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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NFI5
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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	15687	8	15534	9	7342	13	7880	11	14308	9	5288	16	66040	4
	fir	11281	8	5915	12	2377	16	557	27	139	35	643	29	20912	6
	pine	335	33	349	55	231	64	1772	32	2541	26	165	59	5392	17
	larch	846	48	362	41	177	52	2359	15	1381	19	2667	19	7792	10
	Arolla pine	0	.	0	.	0	.	664	32	1586	28	49	74	2298	21
	other conifers	1008	28	206	63	345	37	156	60	610	35	20	100	2345	17
	beech	18171	6	5599	11	3740	14	1281	28	549	27	5998	14	35338	5
	maple	6850	10	3288	15	1749	18	1381	27	373	35	1264	30	14905	7
	ash	3113	13	1316	20	932	27	805	29	410	35	1525	22	8102	8
	oak	1676	30	127	73	53	74	718	38	15	100	870	41	3459	20
	sweet chestnut	0	.	0	.	0	.	75	100	18	100	4138	20	4230	20
	other broadleaves	7760	9	4225	14	2500	20	3744	15	3301	21	10308	11	31839	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	66728	3	36922	6	19447	8	21392	8	25231	7	32934	6	202653	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9075	8	9691	8	5888	11	4944	13	9034	9	3179	14	41810	4
	fir	6495	8	3556	11	1204	17	305	32	106	48	302	34	11969	6
	pine	649	26	295	74	207	46	1872	22	1003	24	16	100	4043	14
	larch	344	38	138	50	120	62	1839	17	1091	25	2537	15	6070	10
	Arolla pine	0	.	0	.	0	.	543	26	1266	22	16	100	1825	17
	other conifers	315	26	104	71	36	71	17	100	35	71	0	.	506	23
	beech	9758	6	3957	11	2716	14	622	36	490	33	3849	16	21391	5
	maple	3858	10	1971	15	1562	16	457	28	194	45	287	32	8330	7
	ash	1569	13	798	24	840	21	383	41	238	74	649	34	4478	10
	oak	1289	21	164	40	21	100	288	38	15	100	793	23	2570	14
	sweet chestnut	0	.	0	.	0	.	37	71	15	100	3758	15	3811	15
	other broadleaves	2632	11	1124	17	1122	24	1223	20	1299	26	4268	13	11667	7
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	35985	3	21798	6	13716	8	12530	8	14787	7	19654	6	118470	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5799	8	6902	8	4476	10	3008	11	5381	9	2015	15	27581	4
	fir	3392	9	2092	13	941	18	287	34	107	37	261	33	7081	6
	pine	960	19	249	48	203	46	678	22	561	26	94	71	2744	12
	larch	96	44	91	56	74	73	1468	17	1060	16	1527	16	4316	9
	Arolla pine	0	.	10	100	0	.	310	31	642	24	0	.	961	19
	other conifers	138	38	7	100	15	100	0	0	0	.	0	.	160	35
	beech	7277	6	2200	12	2039	13	273	32	310	26	1695	16	13793	5
	maple	1700	11	939	17	931	18	64	48	118	42	187	42	3939	8
	ash	1474	13	583	33	473	24	190	38	124	46	151	34	2996	10
	oak	725	17	37	63	23	58	107	43	7	100	297	27	1197	13
	sweet chestnut	0	.	0	.	0	.	87	62	0	.	1382	20	1469	19
	other broadleaves	943	14	157	35	253	25	110	33	135	36	1599	17	3198	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	22504	3	13266	6	9430	7	6582	8	8444	7	9209	7	69435	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4121	6	4962	7	2851	10	1761	12	3602	8	1430	14	18726	3
	fir	2754	7	1237	11	849	14	173	34	88	35	148	32	5250	5
	pine	645	14	138	51	59	44	273	25	363	23	41	52	1519	11
	larch	118	31	30	79	35	82	1171	16	901	15	835	14	3089	8
	Arolla pine	0	.	0	.	0	.	133	33	255	23	0	.	388	19
	other conifers	82	37	0	.	0	.	0	.	0	.	8	100	90	35
	beech	4664	5	1338	14	1428	12	77	55	201	33	649	15	8357	4
	maple	639	12	281	19	340	17	56	60	41	55	44	47	1400	8
	ash	903	12	148	27	252	24	20	73	0	0	51	54	1374	10
	oak	678	15	61	48	32	60	21	75	12	100	155	31	960	12
	sweet chestnut	0	.	7	100	0	.	7	100	6	100	600	20	620	20
	other broadleaves	512	19	106	37	31	52	52	43	42	72	484	18	1227	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	15117	3	8308	5	5877	7	3744	9	5512	7	4443	6	43001	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2289	7	2718	8	1997	9	975	13	1885	9	890	19	10754	4
	fir	1907	8	1201	11	646	17	152	39	92	34	190	29	4189	6
	pine	365	17	50	48	46	53	68	41	198	28	14	100	742	13
	larch	98	28	35	53	27	71	807	14	621	16	538	15	2127	8
	Arolla pine	0	.	0	.	0	.	56	56	135	27	0	.	191	25
	other conifers	116	38	0	.	0	.	0	.	0	.	8	100	124	36
	beech	2756	6	792	15	609	16	34	63	59	44	302	20	4551	5
	maple	266	17	109	28	144	27	0	.	52	41	0	.	571	12
	ash	509	15	59	46	109	29	7	100	0	.	40	63	724	12
	oak	411	16	0	.	6	100	8	100	27	81	57	41	509	14
	sweet chestnut	0	.	0	.	0	.	8	100	6	100	297	26	312	25
	other broadleaves	195	25	44	61	57	40	23	57	25	71	57	42	401	17
