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
grid: 1.4-km grid
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	7823	10	10474	8	14777	6	32338	4	4686	11	70099	3
	fir	6506	7	4716	11	6870	7	2665	12	647	28	21404	4
	pine	262	44	205	60	311	50	5317	13	58	70	6154	12
	larch	42	62	315	59	236	49	5950	10	2845	13	9388	8
	Arolla pine	0	.	0	.	30	74	2050	13	38	61	2118	13
	other conifers	465	31	611	30	227	31	1147	21	40	79	2490	14
	beech	8998	6	8074	6	6669	7	5275	10	6866	11	35882	3
	maple	3190	11	3132	10	2801	12	3757	10	1239	20	14119	5
	ash	1568	15	3076	11	1520	15	2230	13	1620	16	10013	6
	oak	826	31	794	24	44	50	1168	23	908	25	3740	13
	sweet chestnut	0	.	39	61	0	.	118	75	4689	14	4846	14
	other broadleaves	3256	11	4213	9	3226	11	11834	8	10683	8	33212	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	32937	4	35650	4	36710	4	73848	3	34319	4	213464	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4538	9	6771	8	10434	6	20608	4	2838	10	45190	3
	fir	3350	9	2187	11	3611	8	1725	11	383	26	11256	5
	pine	512	27	285	25	251	48	3120	11	86	48	4253	10
	larch	58	59	227	32	39	78	3796	8	2171	12	6291	7
	Arolla pine	0	.	0	.	52	73	1784	13	10	100	1846	13
	other conifers	168	41	294	24	51	45	186	33	0	.	699	17
	beech	6291	7	4967	7	4663	7	3276	10	3724	11	22921	4
	maple	1746	10	1973	12	2236	10	1825	12	280	25	8060	5
	ash	893	16	1453	11	1465	15	1160	14	978	22	5949	7
	oak	968	21	732	17	84	43	409	22	611	20	2803	10
	sweet chestnut	0	.	19	100	0	.	50	53	3477	12	3547	11
	other broadleaves	1040	17	1627	11	1042	17	2932	10	4118	9	10760	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	19562	4	20536	4	23928	4	40872	3	18677	4	123575	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3110	8	3961	8	7352	5	13131	4	1991	11	29545	3
	fir	2327	8	1546	10	2424	7	782	13	254	29	7333	4
	pine	669	18	506	16	208	37	1640	12	161	58	3183	8
	larch	50	36	214	27	16	50	2843	8	1347	11	4470	6
	Arolla pine	0	.	0	.	44	91	929	13	10	100	983	13
	other conifers	75	52	130	38	10	100	13	76	0	.	229	28
	beech	5153	6	3098	7	3199	8	1576	10	1354	12	14379	3
	maple	893	11	758	13	766	13	742	13	163	32	3321	6
	ash	623	14	1246	12	679	17	541	25	285	24	3373	8
	oak	471	21	464	16	66	39	111	35	357	20	1470	10
	sweet chestnut	0	.	21	84	0	.	38	66	1284	13	1344	13
	other broadleaves	512	17	607	15	218	24	663	15	1398	11	3398	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	13883	3	12551	3	14983	3	23008	3	8603	4	73029	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2476	6	2342	7	5406	5	8136	4	1229	11	19589	2
	fir	1756	7	1135	9	1895	7	735	12	147	23	5669	4
	pine	430	16	377	14	130	43	712	11	73	42	1722	8
	larch	34	61	100	26	29	51	2176	8	756	12	3094	6
	Arolla pine	0	.	0	.	13	100	406	14	0	.	419	14
	other conifers	45	52	63	37	0	.	0	.	4	100	113	30
	beech	3084	5	2243	6	1895	8	893	10	679	12	8794	3
	maple	372	12	234	16	254	14	268	15	29	65	1158	7
	ash	331	15	748	10	313	16	190	21	94	27	1677	7
	oak	234	19	441	14	30	47	91	30	131	28	927	10
	sweet chestnut	0	.	8	100	0	.	16	50	487	14	510	13
	other broadleaves	184	24	275	18	76	28	140	21	351	14	1026	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	8947	3	7965	3	10041	3	13764	2	3981	5	44698	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1459	7	1541	8	3478	5	4516	4	737	11	11731	3
	fir	1031	9	956	9	1398	7	486	12	136	26	4007	4
	pine	152	25	249	16	43	47	354	15	19	60	817	10
	larch	19	45	110	23	40	45	1618	8	528	11	2316	6
	Arolla pine	0	.	0	.	0	.	160	18	4	100	164	18
	other conifers	12	100	44	46	0	.	0	.	4	100	60	40
	beech	1365	7	1400	7	897	9	496	15	313	14	4470	4
	maple	110	21	95	25	103	22	84	36	9	71	399	13
	ash	126	19	355	15	84	27	57	30	16	60	638	10
	oak	115	31	296	16	13	58	44	37	31	42	499	13
	sweet chestnut	0	.	12	100	0	.	4	100	293	17	308	17
	other broadleaves	66	51	91	29	23	45	146	32	54	31	379	18
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	0
	total	4455	4	5149	3	6078	3	7963	3	2144	6	25789	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	595	10	544	11	1436	7	1762	6	301	15	4638	4
