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: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern 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	18298	6	15191	6	9067	8	7332	8	15525	7	4686	11	70099	3
	fir	11222	6	5689	8	2720	12	834	23	291	32	647	28	21404	4
	pine	467	36	652	39	264	39	1974	22	2738	17	58	70	6154	12
	larch	357	53	356	38	184	43	3451	12	2196	19	2845	13	9388	8
	Arolla pine	0	.	11	100	65	76	706	21	1299	18	38	61	2118	13
	other conifers	1077	22	153	53	391	30	112	49	718	28	40	79	2490	14
	beech	17072	4	5581	9	4434	10	1156	23	773	20	6866	11	35882	3
	maple	6322	7	3078	11	2067	15	970	18	443	27	1239	20	14119	5
	ash	4644	9	1336	15	1329	18	545	22	540	26	1620	16	10013	6
	oak	1620	20	213	57	54	45	904	27	42	50	908	25	3740	13
	sweet chestnut	39	61	0	.	0	.	118	75	0	.	4689	14	4846	14
	other broadleaves	7469	7	3862	11	3013	15	4254	12	3931	15	10683	8	33212	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	68587	3	36122	4	23586	6	22355	6	28495	6	34319	4	213464	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11309	6	10041	6	6263	8	5450	9	9290	7	2838	10	45190	3
	fir	5537	7	3110	9	1463	13	534	23	229	26	383	26	11256	5
	pine	797	19	280	43	300	30	1630	17	1160	18	86	48	4253	10
	larch	285	28	61	57	117	45	2242	11	1416	14	2171	12	6291	7
	Arolla pine	0	.	32	100	20	100	813	17	972	20	10	100	1846	13
	other conifers	462	22	94	51	21	71	10	100	112	37	0	.	699	17
	beech	11258	5	3925	9	2831	10	653	24	530	22	3724	11	22921	4
	maple	3719	8	1859	11	1678	13	304	28	220	38	280	25	8060	5
	ash	2347	9	861	17	1154	17	369	26	241	39	978	22	5949	7
	oak	1700	14	191	36	10	100	262	26	29	58	611	20	2803	10
	sweet chestnut	19	100	0	.	0	.	41	61	9	100	3477	12	3547	11
	other broadleaves	2667	10	970	17	999	18	1225	14	780	24	4118	9	10760	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	40099	3	21423	4	14856	6	13531	6	14988	5	18677	4	123575	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7071	5	7304	6	4459	7	3072	8	5649	6	1991	11	29545	3
	fir	3873	6	1875	9	1018	12	194	27	119	35	254	29	7333	4
	pine	1174	12	214	36	235	36	661	16	738	18	161	58	3183	8
	larch	264	23	37	58	109	35	1642	11	1072	12	1347	11	4470	6
	Arolla pine	0	.	46	88	26	86	388	19	513	19	10	100	983	13
	other conifers	205	31	4	100	20	71	0	.	0	.	0	.	229	28
	beech	8251	4	2377	10	1904	10	198	25	296	23	1354	12	14379	3
	maple	1651	8	695	14	647	15	70	35	95	40	163	32	3321	6
	ash	1868	9	487	22	461	17	219	55	52	47	285	24	3373	8
	oak	935	13	81	43	35	47	40	46	22	84	357	20	1470	10
	sweet chestnut	21	84	0	.	0	.	38	66	0	.	1284	13	1344	13
	other broadleaves	1119	11	238	24	215	21	244	28	184	29	1398	11	3398	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	26434	2	13357	4	9128	5	6766	6	8741	5	8603	4	73029	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4818	5	5210	5	3062	7	1682	8	3588	6	1229	11	19589	2
	fir	2891	6	1446	8	889	11	174	27	121	22	147	23	5669	4
	pine	807	11	128	43	107	28	224	20	383	16	73	42	1722	8
	larch	134	25	40	40	75	44	1215	11	874	13	756	12	3094	6
	Arolla pine	0	.	24	63	4	100	142	25	249	17	0	.	419	14
	other conifers	109	31	0	.	0	.	0	.	0	.	4	100	113	30
	beech	5327	4	1230	10	1331	10	75	32	152	28	679	12	8794	3
	maple	606	10	193	16	277	14	32	50	21	60	29	65	1158	7
	ash	1079	8	149	21	314	17	31	46	9	72	94	27	1677	7
	oak	675	11	44	41	18	50	24	47	36	58	131	28	927	10
	sweet chestnut	8	100	4	100	0	.	8	71	4	100	487	14	510	13
	other broadleaves	459	14	78	29	74	30	37	37	27	42	351	14	1026	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	16912	2	8546	4	6153	5	3644	6	5462	5	3981	5	44698	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3001	5	3125	6	2038	8	882	10	1949	7	737	11	11731	3
	fir	1987	6	1101	8	567	12	100	30	116	24	136	26	4007	4
	pine	400	14	29	49	55	44	130	29	183	19	19	60	817	10
	larch	130	21	51	40	36	40	890	10	681	15	528	11	2316	6
	Arolla pine	0	.	0	0	0	.	64	30	96	23	4	100	164	18
	other conifers	56	42	0	.	0	.	0	.	0	.	4	100	60	40
	beech	2765	5	718	13	589	12	28	58	58	31	313	14	4470	4
	maple	204	16	79	37	88	25	0	.	20	52	9	71	399	13
	ash	481	12	53	35	76	27	8	71	4	100	16	60	638	10
	oak	411	15	17	60	8	71	8	71	24	53	31	42	499	13
	sweet chestnut	12	100	0	.	0	.	0	.	4	100	293	17	308	17
	other broadleaves	157	27	84	52	34	36	33	43	18	51	54	31	379	18
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	9604	3	5257	4	3490	6	2143	7	3151	7	2144	6	25789	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1139	8	1184	8	913	10	334	15	767	11	301	15	4638	4
