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
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
state 2009/17
		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	7969	10	11312	8	23511	5	6661	9	15434	7	5212	10	70099	3
	fir	6550	7	6309	10	7021	7	496	23	308	24	719	27	21404	4
	pine	240	47	228	55	883	31	1928	22	2796	17	79	58	6154	12
	larch	21	71	411	49	408	34	3123	13	2193	19	3232	12	9388	8
	Arolla pine	0	.	0	.	75	67	706	21	1279	18	58	53	2118	13
	other conifers	486	29	376	21	779	28	71	63	728	28	50	66	2490	14
	beech	8858	6	8886	6	9418	7	790	29	986	18	6943	10	35882	3
	maple	3091	10	3324	10	5397	9	596	23	473	26	1239	20	14119	5
	ash	1319	14	3374	11	2673	12	459	25	569	25	1620	16	10013	6
	oak	816	32	816	23	264	47	894	27	33	58	918	25	3740	13
	sweet chestnut	10	100	29	74	118	75	0	0	0	.	4689	14	4846	14
	other broadleaves	3002	10	4597	9	7075	9	3605	13	3803	15	11130	8	33212	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	32361	4	39662	4	57622	3	19330	6	28601	5	35889	4	213464	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4630	9	7310	8	15928	5	4978	9	9350	7	2994	10	45190	3
	fir	3421	9	2850	10	3920	8	365	27	297	26	404	25	11256	5
	pine	451	26	345	29	505	28	1588	17	1236	18	129	46	4253	10
	larch	30	100	266	28	137	44	2059	11	1424	14	2376	11	6291	7
	Arolla pine	0	.	0	.	52	73	813	17	962	20	20	71	1846	13
	other conifers	186	39	257	24	134	43	0	0	112	37	10	100	699	17
	beech	6304	7	5399	6	6454	7	337	34	704	20	3724	11	22921	4
	maple	1682	10	2100	12	3542	8	157	41	299	32	280	25	8060	5
	ash	827	16	1731	11	1868	12	284	30	261	37	978	22	5949	7
	oak	697	23	1030	17	195	35	241	28	20	71	620	19	2803	10
	sweet chestnut	0	.	19	100	21	71	20	100	0	.	3487	12	3547	11
	other broadleaves	892	15	1819	12	2071	12	1030	16	705	23	4242	9	10760	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	19120	4	23126	4	34827	3	11870	6	15369	5	19264	4	123575	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3098	8	4616	7	11328	4	2762	9	5626	6	2116	11	29545	3
	fir	2348	8	1873	9	2602	7	121	35	135	32	254	29	7333	4
	pine	678	18	490	16	404	27	650	16	790	18	172	55	3183	8
	larch	33	44	239	24	73	36	1588	12	1126	12	1411	11	4470	6
	Arolla pine	0	.	0	.	73	64	388	19	493	20	30	75	983	13
	other conifers	45	69	171	33	13	76	0	.	0	.	0	.	229	28
	beech	5191	6	3394	7	3844	7	142	31	454	20	1354	12	14379	3
	maple	831	11	842	13	1311	10	32	58	141	35	163	32	3321	6
	ash	570	15	1370	11	908	15	183	65	56	44	285	24	3373	8
	oak	438	21	520	15	102	35	30	52	22	84	357	20	1470	10
	sweet chestnut	0	.	21	84	23	100	16	73	0	.	1284	13	1344	13
	other broadleaves	390	17	738	15	477	16	193	32	163	28	1439	11	3398	7
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	13621	4	14275	4	21159	3	6104	6	9006	5	8865	4	73029	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2459	6	2842	7	7886	4	1512	9	3603	6	1287	11	19589	2
	fir	1770	7	1443	8	2039	7	120	31	150	19	147	23	5669	4
	pine	395	17	431	13	213	29	216	20	394	16	73	42	1722	8
	larch	16	61	118	27	85	41	1131	11	913	12	831	11	3094	6
	Arolla pine	0	.	0	.	28	55	142	25	249	17	0	.	419	14
	other conifers	23	70	86	34	0	.	0	.	0	.	4	100	113	30
	beech	3029	5	2633	6	2197	7	47	56	205	23	683	12	8794	3
	maple	357	13	290	14	438	11	20	72	24	53	29	65	1158	7
	ash	276	17	853	9	414	15	17	61	23	53	94	27	1677	7
	oak	204	21	482	13	53	35	16	61	29	62	143	27	927	10
	sweet chestnut	0	.	8	100	12	58	0	.	0	.	491	14	510	13
	other broadleaves	139	27	328	17	159	20	21	45	27	42	351	14	1026	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	8667	3	9515	3	13524	3	3241	7	5616	5	4134	5	44698	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1516	7	1738	7	5028	5	733	11	1937	7	780	11	11731	3
	fir	1071	9	1179	9	1406	7	76	35	139	22	136	26	4007	4
	pine	144	25	257	16	66	31	130	29	200	19	19	60	817	10
	larch	16	50	118	22	69	33	845	11	681	15	587	10	2316	6
	Arolla pine	0	.	0	.	0	0	64	30	96	23	4	100	164	18
	other conifers	0	.	56	42	0	.	0	.	0	.	4	100	60	40
	beech	1279	8	1593	7	1193	9	8	100	85	27	313	14	4470	4
	maple	118	20	104	24	140	24	0	.	29	42	9	71	399	13
