252	6	3429	5	3957	5	596	11	11681	3
	fir	799	11	975	9	1218	8	365	14	68	31	3424	5
	pine	149	21	291	15	45	52	286	19	19	53	791	10
	larch	12	58	82	29	39	37	1191	8	353	12	1676	7
	Arolla pine	0	.	0	.	0	.	134	21	0	.	134	21
	other conifers	0	.	45	48	0	.	0	.	0	.	45	48
	beech	941	8	1050	8	763	11	415	18	170	20	3340	5
	maple	37	34	84	26	35	39	39	34	0	.	196	16
	ash	52	33	215	17	42	36	15	61	6	100	330	13
	oak	137	23	282	17	25	45	26	41	18	59	488	12
	sweet chestnut	0	.	0	.	0	.	23	71	233	19	256	19
	other broadleaves	40	37	45	35	9	71	73	32	28	50	194	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	3616	4	5322	4	5605	4	6524	3	1490	6	22557	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	430	12	732	9	1228	7	1432	7	247	18	4070	4
	fir	246	14	221	16	442	12	165	16	40	39	1114	7
	pine	33	40	55	28	15	59	38	39	4	100	145	19
	larch	0	.	19	45	27	47	417	12	110	20	574	10
	Arolla pine	0	.	0	.	4	100	68	27	0	.	72	26
	other conifers	4	100	25	46	0	.	0	.	0	.	29	42
	beech	209	18	293	15	135	22	73	36	61	27	771	9
	maple	14	58	4	100	7	71	4	100	0	.	29	38
	ash	21	52	56	32	8	71	12	74	0	.	97	24
	oak	33	43	64	31	19	59	4	100	9	71	129	22
	sweet chestnut	0	.	0	.	0	.	5	100	82	26	87	25
	other broadleaves	17	58	24	47	0	.	8	71	9	72	59	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1007	8	1494	6	1886	6	2226	6	562	10	7175	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	131	20	134	18	374	14	498	10	128	22	1264	7
	fir	92	23	98	22	127	19	39	31	13	57	369	11
	pine	0	.	4	100	0	.	8	71	0	.	12	58
	larch	4	100	15	62	0	.	196	17	43	32	258	14
	Arolla pine	0	.	0	.	0	.	47	29	0	.	47	29
	other conifers	0	.	8	71	4	100	0	.	0	.	12	58
	beech	47	31	137	21	31	42	13	59	37	43	265	15
	maple	0	.	6	100	4	100	9	72	4	100	22	46
	ash	0	.	8	71	4	100	0	.	0	.	12	58
	oak	0	.	51	33	10	72	5	100	0	.	66	29
	sweet chestnut	0	.	0	.	0	.	4	100	76	26	80	26
	other broadleaves	0	.	10	72	0	.	0	.	8	71	18	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	275	14	470	11	554	11	818	8	308	14	2425	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21	45	43	33	83	26	160	16	31	39	337	12
	fir	12	58	38	34	66	27	26	41	10	72	151	17
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	59	29	18	50	77	25
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	11	58	40	40	13	58	16	50	8	71	88	24
	maple	0	.	0	.	4	100	8	100	0	.	12	76
	ash	0	.	10	72	0	.	0	.	0	.	10	72
	oak	7	71	29	43	0	.	0	.	0	.	36	37
	sweet chestnut	0	.	0	.	0	.	0	.	65	36	65	36
	other broadleaves	0	.	13	73	0	.	0	.	4	100	18	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	51	28	178	17	164	19	277	13	136	21	806	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	21	45	44	30	19	50	95	21
	fir	4	100	4	100	17	50	8	71	0	.	32	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	4	100	0	.	16	50	9	72	29	38
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	14	58	0	.	0	.	0	.	18	50
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	4	100	39	49	43	45
	other broadleaves	0	.	5	100	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	12	58	38	34	37	33	80	22	67	33	235	15
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	12	58	35	36	12	74	58	29
	fir	0	.	4	100	4	100	0	.	9	72	18	51
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	11	58	28	38
	Arolla pine	0	.	0	.	0	.	12	58	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	0	0	.	19	100	19	100
	maple	0	.	0	.	0	.	4	100	0	.	4	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	74	36	74	36
	other broadleaves	5	100	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	5	100	7	71	16	50	66	25	126	28	220	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22475	5	37479	5	52090	4	72876	3	9686	8	194606	2
	fir	14907	5	12272	6	17840	5	6300	9	1329	19	52647	3
	pine	2691	13	2796	11	982	32	12019	9	457	42	18945	7
	larch	276	31	1132	20	181	39	14107	6	5239	10	20936	5
	Arolla pine	0	.	0	.	122	83	4236	12	13	77	4371	12
	other conifers	634	32	1068	22	170	30	879	28	4	100	2754	14
	beech	27989	4	23599	4	19202	6	11996	8	11285	10	94072	2
	maple	5268	7	4716	10	5139	8	4723	9	812	21	20658	4
	ash	4040	11	6279	8	4278	11	3068	13	2018	16	19683	5
	oak	3124	16	4332	12	419	25	1354	19	2194	18	11422	8
	sweet chestnut	0	.	84	70	0	.	265	46	11352	10	11702	9
	other broadleaves	5174	9	6460	9	5254	11	12309	8	14039	7	43235	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	86577	2	100216	2	105676	2	144132	2	58430	3	495030	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. 1326973. Birmensdorf, Swiss Federal Research Institute WSL

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