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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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	4677	9	2415	10	1434	14	898	14	1381	13	289	17	11094	5
	fir	1640	10	937	12	412	20	42	34	29	33	67	38	3128	7
	pine	221	35	35	75	16	77	459	29	266	27	9	100	1005	17
	larch	35	41	5	62	25	59	277	21	71	27	187	25	600	13
	Arolla pine	0	.	0	.	0	.	62	53	81	32	1	100	144	29
	other conifers	72	27	3	59	21	38	19	85	8	57	0	.	124	22
	beech	3444	7	1006	16	603	14	117	36	140	43	933	19	6242	6
	maple	645	14	395	23	257	21	60	47	35	47	36	32	1427	10
	ash	668	17	241	21	316	23	74	33	61	74	141	27	1501	10
	oak	504	24	39	41	8	71	50	35	2	100	206	43	809	19
	sweet chestnut	1	100	0	.	0	.	2	100	0	.	1034	17	1038	17
	other broadleaves	889	14	439	15	353	22	287	23	289	24	993	12	3250	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12797	5	5516	7	3444	9	2346	10	2362	10	3896	7	30362	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7361	8	5937	9	3720	13	2044	13	3566	11	1050	18	23679	4
	fir	4216	9	1725	13	631	18	111	41	67	51	95	45	6844	7
	pine	481	24	75	68	115	52	459	24	499	23	41	69	1670	13
	larch	202	33	19	73	36	74	770	19	468	25	531	21	2028	11
	Arolla pine	0	.	0	.	0	.	111	37	276	30	0	.	387	24
	other conifers	143	38	4	100	-22	100	0	.	2	100	0	.	127	46
	beech	7733	6	2050	15	1683	17	219	32	203	31	1010	19	12898	5
	maple	1673	11	701	20	747	18	20	165	15	154	65	42	3221	8
	ash	1681	12	528	29	692	21	85	47	61	59	100	37	3148	9
	oak	1035	17	65	63	63	63	40	56	3	100	210	33	1416	14
	sweet chestnut	0	.	0	.	0	.	36	72	4	100	1158	17	1198	17
	other broadleaves	1148	15	268	22	321	22	149	29	132	39	1038	14	3056	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	25673	3	11373	6	7986	9	4045	9	5297	9	5298	7	59671	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13365	6	9543	8	5553	11	2754	13	5638	10	1738	16	38591	4
	fir	6570	8	2741	13	1406	16	203	50	109	44	177	37	11207	6
	pine	2020	14	112	52	121	42	538	21	654	24	81	67	3526	10
	larch	336	33	54	73	9	102	1077	18	792	17	724	22	2992	10
	Arolla pine	0	.	14	71	0	.	143	30	353	27	0	.	510	20
	other conifers	135	48	0	.	0	.	0	.	0	.	12	100	147	45
	beech	11140	6	2447	13	2481	15	151	62	256	35	741	18	17217	5
	maple	1242	12	596	22	701	16	50	72	47	58	17	100	2654	9
	ash	2650	15	328	26	481	27	25	98	38	62	28	73	3549	12
	oak	1315	16	101	83	6	100	13	100	12	100	162	43	1609	15
	sweet chestnut	0	.	0	.	0	.	29	72	5	100	570	24	603	23
	other broadleaves	1142	15	175	38	213	30	98	42	83	48	533	21	2245	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	39914	3	16110	6	10972	8	5080	10	7988	8	4784	8	84848	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17013	6	11691	7	6696	10	2489	12	6134	9	2046	16	46069	3
	fir	7464	7	3856	11	2205	18	234	40	235	34	457	32	14451	5
	pine	2318	12	327	58	90	54	276	38	665	25	30	57	3706	11
	larch	353	26	134	47	38	73	1674	16	1377	15	752	19	4327	9
	Arolla pine	0	.	0	.	0	.	168	40	252	26	0	.	420	22
	other conifers	197	41	0	.	0	.	0	.	0	.	0	.	197	41
	beech	10727	6	2571	15	2075	15	92	62	337	37	516	22	16319	5
	maple	687	16	276	23	366	26	10	100	-1	2190	16	100	1354	12
	ash	2062	14	157	32	350	26	12	100	9	100	-43	90	2547	12
	oak	1296	16	0	.	9	100	0	.	87	71	136	65	1528	15
	sweet chestnut	0	.	0	.	0	.	-5	100	0	.	410	25	406	25
	other broadleaves	457	21	188	53	137	41	19	124	27	77	147	25	975	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	42573	3	19201	6	11966	8	4969	10	9123	7	4466	9	92298	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13495	6	8968	8	5252	11	1767	16	4545	11	1765	15	35792	4
	fir	5230	9	3536	13	1558	17	182	52	272	37	130	47	10908	7
	pine	1137	17	125	63	17	100	134	38	442	35	36	71	1891	14
	larch	238	39	209	52	184	47	1378	17	1335	18	735	20	4078	10
	Arolla pine	0	.	0	0	0	.	29	75	200	35	0	.	230	32
	other conifers	224	54	0	.	0	.	0	.	0	.	0	.	224	54
	beech	6205	8	1810	20	1183	22	16	100	196	45	325	36	9736	7
	maple	373	27	55	51	81	65	0	.	29	71	0	.	538	22
	ash	764	21	94	60	115	52	0	.	0	.	39	100	1014	18
	oak	1072	20	0	.	13	100	0	.	0	.	17	100	1102	19
	sweet chestnut	0	.	0	.	0	.	11	100	8	100	259	31	278	29
	other broadleaves	172	44	187	48	88	55	34	100	0	.	13	100	494	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	28910	4	14984	7	8492	9	3552	12	7026	9	3318	10	66283	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5425	10	4158	11	3219	15	837	27	2019	16	1063	24	16721	6
