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

NFI4
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 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	20431	7	17070	8	9249	11	8218	12	13724	9	3533	15	72225	4
	fir	12533	7	6683	10	2941	17	627	28	367	39	502	32	23653	5
	pine	684	36	512	50	70	61	2922	26	2241	26	81	100	6510	15
	larch	508	67	497	43	191	60	3027	16	936	20	1824	22	6984	11
	Arolla pine	0	.	0	.	0	.	611	31	949	25	32	100	1592	19
	other conifers	1118	24	33	71	249	42	156	53	745	43	0	.	2300	19
	beech	17767	5	6085	11	5063	14	1300	30	634	28	6243	15	37093	4
	maple	5990	10	3078	16	1512	20	820	27	423	37	628	32	12451	7
	ash	4364	12	1338	20	1224	22	521	31	587	41	1498	26	9532	8
	oak	2032	26	215	61	60	58	730	33	15	100	1342	36	4394	17
	sweet chestnut	36	71	0	.	0	.	37	100	0	.	8405	15	8478	15
	other broadleaves	7306	9	4341	13	2981	18	3934	16	3829	22	10274	13	32665	6
	indeterminable	191	36	274	30	69	50	89	45	124	43	99	66	846	17
	total	72960	3	40125	5	23609	8	22992	8	24574	8	34463	6	218723	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11922	8	11082	9	6786	12	5451	13	8133	10	2715	14	46089	4
	fir	6028	9	3356	11	1501	17	416	30	210	38	409	35	11920	6
	pine	1191	20	296	62	167	54	2032	20	1156	26	120	54	4961	12
	larch	257	46	91	67	121	62	1769	17	907	22	1757	17	4902	10
	Arolla pine	0	.	0	.	0	.	581	28	777	27	16	100	1374	19
	other conifers	377	27	35	71	18	100	35	71	53	58	0	.	517	22
	beech	12084	6	4208	11	3232	13	721	31	484	33	3851	16	24580	5
	maple	3504	10	1759	15	1534	17	251	38	179	37	229	38	7455	7
	ash	2285	11	1020	22	1413	19	333	34	261	59	549	32	5862	9
	oak	1841	18	141	38	54	74	234	42	15	100	637	25	2924	13
	sweet chestnut	0	.	0	.	0	.	37	100	15	100	4402	14	4454	14
	other broadleaves	2337	12	970	19	985	27	1018	20	1072	28	4154	12	10536	7
	indeterminable	19	100	105	41	33	71	35	71	36	71	20	100	248	27
	total	41845	3	23064	6	15843	8	12913	9	13298	8	18860	5	125822	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7440	7	7659	7	4661	10	3211	11	5727	9	2075	16	30773	3
	fir	4004	9	2138	11	1132	15	185	41	102	36	254	37	7815	6
	pine	1375	15	188	53	123	64	895	20	777	25	79	54	3438	10
	larch	237	28	86	59	35	53	1421	17	895	18	1092	19	3765	10
	Arolla pine	0	.	0	.	0	.	244	36	455	26	0	.	699	21
	other conifers	127	41	0	.	15	100	0	.	0	.	0	.	142	38
	beech	8279	6	2372	14	1922	13	186	37	373	28	1226	17	14358	5
	maple	1493	11	770	18	744	18	45	54	66	48	74	43	3191	8
	ash	1947	12	659	28	600	20	241	88	41	68	98	36	3586	11
	oak	969	15	38	63	23	74	32	81	7	100	271	29	1339	13
	sweet chestnut	7	100	0	.	0	.	29	73	0	.	1518	18	1553	17
	other broadleaves	1116	15	184	32	317	24	126	39	145	45	1259	15	3146	9
	indeterminable	51	75	46	54	7	100	0	.	7	100	7	100	119	40
	total	27045	3	14139	5	9579	7	6613	9	8596	7	7952	6	73924	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5019	6	5522	7	2930	10	1797	11	3568	8	1363	14	20201	3
	fir	3212	7	1388	11	904	15	199	36	111	32	154	30	5968	5
	pine	861	13	170	54	38	52	188	33	349	23	28	62	1633	11
	larch	117	32	43	59	7	100	1090	17	851	14	600	17	2708	9
	Arolla pine	0	.	10	100	0	.	112	41	212	23	0	.	334	20
	other conifers	109	38	0	.	0	.	0	.	0	.	8	100	117	36
	beech	5271	5	1356	13	1297	14	57	62	211	33	562	18	8754	4
	maple	548	13	199	23	320	17	36	85	15	100	7	100	1125	9
	ash	1099	12	155	25	328	23	27	71	7	100	62	48	1678	9
	oak	653	15	43	54	16	71	21	75	26	77	138	39	897	13
	sweet chestnut	0	.	7	100	0	.	7	100	6	100	601	19	622	18
	other broadleaves	495	19	70	34	80	40	61	40	35	44	306	21	1047	12
	indeterminable	15	71	59	39	20	76	7	100	14	71	0	.	114	28
	total	17398	3	9023	5	5940	7	3603	9	5404	7	3830	7	45198	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3150	7	3100	8	2112	9	1047	13	1877	10	790	16	12076	4
	fir	2009	8	1073	11	577	16	101	39	93	34	163	30	4016	6
	pine	362	16	42	65	24	76	93	45	163	29	14	100	698	13
	larch	98	31	36	66	34	60	811	14	630	16	397	15	2005	8
	Arolla pine	0	.	0	.	0	.	21	58	100	30	0	.	121	27
	other conifers	68	49	0	.	0	.	0	.	0	.	8	100	76	45
	beech	2650	7	637	16	540	17	33	71	79	36	275	19	4215	5
	maple	195	21	107	48	76	32	0	.	35	51	8	100	421	17
	ash	572	16	50	51	85	32	7	100	0	.	0	.	714	14
	oak	319	20	0	.	6	100	8	100	20	76	18	72	371	18
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	259	25	265	24
	other broadleaves	112	35	84	63	36	45	23	57	14	71	39	45	308	23