	fir	475	13	560	11	688	11	231	17	28	37	1982	6
	pine	42	38	67	28	23	45	74	27	4	100	210	16
	larch	4	100	45	38	14	72	630	10	194	16	887	8
	Arolla pine	0	.	0	.	4	100	88	24	0	.	92	23
	other conifers	0	.	40	37	0	.	0	.	0	.	40	37
	beech	320	14	522	10	322	14	142	21	108	23	1413	6
	maple	4	100	24	41	13	78	21	52	0	.	63	30
	ash	43	32	99	28	30	38	12	58	10	71	193	18
	oak	54	39	114	20	9	70	5	100	13	58	194	17
	sweet chestnut	0	.	0	.	0	.	14	77	107	24	121	23
	other broadleaves	24	41	29	45	10	100	17	51	23	55	104	24
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	0
	total	1560	6	2045	5	2550	5	2997	5	787	8	9938	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	197	17	219	15	585	10	978	9	179	18	2158	6
	fir	172	18	172	18	294	13	118	19	41	37	796	8
	pine	4	100	4	100	0	.	13	58	0	.	21	45
	larch	4	100	0	.	17	50	303	14	85	24	409	12
	Arolla pine	0	.	0	.	0	.	46	31	0	.	46	31
	other conifers	4	100	29	52	0	.	0	.	0	.	33	47
	beech	116	21	184	17	97	24	36	36	49	37	483	11
	maple	5	100	0	.	4	100	13	58	0	.	21	45
	ash	20	53	51	34	14	59	8	71	0	.	93	24
	oak	22	45	49	41	5	100	13	58	4	100	93	26
	sweet chestnut	0	.	0	.	0	.	5	100	97	23	103	23
	other broadleaves	6	100	17	50	0	.	8	71	10	73	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	550	10	725	9	1016	7	1539	7	466	11	4295	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	45	35	81	24	148	23	302	13	92	23	668	9
	fir	43	32	61	27	93	23	36	33	8	71	241	14
	pine	0	.	4	100	0	.	4	100	0	.	8	71
	larch	0	.	4	100	0	.	96	22	51	34	151	19
	Arolla pine	0	.	0	.	0	.	36	33	0	.	36	33
	other conifers	4	100	4	100	4	100	0	.	0	.	12	58
	beech	20	45	63	29	21	52	16	50	16	50	136	19
	maple	0	.	6	100	4	100	4	100	0	.	13	59
	ash	0	.	14	58	4	100	0	.	0	.	18	50
	oak	14	59	29	38	14	58	0	.	0	.	58	28
	sweet chestnut	0	.	0	.	0	.	4	100	57	34	61	32
	other broadleaves	10	73	4	100	0	.	0	.	4	100	19	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	136	19	269	13	288	15	498	10	229	15	1420	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	40	33	75	28	23	40	149	18
	fir	24	47	8	71	13	58	16	50	10	72	71	26
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	51	29	10	100	62	30
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	4	100	9	71	12	58	0	.	8	71	33	36
	maple	0	.	0	.	0	.	0	.	4	100	4	100
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	10	73	9	100	0	.	0	.	0	.	19	61
	sweet chestnut	0	.	0	.	0	.	0	.	20	53	20	53
	other broadleaves	0	.	17	80	0	.	4	100	0	.	21	67
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	42	35	59	34	64	26	154	18	75	27	394	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	17	50	91	32	28	41	148	22
	fir	0	.	12	58	8	71	8	71	8	71	36	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	15	61	32	39
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	11	100	11	100
	maple	0	.	0	.	0	.	12	75	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	7	71	0	.	0	.	0	.	11	58
	sweet chestnut	0	.	0	.	0	.	0	.	77	38	77	38
	other broadleaves	15	74	22	100	0	.	0	.	4	100	41	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	23	54	50	49	25	41	135	24	143	24	376	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20252	6	25949	5	43672	4	81937	3	12103	8	183914	2
	fir	15683	5	11354	7	17294	5	6801	8	1662	19	52794	3
	pine	2069	15	1696	13	965	37	11234	9	402	43	16367	7
	larch	211	31	1015	22	392	35	17479	6	8002	8	27100	5
	Arolla pine	0	.	0	.	143	75	5515	10	62	56	5720	10
	other conifers	773	27	1220	21	292	27	1346	21	49	68	3680	12
	beech	25351	4	20559	4	17776	5	11709	8	13128	9	88523	3
	maple	6319	7	6221	8	6181	9	6725	8	1724	17	27171	4
	ash	3604	10	7046	7	4109	11	4197	11	3003	14	21958	4
	oak	2717	17	2936	11	265	22	1840	20	2056	17	9815	8
	sweet chestnut	0	.	99	78	0	.	250	57	10587	10	10937	10
	other broadleaves	5114	11	6902	8	4595	10	15745	7	16645	7	49001	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	82094	2	84998	2	95684	2	164778	2	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. 1327022. Birmensdorf, Swiss Federal Research Institute WSL

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