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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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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NFI3
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 2004/07
		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	8644	10	13182	8	14875	6	31328	4	4087	11	72116	3
	fir	6546	7	3962	10	6440	8	2688	11	710	23	20347	4
	pine	365	34	332	44	416	42	5395	12	38	79	6547	11
	larch	19	71	286	31	108	65	5590	9	2652	13	8655	7
	Arolla pine	0	.	0	.	11	100	1542	14	10	100	1562	14
	other conifers	414	30	682	31	126	38	1057	23	10	100	2289	15
	beech	8796	6	8603	6	7192	7	5293	10	6799	10	36683	3
	maple	2723	10	3179	12	2889	13	3198	10	922	20	12911	6
	ash	1666	14	2877	13	1711	15	1972	13	1770	16	9996	6
	oak	938	28	958	22	43	50	992	21	1196	24	4126	12
	sweet chestnut	0	.	79	71	0	.	84	71	5742	13	5905	13
	other broadleaves	3262	11	4104	10	3225	11	11229	8	10936	8	32755	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	33373	4	38242	4	37035	4	70368	3	34872	4	213891	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5063	8	7688	8	11314	6	19587	4	2752	11	46404	3
	fir	3112	9	2458	11	3685	8	1574	12	365	26	11195	5
	pine	601	22	414	25	219	57	3247	11	96	47	4577	9
	larch	58	58	288	32	14	100	3605	9	1869	12	5834	7
	Arolla pine	0	.	0	.	73	78	1646	14	20	71	1738	13
	other conifers	230	37	287	26	30	58	186	32	0	.	733	18
	beech	7195	6	5663	7	4967	8	3526	10	3430	11	24781	4
	maple	1733	9	1665	13	2086	11	1627	12	335	26	7447	5
	ash	1257	15	1580	12	1720	14	1390	21	733	24	6680	7
	oak	1039	21	853	19	154	35	372	28	765	21	3184	10
	sweet chestnut	0	.	19	100	0	.	41	61	3206	12	3266	12
	other broadleaves	1196	13	1511	12	1084	16	2644	11	4276	9	10710	5
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	21486	3	22427	4	25346	4	39445	3	17846	4	126550	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3376	7	3969	7	7884	6	12699	4	1778	12	29706	3
	fir	2384	9	1588	10	2449	7	842	12	247	31	7511	5
	pine	751	18	587	15	175	32	1750	11	190	56	3453	8
	larch	56	36	157	29	13	58	2661	9	1132	13	4020	7
	Arolla pine	0	.	0	.	34	100	786	14	0	.	820	14
	other conifers	56	58	85	49	0	.	10	100	0	.	151	36
	beech	5477	5	3662	7	3236	9	1543	10	1273	12	15191	4
	maple	890	12	658	14	649	14	601	14	151	31	2949	6
	ash	641	18	1336	12	730	18	446	22	205	24	3358	8
	oak	509	17	709	18	60	41	141	35	331	23	1749	10
	sweet chestnut	0	.	22	71	0	.	46	57	1155	13	1223	12
	other broadleaves	552	19	587	16	209	28	663	16	1212	12	3223	7
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	14692	3	13360	3	15439	4	22188	3	7675	5	73354	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2458	6	3064	6	5610	5	7708	4	1125	11	19965	2
	fir	1753	7	1309	8	1944	7	647	12	150	24	5803	4
	pine	483	15	554	14	128	42	729	12	80	40	1975	8
	larch	34	69	156	25	41	47	2090	7	686	12	3007	6
	Arolla pine	0	.	0	.	4	100	401	14	0	.	405	14
	other conifers	27	78	53	38	0	.	0	.	4	100	85	35
	beech	2985	5	2431	6	1865	8	882	11	597	12	8758	3
	maple	351	13	264	18	270	15	187	17	17	80	1089	8
	ash	295	16	719	11	257	17	223	33	42	42	1537	8
	oak	192	19	508	13	33	49	86	34	120	33	938	10
	sweet chestnut	0	.	12	100	0	.	8	70	455	16	474	15
	other broadleaves	138	26	276	19	56	35	134	22	252	15	856	10
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	8715	3	9344	3	10208	3	13096	3	3529	5	44892	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1592	7	1887	7	3589	6	4296	4	682	11	12045	3
	fir	979	11	1111	9	1380	7	487	12	105	26	4062	5
	pine	167	24	278	15	38	43	307	16	16	61	806	10
	larch	12	58	107	28	33	44	1477	7	480	11	2109	6
	Arolla pine	0	.	0	.	0	.	133	20	4	100	136	20
	other conifers	0	.	53	37	0	.	0	.	4	100	57	35
	beech	1127	8	1367	7	801	10	525	16	240	16	4060	4
	maple	62	26	81	29	92	25	41	35	4	100	281	14
	ash	100	23	301	15	78	28	56	30	16	60	551	11
	oak	107	30	325	14	18	50	28	38	47	40	526	12
	sweet chestnut	0	.	4	100	0	.	14	78	267	17	285	17
	other broadleaves	51	29	66	38	28	48	183	30	53	35	381	17
