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 NFI1-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	15545	8	13503	10	6469	14	7285	11	12176	10	3766	18	58743	4
	fir	11187	8	5893	12	2230	17	537	28	139	35	608	31	20594	6
	pine	335	33	349	55	35	100	1772	32	2032	29	65	100	4588	19
	larch	846	48	362	41	85	65	1796	18	579	24	1165	28	4833	13
	Arolla pine	0	.	0	.	0	.	628	34	877	43	32	100	1538	28
	other conifers	1008	28	33	71	287	40	156	60	483	41	20	100	1987	19
	beech	17936	6	5563	11	3579	15	1193	29	549	27	5708	14	34528	5
	maple	6733	10	2977	15	1426	20	788	27	333	38	736	33	12992	7
	ash	2925	13	1199	21	753	31	527	31	294	38	1407	23	7104	9
	oak	1676	30	127	73	18	100	718	38	15	100	838	42	3392	20
	sweet chestnut	0	.	0	.	0	.	0	0	18	100	4105	20	4123	20
	other broadleaves	7341	9	2890	14	1671	19	3113	16	2513	25	8559	13	26087	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	65530	4	32895	6	16553	9	18513	8	20008	8	27009	7	180509	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8962	8	8282	9	5175	11	4484	14	7370	10	2558	16	36831	4
	fir	6421	8	3556	11	1186	17	305	32	106	48	284	36	11859	6
	pine	649	26	278	79	146	56	1834	22	965	24	16	100	3887	14
	larch	344	38	121	55	102	71	1407	18	601	27	1665	19	4240	11
	Arolla pine	0	.	0	.	0	.	490	28	811	27	16	100	1317	19
	other conifers	315	26	35	71	36	71	17	100	35	71	0	.	437	21
	beech	9674	6	3843	12	2564	15	622	36	437	35	3606	17	20745	5
	maple	3715	10	1835	15	1428	17	321	32	175	49	238	33	7713	7
	ash	1509	14	771	24	730	21	198	45	186	90	614	35	4008	11
	oak	1272	21	119	41	21	100	252	42	15	100	671	25	2350	14
	sweet chestnut	0	.	0	.	0	.	18	100	15	100	3666	16	3699	16
	other broadleaves	2453	11	965	17	748	30	1037	20	1033	30	3543	14	9779	7
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	35314	3	19806	6	12135	8	10985	9	11749	8	16877	6	106866	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5687	8	6161	8	4032	10	2714	12	4770	9	1878	16	25241	4
	fir	3377	9	2043	13	927	19	287	34	107	37	247	34	6989	6
	pine	960	19	242	49	151	51	678	22	552	27	94	71	2676	12
	larch	96	44	91	56	28	100	1280	18	805	18	1025	19	3325	10
	Arolla pine	0	.	10	100	0	.	262	34	475	26	0	.	747	20
	other conifers	138	38	0	.	15	100	0	0	0	.	0	.	153	36
	beech	7234	6	2145	12	1954	13	273	32	310	26	1630	16	13546	5
	maple	1674	11	939	17	808	18	40	62	90	46	104	56	3656	8
	ash	1466	13	537	35	473	24	160	43	103	51	126	38	2865	11
	oak	725	17	37	63	23	58	81	51	7	100	272	29	1146	14
	sweet chestnut	0	.	0	.	0	.	49	79	0	.	1373	20	1422	19
	other broadleaves	876	15	157	35	253	25	55	44	124	39	1368	19	2833	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	22233	4	12361	6	8665	7	5879	9	7343	7	8116	7	64597	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4054	6	4468	7	2529	11	1659	12	3279	9	1341	15	17331	3
	fir	2739	7	1237	11	797	15	173	34	88	35	148	32	5182	5
	pine	645	14	131	53	29	50	262	26	356	24	34	60	1457	11
	larch	118	31	30	79	7	100	1043	17	711	17	668	16	2576	9
	Arolla pine	0	.	0	.	0	.	105	36	219	25	0	.	324	21
	other conifers	82	37	0	.	0	.	0	.	0	.	8	100	90	35
	beech	4657	5	1230	13	1387	13	77	55	201	33	628	16	8180	4
	maple	625	12	281	19	326	17	56	60	23	57	44	47	1355	8
	ash	868	13	148	27	252	24	20	73	0	0	51	54	1339	10
	oak	661	15	61	48	25	72	21	75	12	100	133	34	913	13
	sweet chestnut	0	.	7	100	0	.	7	100	6	100	600	20	620	20
	other broadleaves	505	19	106	37	31	52	45	47	14	71	431	20	1131	12
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	14955	3	7699	6	5383	7	3469	9	4909	7	4085	7	40500	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2242	8	2539	8	1803	10	966	13	1755	10	862	19	10166	4
	fir	1875	8	1185	11	627	17	142	41	92	34	183	30	4104	6
	pine	365	17	43	54	31	71	68	41	184	29	14	100	706	13
	larch	98	28	29	61	27	71	744	15	559	17	442	17	1898	9
	Arolla pine	0	.	0	.	0	.	42	71	135	27	0	.	177	27
	other conifers	116	38	0	.	0	.	0	.	0	.	8	100	124	36
	beech	2696	6	773	15	574	17	26	77	59	44	295	20	4423	5
	maple	266	17	109	28	132	28	0	.	42	45	0	.	549	13
