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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NFI3
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	1232	17	2280	11	2693	8	899	17	1510	11	352	16	8965	5
	fir	788	11	909	19	945	12	29	40	45	28	57	38	2774	8
	pine	44	40	88	42	45	53	314	27	243	27	8	73	742	16
	larch	0	0	27	54	44	56	275	18	91	25	208	22	645	12
	Arolla pine	0	.	0	.	0	.	107	56	86	44	3	79	197	36
	other conifers	42	32	50	31	18	60	6	65	26	44	3	100	144	18
	beech	1378	11	1417	10	1524	14	57	39	84	26	941	17	5400	6
	maple	349	16	484	20	584	14	58	35	48	39	65	34	1588	9
	ash	242	29	549	22	377	16	56	49	83	42	159	36	1467	11
	oak	134	38	215	31	18	42	67	38	0	.	174	32	609	17
	sweet chestnut	8	100	0	.	0	.	0	0	0	.	898	21	907	21
	other broadleaves	356	16	494	14	760	13	380	20	349	27	1192	15	3530	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4573	7	6513	6	7010	6	2249	10	2565	9	4059	8	26969	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2689	12	4587	10	7673	8	2026	15	3347	10	1086	17	21409	4
	fir	1774	13	1878	14	2146	11	124	53	92	41	152	39	6167	7
	pine	221	36	291	32	159	44	547	22	486	27	79	53	1783	13
	larch	11	100	129	44	50	55	599	20	281	24	577	18	1647	11
	Arolla pine	0	.	0	.	0	.	184	34	278	24	11	79	472	19
	other conifers	64	45	112	37	-8	251	10	100	6	73	5	100	189	30
	beech	3822	10	3425	8	3329	10	130	47	246	27	1403	19	12355	5
	maple	823	15	841	15	1575	13	19	59	85	54	105	40	3448	8
	ash	528	22	1220	16	1320	20	105	67	94	55	165	36	3431	10
	oak	235	29	850	22	48	43	54	70	25	100	284	31	1495	15
	sweet chestnut	0	.	0	.	0	.	17	100	0	.	1345	17	1362	17
	other broadleaves	356	20	662	18	782	19	217	27	187	34	1378	14	3581	8
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	10524	6	13994	5	17074	5	4033	10	5126	8	6589	7	57339	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3614	10	5683	9	12104	7	2365	14	5142	10	1921	18	30829	4
	fir	2758	12	2687	12	3113	10	139	55	192	37	179	36	9067	6
	pine	598	21	1004	20	189	35	595	22	750	26	50	64	3185	11
	larch	50	60	188	34	14	129	1049	19	857	21	966	23	3124	11
	Arolla pine	0	.	0	.	12	100	157	31	275	29	12	100	456	21
	other conifers	1	100	126	52	0	.	0	.	0	.	0	.	127	51
	beech	5807	8	5076	9	4200	10	132	49	392	33	796	18	16403	5
	maple	948	15	676	18	1110	14	30	70	74	50	56	54	2894	8
	ash	681	20	1977	15	1094	22	-17	92	84	48	86	50	3906	10
	oak	298	31	874	20	79	46	20	78	7	100	120	42	1398	15
	sweet chestnut	0	.	0	.	0	.	19	70	0	.	788	20	807	20
	other broadleaves	335	29	910	19	401	23	116	43	75	44	669	19	2506	10
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	total	15089	5	19202	5	22316	5	4605	10	7848	8	5642	8	74702	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4761	10	8040	8	15246	6	2675	13	6475	10	2230	15	39426	4
	fir	3911	10	3949	10	4381	9	201	44	270	32	353	31	13064	5
	pine	665	23	1639	19	370	52	235	35	657	25	77	63	3643	12
	larch	107	51	178	35	91	62	1661	16	1373	15	1066	18	4477	9
	Arolla pine	0	.	0	.	0	0	112	39	253	25	0	.	365	21
	other conifers	0	.	255	41	0	.	0	.	0	.	16	100	271	39
	beech	5404	8	6211	8	4105	11	104	63	421	32	722	18	16966	5
	maple	832	18	417	22	813	16	12	100	74	51	18	100	2167	10
	ash	483	26	1942	15	862	31	24	100	9	100	43	138	3362	12
	oak	250	32	1157	18	61	62	19	100	23	100	230	49	1741	15
	sweet chestnut	0	.	0	.	12	71	0	.	0	.	537	28	549	28
	other broadleaves	188	34	622	23	212	36	-27	130	55	53	310	24	1361	14
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	16601	5	24408	4	26154	5	5017	10	9610	8	5603	8	87392	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4676	10	8144	9	14585	6	1661	15	4844	11	1983	16	35895	4
	fir	2720	13	4367	10	4460	10	154	51	463	32	329	33	12494	6
	pine	258	30	942	18	134	46	178	43	439	29	20	100	1970	13
	larch	21	100	429	39	242	45	1582	17	1751	16	1042	16	5068	9
	Arolla pine	0	.	0	.	0	.	12	100	182	30	0	.	194	29
	other conifers	44	100	271	44	0	.	0	.	0	.	26	100	341	38
	beech	3357	11	5149	10	3047	13	60	71	286	35	615	21	12515	6
	maple	150	37	297	39	181	38	0	.	60	61	0	.	687	22
	ash	399	27	1344	19	330	36	0	.	19	100	0	.	2092	14
	oak	74	52	925	20	15	100	0	.	12	100	101	53	1126	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	323	29	323	29
	other broadleaves	97	46	226	50	521	47	77	64	18	100	11	204	950	29
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	11796	6	22094	5	23516	5	3723	12	8075	8	4450	9	73654	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2332	16	4376	13	7319	9	803	26	2642	15	1175	19	18647	6