	fir	1035	8	611	12	234	17	27	55	46	31	28	37	1982	6
	pine	109	23	19	51	8	71	12	58	58	31	4	100	210	16
	larch	49	36	16	62	10	73	398	14	221	16	194	16	887	8
	Arolla pine	0	.	10	72	4	100	19	45	59	30	0	.	92	23
	other conifers	40	37	0	.	0	.	0	.	0	.	0	.	40	37
	beech	841	8	231	17	205	18	8	71	20	45	108	23	1413	6
	maple	28	38	20	52	14	77	0	.	0	.	0	.	63	30
	ash	142	21	21	45	20	45	0	.	0	.	10	71	193	18
	oak	167	19	5	100	5	100	5	100	0	.	13	58	194	17
	sweet chestnut	0	.	0	.	0	.	14	77	0	.	107	24	121	23
	other broadleaves	54	31	7	71	14	78	0	.	6	100	23	55	104	24
	indeterminable	0	0	0	0	0	.	0	0	0	.	0	.	0	0
	total	3605	4	2124	6	1428	8	817	10	1178	8	787	8	9938	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	416	11	537	11	459	13	195	23	370	14	179	18	2158	6
	fir	344	13	201	16	162	19	20	45	29	38	41	37	796	8
	pine	8	71	0	.	0	.	0	.	13	58	0	.	21	45
	larch	4	100	8	71	17	50	204	18	90	22	85	24	409	12
	Arolla pine	0	.	0	.	0	.	27	43	19	45	0	.	46	31
	other conifers	33	47	0	.	0	.	0	.	0	.	0	.	33	47
	beech	300	13	59	30	59	30	4	100	11	74	49	37	483	11
	maple	5	100	12	58	5	100	0	.	0	.	0	.	21	45
	ash	71	29	4	100	14	59	0	.	4	100	0	.	93	24
	oak	71	31	0	.	9	71	0	.	8	71	4	100	93	26
	sweet chestnut	0	.	0	.	0	.	5	100	0	.	97	23	103	23
	other broadleaves	23	46	4	100	0	.	4	100	0	.	10	73	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1275	7	825	8	726	10	459	14	544	11	466	11	4295	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	125	20	173	18	95	33	62	34	122	19	92	23	668	9
	fir	104	21	68	26	45	32	8	71	8	71	8	71	241	14
	pine	4	100	0	.	0	.	0	.	4	100	0	.	8	71
	larch	4	100	0	.	0	.	61	27	35	40	51	34	151	19
	Arolla pine	0	.	0	.	0	.	16	50	20	45	0	.	36	33
	other conifers	8	71	4	100	0	.	0	.	0	.	0	.	12	58
	beech	83	25	12	58	21	52	4	100	0	.	16	50	136	19
	maple	6	100	4	100	4	100	0	.	0	.	0	.	13	59
	ash	14	58	0	.	4	100	0	.	0	.	0	.	18	50
	oak	43	32	4	100	10	72	0	.	0	.	0	.	58	28
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	57	34	61	32
	other broadleaves	14	59	0	.	0	.	0	.	0	.	4	100	19	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	405	11	264	14	178	21	156	19	188	16	229	15	1420	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	44	35	54	33	4	100	12	58	23	40	149	18
	fir	32	40	13	58	8	71	0	.	8	71	10	72	71	26
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	35	36	17	50	10	100	62	30
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	beech	13	58	8	71	4	100	0	.	0	.	8	71	33	36
	maple	0	.	0	.	0	.	0	.	0	.	4	100	4	100
	ash	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	oak	19	61	0	.	0	.	0	.	0	.	0	.	19	61
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	20	53	20	53
	other broadleaves	17	80	0	.	0	.	4	100	0	.	0	.	21	67
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	101	25	65	28	66	29	47	30	41	32	75	27	394	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	17	50	24	41	8	71	60	45	28	41	148	22
	fir	12	58	8	71	4	100	0	.	4	100	8	71	36	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	12	58	15	61	32	39
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	maple	0	.	8	100	4	100	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	11	58	0	.	0	.	0	.	0	.	0	.	11	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	77	38	77	38
	other broadleaves	37	67	0	.	0	.	0	.	0	.	4	100	41	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	72	38	34	40	31	35	16	50	80	35	143	24	376	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	46201	4	42826	4	26433	6	19020	7	37330	5	12103	8	183914	2
	fir	27038	4	14122	6	7111	8	1890	18	971	19	1662	19	52794	3
	pine	3766	10	1322	32	970	26	4630	15	5277	13	402	43	16367	7
	larch	1227	19	569	28	547	29	10142	8	6613	10	8002	8	27100	5
	Arolla pine	0	.	122	80	119	72	2182	15	3235	14	62	56	5720	10
	other conifers	1993	16	255	50	431	28	122	47	830	27	49	68	3680	12
	beech	45910	3	14142	7	11378	7	2125	21	1839	17	13128	9	88523	3
	maple	12541	5	5948	9	4784	11	1376	16	799	27	1724	17	27171	4
	ash	10649	6	2911	12	3372	12	1173	22	850	27	3003	14	21958	4
	oak	5653	10	555	34	148	27	1242	24	161	49	2056	17	9815	8
	sweet chestnut	99	78	4	100	0	.	229	62	17	100	10587	10	10937	10
	other broadleaves	12016	6	5242	10	4350	13	5800	11	4947	15	16645	7	49001	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	167092	2	88018	3	59643	4	49933	5	62869	4	69424	3	496978	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. 1327019. Birmensdorf, Swiss Federal Research Institute WSL

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