	ash	497	15	59	46	109	29	7	100	0	.	33	72	706	13
	oak	401	16	0	.	6	100	8	100	27	81	57	41	499	14
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	297	26	303	26
	other broadleaves	180	26	44	61	57	40	23	57	14	100	38	52	357	18
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	8737	3	4782	6	3365	7	2027	10	2873	8	2228	10	24013	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1203	10	1175	10	992	13	366	19	675	14	344	17	4755	5
	fir	923	11	587	15	221	21	20	73	53	39	30	50	1832	8
	pine	75	33	17	72	7	100	7	100	58	35	0	.	164	22
	larch	34	45	7	100	6	100	401	20	336	19	186	23	970	11
	Arolla pine	0	.	0	.	0	.	7	100	22	58	0	.	29	50
	other conifers	61	46	0	.	0	.	0	.	0	.	8	100	69	42
	beech	882	10	177	26	180	25	20	73	41	41	142	29	1441	8
	maple	55	39	29	51	27	76	0	.	0	.	0	.	112	30
	ash	166	22	17	72	41	52	0	.	0	.	10	100	233	19
	oak	134	29	0	0	0	.	0	.	6	100	8	100	148	27
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	75	31	82	30
	other broadleaves	50	43	31	82	15	72	7	100	7	100	15	71	125	30
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	3583	5	2039	8	1488	11	835	13	1198	10	818	10	9961	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	445	14	504	13	466	17	163	29	295	18	181	28	2054	7
	fir	434	14	277	18	167	26	37	60	34	45	30	49	979	10
	pine	34	45	0	.	0	.	0	.	14	71	0	.	48	38
	larch	7	100	14	71	22	58	163	28	106	27	86	28	398	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	276	18	111	28	37	65	6	100	18	74	44	41	493	14
	maple	23	58	6	100	0	.	0	.	0	.	0	.	29	50
	ash	32	50	8	100	13	71	0	.	6	100	0	.	59	36
	oak	59	36	0	.	0	.	0	.	7	100	0	.	66	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	67	40	67	40
	other broadleaves	43	41	6	100	0	.	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1393	8	926	10	706	14	398	19	502	13	414	16	4338	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	137	25	114	26	112	26	43	41	113	30	49	47	568	12
	fir	183	22	89	33	41	46	0	.	15	71	7	100	335	16
	pine	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	72	40	56	43	50	51	178	26
	Arolla pine	0	.	0	.	0	.	14	100	15	71	0	.	29	61
	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	217	20	219	19	128	28	199	22	188	24	1393	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	35	45	46	41	29	50	22	58	49	43	18	72	199	20
	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	7	100	0	.	43	41
	Arolla pine	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	133	24	52	38	63	33	73	31	63	37	81	37	465	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21	58	29	50	28	50	7	100	23	58	18	72	126	24
	fir	15	71	7	100	0	.	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	14	100	22	74
	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	.	44	57	44	57
	other broadleaves	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	59	40	36	45	28	50	14	71	37	45	76	41	251	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38330	5	36822	6	21635	8	17710	10	30504	7	11014	13	156015	3
	fir	27189	6	14880	9	6216	12	1501	24	634	26	1563	25	51983	4
	pine	3070	13	1060	48	398	46	4621	22	4169	20	223	72	13541	11
	larch	1543	32	653	34	277	52	6942	12	3766	13	5302	15	18482	7
	Arolla pine	0	.	10	100	0	.	1592	24	2581	24	49	100	4231	17
	other conifers	1795	20	74	46	339	36	173	56	518	40	44	70	2942	15
	beech	43415	4	13848	9	10304	10	2216	28	1615	24	12090	13	83489	3
	maple	13092	7	6176	11	4162	13	1205	25	663	31	1122	28	26420	5
	ash	7495	8	2739	19	2389	16	912	29	589	52	2240	24	16364	7
	oak	4987	15	344	39	112	47	1080	34	91	72	1978	26	8591	12
	sweet chestnut	0	.	7	100	0	.	90	65	45	72	10308	14	10450	13
	other broadleaves	11464	8	4199	12	2774	17	4280	15	3705	25	13962	11	40383	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	152379	2	80813	4	48605	6	42322	7	48881	6	59893	5	432892	2

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. 1360130. Birmensdorf, Swiss Federal Research Institute WSL

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