	fir	1580	17	2357	14	3017	15	121	58	74	71	115	50	7263	9
	pine	111	52	228	38	44	100	0	.	237	36	0	.	619	23
	larch	0	.	122	50	88	71	1075	23	642	24	645	25	2572	13
	Arolla pine	0	.	0	.	0	.	17	100	77	50	0	.	94	45
	other conifers	46	100	346	53	0	.	0	.	0	.	0	.	392	49
	beech	581	27	2034	15	1455	21	0	.	57	71	249	31	4377	11
	maple	83	79	98	58	55	59	0	.	0	.	0	.	236	39
	ash	83	59	396	36	163	53	0	.	22	100	45	100	710	25
	oak	114	80	472	36	0	.	0	.	15	100	0	.	601	32
	sweet chestnut	0	.	0	.	15	100	0	.	0	.	167	37	182	35
	other broadleaves	58	71	53	71	95	58	0	.	0	.	19	100	226	36
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	4988	10	10483	8	12251	7	2016	17	3766	12	2415	12	35919	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	818	25	1621	19	4404	12	489	33	1150	21	934	31	9416	8
	fir	855	25	1304	21	1126	21	33	100	319	42	145	57	3781	12
	pine	0	.	37	100	0	.	0	.	0	.	0	.	37	100
	larch	42	100	43	100	49	100	565	29	442	31	373	33	1514	17
	Arolla pine	0	.	0	.	0	.	116	47	36	71	31	100	183	37
	other conifers	51	100	69	100	0	.	0	.	0	.	0	.	120	72
	beech	158	50	1017	29	305	42	0	.	109	71	164	41	1752	20
	maple	0	.	0	.	0	.	0	.	22	100	0	.	22	100
	ash	0	.	379	39	31	100	0	.	0	.	0	.	410	37
	oak	0	.	325	36	29	100	0	.	0	.	0	.	354	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	95	49	95	49
	other broadleaves	0	.	57	71	0	.	0	.	0	.	21	100	78	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1924	17	4852	11	5942	10	1203	21	2077	16	1764	19	17762	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	335	46	355	41	1252	28	248	53	479	34	366	39	3034	16
	fir	410	38	379	38	463	36	0	.	72	100	144	71	1468	20
	pine	0	.	44	100	0	.	0	.	51	100	0	.	95	71
	larch	0	.	0	.	0	.	330	37	171	52	199	56	700	27
	Arolla pine	0	.	0	.	0	.	30	100	27	100	0	.	57	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	447	39	182	51	37	100	0	.	21	100	686	30
	maple	0	.	0	.	20	100	0	.	0	.	0	.	20	100
	ash	0	.	82	100	0	.	0	.	0	.	0	.	82	100
	oak	0	.	218	46	0	.	0	.	0	.	0	.	218	46
	sweet chestnut	0	.	0	.	0	.	21	100	0	.	150	46	171	42
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	744	29	1525	20	1917	21	666	28	800	26	880	25	6532	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	61	100	390	39	0	0	152	59	91	100	694	30
	fir	68	100	125	71	0	.	0	.	0	.	133	71	326	45
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	92	71	47	100	0	.	139	58
	Arolla pine	0	.	0	.	0	.	37	100	0	.	0	.	37	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	257	59	0	.	0	.	0	.	0	.	257	59
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	58	100	0	.	0	.	0	.	0	.	58	100
	oak	0	.	128	100	0	.	0	.	0	.	0	.	128	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	150	60	150	60
	other broadleaves	0	.	169	79	0	.	0	.	0	.	0	.	169	79
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	68	100	798	34	390	39	129	58	199	51	374	42	1957	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	62	100	228	58	83	71	48	100	123	72	88	100	634	33
	fir	0	.	0	.	91	100	0	.	0	.	0	.	91	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	184	78	184	78
	Arolla pine	0	.	0	.	0	.	35	100	35	100	0	.	69	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	379	57	379	57
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	62	100	228	58	175	62	83	72	158	60	651	42	1357	26
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20519	6	35374	6	65750	5	11215	10	25863	7	10226	11	168948	2
	fir	14863	8	17955	8	19743	7	801	36	1526	22	1607	25	56495	4
	pine	1896	16	4272	13	941	36	1870	18	2862	18	233	45	12074	8
	larch	232	37	1117	22	579	30	7227	13	5655	12	5260	13	20071	7
	Arolla pine	0	.	0	.	12	100	807	24	1248	20	58	94	2124	15
	other conifers	248	58	1229	32	10	190	16	86	32	42	49	87	1584	27
	beech	20507	5	25032	6	18148	7	519	37	1596	23	4909	13	70711	3
	maple	3185	10	2813	11	4338	9	119	38	363	33	244	32	11061	5
	ash	2416	16	7948	11	4176	16	168	48	312	38	498	34	15518	7
	oak	1105	19	5165	13	250	33	160	46	81	72	909	21	7670	10
	sweet chestnut	8	100	0	.	27	83	57	62	0	.	4833	13	4925	13
	other broadleaves	1390	15	3194	13	2772	15	763	22	684	22	3600	11	12401	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	66368	3	104098	3	116745	3	23722	7	40224	6	32426	4	383583	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. 1217946. Birmensdorf, Swiss Federal Research Institute WSL

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