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
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	1102	14	2038	12	3070	7	913	16	1862	10	596	15	9582	4
	fir	893	11	1095	15	994	12	74	66	50	37	64	38	3169	7
	pine	18	59	65	67	38	90	358	29	262	23	10	73	751	18
	larch	0	0	49	63	60	38	356	18	163	23	283	21	911	11
	Arolla pine	0	.	0	.	0	.	91	46	181	32	5	77	277	26
	other conifers	15	164	34	56	42	37	8	66	35	36	3	100	138	27
	beech	1332	10	1147	10	1357	11	155	53	98	30	929	15	5019	6
	maple	431	16	517	21	730	13	80	34	56	36	126	28	1941	8
	ash	250	27	469	20	459	16	150	55	90	42	214	31	1633	11
	oak	110	41	128	31	29	55	60	33	0	.	153	33	479	17
	sweet chestnut	2	100	0	0	19	100	0	0	0	.	836	19	857	18
	other broadleaves	321	16	588	12	861	14	411	18	507	22	1367	14	4054	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4473	6	6131	6	7659	5	2656	10	3306	8	4586	7	28812	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2612	13	4255	10	7608	7	2088	15	3552	10	1265	16	21379	4
	fir	1756	14	1391	13	1950	12	141	42	76	40	167	39	5480	7
	pine	208	44	269	31	177	39	571	23	399	27	68	53	1692	13
	larch	0	0	113	47	81	48	727	18	406	23	863	16	2188	10
	Arolla pine	0	.	0	.	0	.	233	29	291	25	7	71	531	19
	other conifers	83	40	81	48	38	62	0	0	10	59	4	100	217	26
	beech	3438	10	3133	8	3283	9	133	46	280	31	1641	17	11907	5
	maple	756	14	930	16	1685	13	48	39	208	42	127	39	3755	8
	ash	405	21	1158	16	1195	16	128	40	86	45	251	29	3224	9
	oak	223	28	669	23	61	38	71	43	0	0	264	25	1289	14
	sweet chestnut	0	.	0	.	8	100	15	100	0	.	1458	16	1481	16
	other broadleaves	318	21	692	17	754	17	339	23	249	30	1623	14	3975	8
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	0	0	0
	total	9799	6	12691	5	16840	5	4495	10	5557	8	7739	6	57120	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3622	10	5761	9	13013	6	2705	13	5643	9	2153	16	32896	4
	fir	2794	12	2525	12	3357	10	146	49	148	32	254	35	9224	6
	pine	591	24	704	20	218	33	643	21	722	27	55	56	2932	11
	larch	55	54	223	32	61	53	1269	17	919	19	1205	17	3732	9
	Arolla pine	0	.	0	.	0	.	178	31	336	24	28	100	542	19
	other conifers	12	101	139	44	-14	145	0	.	0	.	0	.	137	48
	beech	5459	8	4211	10	4155	10	141	38	428	26	996	16	15389	5
	maple	813	15	670	20	1479	12	11	102	171	47	64	53	3207	8
	ash	726	18	1781	14	1197	19	35	77	51	55	95	40	3885	9
	oak	316	27	722	19	46	53	34	54	6	100	231	29	1355	13
	sweet chestnut	0	.	6	100	24	100	19	71	0	.	997	20	1045	19
	other broadleaves	359	22	858	21	428	20	109	40	85	38	1064	16	2903	10
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	14747	5	17599	5	23963	4	5290	9	8508	7	7141	7	77247	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4919	9	6420	8	16237	6	2621	13	6844	9	2358	16	39400	3
	fir	3876	10	3528	11	4664	9	227	41	320	26	296	30	12911	5
	pine	618	24	1027	18	362	55	233	31	565	24	45	53	2851	12
	larch	41	73	204	39	88	60	1777	18	1509	15	1030	15	4649	9
	Arolla pine	0	.	0	.	-7	100	145	36	312	23	0	.	451	20
	other conifers	-6	100	245	42	0	.	0	.	0	.	20	100	259	40
	beech	5739	7	5970	8	4716	10	118	62	395	41	788	18	17726	4
	maple	668	17	397	23	772	16	38	100	60	60	9	100	1944	10
	ash	549	25	1912	14	867	20	21	100	21	72	109	50	3480	10
	oak	307	31	1129	17	79	55	19	76	13	100	272	37	1819	13
	sweet chestnut	0	.	0	.	17	71	0	.	0	.	649	20	666	20
	other broadleaves	167	32	697	22	292	29	-6	741	57	59	380	23	1587	13
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	16878	4	21529	4	28089	4	5192	10	10098	7	5957	7	87743	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4640	10	6451	10	15789	6	2034	14	5461	11	2162	16	36538	4
	fir	3344	12	4201	11	4726	10	156	51	416	34	518	30	13361	6
	pine	286	29	837	19	182	43	172	48	420	29	38	100	1935	13
	larch	66	58	305	37	125	54	2099	15	1832	16	1170	14	5598	8
	Arolla pine	0	.	0	.	0	.	45	50	166	32	0	.	210	27
	other conifers	0	.	248	51	0	.	0	.	0	.	30	100	278	47
	beech	4076	11	5442	9	3410	12	36	100	274	38	568	24	13806	6
	maple	294	27	297	35	325	34	0	.	113	43	20	100	1049	17
	ash	404	27	1722	18	340	33	15	100	20	100	33	100	2534	14
	oak	54	58	1001	20	57	58	0	.	28	100	62	58	1201	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	463	28	463	28
	other broadleaves	95	60	466	39	501	47	43	73	18	145	103	52	1225	25
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	13259	6	20969	5	25454	5	4600	10	8748	8	5169	9	78198	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2495	14	3639	13	9073	8	913	24	2830	13	1422	18	20372	5
	fir	2061	16	3250	13	3627	14	46	100	213	38	99	59	9296	8
