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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NFI4
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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	7162	11	11041	11	13135	9	26594	6	3155	16	61087	4
	fir	6638	9	4752	13	6802	10	2516	14	465	33	21173	6
	pine	180	40	327	70	278	79	4388	19	65	100	5237	17
	larch	0	0	472	72	276	58	3363	13	1493	25	5604	12
	Arolla pine	0	.	0	.	0	.	1422	20	32	100	1455	20
	other conifers	470	35	576	35	146	35	807	31	0	.	1999	18
	beech	9245	8	7584	8	6933	10	5045	13	6054	15	34862	5
	maple	3093	14	2600	15	2357	17	3103	14	591	33	11744	7
	ash	1725	21	2431	15	1432	18	1870	18	1330	27	8788	9
	oak	1031	41	688	35	78	50	837	31	1043	37	3676	18
	sweet chestnut	0	.	17	100	0	.	0	0	5688	18	5705	18
	other broadleaves	2695	13	3699	13	2499	13	9333	12	8862	14	27088	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	32239	4	34186	5	33936	5	59279	4	28778	7	188417	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4567	12	6496	11	9762	9	16870	6	2480	14	40176	4
	fir	3456	12	2099	14	3381	12	1644	14	356	38	10936	6
	pine	625	33	323	29	226	75	2969	16	84	65	4226	13
	larch	17	100	223	52	51	100	2308	14	1653	18	4251	11
	Arolla pine	0	.	0	.	0	.	1322	19	16	100	1338	19
	other conifers	120	43	257	34	35	71	87	45	0	.	499	22
	beech	6446	9	5165	8	5102	9	3107	13	3706	17	23526	5
	maple	1536	12	1845	16	2196	14	1456	15	229	38	7262	7
	ash	796	20	1337	15	1755	17	1108	20	494	35	5489	9
	oak	941	28	777	23	87	50	322	33	600	24	2727	14
	sweet chestnut	0	.	0	.	0	.	52	76	3902	15	3954	15
	other broadleaves	752	18	1362	16	996	25	2418	15	3424	12	8952	7
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	19256	5	19882	5	23591	5	33664	4	16945	6	113338	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3158	11	3903	10	6932	7	11824	5	1989	16	27807	4
	fir	2259	12	1506	13	2433	10	840	18	254	37	7292	6
	pine	682	21	493	23	169	58	1611	16	43	71	2999	11
	larch	62	48	168	35	14	71	2218	12	1068	19	3530	10
	Arolla pine	0	.	0	.	0	.	668	20	0	.	668	20
	other conifers	45	64	82	53	0	.	15	100	0	.	142	38
	beech	5041	7	3036	9	3113	12	1563	13	1174	17	13926	5
	maple	800	16	678	16	861	16	720	19	74	43	3134	8
	ash	540	18	1361	15	825	22	655	36	90	38	3471	11
	oak	405	25	526	19	29	77	70	47	271	29	1301	13
	sweet chestnut	0	.	7	100	0	.	29	73	1353	19	1388	18
	other broadleaves	372	24	699	19	232	27	409	21	1128	16	2840	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	13363	4	12459	5	14610	4	20622	4	7445	7	68500	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2228	8	2484	8	4846	6	7696	5	1236	14	18491	3
	fir	1841	10	1230	11	1611	9	787	16	132	31	5601	5
	pine	386	21	405	18	143	63	532	18	28	62	1493	12
	larch	7	100	102	35	7	100	1856	11	563	16	2535	9
	Arolla pine	0	.	0	.	0	.	320	21	0	.	320	21
	other conifers	28	71	81	45	0	.	0	.	8	100	117	36
	beech	2879	7	2270	7	1853	11	960	14	540	19	8502	4
	maple	362	16	179	23	311	18	239	20	7	100	1097	9
	ash	267	22	811	14	286	23	214	26	62	48	1642	9
	oak	250	24	403	19	44	53	62	44	138	39	897	13
	sweet chestnut	0	.	0	.	0	.	21	58	517	19	538	19
	other broadleaves	142	41	319	21	75	39	144	26	281	21	962	12
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	8390	4	8284	4	9175	4	12830	4	3515	7	42194	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1371	9	1654	10	3107	7	4224	6	755	16	11112	4
	fir	935	12	1019	11	1244	10	474	16	163	30	3835	6
	pine	104	29	251	19	52	53	249	24	14	100	671	13
	larch	14	70	84	35	21	74	1414	10	397	15	1930	8
	Arolla pine	0	.	0	.	0	.	114	28	0	.	114	28
	other conifers	21	100	47	55	0	.	0	.	8	100	76	45
	beech	1211	10	1371	9	779	14	482	16	245	21	4087	6
	maple	105	25	90	34	94	29	124	42	8	100	421	17
	ash	103	26	469	18	73	39	63	39	0	.	708	14
	oak	58	43	254	22	0	.	34	53	18	72	364	18
	sweet chestnut	0	.	0	.	0	.	6	100	243	26	249	25
	other broadleaves	36	52	76	45	25	58	126	40	39	45	301	22
