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: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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	2944	9	1861	9	996	14	907	16	1591	11	432	18	8732	5
	fir	1745	10	863	13	362	23	96	52	33	36	56	41	3155	7
	pine	83	54	18	186	11	59	356	29	213	27	7	100	688	19
	larch	49	63	46	44	15	70	317	19	81	23	168	29	676	13
	Arolla pine	0	.	0	.	0	.	90	47	132	41	2	100	224	30
	other conifers	69	42	2	71	11	152	11	56	29	42	0	.	122	30
	beech	2353	7	800	15	624	16	193	45	67	38	884	16	4920	6
	maple	915	13	394	18	216	24	105	30	48	40	69	30	1747	9
	ash	686	17	168	24	207	23	71	39	79	47	195	33	1407	11
	oak	235	26	19	78	10	69	58	34	2	100	140	36	464	18
	sweet chestnut	2	100	0	.	0	.	0	0	0	.	813	19	814	19
	other broadleaves	830	10	369	16	313	21	405	18	420	26	1150	16	3488	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	9910	4	4538	6	2766	9	2610	10	2696	9	3915	8	26435	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6344	8	4333	10	2802	13	2117	15	3104	11	1081	17	19780	4
	fir	2833	10	1507	13	649	20	212	36	74	41	148	43	5423	7
	pine	477	26	79	62	85	56	589	23	369	28	45	61	1642	13
	larch	113	47	48	67	48	63	602	19	283	27	602	21	1695	12
	Arolla pine	0	.	0	.	0	.	213	31	223	26	3	100	440	20
	other conifers	163	31	12	74	6	100	4	100	10	59	0	.	195	27
	beech	6234	7	1899	13	1479	14	320	33	162	37	1580	18	11674	5
	maple	1644	11	899	18	677	20	101	36	68	55	120	41	3509	8
	ash	1370	13	511	26	702	20	116	35	56	62	210	33	2966	9
	oak	853	19	53	41	10	100	59	49	6	100	235	27	1216	15
	sweet chestnut	0	.	0	.	0	.	15	100	5	100	1435	16	1455	15
	other broadleaves	934	14	406	22	281	31	281	22	239	32	1326	14	3468	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	20965	4	9748	7	6739	8	4629	10	4597	9	6785	7	53463	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8679	7	7186	8	5013	11	2941	13	5303	9	2033	17	31155	4
	fir	4739	9	2449	11	1407	16	205	39	110	39	245	36	9155	6
	pine	1289	16	124	50	81	48	659	21	688	28	39	67	2879	11
	larch	266	29	41	71	36	52	1169	17	791	20	962	20	3265	10
	Arolla pine	0	.	0	.	0	.	159	32	315	25	0	.	474	20
	other conifers	151	42	0	.	-19	100	0	.	0	.	0	.	131	50
	beech	9291	6	2365	13	2128	14	148	36	265	31	996	16	15193	5
	maple	1388	12	765	17	752	17	8	263	35	75	64	53	3011	8
	ash	2276	12	675	29	661	21	21	116	42	63	95	40	3770	9
	oak	1024	15	31	59	27	75	19	78	6	100	208	31	1316	13
	sweet chestnut	6	100	0	.	0	.	19	71	0	.	997	20	1022	20
	other broadleaves	1163	17	202	33	216	24	77	46	78	41	895	17	2632	10
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	30273	3	13838	6	10301	7	5427	9	7632	8	6533	7	74003	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10403	6	9656	8	5725	10	2907	12	6510	9	2322	16	37523	3
	fir	6850	8	2797	11	2093	15	405	36	242	32	282	31	12669	6
	pine	1598	14	274	71	63	54	219	32	548	25	36	61	2737	12
	larch	245	35	88	60	0	0	1651	18	1345	15	825	17	4154	9
	Arolla pine	0	.	-7	100	0	.	118	41	294	24	0	.	406	21
	other conifers	239	43	0	.	0	.	0	.	0	.	20	100	259	40
	beech	10996	5	2735	13	2445	14	88	61	337	45	764	18	17365	4
	maple	938	15	371	24	489	20	48	82	22	100	9	100	1876	10
	ash	2360	12	279	34	629	24	40	71	9	100	109	50	3426	10
	oak	1378	16	65	62	26	71	19	76	54	79	226	41	1768	14
	sweet chestnut	0	.	7	100	0	.	10	100	5	100	644	21	666	20
	other broadleaves	797	20	89	37	141	42	22	231	57	59	353	24	1461	14
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	35805	3	16354	6	11611	7	5528	10	9424	7	5589	8	84310	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10160	7	8847	9	6433	10	2576	14	5165	11	2077	17	35258	4
	fir	6612	8	3418	12	2042	17	216	44	370	38	518	30	13176	6
	pine	1139	16	94	64	63	75	172	48	403	30	38	100	1910	13
	larch	344	34	75	59	128	60	1992	15	1681	17	919	15	5138	9
	Arolla pine	0	.	0	.	0	.	34	58	166	32	0	.	200	28
	other conifers	248	51	0	.	0	.	0	.	0	.	30	100	278	47
	beech	8889	7	2115	16	1481	18	91	72	242	39	549	24	13366	6
	maple	558	23	260	41	121	41	0	.	83	53	20	100	1042	17
	ash	2095	16	153	54	238	36	15	100	0	.	0	.	2501	14
	oak	1024	20	0	.	16	100	21	100	50	71	39	71	1150	18
	sweet chestnut	0	.	0	.	0	.	0	.	8	100	455	28	463	28
	other broadleaves	358	37	293	63	88	48	43	73	30	75	69	60	882	27
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	31426	4	15255	7	10609	8	5159	10	8198	9	4716	9	75363	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5257	10	4901	11	4086	13	1204	22	2589	14	1400	19	19437	5
