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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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
number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7232	11	11139	11	14923	9	31325	5	4174	14	68792	4
	fir	6733	9	4752	13	6802	10	2599	14	465	33	21351	6
	pine	180	40	327	70	278	79	5103	17	65	100	5952	16
	larch	0	0	472	72	276	58	4769	11	2978	17	8495	10
	Arolla pine	0	.	0	.	0	.	2137	18	32	100	2170	18
	other conifers	470	35	576	35	146	35	1072	27	0	.	2264	17
	beech	9368	8	7714	8	6951	10	5294	13	6354	14	35681	5
	maple	3148	14	2655	15	2688	16	3731	13	1064	30	13285	7
	ash	1748	20	2556	14	1577	18	2543	16	1434	25	9860	8
	oak	1031	41	688	35	78	50	889	29	1096	35	3782	18
	sweet chestnut	0	.	17	100	0	.	147	100	5834	18	5998	17
	other broadleaves	2877	13	3805	12	3454	14	11838	11	10580	12	32553	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	32787	4	34700	5	37172	5	71447	4	34076	6	210183	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4639	12	6612	11	11082	8	19176	6	2936	13	44446	4
	fir	3475	12	2099	14	3415	12	1681	14	374	36	11044	6
	pine	625	33	323	29	226	75	3099	15	84	65	4357	13
	larch	17	100	223	52	51	100	3150	13	2412	15	5853	9
	Arolla pine	0	.	0	.	0	.	1668	18	16	100	1684	18
	other conifers	120	43	257	34	35	71	157	51	0	.	569	23
	beech	6558	8	5183	8	5180	9	3329	13	3866	16	24116	5
	maple	1672	12	1845	16	2361	13	1749	15	245	37	7873	7
	ash	796	20	1356	15	1941	18	1423	18	601	30	6117	9
	oak	941	28	810	23	132	47	378	29	688	23	2949	13
	sweet chestnut	0	.	0	.	0	.	73	62	4156	15	4229	15
	other broadleaves	805	18	1447	16	1225	23	2783	14	4274	13	10535	7
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	19648	5	20155	5	25648	5	38668	4	19653	5	123772	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3234	11	3903	10	7708	6	12912	5	2128	15	29885	3
	fir	2267	12	1506	13	2446	10	874	18	272	35	7364	6
	pine	682	21	493	23	169	58	1697	15	43	71	3085	11
	larch	62	48	168	35	14	71	2634	11	1374	16	4253	9
	Arolla pine	0	.	0	.	0	.	790	19	0	.	790	19
	other conifers	45	64	82	53	0	.	22	76	0	.	149	36
	beech	5062	7	3054	9	3259	12	1603	13	1174	17	14151	5
	maple	807	16	678	16	924	16	840	17	74	43	3324	8
	ash	590	18	1369	15	825	22	731	33	90	38	3606	11
	oak	405	25	526	19	38	63	77	43	304	27	1350	13
	sweet chestnut	0	.	7	100	0	.	71	66	1353	19	1430	18
	other broadleaves	399	23	706	19	232	27	510	18	1356	15	3202	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	13553	4	12492	5	15616	4	22760	4	8168	6	72590	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2293	8	2495	8	5278	6	8257	5	1277	14	19601	3
	fir	1849	10	1238	11	1663	9	828	15	140	30	5716	5
	pine	386	21	405	18	143	63	608	17	35	53	1576	11
	larch	7	100	102	35	7	100	2080	11	672	14	2868	9
	Arolla pine	0	.	0	.	0	.	359	19	0	.	359	19
	other conifers	28	71	81	45	0	.	0	.	8	100	117	36
	beech	2916	7	2270	7	1932	11	1022	13	562	18	8702	4
	maple	376	16	179	23	311	18	262	20	7	100	1135	9
	ash	267	22	834	14	286	23	214	26	62	48	1665	9
	oak	250	24	413	18	44	53	70	41	153	37	931	13
	sweet chestnut	0	.	0	.	0	.	21	58	517	19	538	19
	other broadleaves	142	41	345	20	75	39	168	26	304	20	1033	12
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	8514	4	8362	4	9739	4	13890	3	3738	7	44241	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1401	9	1654	10	3394	7	4396	5	790	15	11636	4
	fir	950	12	1036	11	1244	10	498	15	163	30	3890	6
	pine	104	29	251	19	52	53	263	23	14	100	685	13
	larch	14	70	84	35	21	74	1554	10	495	14	2168	8
	Arolla pine	0	.	0	.	0	.	121	27	0	.	121	27
	other conifers	21	100	47	55	0	.	0	.	8	100	76	45
	beech	1302	10	1384	9	817	14	488	16	253	20	4244	5
	maple	112	25	90	34	107	28	124	42	8	100	441	17
	ash	103	26	469	18	73	39	63	39	13	100	721	14
	oak	58	43	273	21	0	.	34	53	18	72	383	17
	sweet chestnut	0	.	0	.	0	.	6	100	243	26	249	25
	other broadleaves	86	62	83	42	25	58	206	39	55	43	454	23
