Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI5
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	923	15	2022	13	3192	8	948	15	2072	10	648	15	9805	5
	fir	937	11	1015	13	948	15	52	40	32	35	66	35	3049	7
	pine	27	47	-9	341	53	47	212	34	229	29	22	55	534	20
	larch	1	100	133	48	48	38	465	22	179	20	301	21	1126	12
	Arolla pine	0	.	0	.	0	.	120	56	142	29	5	65	267	29
	other conifers	58	36	57	28	80	36	7	66	37	32	3	72	242	17
	beech	1291	10	1420	9	1327	13	72	34	112	30	953	14	5176	5
	maple	469	20	537	17	712	13	138	30	53	34	180	31	2089	8
	ash	162	20	426	21	407	21	111	32	69	33	215	24	1391	10
	oak	109	44	118	35	19	64	61	35	0	.	97	41	405	20
	sweet chestnut	0	.	0	.	12	100	0	0	5	100	668	20	684	20
	other broadleaves	292	16	788	12	860	11	434	17	429	21	1272	12	4073	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4268	6	6507	6	7658	5	2620	10	3358	8	4430	6	28841	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1811	12	3657	11	7231	7	2084	16	4326	10	1408	15	20517	4
	fir	2074	12	1919	14	2131	13	131	41	59	35	132	35	6446	7
	pine	241	42	170	35	178	48	613	24	470	25	33	87	1705	14
	larch	0	.	178	40	110	51	827	18	536	26	1022	17	2673	11
	Arolla pine	0	.	0	.	0	.	187	29	370	23	19	77	576	18
	other conifers	46	48	100	31	44	56	0	.	11	59	4	100	205	22
	beech	2901	10	2600	9	2998	10	200	46	304	31	1763	17	10766	5
	maple	846	13	1018	17	1785	11	101	36	115	35	91	39	3956	7
	ash	321	26	654	19	841	19	154	36	123	69	315	33	2408	11
	oak	175	32	561	26	96	38	79	42	0	0	309	24	1220	15
	sweet chestnut	0	.	0	.	7	100	9	100	0	.	1415	16	1431	15
	other broadleaves	357	22	855	14	836	16	439	24	341	26	1776	15	4604	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	8773	6	11713	6	16256	5	4822	10	6656	8	8288	6	56508	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3197	11	4615	10	12150	6	2794	13	5641	9	2257	18	30654	4
	fir	2561	12	2284	13	3341	11	189	41	172	31	372	34	8919	7
	pine	555	27	463	22	325	33	538	22	574	26	52	88	2506	11
	larch	8	100	111	51	82	52	1498	20	1053	16	1364	17	4117	10
	Arolla pine	0	.	0	.	12	100	204	31	428	23	14	100	658	18
	other conifers	41	65	97	49	-20	125	0	0	0	.	0	.	119	50
	beech	4967	8	4022	9	4325	9	159	38	375	26	1472	15	15320	5
	maple	1005	15	891	15	1791	12	5	100	209	36	170	43	4072	8
	ash	441	21	1530	14	1212	22	84	62	62	49	105	38	3433	10
	oak	241	29	552	22	59	46	60	46	12	71	267	26	1192	13
	sweet chestnut	0	.	0	.	19	100	33	74	0	.	904	19	956	19
	other broadleaves	266	27	643	18	341	22	120	50	97	32	1274	17	2741	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	13282	5	15208	5	23636	4	5686	10	8624	7	8252	8	74688	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3848	9	5829	9	15517	6	2897	13	7040	9	2756	16	37889	3
	fir	3513	10	3239	11	4291	9	218	42	255	29	300	32	11815	5
	pine	506	22	837	19	300	49	347	26	652	25	63	57	2705	11
	larch	67	53	204	37	70	84	1896	17	1635	15	1334	14	5206	9
	Arolla pine	0	.	0	.	0	.	171	31	320	24	0	.	490	19
	other conifers	24	77	150	43	0	.	0	.	0	.	16	100	191	36
