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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NFI2
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		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 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	4501	7	2423	7	1441	10	872	10	1428	9	475	20	11140	4
	fir	1765	8	909	9	393	16	89	43	35	24	67	30	3259	5
	pine	184	27	45	46	45	38	443	19	292	19	11	71	1020	11
	larch	95	40	5	55	15	53	311	15	134	22	239	31	799	12
	Arolla pine	0	.	3	100	1	100	55	36	73	26	1	100	133	21
	other conifers	140	26	7	47	17	31	12	85	32	41	0	.	208	19
	beech	3334	5	984	13	690	13	138	28	106	32	989	13	6241	4
	maple	723	11	388	14	287	18	64	29	44	36	45	32	1551	7
	ash	793	12	193	19	294	18	79	26	55	50	214	20	1628	8
	oak	423	18	36	36	10	50	52	25	6	64	205	34	732	14
	sweet chestnut	6	66	0	.	0	.	17	75	0	.	1004	13	1027	12
	other broadleaves	1003	9	492	12	372	18	389	14	336	21	1087	8	3679	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12967	3	5485	5	3565	7	2521	7	2541	7	4337	6	31417	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7052	6	6193	6	3580	10	2134	11	3867	8	969	14	23795	3
	fir	3700	7	1668	10	937	13	179	32	86	29	122	28	6691	5
	pine	504	16	99	38	182	30	542	18	526	17	81	57	1934	9
	larch	263	25	13	64	40	51	905	13	546	16	603	16	2370	8
	Arolla pine	0	.	15	100	12	71	179	25	260	22	0	.	466	16
	other conifers	154	31	6	71	-5	285	0	.	13	56	0	.	167	31
	beech	8123	5	1996	11	1681	12	175	26	197	31	998	14	13170	4
	maple	1841	8	659	14	686	14	29	72	32	55	98	32	3344	6
	ash	1835	10	435	22	536	19	101	31	46	48	174	25	3127	7
	oak	1072	13	96	38	50	50	40	39	4	71	250	21	1512	10
	sweet chestnut	8	73	0	.	0	.	33	53	3	100	1102	13	1145	12
	other broadleaves	1198	10	381	17	400	16	234	22	145	26	1099	11	3457	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	25750	3	11559	5	8099	6	4550	7	5725	6	5496	5	61179	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13012	5	10229	6	5897	8	3140	9	5594	7	1731	13	39604	3
	fir	6401	6	3047	9	1641	11	230	29	112	32	191	39	11623	4
	pine	2085	10	133	34	236	30	542	17	711	17	154	44	3862	7
	larch	300	25	47	50	130	49	1306	12	906	12	808	15	3498	7
	Arolla pine	0	.	22	70	0	.	189	23	351	21	0	.	562	15
	other conifers	125	34	0	.	0	.	0	.	0	.	7	100	132	33
	beech	11658	4	2474	10	2586	10	98	60	202	29	849	13	17866	3
	maple	1414	10	492	17	573	13	38	57	30	51	24	68	2572	7
	ash	2395	11	338	20	463	21	36	58	32	47	84	38	3347	8
	oak	1429	11	92	56	15	59	8	100	39	83	170	28	1751	10
	sweet chestnut	14	100	0	.	0	.	22	59	3	100	574	16	613	16
	other broadleaves	1127	13	219	30	189	23	109	31	82	35	642	16	2368	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	39960	2	17094	4	11730	6	5718	7	8062	6	5232	6	87796	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16952	4	12578	5	6756	7	2617	9	6665	7	1850	12	47417	2
	fir	7642	5	3908	8	2166	12	196	33	303	23	345	26	14560	4
	pine	2178	9	240	46	148	29	337	25	716	17	63	50	3682	8
	larch	424	21	133	37	112	46	1940	11	1363	11	781	13	4753	6
	Arolla pine	0	.	0	.	1	100	171	27	272	19	6	100	449	15
	other conifers	136	37	0	.	0	.	0	.	0	.	0	.	136	37
	beech	10760	5	2689	11	2223	11	54	62	222	34	610	15	16557	4
	maple	678	13	254	21	345	20	6	100	0	3091	13	73	1297	10
	ash	1741	11	179	26	309	21	17	58	20	58	-4	697	2262	9
	oak	1452	12	14	72	11	71	9	73	90	55	126	43	1701	11
	sweet chestnut	11	100	0	.	0	.	-3	100	0	.	460	20	468	20
	other broadleaves	514	15	140	46	128	34	62	56	3	663	175	19	1021	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	42487	2	20135	4	12198	6	5405	7	9654	6	4424	6	94303	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12744	5	9797	6	5686	8	2032	13	5202	8	1646	12	37107	3
	fir	6113	7	3604	9	1503	13	200	38	277	28	203	32	11900	5
	pine	1309	13	73	63	84	52	229	36	402	25	47	51	2145	10
	larch	290	26	150	43	175	36	1519	12	1256	13	851	13	4241	7
	Arolla pine	0	.	0	0	0	.	44	42	202	25	0	.	246	22
	other conifers	138	52	0	.	0	.	0	.	0	.	0	.	138	52
	beech	6482	6	2068	15	1372	15	25	59	155	37	334	25	10436	5
	maple	334	22	77	41	98	42	0	.	17	71	0	.	526	17
	ash	845	15	99	49	95	42	0	.	0	.	21	100	1060	14
	oak	1305	14	52	45	35	60	23	77	8	100	41	60	1464	13
	sweet chestnut	0	.	0	.	0	.	34	84	5	100	393	20	432	20
	other broadleaves	244	29	115	45	64	49	27	75	0	.	56	60	507	20
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	29804	3	16034	5	9112	7	4133	9	7525	6	3593	7	70201	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5656	7	4734	8	3205	12	979	19	2591	11	922	19	18088	4
	fir	2216	11	1864	13	871	18	116	36	168	34	198	40	5433	7
