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
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9645	10	15474	7	27578	5	8146	9	16748	7	5059	10	82649	3
	fir	7295	7	5847	9	7592	7	716	31	443	21	789	22	22681	4
	pine	443	30	458	34	1032	25	2967	17	3507	17	117	51	8525	10
	larch	70	62	325	28	332	34	3855	12	2048	15	3580	11	10210	7
	Arolla pine	0	.	0	.	33	75	638	22	1064	18	30	75	1765	14
	other conifers	472	30	514	33	625	29	46	64	860	31	20	71	2538	15
	beech	9199	6	9843	6	10814	7	779	30	1113	17	7037	10	38785	3
	maple	3054	11	3218	11	5331	9	496	24	426	28	944	20	13469	5
	ash	1512	14	3320	12	2891	11	475	26	577	28	1852	16	10627	6
	oak	1041	27	1218	21	322	36	729	25	62	61	1347	23	4719	11
	sweet chestnut	40	100	48	100	107	73	20	100	0	.	8161	11	8376	11
	other broadleaves	3533	10	4739	10	8623	8	4153	14	4150	16	13331	8	38528	4
	indeterminable	39	50	57	41	398	23	189	28	201	25	170	27	1054	12
	total	36345	4	45061	4	65678	3	23207	6	31199	6	42436	4	243926	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5230	8	8938	7	19078	5	5345	10	9312	7	3290	10	51194	3
	fir	3490	8	2973	9	4528	7	359	26	372	26	431	24	12153	4
	pine	669	19	561	22	618	28	1902	15	1533	18	137	44	5420	9
	larch	50	72	307	30	156	37	2371	13	1290	13	2195	12	6368	7
	Arolla pine	0	.	0	.	105	59	714	19	950	20	30	58	1799	13
	other conifers	211	38	288	26	121	46	13	100	91	40	10	100	733	18
	beech	7388	7	6418	6	7179	7	327	30	781	21	3550	11	25643	3
	maple	1751	9	1879	12	3323	8	109	51	233	31	345	26	7639	5
	ash	1174	16	2093	11	2414	14	190	40	282	36	753	24	6906	7
	oak	817	22	1222	18	298	33	171	36	51	72	794	20	3353	10
	sweet chestnut	0	.	19	100	32	77	20	100	0	.	3515	11	3586	11
	other broadleaves	1014	13	1924	11	2387	11	961	17	809	23	4870	9	11966	5
	indeterminable	21	71	0	.	103	32	20	71	60	47	34	58	238	22
	total	21813	4	26624	4	40343	3	12501	7	15765	5	19953	4	136999	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3521	7	5088	7	13001	4	3265	9	6010	6	1963	11	32847	3
	fir	2569	8	1981	9	2787	7	164	33	203	24	251	31	7955	4
	pine	718	18	776	19	392	23	852	16	889	17	204	53	3831	8
	larch	36	48	197	27	83	44	1543	12	1147	13	1297	12	4302	7
	Arolla pine	0	.	0	.	43	80	356	19	432	21	10	100	841	14
	other conifers	19	71	122	42	10	100	0	.	0	.	0	.	151	36
	beech	5511	6	4204	7	3875	8	119	37	460	22	1327	12	15495	3
	maple	904	12	747	13	1039	11	46	55	101	38	155	30	2993	6
	ash	601	19	1481	11	919	16	115	56	101	44	209	24	3424	8
	oak	463	19	834	16	126	35	33	50	30	87	331	23	1817	10
	sweet chestnut	0	.	22	71	27	87	16	73	0	.	1303	12	1367	12
	other broadleaves	480	20	768	14	568	17	193	27	154	36	1333	12	3496	7
	indeterminable	0	.	0	.	59	36	10	100	59	52	4	100	132	29
	total	14822	3	16219	4	22929	3	6711	6	9585	5	8388	5	78654	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2640	6	3744	6	8648	4	1688	8	3641	6	1324	10	21684	2
	fir	1844	7	1690	8	2076	7	113	28	149	22	175	24	6046	4
	pine	453	16	639	13	241	26	250	23	440	16	80	40	2104	8
	larch	24	53	166	26	96	35	1144	10	891	10	834	11	3156	6
	Arolla pine	0	.	0	.	13	58	154	24	258	17	0	.	425	14
	other conifers	0	.	81	36	0	.	0	.	0	.	4	100	85	35
	beech	2965	6	2840	6	2265	7	43	56	216	24	613	12	8943	3
	maple	366	15	303	14	383	12	12	75	26	47	17	80	1108	8
	ash	242	17	860	10	385	21	8	100	21	51	46	39	1562	8
	oak	153	21	564	13	61	36	16	78	28	65	131	31	954	10
	sweet chestnut	0	.	19	100	13	58	0	.	0	.	507	15	540	15
	other broadleaves	80	29	357	17	153	21	12	58	20	53	290	15	913	9
	indeterminable	0	.	5	100	14	60	12	58	4	100	0	.	35	36
	total	8768	3	11268	3	14349	3	3452	6	5694	5	4023	5	47555	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1671	7	2243	7	5537	5	735	11	1993	7	801	11	12980	3
	fir	1050	10	1346	8	1513	7	79	33	147	21	109	26	4244	5
	pine	168	24	302	15	54	38	106	30	200	20	16	61	845	10
	larch	8	71	115	26	83	30	794	11	611	11	586	10	2197	6
	Arolla pine	0	.	0	.	8	100	37	33	107	24	4	100	156	19
	other conifers	4	100	48	39	0	.	0	.	0	.	4	100	57	35
	beech	1084	8	1585	7	1123	10	18	60	93	26	256	16	4159	4
	maple	67	25	85	28	109	23	0	.	15	61	4	100	281	14
	ash	88	23	349	14	79	26	4	100	19	51	16	60	555	11
	oak	76	36	369	14	26	41	4	100	12	58	51	38	539	11
	sweet chestnut	0	.	4	100	10	100	0	.	0	.	288	17	302	16
	other broadleaves	32	35	100	29	146	35	39	44	10	72	53	35	381	17
	indeterminable	0	.	0	.	21	45	8	71	27	49	0	.	56	31
	total	4249	4	6546	4	8710	4	1823	8	3235	5	2189	6	26752	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	550	12	915	9	1974	6	300	20	751	10	332	14	4822	4