	fir	2059	16	1697	18	691	25	60	71	147	50	151	51	4806	10
	pine	260	36	43	100	32	100	0	.	97	50	0	.	433	27
	larch	64	71	72	71	160	52	758	22	490	27	409	26	1954	13
	Arolla pine	0	.	0	.	0	.	40	71	49	58	0	.	88	45
	other conifers	137	63	0	.	0	.	0	.	0	.	0	.	137	63
	beech	2309	16	564	27	236	48	0	.	81	74	224	37	3414	13
	maple	59	73	21	100	16	100	0	.	0	.	0	.	95	53
	ash	373	34	31	100	42	100	0	.	43	100	0	.	489	29
	oak	238	39	0	.	48	100	0	.	0	.	0	.	286	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	121	36	121	36
	other broadleaves	142	52	49	71	0	.	0	.	0	.	26	100	217	40
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11067	7	6634	9	4444	13	1695	17	2925	12	1995	15	28760	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1445	18	886	22	1619	19	382	36	958	22	736	30	6027	9
	fir	1125	22	795	30	168	50	34	100	164	59	52	100	2338	16
	pine	40	100	0	.	0	.	0	.	65	72	0	.	106	59
	larch	82	71	34	100	0	.	562	30	361	32	182	51	1221	19
	Arolla pine	0	.	0	.	0	.	69	50	72	59	0	.	141	39
	other conifers	0	.	59	100	0	.	0	.	0	.	0	.	59	100
	beech	1181	24	214	52	76	100	0	.	0	.	66	58	1537	21
	maple	0	.	0	.	52	73	0	.	0	.	0	.	52	73
	ash	39	100	0	.	51	100	0	.	0	.	0	.	90	71
	oak	254	41	0	.	25	100	0	.	0	.	0	.	279	39
	sweet chestnut	0	.	0	.	0	.	27	100	0	.	197	36	224	34
	other broadleaves	35	100	0	.	0	.	0	.	0	.	20	100	55	73
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4202	11	1987	16	1991	17	1075	21	1620	17	1253	21	12128	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	545	32	344	36	251	42	140	51	219	42	27	100	1527	17
	fir	331	41	514	36	53	100	0	.	0	.	124	72	1021	25
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	148	50	73	71	37	100	258	38
	Arolla pine	0	.	0	.	0	.	24	100	0	.	0	.	24	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	254	63	34	100	81	71	39	100	20	100	0	.	428	42
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	109	71	0	.	0	.	0	.	0	.	0	.	109	71
	oak	185	63	0	.	0	.	0	.	0	.	0	.	185	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	226	46	226	46
	other broadleaves	148	72	0	.	0	.	0	.	0	.	0	.	148	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1573	20	892	25	385	34	351	34	312	34	414	35	3926	12
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	211	58	0	.	439	36	48	100	61	100	85	100	844	27
	fir	57	100	75	100	67	100	0	.	0	.	0	.	198	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	87	71	0	.	60	100	147	59
	Arolla pine	0	.	0	.	0	.	35	100	0	.	0	.	35	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	228	58	0	.	0	.	0	.	0	.	0	.	228	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	60	100	0	.	0	.	0	.	0	.	0	.	60	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	121	53	121	53
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	556	36	75	100	506	34	171	50	61	100	266	46	1633	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	99	71	0	.	0	.	134	72	71	100	304	46
	fir	0	.	0	.	0	.	0	.	0	.	96	100	96	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	51	100	69	100	120	71
	Arolla pine	0	.	0	.	0	.	37	100	33	100	0	.	70	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	.	485	49	485	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	99	71	0	.	37	100	218	52	721	38	1075	29
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	63536	4	44040	6	28183	8	11361	10	24656	7	8870	12	180646	2
	fir	28692	6	15877	8	7190	12	867	30	1023	27	1348	28	54997	4
	pine	6478	10	718	45	390	37	1866	19	2688	17	196	47	12336	8
	larch	1310	21	526	32	452	35	6731	12	5018	12	3687	14	17724	6
	Arolla pine	0	.	14	71	0	.	718	23	1316	20	1	100	2050	15
	other conifers	907	31	66	89	-1	1590	19	85	10	58	12	100	1013	28
	beech	43221	4	10695	10	8419	12	633	31	1233	27	3816	13	68018	3
	maple	4680	8	2044	13	2221	13	139	37	124	43	133	34	9341	6
	ash	8346	10	1380	19	2048	17	195	34	212	52	265	29	12447	7
	oak	5959	10	205	47	172	43	103	37	104	72	731	25	7274	9
	sweet chestnut	1	100	0	.	0	.	100	57	17	100	4580	13	4698	13
	other broadleaves	4134	11	1306	19	1112	16	587	21	531	23	2770	11	10440	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	167264	2	76871	4	50186	6	23321	7	36933	6	26410	5	380985	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. 1217897. Birmensdorf, Swiss Federal Research Institute WSL

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