	pine	374	23	50	62	19	100	9	100	103	46	10	100	564	19
	larch	89	45	106	59	117	46	935	16	451	21	386	21	2084	10
	Arolla pine	0	.	0	.	10	100	57	42	98	35	0	.	165	26
	other conifers	135	44	0	.	0	.	0	.	0	.	0	.	135	44
	beech	2583	12	468	24	366	30	0	.	49	74	151	32	3617	10
	maple	84	54	25	71	9	100	0	.	0	.	0	.	118	42
	ash	345	28	17	100	42	72	0	.	26	100	0	.	430	25
	oak	443	25	21	100	45	73	0	.	7	100	3	83	520	23
	sweet chestnut	0	.	0	.	0	.	12	100	0	.	167	27	179	26
	other broadleaves	151	39	28	71	0	.	0	.	0	.	26	72	205	32
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12077	5	7312	7	4685	10	2108	12	3493	9	1864	11	31540	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1543	14	1479	15	1603	16	427	25	1110	17	626	23	6787	7
	fir	1147	16	740	22	200	35	21	100	154	46	84	58	2345	11
	pine	23	100	0	.	0	.	0	.	35	72	0	.	58	59
	larch	100	52	21	100	0	.	576	21	288	27	189	33	1173	14
	Arolla pine	0	.	0	.	0	.	48	45	81	38	0	.	129	29
	other conifers	47	72	34	100	0	.	0	.	0	.	0	.	81	59
	beech	1109	18	181	40	58	80	0	.	0	.	106	40	1454	15
	maple	36	100	10	100	30	73	0	.	0	.	6	100	82	53
	ash	30	81	0	.	30	100	0	.	0	.	0	.	60	64
	oak	257	33	0	.	57	61	0	.	0	.	0	.	313	29
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	215	28	229	27
	other broadleaves	57	75	0	.	0	.	0	.	0	.	26	72	82	56
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4347	9	2465	12	1977	14	1087	15	1668	14	1251	15	12795	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	504	27	566	24	378	27	211	34	282	30	167	40	2107	12
	fir	396	29	574	27	104	59	14	100	28	100	67	72	1182	18
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	168	36	144	52	85	52	397	27
	Arolla pine	0	.	0	.	0	.	13	100	17	100	0	.	30	71
	other conifers	35	100	0	.	0	.	0	.	0	.	0	.	35	100
	beech	390	34	39	71	86	60	21	100	12	100	27	71	576	25
	maple	0	.	24	100	15	100	0	.	0	.	0	.	39	72
	ash	64	71	0	.	0	.	0	.	0	.	0	.	64	71
	oak	253	37	0	.	0	.	0	.	0	.	0	.	253	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	205	34	205	34
	other broadleaves	82	72	0	.	0	.	0	.	0	.	20	100	102	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1724	15	1202	19	583	23	427	23	482	24	572	21	4990	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	120	58	64	58	300	33	26	100	66	71	134	54	710	22
	fir	67	71	124	58	75	71	0	.	44	100	0	.	309	36
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	29	100	0	.	0	.	87	51	0	.	55	72	171	39
	Arolla pine	0	.	0	.	0	.	19	100	12	100	0	.	31	72
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	178	50	0	.	0	.	0	.	0	.	0	.	178	50
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	34	100	0	.	0	.	0	.	0	.	0	.	34	100
	sweet chestnut	0	.	0	.	0	.	0	.	17	100	159	48	176	44
	other broadleaves	34	100	0	.	0	.	0	.	0	.	0	.	34	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	461	29	188	43	375	30	132	41	139	48	347	32	1643	15
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	139	51	74	78	0	.	162	46	110	74	485	30
	fir	42	100	50	100	0	.	0	.	0	.	101	71	193	50
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	33	100	69	59	105	58	207	39
	Arolla pine	0	.	0	.	0	.	37	71	18	100	0	.	55	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	0	0	.	0	.	232	100	232	100
	maple	0	.	0	.	18	100	0	.	0	.	0	.	18	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	35	100	0	.	0	.	0	.	0	.	0	.	35	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	657	38	657	38
	other broadleaves	40	100	0	.	0	.	0	.	0	.	0	.	40	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	117	58	189	46	92	66	70	60	249	35	1206	30	1922	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	62084	3	48202	4	28918	6	12438	7	26967	5	8630	8	187239	2
	fir	29489	4	16488	6	7891	9	1044	22	1206	19	1378	21	57496	3
	pine	6656	8	640	31	714	23	2103	15	2785	13	365	38	13264	6
	larch	1589	16	474	31	590	28	7779	8	5158	9	4101	9	19692	5
	Arolla pine	0	.	40	82	24	73	812	17	1385	15	6	91	2267	11
	other conifers	910	22	47	75	12	114	12	85	45	40	7	100	1033	19
	beech	44617	3	10897	8	9061	8	511	24	943	23	4297	10	70327	2
	maple	5110	6	1929	10	2062	10	137	25	123	31	186	25	9546	4
	ash	8048	7	1260	14	1769	13	233	25	180	38	489	19	11979	5
	oak	6702	8	310	26	222	31	131	25	154	55	796	19	8315	7
	sweet chestnut	39	82	0	.	0	.	131	54	28	73	4936	10	5133	10
	other broadleaves	4449	8	1376	14	1151	12	822	16	565	19	3131	8	11494	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	169693	1	81663	3	52416	4	26153	5	39539	4	28322	3	397785	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. 1217918. Birmensdorf, Swiss Federal Research Institute WSL

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