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	3958	5	5316	5	5396	5	7311	4	1889	8	23869	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	550	14	547	14	1341	10	1590	8	358	18	4386	5
	fir	399	17	589	14	561	16	255	21	22	58	1825	8
	pine	15	71	41	41	25	59	57	39	0	.	138	24
	larch	0	.	27	50	0	.	565	16	187	23	779	13
	Arolla pine	0	.	0	.	0	.	43	41	0	.	43	41
	other conifers	0	.	54	43	0	.	0	.	0	.	54	43
	beech	284	19	433	14	247	21	128	27	121	31	1212	9
	maple	7	100	28	50	24	79	24	59	0	.	83	34
	ash	52	38	81	43	37	45	6	100	10	100	187	24
	oak	28	59	88	29	0	.	0	.	15	71	131	25
	sweet chestnut	0	.	0	.	0	.	7	100	68	33	76	31
	other broadleaves	23	58	20	58	18	100	18	57	8	100	88	33
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	1357	9	1908	7	2253	7	2694	6	790	11	9002	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	160	24	261	19	513	14	714	12	182	24	1830	8
	fir	138	25	137	26	277	18	128	24	30	49	709	11
	pine	7	100	7	100	0	.	14	71	0	.	28	50
	larch	7	100	0	.	16	71	302	18	71	34	396	15
	Arolla pine	0	.	0	.	0	.	50	37	0	.	50	37
	other conifers	7	100	42	57	0	.	0	.	0	.	49	51
	beech	64	37	146	26	73	36	53	39	44	41	380	15
	maple	9	100	0	.	6	100	8	100	0	.	23	58
	ash	14	71	34	50	18	72	14	71	0	.	79	32
	oak	18	70	28	50	8	100	15	71	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	0	.	76	37	76	37
	other broadleaves	0	.	21	58	0	.	6	100	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	424	14	675	12	911	11	1303	9	410	15	3722	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	54	80	31	140	35	277	20	63	35	596	14
	fir	51	38	54	39	49	43	36	45	8	100	197	20
	pine	0	.	7	100	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	93	31	29	61	122	28
	Arolla pine	0	.	0	.	0	.	22	58	0	.	22	58
	other conifers	0	.	8	100	7	100	0	.	0	.	15	71
	beech	0	0	49	42	16	71	22	58	22	58	108	27
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	17	72	7	100	0	.	0	.	24	58
	oak	0	.	43	41	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	8	100	45	46	53	42
	other broadleaves	7	100	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	94	30	256	18	220	25	471	14	167	23	1207	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	7	100	28	50	53	38	7	100	103	27
	fir	22	58	13	71	8	100	7	100	18	72	68	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	30	50	0	.	30	50
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	7	100	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	7	100
	oak	0	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	30	61	30	61
	other broadleaves	0	.	23	100	0	.	0	.	0	.	23	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	73	44	44	41	97	28	54	42	298	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	14	71	14	71	52	38	19	72	105	27
	fir	0	.	14	71	7	100	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	14	100	22	74
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	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	.	51	49	51	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	28	50	21	58	73	32	83	38	213	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19246	8	26488	7	39819	6	69894	4	10246	11	165693	3
	fir	15738	7	11412	9	16374	7	6687	11	1447	26	51657	4
	pine	1999	19	1853	19	892	57	9828	14	234	66	14807	10
	larch	107	34	1075	35	385	51	12156	8	5475	14	19199	7
	Arolla pine	0	.	0	.	0	.	3981	15	49	100	4030	15
	other conifers	691	29	1147	24	188	30	910	29	16	100	2952	15
	beech	25170	5	20059	5	18123	7	11359	10	11906	13	86616	3
	maple	5912	9	5419	12	5850	11	5681	12	909	28	23771	5
	ash	3498	14	6547	9	4433	13	3930	16	1987	26	20396	6
	oak	2731	24	2823	16	246	32	1340	27	2085	25	9224	11
	sweet chestnut	0	.	23	76	0	.	124	52	11972	13	12120	13
	other broadleaves	4026	12	6220	11	3844	14	12455	11	13749	11	40295	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	79116	3	83067	3	90155	3	138344	3	60076	4	450759	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. 1327003. Birmensdorf, Swiss Federal Research Institute WSL

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