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	8913	3	5009	6	3641	7	2141	9	3100	7	2392	10	25195	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1217	9	1288	10	1055	12	397	18	750	13	364	16	5071	5
	fir	931	11	587	15	233	20	20	73	53	39	30	50	1853	8
	pine	75	33	17	72	7	100	7	100	58	35	7	100	172	21
	larch	34	45	7	100	6	100	429	19	336	19	209	21	1021	11
	Arolla pine	0	.	0	.	0	.	14	71	22	58	0	.	36	45
	other conifers	61	46	0	.	0	.	0	.	0	.	8	100	69	42
	beech	944	10	177	26	180	25	20	73	41	41	151	28	1511	8
	maple	63	37	41	47	35	63	0	.	0	.	0	.	140	27
	ash	166	22	24	58	41	52	0	.	0	.	10	100	241	19
	oak	142	28	0	0	0	.	0	.	6	100	8	100	156	26
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	75	31	82	30
	other broadleaves	57	39	31	82	22	58	7	100	18	72	15	71	151	27
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	3689	5	2172	8	1580	10	901	13	1283	10	877	10	10501	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	452	14	558	13	472	17	178	27	309	17	188	27	2158	7
	fir	454	13	277	18	167	26	37	60	34	45	30	49	999	10
	pine	34	45	0	.	0	.	0	.	14	71	0	.	48	38
	larch	7	100	14	71	22	58	180	26	117	26	93	27	433	15
	Arolla pine	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	41	58	0	.	0	.	0	.	0	.	0	.	41	58
	beech	293	18	111	28	53	50	6	100	18	74	44	41	525	13
	maple	23	58	13	71	0	.	0	.	0	.	0	.	36	45
	ash	32	50	8	100	13	71	0	.	6	100	6	100	65	34
	oak	69	34	0	.	0	.	0	.	7	100	0	.	76	32
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	67	40	75	37
	other broadleaves	43	41	6	100	0	.	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1447	8	987	10	727	13	438	17	527	13	435	15	4562	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	137	25	131	25	132	25	43	41	141	26	75	37	659	11
	fir	183	22	89	33	51	42	0	.	15	71	7	100	345	16
	pine	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	81	37	56	43	80	40	218	23
	Arolla pine	0	.	0	.	0	.	21	75	15	71	0	.	36	53
	other conifers	34	53	7	100	0	.	0	.	0	.	0	.	41	47
	beech	28	50	7	100	23	58	0	0	0	.	37	45	95	28
	maple	0	.	0	.	8	100	0	.	0	.	0	.	8	100
	ash	16	71	0	.	17	72	0	.	0	.	0	.	33	51
	oak	29	50	0	.	18	71	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	45	46	45	46
	other broadleaves	8	100	0	.	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	441	14	234	19	250	18	145	26	226	21	244	21	1541	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	35	45	71	34	29	50	22	58	49	43	25	59	231	19
	fir	35	45	6	100	21	58	0	.	0	.	25	58	88	29
	pine	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	36	45	18	72	7	100	61	36
	Arolla pine	0	.	0	.	0	.	14	71	0	.	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	32	52	0	.	7	100	0	.	0	.	0	.	39	46
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	17	71	0	.	0	.	0	.	0	.	0	.	17	71
	oak	14	71	0	.	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	38	60	45	53
	other broadleaves	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	140	23	77	32	63	33	80	30	73	35	95	33	529	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21	58	29	50	36	45	7	100	30	50	18	72	140	23
	fir	15	71	7	100	0	.	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	15	71	14	100	29	61
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	50	51	50	51
	other broadleaves	27	61	0	.	0	.	0	.	0	.	0	.	27	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	79	36	36	45	36	45	14	71	51	38	83	39	299	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38834	5	41884	6	24277	8	19216	9	35488	7	13471	12	173170	3
	fir	27447	5	14967	9	6490	12	1532	24	634	26	1637	24	52707	4
	pine	3070	13	1098	47	753	36	4670	21	4745	19	338	53	14674	10
	larch	1543	32	677	33	461	47	8371	11	5596	11	8507	12	25155	6
	Arolla pine	0	.	10	100	0	.	1791	22	3947	20	65	79	5813	15
	other conifers	1795	20	324	63	396	34	173	56	644	34	44	70	3376	15
	beech	43923	4	14179	9	10795	10	2313	27	1669	23	12725	12	85602	3
	maple	13400	7	6643	12	4776	12	1958	25	778	29	1781	26	29336	5
	ash	7799	8	2937	18	2678	15	1405	28	778	43	2433	22	18030	6
	oak	5049	15	389	36	154	41	1142	32	91	72	2180	24	9004	11
	sweet chestnut	0	.	7	100	0	.	238	67	45	72	10448	13	10739	13
	other broadleaves	12184	8	5692	12	3986	17	5160	14	4819	21	16738	10	48580	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	155043	2	88808	4	54766	6	47968	6	59235	5	70366	4	476187	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. 1327031. Birmensdorf, Swiss Federal Research Institute WSL

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