	beech	5268	8	5404	8	5122	10	141	60	560	28	1050	16	17545	5
	maple	834	15	526	20	819	15	58	100	87	44	52	49	2376	9
	ash	587	22	1528	15	835	21	9	100	11	100	106	61	3076	10
	oak	337	33	1043	17	103	49	20	76	0	.	242	30	1745	13
	sweet chestnut	0	.	0	.	16	72	0	.	0	.	844	22	859	22
	other broadleaves	331	29	570	20	255	29	50	52	57	73	721	20	1985	11
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	15316	4	19331	5	27327	4	5808	10	10616	7	7485	7	85882	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3904	10	4637	10	14665	6	2416	14	5775	10	2497	18	33895	4
	fir	3406	11	4286	12	5724	9	275	49	395	30	612	28	14699	6
	pine	329	27	794	22	242	39	145	42	522	27	27	100	2060	13
	larch	68	58	270	33	161	49	2051	15	1706	16	1312	14	5568	8
	Arolla pine	0	.	0	.	0	.	109	48	246	27	0	.	355	24
	other conifers	23	71	385	43	0	.	0	.	0	.	28	100	436	39
	beech	3911	11	5927	8	3791	12	15	100	276	39	708	20	14628	5
	maple	405	24	347	27	608	21	0	.	160	34	0	.	1519	13
	ash	321	32	1551	18	466	27	15	100	22	100	88	64	2464	13
	oak	105	51	1209	17	41	71	0	.	48	100	121	42	1525	15
	sweet chestnut	0	.	0	.	12	100	0	.	0	.	545	27	557	26
	other broadleaves	88	72	532	29	331	37	34	75	42	71	104	42	1130	19
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	12560	6	19939	5	26042	4	5061	10	9193	8	6042	9	78837	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2916	13	3354	13	10198	8	1056	18	3277	13	1479	17	22280	5
	fir	2113	17	2677	14	3958	13	32	100	329	37	138	50	9246	8
	pine	47	71	357	33	44	100	17	100	219	35	14	100	698	22
	larch	0	.	167	45	23	100	1514	20	1320	20	816	20	3841	11
	Arolla pine	0	.	0	.	0	.	39	71	67	59	0	.	107	45
	other conifers	0	.	341	46	0	.	0	.	0	.	36	100	377	43
	beech	1409	17	3196	12	1721	20	22	100	196	38	500	29	7044	8
	maple	89	58	203	47	260	42	0	.	0	.	0	.	551	28
	ash	225	42	676	24	187	46	0	.	0	.	55	100	1143	19
	oak	94	72	561	32	26	100	0	.	0	.	66	71	747	26
	sweet chestnut	0	.	0	.	16	100	0	.	0	.	178	33	194	31
	other broadleaves	72	100	191	42	265	57	26	100	74	78	47	73	674	29
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	6964	9	11723	7	16698	6	2706	14	5482	10	3328	10	46901	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1287	21	1919	18	6022	10	611	39	1622	18	1088	28	12548	7
	fir	1196	20	1863	17	2606	16	184	61	314	42	195	50	6357	10
	pine	41	100	161	50	0	.	0	.	62	71	0	.	264	38
	larch	41	100	84	71	112	59	687	28	564	26	536	27	2026	15
	Arolla pine	0	.	0	.	0	.	87	50	65	58	0	.	151	38
	other conifers	0	.	277	60	0	.	0	.	0	.	0	.	277	60
	beech	345	40	1531	20	933	26	0	.	118	70	170	41	3097	14
	maple	72	72	34	100	46	72	0	.	0	.	0	.	152	46
	ash	79	71	148	59	81	71	0	.	33	100	30	100	371	34
	oak	0	.	447	34	0	.	0	.	10	100	0	.	457	34
	sweet chestnut	0	.	0	.	18	100	0	.	0	.	168	38	186	36
	other broadleaves	0	.	216	41	38	100	0	.	0	.	27	100	281	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3061	13	6680	9	9856	8	1568	22	2788	13	2214	16	26166	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	345	48	909	26	1694	18	242	47	913	27	440	39	4545	12
