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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NFI3
number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/07
		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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	8304	14	13148	10	20014	7	6258	14	12158	10	3447	15	63328	4
	fir	6576	9	5386	13	6563	10	350	40	429	29	569	32	19872	6
	pine	246	43	548	50	458	54	2524	25	2015	27	83	72	5875	15
	larch	0	0	252	54	475	38	2324	18	777	24	2106	21	5934	11
	Arolla pine	0	.	0	.	0	.	557	32	740	29	53	76	1350	21
	other conifers	384	33	393	28	348	51	78	64	483	44	17	100	1703	19
	beech	8910	9	9608	8	10108	9	776	38	919	27	5920	15	36241	5
	maple	2447	13	3059	16	4193	14	441	34	339	37	490	31	10970	8
	ash	1303	21	2634	15	2320	16	390	46	561	40	1205	29	8413	9
	oak	973	40	1057	29	193	51	588	36	0	.	1319	35	4130	17
	sweet chestnut	66	100	0	.	0	.	0	0	0	.	6351	17	6418	17
	other broadleaves	2895	15	3618	13	5758	12	3321	20	2985	25	9196	14	27775	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	32105	6	39703	5	50430	5	17607	9	21406	8	30756	7	192008	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5050	12	8040	10	15908	7	4320	14	7578	10	2615	14	43511	4
	fir	3363	13	2957	13	4009	10	260	42	207	40	375	38	11172	6
	pine	509	32	535	33	452	50	1650	21	1132	26	171	55	4450	13
	larch	17	100	208	42	119	52	1599	18	728	24	1566	17	4237	10
	Arolla pine	0	.	0	.	0	.	507	32	828	24	35	71	1370	19
	other conifers	154	43	207	37	50	58	22	100	35	71	17	100	484	23
	beech	7213	9	6759	8	6736	9	375	41	725	29	3554	17	25362	5
	maple	1602	13	1588	15	3223	11	54	58	295	34	193	37	6954	7
	ash	936	22	1889	15	2521	20	232	52	274	59	389	35	6242	10
	oak	533	29	1575	22	114	41	164	59	36	100	686	27	3107	14
	sweet chestnut	0	.	0	.	0	.	37	100	0	.	3762	15	3799	15
	other broadleaves	799	20	1575	15	1630	18	733	24	613	34	3574	11	8925	7
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	20177	5	25332	5	34762	5	9954	9	12452	8	16937	6	119613	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3385	10	4376	9	10698	6	2397	13	4916	9	1951	16	27722	4
	fir	2383	12	1841	12	2417	10	140	58	163	36	195	35	7139	6
	pine	636	20	744	22	209	36	686	21	806	25	85	52	3165	11
	larch	38	61	152	35	31	80	1123	19	837	21	1070	20	3250	11
	Arolla pine	0	.	0	.	10	100	241	32	368	27	18	100	637	20
	other conifers	17	100	103	56	0	.	0	.	0	.	0	.	120	50
	beech	5094	8	4061	9	3672	12	165	46	521	28	1099	17	14612	5
	maple	811	15	609	18	1161	14	59	71	115	40	59	56	2813	9
	ash	578	22	1662	14	929	23	121	89	93	55	119	42	3501	10
	oak	349	26	669	19	83	45	26	78	7	100	199	35	1334	13
	sweet chestnut	0	.	0	.	0	.	29	73	0	.	1058	18	1087	18
	other broadleaves	315	30	777	18	436	23	154	41	85	47	889	17	2657	10
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	total	13607	5	14992	5	19644	5	5140	9	7911	7	6743	7	68037	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2435	9	3437	8	7214	6	1537	12	3365	10	1240	14	19227	3
	fir	1796	10	1620	10	1891	9	112	43	125	31	176	31	5720	5
	pine	380	22	704	17	197	48	178	37	375	24	56	66	1890	11
	larch	43	53	82	35	43	59	992	16	805	15	638	16	2603	9
	Arolla pine	0	.	0	.	0	0	105	42	206	23	0	.	311	21
	other conifers	0	.	116	42	0	.	0	.	0	.	8	100	124	40
	beech	2716	7	2732	8	2132	11	72	61	281	27	513	18	8445	4
	maple	440	19	230	21	466	15	7	100	46	47	8	100	1197	10
	ash	245	23	865	14	414	30	15	100	7	100	37	66	1583	12
	oak	146	28	515	18	37	66	19	100	14	100	138	46	869	14
	sweet chestnut	0	.	0	.	15	70	0	.	0	.	436	22	451	21
	other broadleaves	109	33	334	23	116	33	15	71	35	53	246	22	856	13
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	8310	5	10635	4	12525	4	3053	9	5258	7	3495	7	43276	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1534	9	2304	9	4635	6	678	14	1611	10	718	15	11481	4
	fir	823	12	1256	10	1256	10	59	53	142	30	104	33	3640	6
	pine	104	27	285	18	52	48	69	42	152	29	7	100	669	12
	larch	7	100	110	36	78	44	632	16	627	15	435	15	1889	9
	Arolla pine	0	.	0	.	0	.	7	100	100	30	0	.	106	29
	other conifers	8	100	68	45	0	.	0	.	0	.	8	100	83	39
	beech	1036	11	1440	9	970	12	25	72	99	32	243	20	3813	6
	maple	58	35	88	38	97	29	0	.	27	60	0	.	270	19
	ash	121	26	367	18	100	32	0	.	7	100	0	.	596	13
	oak	28	50	292	20	6	100	0	.	7	100	39	52	372	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	212	26	212	26
	other broadleaves	36	45	78	47	148	41	30	61	7	100	22	58	322	24