	fir	427	12	486	11	636	11	51	36	35	33	43	32	1678	6
	pine	35	43	82	26	14	59	8	71	62	31	8	100	210	17
	larch	0	.	42	32	47	47	327	15	161	18	207	16	784	9
	Arolla pine	0	.	0	.	12	58	23	41	55	32	0	.	90	23
	other conifers	4	100	41	50	0	.	0	.	0	.	0	.	45	46
	beech	234	19	509	11	323	15	4	100	8	71	97	22	1175	8
	maple	10	72	17	50	25	47	0	.	0	.	0	.	51	31
	ash	34	39	95	26	25	46	0	.	4	100	6	100	164	19
	oak	41	50	147	24	12	71	0	.	8	71	15	72	221	19
	sweet chestnut	0	.	0	.	14	77	0	.	0	.	103	23	117	23
	other broadleaves	12	58	24	45	19	52	0	.	0	.	16	61	71	27
	indeterminable	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	total	1346	7	2358	6	3105	5	713	12	1083	8	826	8	9431	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	178	18	273	14	929	8	91	23	299	14	198	18	1968	6
	fir	166	19	178	16	243	14	8	71	35	36	25	40	654	9
	pine	0	.	8	71	0	.	0	.	0	.	0	.	8	71
	larch	4	100	9	71	8	71	143	22	69	27	112	21	344	13
	Arolla pine	0	.	0	.	0	.	27	43	20	45	4	100	50	30
	other conifers	4	100	13	58	0	.	0	.	0	.	0	.	17	50
	beech	56	28	207	17	49	31	0	.	19	52	32	35	363	12
	maple	0	.	6	100	8	71	0	.	5	100	0	.	19	51
	ash	0	.	48	32	4	100	0	.	0	.	0	.	52	31
	oak	4	100	79	28	19	50	0	.	0	.	0	.	102	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	102	25	102	25
	other broadleaves	0	.	19	51	4	100	0	.	0	.	8	71	31	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	411	12	839	8	1265	7	268	15	447	11	480	11	3710	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	82	46	52	30	250	17	43	43	67	27	80	24	574	12
	fir	65	28	49	29	54	28	0	.	12	58	15	58	195	15
	pine	0	.	4	100	0	.	0	.	4	100	0	.	8	71
	larch	0	.	0	.	0	.	70	25	28	43	58	34	155	19
	Arolla pine	0	.	0	.	0	.	8	71	12	58	0	.	20	45
	other conifers	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	beech	4	100	73	30	25	41	4	100	0	.	4	100	109	23
	maple	0	.	0	.	4	100	0	.	0	.	4	100	8	71
	ash	0	.	10	72	0	.	0	.	0	.	0	.	10	72
	oak	0	.	26	42	0	.	0	.	0	.	0	.	26	42
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	61	30	66	29
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	151	29	218	16	332	14	129	21	123	20	221	16	1174	7
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	71	12	58	98	23	8	71	32	35	27	41	185	16
	fir	8	71	17	50	8	71	0	.	0	.	18	51	51	29
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	8	71	9	72	33	36
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	22	45	0	.	0	.	0	.	8	71	31	38
	maple	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	ash	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	oak	0	.	16	63	0	.	0	.	0	.	0	.	16	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	29	46	29	46
	other broadleaves	0	.	21	64	4	100	0	.	0	.	4	100	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	16	50	94	25	114	22	32	35	45	30	95	23	395	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	13	58	27	38	4	100	17	50	21	53	86	23
	fir	0	.	0	.	19	51	0	.	4	100	4	100	27	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	8	71	8	71	19	53	36	36
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	19	100	19	100
	maple	0	.	0	.	12	75	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	70	38	70	38
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	9	71	16	50	58	29	20	45	33	35	133	27	270	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	23528	6	36752	5	77120	3	19624	8	38870	5	13096	7	208990	2
	fir	16914	5	14566	6	19457	5	1488	25	1400	16	1859	17	55684	3
	pine	2486	14	2830	11	2351	19	6085	14	6636	13	562	40	20951	7
	larch	192	44	1162	17	807	24	10269	8	6261	9	8896	8	27586	5
	Arolla pine	0	.	0	.	214	55	1973	16	2905	15	77	64	5170	11
	other conifers	715	29	1110	21	756	30	59	70	951	30	39	62	3630	13
	beech	26441	4	25702	4	25654	5	1294	28	2690	16	12941	9	94722	3
	maple	6152	7	6256	8	10237	7	663	22	807	24	1469	18	25584	4
	ash	3651	11	8259	7	6716	10	791	30	1003	27	2883	15	23304	5
	oak	2594	18	4480	11	864	26	952	24	193	54	2668	17	11751	8
	sweet chestnut	40	100	113	92	203	72	61	84	0	.	14138	9	14555	9
	other broadleaves	5157	9	7952	8	11905	7	5358	13	5143	16	19905	7	55420	4
	indeterminable	60	41	62	38	599	17	239	26	351	22	208	26	1518	10
	total	87931	3	109242	2	156883	2	48858	5	67210	4	78742	3	548866	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. 1296644. Birmensdorf, Swiss Federal Research Institute WSL

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