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	4152	5	5371	4	5733	5	7753	4	2060	8	25069	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	564	14	547	14	1487	9	1682	8	366	18	4645	5
	fir	407	16	596	14	607	16	255	21	22	58	1886	8
	pine	15	71	41	41	25	59	57	39	7	100	145	23
	larch	0	.	27	50	0	.	572	16	201	22	800	13
	Arolla pine	0	.	0	.	0	.	49	38	0	.	49	38
	other conifers	0	.	54	43	0	.	0	.	0	.	54	43
	beech	329	18	439	14	257	20	134	26	121	31	1281	9
	maple	7	100	28	50	24	79	38	52	0	.	96	32
	ash	52	38	81	43	37	45	14	71	10	100	195	23
	oak	28	59	99	28	0	.	0	.	15	71	142	24
	sweet chestnut	0	.	0	.	0	.	27	77	68	33	95	32
	other broadleaves	23	58	20	58	18	100	30	52	8	100	99	31
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	1425	8	1932	7	2455	8	2857	6	819	11	9488	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	167	23	261	19	539	14	792	12	196	23	1956	7
	fir	138	25	137	26	287	17	128	24	30	49	719	11
	pine	7	100	7	100	0	.	14	71	0	.	28	50
	larch	7	100	0	.	16	71	328	17	94	32	444	15
	Arolla pine	0	.	0	.	0	.	50	37	0	.	50	37
	other conifers	7	100	42	57	0	.	0	.	0	.	49	51
	beech	105	33	146	26	83	34	53	39	44	41	430	15
	maple	9	100	0	.	6	100	16	71	0	.	30	50
	ash	14	71	34	50	18	72	14	71	0	.	79	32
	oak	18	70	28	50	8	100	15	71	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	10	100	76	37	85	34
	other broadleaves	0	.	21	58	0	.	6	100	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	472	14	675	12	957	10	1424	8	447	14	3974	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	54	80	31	162	32	324	18	90	31	692	13
	fir	51	38	54	39	49	43	36	45	8	100	197	20
	pine	0	.	7	100	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	115	29	53	43	167	24
	Arolla pine	0	.	0	.	0	.	36	45	0	.	36	45
	other conifers	0	.	8	100	7	100	0	.	0	.	15	71
	beech	0	0	49	42	16	71	22	58	22	58	108	27
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	17	72	7	100	0	.	0	.	24	58
	oak	0	.	43	41	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	8	100	45	46	53	42
	other broadleaves	7	100	7	100	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	94	30	263	18	241	24	553	13	217	20	1369	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	7	100	51	38	53	38	14	71	132	24
	fir	22	58	13	71	8	100	7	100	18	72	68	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	30	50	0	.	30	50
	Arolla pine	0	.	0	.	0	.	7	100	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	7	100	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	7	100
	oak	0	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	30	61	30	61
	other broadleaves	0	.	30	81	0	.	0	.	0	.	30	81
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	80	41	67	33	97	28	61	39	334	17
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	14	71	14	71	59	35	19	72	112	26
	fir	0	.	21	58	7	100	0	.	0	.	28	50
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	15	71	14	100	29	61
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	57	45	57	45
	other broadleaves	0	.	37	100	0	.	0	.	0	.	37	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	72	56	21	58	88	29	90	36	278	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19580	7	26713	7	44639	5	78975	4	11990	10	181897	3
	fir	15891	7	11450	9	16528	7	6905	11	1491	25	52265	4
	pine	1999	19	1853	19	892	57	10848	13	249	63	15841	10
	larch	107	34	1075	35	385	51	15247	7	8293	11	25108	6
	Arolla pine	0	.	0	.	0	.	5231	14	49	100	5280	14
	other conifers	691	29	1147	24	188	30	1251	27	16	100	3293	15
	beech	25639	5	20246	5	18502	7	11944	10	12396	13	88727	3
	maple	6132	9	5474	12	6421	11	6766	11	1398	26	26191	5
	ash	3572	14	6723	9	4765	14	5002	14	2211	24	22274	6
	oak	2731	24	2896	16	300	30	1464	25	2274	23	9664	11
	sweet chestnut	0	.	23	76	0	.	363	68	12379	13	12765	13
	other broadleaves	4338	11	6501	10	5028	14	15541	10	16583	10	47992	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	80681	3	84101	3	97649	3	159537	2	69328	4	491296	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. 1327012. Birmensdorf, Swiss Federal Research Institute WSL

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