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	3756	6	6287	5	7343	5	1500	11	2779	7	1788	8	23453	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	523	15	866	13	1676	8	234	24	612	15	320	18	4231	5
	fir	347	17	490	14	608	15	29	61	14	71	29	50	1517	9
	pine	29	50	48	38	10	100	0	.	64	37	0	.	151	23
	larch	0	.	27	50	21	74	314	22	167	24	178	24	707	13
	Arolla pine	0	.	0	.	0	.	7	100	29	50	0	.	36	45
	other conifers	8	100	62	54	0	.	0	.	0	.	0	.	70	49
	beech	132	26	420	14	317	20	0	.	14	71	85	30	967	10
	maple	17	72	22	58	23	59	0	.	0	.	0	.	63	36
	ash	22	58	86	36	36	52	0	.	6	100	11	100	162	25
	oak	31	81	109	35	0	.	0	.	7	100	0	.	148	31
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	75	34	83	33
	other broadleaves	14	70	15	71	20	58	0	.	0	.	8	100	56	35
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	1121	10	2147	7	2718	7	584	17	912	12	707	11	8190	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	146	25	263	19	806	12	99	31	215	20	168	27	1696	8
	fir	146	24	198	21	176	21	7	100	47	42	22	57	598	12
	pine	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	larch	7	100	9	100	7	100	137	29	100	30	78	33	338	17
	Arolla pine	0	.	0	.	0	.	42	47	14	71	7	100	64	37
	other conifers	8	100	7	100	0	.	0	.	0	.	0	.	15	71
	beech	28	50	168	28	66	37	0	.	18	74	44	41	324	18
	maple	0	.	0	.	0	.	0	.	10	100	0	.	10	100
	ash	0	.	60	39	7	100	0	.	0	.	0	.	67	36
	oak	0	.	60	36	8	100	0	.	0	.	0	.	67	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	46	46	46	46
	other broadleaves	0	.	14	71	0	.	0	.	0	.	7	100	21	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	335	17	785	11	1071	10	285	20	405	15	374	16	3253	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	45	48	42	41	190	28	56	55	70	35	61	39	464	16
	fir	51	38	49	38	56	36	0	.	7	100	18	71	182	20
	pine	0	.	7	100	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	65	37	35	53	44	52	143	26
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	64	39	29	50	7	100	0	.	7	100	108	28
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	10	100	0	.	0	.	0	.	0	.	10	100
	oak	0	.	39	46	0	.	0	.	0	.	0	.	39	46
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	45	46	53	42
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	96	30	211	20	282	21	143	29	127	26	175	24	1034	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	7	100	57	39	0	0	21	58	11	100	95	30
	fir	7	100	14	71	0	.	0	.	0	.	18	72	39	45
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	7	100	0	.	21	58
	Arolla pine	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	22	58	0	.	0	.	0	.	0	.	22	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	oak	0	.	16	100	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	37	59	37	59
	other broadleaves	0	.	28	79	0	.	0	.	0	.	0	.	28	79
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	94	35	57	39	21	58	27	50	66	42	272	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	23	58	14	71	7	100	15	71	8	100	74	32
	fir	0	.	0	.	7	100	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	22	74	22	74
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	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	.	53	50	53	50
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	23	58	21	58	14	71	22	58	82	39	170	24
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21428	8	32504	6	61212	5	15585	11	30561	7	10540	11	171831	3
	fir	15493	7	13812	8	16984	7	958	34	1134	22	1505	25	49886	4
	pine	1904	19	2877	15	1378	38	5107	20	4551	19	403	49	16220	9
	larch	113	36	840	24	773	30	7199	12	4083	13	6136	14	19143	7
	Arolla pine	0	.	0	.	10	100	1488	25	2299	21	113	77	3910	16
	other conifers	579	26	955	22	398	49	100	70	518	43	51	75	2600	15
	beech	25128	6	25275	6	24029	7	1420	36	2577	23	11465	13	89894	3
	maple	5375	9	5596	11	9169	10	560	33	832	29	750	27	22282	5
	ash	3206	15	7580	10	6328	14	757	49	948	41	1761	27	20580	7
	oak	2060	26	4331	16	441	32	798	35	71	72	2382	26	10082	11
	sweet chestnut	66	100	0	.	22	74	74	77	0	.	12075	13	12238	13
	other broadleaves	4169	13	6440	11	8108	11	4254	19	3726	23	13942	11	40639	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	79520	4	100210	3	128852	3	38302	7	51299	6	61122	4	459304	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. 1326982. Birmensdorf, Swiss Federal Research Institute WSL

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