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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI5
total 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
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 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	7082	12	11799	10	28709	6	9219	11	16766	8	6307	14	79881	4
	fir	7757	10	6168	11	7900	10	473	32	243	31	750	26	23291	6
	pine	345	31	187	43	789	39	2601	27	3396	24	252	52	7569	15
	larch	17	100	913	47	565	36	3949	15	2181	18	3869	15	11495	9
	Arolla pine	0	.	0	.	0	.	795	29	1726	27	67	61	2588	20
	other conifers	507	37	390	23	883	36	156	62	945	36	37	71	2917	18
	beech	9594	8	10776	9	10384	9	1115	31	1001	23	6630	13	39500	5
	maple	3679	14	3740	13	5742	11	1143	32	371	35	1390	28	16065	7
	ash	1605	18	2636	15	3185	14	938	31	485	34	1660	22	10509	8
	oak	1158	42	1009	25	230	51	805	35	0	.	1200	37	4403	17
	sweet chestnut	0	.	0	.	94	100	37	100	18	100	7956	15	8105	15
	other broadleaves	3242	12	6223	11	9250	11	4365	15	3963	19	13294	11	40339	5
	indeterminable	123	48	85	45	298	24	74	50	250	39	110	41	940	16
	total	35109	5	43927	5	68029	4	25670	8	31345	7	43522	5	247602	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3938	11	6795	10	18460	6	5214	13	10768	8	3837	13	49013	4
	fir	4442	10	3545	12	4402	9	239	35	224	32	407	30	13259	6
	pine	511	31	435	32	547	44	2170	20	1350	21	123	63	5135	12
	larch	0	.	362	37	188	45	2468	16	1690	22	3105	14	7812	9
	Arolla pine	0	.	0	.	0	.	614	26	1247	23	53	76	1914	17
	other conifers	120	47	213	29	121	62	0	.	70	50	17	100	542	22
	beech	6002	9	5266	8	6506	9	433	44	844	28	4075	16	23126	5
	maple	1998	12	1932	16	3974	10	274	37	280	40	306	31	8765	7
	ash	724	19	1240	16	2036	15	443	37	296	63	710	31	5449	9
	oak	511	30	976	25	225	34	270	40	54	100	887	22	2923	13
	sweet chestnut	0	.	0	.	19	100	18	100	0	.	4512	14	4549	14
	other broadleaves	981	21	2087	12	2855	14	1406	19	1374	23	5404	12	14106	6
	indeterminable	54	58	17	100	122	38	18	100	75	62	18	100	305	26
	total	19282	5	22869	5	39454	4	13568	8	18272	7	23453	5	136898	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3229	11	3818	10	12992	6	3441	11	6131	8	2366	14	31976	3
	fir	2213	11	2017	13	3087	10	183	40	156	29	301	30	7956	6
	pine	749	23	401	24	441	35	833	20	749	23	155	49	3329	11
	larch	15	71	96	44	127	49	1851	18	1303	15	1698	14	5089	9
	Arolla pine	0	.	0	.	10	100	342	31	641	24	15	100	1007	19
	other conifers	37	68	101	46	22	76	17	100	0	.	0	.	177	33
	beech	4547	8	3368	8	4281	9	223	37	503	24	1780	15	14703	4
	maple	939	16	843	14	1846	12	7	100	215	35	204	40	4055	8
	ash	540	25	1260	15	1143	20	129	48	131	43	169	32	3372	10
	oak	301	31	499	20	61	54	107	45	14	71	313	27	1295	13
	sweet chestnut	0	.	0	.	38	100	49	79	0	.	1646	18	1733	17
	other broadleaves	387	23	668	18	527	19	121	35	160	31	1868	16	3732	10
	indeterminable	34	71	32	62	91	37	7	100	34	71	26	78	224	25
	total	12992	5	13104	5	24665	4	7310	8	10036	6	10540	6	78647	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2269	9	2712	9	8827	5	1833	12	4061	8	1599	13	21301	3
	fir	1648	10	1502	10	2174	9	117	41	125	28	184	29	5751	5
	pine	346	20	412	17	203	41	310	27	401	23	41	52	1713	10
	larch	35	53	90	36	36	68	1255	16	993	14	993	13	3402	8
	Arolla pine	0	.	0	.	0	.	160	30	262	22	0	.	422	18
	other conifers	14	71	69	42	0	.	0	.	0	.	8	100	90	35
	beech	2680	8	2488	8	2582	9	106	52	320	26	703	15	8879	4
	maple	459	14	270	19	555	13	30	100	56	44	44	47	1414	8
	ash	303	21	781	14	396	19	15	100	14	71	66	44	1574	9
	oak	200	30	516	16	63	44	21	75	0	.	185	28	985	12
	sweet chestnut	0	.	0	.	23	58	0	.	0	.	727	18	750	18
	other broadleaves	233	34	340	18	181	28	59	39	56	57	524	18	1393	11
	indeterminable	15	71	0	.	62	43	7	100	26	60	7	100	117	29
	total	8201	4	9180	4	15102	4	3913	9	6315	6	5081	6	47791	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1400	10	1454	10	5235	5	999	12	2237	9	1040	20	12365	4
	fir	1038	11	1222	12	1704	9	112	45	131	29	197	28	4404	6
	pine	121	26	267	21	101	37	78	46	220	27	21	74	808	13
	larch	22	58	76	32	50	47	835	14	655	16	609	14	2247	8
	Arolla pine	0	.	0	.	0	.	70	47	149	27	0	.	219	23
	other conifers	14	71	102	43	0	.	0	.	0	.	8	100	124	36
	beech	1301	10	1763	8	1270	11	7	100	104	37	324	19	4769	5
	maple	151	23	123	26	232	21	0	.	73	34	0	.	578	12
	ash	96	32	436	17	164	25	7	100	7	100	40	63	751	12
	oak	42	47	378	16	14	71	0	.	21	100	63	38	518	14
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	343	24	352	23
	other broadleaves	28	61	196	26	134	33	16	71	25	71	57	42	456	17