	pine	53	72	258	36	56	100	0	.	199	39	18	100	584	24
	larch	0	.	126	51	57	76	1127	24	868	21	757	23	2935	13
	Arolla pine	0	.	0	.	0	.	33	72	86	46	0	.	119	38
	other conifers	48	100	247	47	0	.	0	.	0	.	0	.	296	42
	beech	1203	19	2687	14	1581	20	0	.	121	51	406	31	5997	9
	maple	35	100	133	51	193	47	0	.	0	.	0	.	360	33
	ash	178	42	469	38	233	41	0	.	0	.	46	100	926	24
	oak	67	75	516	27	0	.	0	.	0	.	46	71	628	24
	sweet chestnut	0	.	0	.	50	76	0	.	0	.	143	35	193	32
	other broadleaves	58	71	212	48	91	58	0	.	21	100	16	100	397	31
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	6196	9	11535	7	14961	7	2118	17	4338	10	2953	12	42102	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	914	23	1864	18	5349	11	481	40	1365	21	990	25	10964	8
	fir	880	25	1356	22	1854	18	78	71	343	43	178	51	4689	11
	pine	29	100	43	100	0	.	0	.	55	71	0	.	127	51
	larch	43	100	73	72	57	100	710	25	512	27	629	30	2023	15
	Arolla pine	0	.	0	.	0	.	100	45	43	71	0	.	143	38
	other conifers	65	100	281	57	0	.	0	.	0	.	0	.	346	50
	beech	514	37	1242	24	591	32	0	.	116	71	158	41	2621	16
	maple	60	100	0	.	45	76	0	.	32	100	0	.	137	56
	ash	36	100	292	41	104	71	0	.	30	100	0	.	462	32
	oak	0	.	335	39	26	100	0	.	12	100	0	.	373	36
	sweet chestnut	0	.	0	.	19	100	0	.	0	.	162	36	181	34
	other broadleaves	0	.	130	62	34	100	0	.	0	.	21	100	184	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2542	15	5616	10	8078	9	1369	20	2507	15	2138	15	22251	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	275	53	723	28	1959	22	260	57	761	26	553	31	4531	13
	fir	422	38	577	34	482	40	29	100	84	100	67	100	1662	20
	pine	0	.	47	100	0	.	0	.	54	100	0	.	102	71
	larch	0	.	0	.	0	.	293	42	307	44	351	40	951	24
	Arolla pine	0	.	0	.	0	.	67	58	28	100	19	100	114	45
	other conifers	72	100	71	100	0	.	0	.	0	.	0	.	142	71
	beech	0	.	436	39	169	60	39	100	0	.	82	58	726	28
	maple	0	.	0	.	23	100	0	.	0	.	0	.	23	100
	ash	0	.	207	59	0	.	0	.	0	.	0	.	207	59
	oak	0	.	301	41	0	.	0	.	0	.	0	.	301	41
	sweet chestnut	0	.	0	.	0	.	30	100	0	.	134	46	164	42
	other broadleaves	0	.	92	71	0	.	0	.	0	.	0	.	92	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	768	29	2453	16	2633	18	717	28	1235	21	1207	20	9013	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	65	100	139	71	504	32	51	100	154	72	84	71	996	24
	fir	225	58	195	60	65	100	0	.	0	.	145	71	629	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	94	71	61	100	64	100	218	50
	Arolla pine	0	.	0	.	0	.	28	100	0	.	0	.	28	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	54	100	41	100	0	.	0	.	0	.	96	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	56	100	0	.	0	.	0	.	0	.	56	100
	oak	0	.	168	100	0	.	0	.	0	.	0	.	168	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	102	62	102	62
	other broadleaves	0	.	212	75	0	.	0	.	0	.	0	.	212	75
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	290	50	824	35	610	30	172	51	214	59	395	38	2506	17
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	67	100	209	58	169	50	53	100	267	50	204	71	970	27
	fir	0	.	153	71	188	75	0	.	0	.	0	.	341	52
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	55	100	215	80	270	67
	Arolla pine	0	.	0	.	0	.	38	100	35	100	0	.	73	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	417	49	417	49
	other broadleaves	0	.	400	100	0	.	0	.	0	.	0	.	400	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	67	100	762	57	357	46	91	72	357	42	836	36	2470	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20710	6	31499	6	72772	4	12118	10	28740	7	11788	10	177626	2
	fir	16251	8	18270	8	21906	7	897	33	1650	23	1788	25	60763	4
	pine	1802	19	3250	13	1034	36	1977	19	2676	18	235	41	10974	8
	larch	204	39	1093	20	530	27	8451	12	6631	12	6566	11	23475	6
	Arolla pine	0	.	0	.	-7	100	958	22	1478	18	60	92	2489	13
	other conifers	290	66	1346	33	67	65	8	66	45	35	57	89	1813	27
	beech	21761	5	24321	6	19302	7	622	37	1711	24	5569	12	73286	3
	maple	3056	9	2943	11	5252	8	177	36	641	34	347	25	12416	5
	ash	2549	14	8067	10	4395	12	349	37	299	35	748	25	16407	6
	oak	1077	18	4969	13	298	26	184	40	59	100	1028	20	7615	9
	sweet chestnut	2	100	6	100	137	79	65	63	0	.	5360	12	5569	12
	other broadleaves	1317	15	4345	16	2960	15	896	20	936	20	4574	11	15029	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	69020	3	100110	3	128644	3	26701	7	44867	5	38120	4	407462	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. 1218012. Birmensdorf, Swiss Federal Research Institute WSL

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