	fir	504	36	1379	23	737	33	0	.	220	58	50	100	2891	16
	pine	0	.	49	100	0	.	0	.	0	.	0	.	49	100
	larch	0	.	0	.	0	.	334	46	303	44	592	33	1230	23
	Arolla pine	0	.	0	.	0	.	69	75	35	100	34	100	138	51
	other conifers	0	.	376	46	0	.	0	.	0	.	0	.	376	46
	beech	0	.	217	50	206	51	0	0	0	.	171	45	593	29
	maple	0	.	0	.	0	.	0	.	27	100	0	.	27	100
	ash	0	.	212	58	70	100	0	.	0	.	0	.	282	50
	oak	0	.	261	45	37	100	0	.	0	.	0	.	298	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	153	46	153	46
	other broadleaves	0	.	70	100	0	.	0	.	0	.	0	.	70	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	849	29	3473	14	2744	15	645	30	1499	21	1442	21	10652	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	127	71	169	58	734	28	124	61	317	41	215	58	1685	19
	fir	214	58	126	71	340	45	0	.	0	.	204	58	883	28
	pine	0	.	0	.	0	.	0	.	57	100	0	.	57	100
	larch	0	.	0	.	0	.	180	50	121	72	86	71	387	36
	Arolla pine	0	.	0	.	0	.	57	71	0	.	0	.	57	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	394	47	55	100	0	.	0	.	0	.	449	43
	maple	0	.	0	.	25	100	0	.	0	.	0	.	25	100
	ash	0	.	119	71	0	.	0	.	0	.	0	.	119	71
	oak	0	.	89	71	0	.	0	.	0	.	0	.	89	71
	sweet chestnut	0	.	0	.	0	.	29	100	0	.	145	59	174	52
	other broadleaves	0	.	55	100	0	.	0	.	0	.	0	.	55	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	340	45	951	27	1154	23	391	33	495	34	648	31	3980	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	81	100	227	58	411	36	64	100	306	50	184	72	1274	24
	fir	285	59	0	.	133	100	0	.	0	.	0	.	418	51
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	54	100	182	77	236	63
	Arolla pine	0	.	0	.	0	.	39	100	36	100	0	.	75	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	.	194	100	0	.	0	.	0	.	0	.	194	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	411	55	411	55
	other broadleaves	0	.	223	59	0	.	0	.	0	.	0	.	223	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	367	51	643	42	544	36	103	73	396	42	777	38	2830	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18439	6	27337	6	71814	4	13237	10	31290	7	12973	12	175091	2
	fir	16803	7	18788	8	24208	7	1081	31	1776	22	2068	24	64723	4
	pine	1745	18	2823	14	1142	31	1872	19	2784	17	211	44	10577	8
	larch	185	41	1148	19	606	29	9453	12	7472	11	7545	10	26409	6
	Arolla pine	0	.	0	.	12	100	1082	21	1708	18	73	89	2875	13
	other conifers	192	32	1785	31	104	50	7	66	48	32	88	92	2224	25
	beech	20091	5	24711	6	20478	7	611	37	1941	22	6788	11	74620	3
	maple	3719	9	3556	10	6044	8	301	34	652	27	494	25	14767	5
	ash	2138	15	6842	11	4099	14	373	35	321	37	913	30	14686	7
	oak	1062	19	5035	13	383	27	220	38	70	91	1103	20	7872	10
	sweet chestnut	0	.	0	.	99	73	71	62	5	100	5430	12	5605	12
	other broadleaves	1406	16	4142	11	2926	13	1102	20	1040	20	5220	11	15836	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	65779	3	96167	3	131916	3	29411	7	49106	5	42905	4	415285	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. 1218075. Birmensdorf, Swiss Federal Research Institute WSL

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