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	4198	4	5579	4	6058	4	7546	3	1919	6	25300	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	549	12	729	10	1327	7	1638	7	285	15	4529	4
	fir	380	12	415	13	570	11	213	18	39	34	1617	6
	pine	39	40	66	28	19	51	70	28	4	100	198	16
	larch	4	100	31	39	23	52	540	11	151	18	748	9
	Arolla pine	0	.	0	.	4	100	86	24	0	.	90	23
	other conifers	4	100	41	50	0	.	0	.	0	.	45	46
	beech	285	17	387	13	285	15	106	29	97	22	1160	8
	maple	10	72	17	50	4	100	21	52	0	.	51	31
	ash	46	37	86	26	14	58	11	58	6	100	164	19
	oak	81	35	97	29	11	71	13	59	15	72	218	20
	sweet chestnut	0	.	0	.	0	.	9	71	89	25	98	24
	other broadleaves	23	45	12	58	0	.	11	58	12	58	59	27
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0
	total	1420	7	1882	6	2256	5	2720	5	698	9	8976	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	181	17	217	16	534	11	767	9	171	20	1871	6
	fir	162	20	145	18	238	14	77	24	25	40	646	9
	pine	0	.	8	71	0	.	0	.	0	.	8	71
	larch	4	100	4	100	5	100	252	16	67	25	333	13
	Arolla pine	0	.	0	.	0	.	50	30	0	.	50	30
	other conifers	4	100	9	71	4	100	0	.	0	.	17	50
	beech	64	26	174	19	29	47	43	32	32	35	343	13
	maple	0	.	6	100	4	100	10	71	0	.	19	51
	ash	8	100	27	41	8	71	4	100	0	.	48	32
	oak	9	71	62	31	22	51	9	71	0	.	102	24
	sweet chestnut	0	.	0	.	0	.	0	.	84	27	84	27
	other broadleaves	6	100	12	58	0	.	4	100	8	71	31	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	437	11	665	9	844	8	1217	7	388	12	3551	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	82	46	39	35	124	26	234	15	73	26	552	12
	fir	69	27	24	41	50	29	29	38	15	58	187	16
	pine	0	.	4	100	0	.	4	100	0	.	8	71
	larch	0	.	0	.	0	.	113	21	38	41	151	19
	Arolla pine	0	.	0	.	0	.	20	45	0	.	20	45
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	11	74	41	42	29	42	12	58	4	100	97	25
	maple	0	.	0	.	0	.	4	100	4	100	8	71
	ash	0	.	10	72	0	.	0	.	0	.	10	72
	oak	4	100	23	46	0	.	0	.	0	.	26	42
	sweet chestnut	0	.	0	.	0	.	4	100	57	32	62	30
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	166	27	144	19	203	19	420	11	190	17	1124	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	71	4	100	58	33	73	24	27	41	170	17
	fir	4	100	8	71	17	50	0	.	13	59	43	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	24	41	9	72	33	36
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	18	51	0	.	0	.	8	71	31	38
	maple	0	.	0	.	4	100	0	.	0	.	4	100
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	4	100	13	76	0	.	0	.	0	.	16	63
	sweet chestnut	0	.	0	.	0	.	0	.	20	59	20	59
	other broadleaves	4	100	17	75	0	.	4	100	4	100	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	24	41	64	31	79	28	109	19	82	25	358	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	8	71	45	30	17	61	82	24
	fir	0	.	7	100	8	71	8	71	4	100	27	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	15	61	32	39
	Arolla pine	0	.	0	.	0	.	12	58	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	19	100	19	100
	maple	0	.	0	.	0	.	12	75	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	70	38	70	38
	other broadleaves	5	100	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	9	71	18	52	17	50	93	22	125	29	262	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21959	6	30787	5	45322	4	78376	3	10997	8	187441	2
	fir	15388	5	11028	7	16783	5	6565	8	1673	18	51437	3
	pine	2406	13	2242	12	996	34	11502	9	424	43	17570	7
	larch	187	33	1029	18	237	39	16368	6	7100	9	24922	5
	Arolla pine	0	.	0	.	126	83	4684	11	33	66	4843	11
	other conifers	735	27	1214	22	159	32	1254	23	18	71	3381	13
	beech	25943	4	22345	4	18405	6	11931	8	12498	9	91123	3
	maple	5769	7	5869	9	5997	9	5701	8	1434	18	24770	4
	ash	4012	10	6941	8	4519	11	4102	13	2772	15	22346	5
	oak	2882	17	3551	11	340	23	1641	19	2473	18	10889	8
	sweet chestnut	0	.	135	70	0	.	207	53	11145	10	11487	9
	other broadleaves	5238	9	6584	8	4602	10	14872	8	16754	7	48050	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	84521	2	91726	2	97486	2	157203	2	67323	3	498258	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. 1326993. Birmensdorf, Swiss Federal Research Institute WSL

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