	indeterminable	0	.	0	.	19	58	14	71	0	.	0	.	34	45
	total	4212	5	6018	5	8932	4	2139	9	3622	7	2703	10	27626	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	691	13	702	12	2553	7	339	18	812	12	437	16	5535	5
	fir	517	15	563	14	872	12	7	100	65	37	37	45	2061	7
	pine	15	71	75	33	10	100	7	100	65	37	7	100	179	21
	larch	0	.	34	45	7	100	444	19	349	19	245	20	1078	11
	Arolla pine	0	.	0	.	0	.	14	71	22	58	0	.	36	45
	other conifers	0	.	61	46	0	.	0	.	0	.	8	100	69	42
	beech	319	17	669	11	389	20	7	100	47	38	151	28	1582	8
	maple	21	58	41	47	77	38	0	.	0	.	0	.	140	27
	ash	53	43	140	24	44	46	0	.	0	.	10	100	247	19
	oak	20	73	115	31	15	71	0	.	0	.	21	58	170	25
	sweet chestnut	0	.	0	.	16	71	0	.	0	.	98	27	114	25
	other broadleaves	14	100	43	41	60	49	7	100	18	72	15	71	158	26
	indeterminable	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	total	1649	8	2443	6	4043	6	834	13	1377	10	1029	9	11375	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	231	20	315	17	1174	9	131	33	323	17	233	24	2407	6
	fir	211	19	338	16	424	16	37	60	40	41	30	49	1080	9
	pine	7	100	27	50	0	.	0	.	14	71	0	.	48	38
	larch	7	100	15	71	21	58	159	28	117	26	115	26	433	15
	Arolla pine	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	0	.	41	58	0	.	0	.	0	.	0	.	41	58
	beech	59	39	273	19	159	24	0	.	25	79	44	41	560	13
	maple	16	71	7	100	13	71	0	.	0	.	0	.	36	45
	ash	15	71	23	59	14	71	0	.	6	100	6	100	65	34
	oak	0	.	69	34	0	.	0	.	7	100	0	.	76	32
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	104	33	112	31
	other broadleaves	0	.	43	41	6	100	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	546	12	1151	9	1820	8	355	20	554	13	539	14	4965	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	125	25	270	17	37	45	141	26	88	33	707	11
	fir	65	36	167	23	99	32	0	.	21	58	7	100	360	16
	pine	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	60	43	63	40	123	32	245	22
	Arolla pine	0	.	0	.	0	.	28	71	7	100	7	100	43	52
	other conifers	0	.	41	47	0	.	0	.	0	.	0	.	41	47
	beech	0	.	28	50	30	50	7	100	0	.	37	45	102	27
	maple	0	.	0	.	0	.	0	.	8	100	0	.	8	100
	ash	0	.	23	58	10	100	0	.	0	.	0	.	33	51
	oak	0	.	37	45	10	100	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	53	47	53	47
	other broadleaves	0	.	8	100	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	111	29	435	13	420	14	132	28	240	20	315	19	1654	7
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	20	58	107	27	22	58	49	43	25	59	238	18
	fir	29	50	14	71	34	45	0	.	0	.	25	58	102	27
	pine	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	29	50	18	72	15	71	61	36
	Arolla pine	0	.	0	.	0	.	22	58	0	.	0	.	22	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	44	46	7	100	0	.	0	.	0	.	51	42
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	17	71	0	.	0	.	0	.	0	.	17	71
	oak	0	.	14	71	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	45	53	53	48
	other broadleaves	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	44	41	116	26	155	22	80	30	73	35	110	30	577	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	71	21	58	66	33	7	100	30	50	18	72	156	22
	fir	26	59	0	.	7	100	0	.	0	.	0	.	33	51
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	22	74	29	61
	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	.	16	100	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	50	51	50	51
	other broadleaves	0	.	27	61	0	.	0	.	0	.	0	.	27	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	40	46	65	40	73	32	14	71	44	41	90	37	327	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18916	7	27761	7	78393	4	21243	10	41317	7	15950	11	203580	3
	fir	17945	7	15537	8	20703	7	1169	29	1005	22	1938	22	58298	4
	pine	2093	19	1811	15	2090	30	5999	19	6201	18	600	43	18794	10
	larch	95	37	1586	32	993	29	11050	11	7376	11	10792	11	31892	6
	Arolla pine	0	.	0	.	10	100	2079	22	4083	20	142	65	6313	15
	other conifers	691	31	1017	19	1026	36	173	65	1015	34	78	59	4002	15
	beech	24502	6	24675	6	25610	7	1899	31	2844	21	13744	12	93274	3
	maple	7263	10	6956	10	12446	8	1454	31	1003	27	1944	25	31067	5
	ash	3337	14	6557	10	6993	11	1532	29	939	40	2661	22	22019	6
	oak	2230	28	3630	16	617	28	1203	32	96	73	2670	23	10446	11
	sweet chestnut	0	.	0	.	207	90	112	83	18	100	15534	12	15870	12
	other broadleaves	4885	12	9642	9	13014	10	5974	14	5596	18	21170	9	60281	5
	indeterminable	226	33	135	34	592	17	128	37	385	29	160	37	1627	12
	total	82186	3	99307	3	162693	3	54015	7	71878	5	87383	4	557462	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. 1296800. Birmensdorf, Swiss Federal Research Institute WSL

back