	indeterminable	0	.	7	100	7	100	14	71	0	.	0	.	29	50
	total	9536	3	5135	6	3496	7	2159	10	3019	8	1971	8	25316	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1120	10	1197	10	982	12	324	21	656	13	373	18	4652	5
	fir	1008	11	644	15	171	23	15	100	47	38	29	50	1914	8
	pine	55	35	18	72	7	100	0	.	65	40	0	.	145	25
	larch	34	45	14	71	6	100	339	22	241	22	194	23	828	12
	Arolla pine	0	.	0	.	0	.	7	100	35	45	0	.	43	41
	other conifers	54	43	0	.	0	.	0	.	0	.	0	.	54	43
	beech	738	11	209	23	147	26	6	100	21	58	121	31	1242	9
	maple	35	45	23	59	25	77	0	.	0	.	0	.	83	34
	ash	141	29	23	58	20	58	0	.	0	.	10	100	194	23
	oak	123	25	0	.	0	.	0	.	0	.	15	71	138	24
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	83	30	90	28
	other broadleaves	43	41	12	70	32	65	0	.	0	.	8	100	95	31
	indeterminable	0	.	6	100	0	.	7	100	0	.	0	.	13	71
	total	3351	5	2146	8	1390	10	705	15	1066	10	833	11	9491	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	437	15	523	15	462	17	101	33	222	22	203	23	1949	8
	fir	281	18	195	20	168	24	21	58	35	45	30	49	730	11
	pine	14	71	0	.	0	.	0	.	14	71	0	.	28	50
	larch	7	100	0	.	23	58	195	24	100	28	71	34	396	15
	Arolla pine	0	.	0	.	0	.	35	44	14	71	0	.	50	37
	other conifers	49	51	0	.	0	.	0	.	0	.	0	.	49	51
	beech	216	21	62	36	48	46	6	100	18	74	44	41	395	15
	maple	9	100	6	100	8	100	0	.	0	.	0	.	23	58
	ash	48	41	7	100	18	72	0	.	6	100	0	.	79	32
	oak	46	41	0	.	16	71	0	.	7	100	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	92	37	92	37
	other broadleaves	21	58	6	100	0	.	0	.	0	.	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1128	9	800	12	743	13	358	17	417	15	447	14	3893	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	116	27	151	25	94	49	68	49	111	26	77	32	617	14
	fir	112	26	51	43	27	50	7	100	7	100	8	100	212	19
	pine	7	100	0	.	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	51	42	49	43	43	52	144	26
	Arolla pine	0	.	0	.	0	.	7	100	15	71	0	.	22	58
	other conifers	8	100	7	100	0	.	0	.	0	.	0	.	15	71
	beech	55	39	14	71	16	71	7	100	0	.	22	58	115	26
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	17	72	0	.	7	100	0	.	0	.	0	.	24	58
	oak	43	41	0	.	0	.	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	57	42	65	39
	other broadleaves	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	365	15	223	20	150	33	148	28	190	20	207	21	1284	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	21	58	50	38	15	71	15	72	7	100	123	24
	fir	42	41	8	100	7	100	0	.	0	.	18	72	75	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	22	58	8	100	0	.	30	50
	Arolla pine	0	.	0	.	0	.	14	71	0	.	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	7	100	0	.	7	100	0	.	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	oak	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	37	52	37	52
	other broadleaves	23	100	0	.	0	.	0	.	0	.	0	.	23	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	110	33	30	50	65	33	51	38	23	58	62	39	340	16
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21	58	14	71	28	50	7	100	23	58	19	72	112	26
	fir	14	71	7	100	0	.	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	14	100	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	.	51	49	51	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	35	45	22	58	28	50	14	71	37	45	83	38	220	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	49671	5	46340	6	27354	8	20240	10	34056	7	11156	11	188816	3
	fir	29243	5	15544	8	7429	12	1570	25	973	26	1567	25	56325	4
	pine	4548	12	1225	45	429	46	6130	19	4773	19	321	57	17427	10
	larch	1258	31	766	33	417	47	8725	11	4625	12	5992	14	21782	7
	Arolla pine	0	.	10	100	0	.	1639	24	2565	20	49	100	4263	15
	other conifers	1909	18	74	46	282	39	191	54	797	41	16	100	3270	15
	beech	47068	4	14944	9	12273	10	2316	28	1821	24	12345	13	90766	3
	maple	11773	7	5943	12	4225	13	1151	27	717	30	946	27	24756	5
	ash	10480	8	3253	16	3694	15	1129	35	902	46	2218	25	21677	6
	oak	6042	14	437	38	175	38	1025	33	91	72	2422	26	10191	11
	sweet chestnut	43	62	7	100	0	.	126	75	28	100	15504	12	15707	12
	other broadleaves	11460	8	5667	12	4431	17	5162	16	5096	22	16046	10	47861	5
	indeterminable	276	29	497	22	136	33	154	32	181	35	126	59	1370	13
	total	173771	2	94706	4	60845	6	49558	7	56624	6	68707	4	504211	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. 1296672. Birmensdorf, Swiss Federal Research Institute WSL

back