	fir	4912	11	2942	16	849	23	46	100	179	41	99	59	9027	8
	pine	252	36	83	75	31	100	0	.	199	39	0	.	565	24
	larch	126	51	57	76	26	100	1132	24	842	21	685	25	2868	13
	Arolla pine	0	.	0	.	0	.	19	100	86	46	0	.	105	42
	other conifers	296	42	0	.	0	.	0	.	0	.	0	.	296	42
	beech	3514	12	1017	24	647	31	24	100	101	58	406	31	5708	9
	maple	167	45	59	59	92	79	0	.	0	.	0	.	318	35
	ash	615	31	108	58	113	58	0	.	0	.	46	100	882	25
	oak	534	26	0	.	0	.	0	.	0	.	46	71	580	25
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	143	35	158	33
	other broadleaves	197	41	58	71	105	75	0	.	0	.	16	100	376	32
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	15870	6	9224	9	5950	11	2440	16	3995	11	2841	12	40321	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2547	15	2497	15	2628	17	603	35	1155	24	946	26	10376	8
	fir	1768	18	1310	20	1034	25	78	71	248	46	178	51	4616	11
	pine	73	72	0	.	0	.	0	.	55	71	0	.	127	51
	larch	43	100	0	.	130	60	853	25	454	28	352	35	1832	16
	Arolla pine	0	.	0	.	0	.	100	45	43	71	0	.	143	38
	other conifers	346	50	0	.	0	.	0	.	0	.	0	.	346	50
	beech	1408	22	329	41	315	50	20	100	116	71	158	41	2346	16
	maple	60	100	13	100	32	100	0	.	0	.	0	.	106	66
	ash	274	41	45	100	113	71	0	.	30	100	0	.	462	32
	oak	308	41	0	.	52	71	0	.	12	100	0	.	373	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	162	36	162	36
	other broadleaves	130	62	34	100	0	.	0	.	0	.	21	100	184	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6958	9	4229	12	4305	14	1653	19	2113	16	1816	16	21073	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	910	27	1079	25	604	51	296	51	686	26	433	36	4008	14
	fir	852	27	400	47	229	50	29	100	84	100	67	100	1662	20
	pine	47	100	0	.	0	.	0	.	54	100	0	.	102	71
	larch	0	.	0	.	0	.	289	42	226	48	142	63	657	28
	Arolla pine	0	.	0	.	0	.	23	100	48	72	0	.	70	58
	other conifers	72	100	71	100	0	.	0	.	0	.	0	.	142	71
	beech	368	42	123	74	113	72	39	100	0	.	82	58	726	28
	maple	0	.	0	.	23	100	0	.	0	.	0	.	23	100
	ash	141	72	0	.	65	100	0	.	0	.	0	.	207	59
	oak	301	41	0	.	0	.	0	.	0	.	0	.	301	41
	sweet chestnut	0	.	0	.	0	.	30	100	0	.	134	46	164	42
	other broadleaves	41	100	0	.	0	.	0	.	0	.	0	.	41	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2733	15	1672	20	1035	33	706	29	1098	21	859	23	8103	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	125	71	165	61	272	46	51	100	154	72	37	100	803	28
	fir	331	46	89	100	65	100	0	.	0	.	145	71	629	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	157	58	61	100	0	.	218	50
	Arolla pine	0	.	0	.	0	.	28	100	0	.	0	.	28	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	54	100	0	.	41	100	0	.	0	.	0	.	96	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	56	100	0	.	0	.	0	.	0	.	0	.	56	100
	oak	168	100	0	.	0	.	0	.	0	.	0	.	168	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	102	62	102	62
	other broadleaves	143	100	0	.	0	.	0	.	0	.	0	.	143	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	878	33	254	53	378	39	236	46	214	59	284	44	2244	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	208	58	106	74	131	58	53	100	195	58	204	71	897	28
	fir	153	71	126	100	0	.	0	.	0	.	0	.	279	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	52	100	163	100	215	80
	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	.	385	52	385	52
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	361	45	232	64	131	58	91	72	282	46	752	39	1849	22
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47578	4	40631	6	28690	8	13654	10	26451	7	10966	11	167969	2
	fir	30796	6	15900	8	8729	11	1287	28	1341	24	1738	25	59792	4
	pine	4958	11	671	52	333	40	1995	19	2528	19	164	50	10650	8
	larch	1185	19	355	33	383	36	8161	12	5815	13	4818	13	20718	7
	Arolla pine	0	.	-7	100	0	.	823	24	1342	19	5	100	2164	15
	other conifers	1583	30	84	85	-2	1200	15	58	39	39	50	100	1770	27
	beech	43108	4	11383	9	9272	10	922	30	1290	28	5418	12	71394	3
	maple	5669	7	2760	12	2402	13	262	31	256	35	281	29	11631	5
	ash	9873	9	1940	20	2730	16	263	34	215	41	655	28	15676	7
	oak	5826	11	168	36	141	42	176	42	130	71	894	21	7336	10
	sweet chestnut	8	82	7	100	0	.	90	53	18	100	5269	12	5391	12
	other broadleaves	4595	11	1451	22	1144	19	829	19	825	24	3831	11	12674	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	155178	2	75344	4	53824	6	28478	7	40250	6	34090	4	387165	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. 1217982. Birmensdorf, Swiss Federal Research Institute WSL

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