	ash	97	21	410	14	99	25	8	71	8	71	16	60	638	10
	oak	98	37	313	15	29	42	4	100	20	60	35	39	499	13
	sweet chestnut	0	.	12	100	0	.	0	.	0	.	297	17	308	17
	other broadleaves	20	53	147	28	112	40	29	47	14	59	58	30	379	18
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	4358	4	5927	4	8142	3	1897	7	3207	7	2257	6	25789	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	587	10	693	10	2011	6	244	17	779	11	324	15	4638	4
	fir	484	13	641	10	751	10	27	55	50	29	28	37	1982	6
	pine	38	40	83	25	14	59	12	58	58	31	4	100	210	16
	larch	0	.	49	36	45	57	343	15	213	16	238	14	887	8
	Arolla pine	0	.	0	.	14	59	19	45	59	30	0	.	92	23
	other conifers	4	100	36	40	0	.	0	.	0	.	0	.	40	37
	beech	271	15	644	9	363	13	4	100	24	41	108	23	1413	6
	maple	4	100	24	41	34	44	0	.	0	.	0	.	63	30
	ash	31	38	115	25	37	33	0	.	0	.	10	71	193	18
	oak	38	52	134	19	10	71	0	.	0	.	13	58	194	17
	sweet chestnut	0	.	0	.	14	77	0	.	0	.	107	24	121	23
	other broadleaves	16	50	48	37	11	57	0	.	6	100	23	55	104	24
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	1473	7	2469	5	3305	5	650	11	1188	8	854	8	9938	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	193	17	271	14	1006	8	128	28	366	15	194	18	2158	6
	fir	172	18	228	16	300	13	20	45	36	36	41	37	796	8
	pine	4	100	4	100	0	.	0	.	13	58	0	.	21	45
	larch	4	100	9	71	16	50	164	20	82	23	134	21	409	12
	Arolla pine	0	.	0	.	0	.	27	43	19	45	0	.	46	31
	other conifers	4	100	28	52	0	.	0	.	0	.	0	.	33	47
	beech	92	24	233	15	93	24	0	.	16	60	49	37	483	11
	maple	5	100	0	.	12	58	0	.	5	100	0	.	21	45
	ash	20	53	60	31	10	72	0	.	4	100	0	.	93	24
	oak	8	71	67	33	5	100	0	.	8	71	4	100	93	26
	sweet chestnut	0	.	0	.	5	100	0	.	0	.	97	23	103	23
	other broadleaves	0	.	19	51	12	58	0	.	0	.	10	73	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	502	11	919	8	1457	7	338	16	548	11	530	10	4295	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41	38	92	22	267	16	50	39	121	19	96	22	668	9
	fir	43	32	76	25	102	21	4	100	8	71	8	71	241	14
	pine	0	.	4	100	0	.	0	.	4	100	0	.	8	71
	larch	0	.	4	100	0	.	57	28	31	43	59	31	151	19
	Arolla pine	0	.	0	.	0	.	16	50	16	50	4	100	36	33
	other conifers	4	100	8	71	0	.	0	.	0	.	0	.	12	58
	beech	13	58	87	25	16	50	4	100	0	.	16	50	136	19
	maple	0	.	6	100	7	71	0	.	0	.	0	.	13	59
	ash	0	.	18	50	0	.	0	.	0	.	0	.	18	50
	oak	14	59	34	36	10	72	0	.	0	.	0	.	58	28
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	57	34	61	32
	other broadleaves	0	.	14	59	0	.	0	.	0	.	4	100	19	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	114	21	342	12	403	12	136	20	180	16	245	15	1420	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	12	58	90	25	4	100	16	50	23	40	149	18
	fir	24	47	13	58	16	50	0	.	8	71	10	72	71	26
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	31	38	13	58	18	65	62	30
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	beech	4	100	9	71	12	58	0	.	0	.	8	71	33	36
	maple	0	.	0	.	0	.	0	.	0	.	4	100	4	100
	ash	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	oak	6	100	13	77	0	.	0	.	0	.	0	.	19	61
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	20	53	20	53
	other broadleaves	0	.	17	80	4	100	0	.	0	.	0	.	21	67
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	38	37	71	30	122	21	38	34	41	32	83	25	394	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	12	58	40	32	4	100	56	47	32	38	148	22
	fir	0	.	8	71	16	50	0	.	4	100	8	71	36	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	8	71	19	53	32	39
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	maple	0	.	0	.	12	75	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	11	58	0	.	0	.	0	.	0	.	11	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	77	38	77	38
	other broadleaves	15	74	22	100	0	.	0	.	0	.	4	100	41	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	19	62	54	46	69	26	12	58	72	38	151	23	376	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20500	6	28899	5	67094	3	17075	7	37288	5	13058	8	183914	2
	fir	15883	5	14620	6	18173	5	1228	22	1134	16	1755	19	52794	3
	pine	1949	15	1843	13	2085	22	4523	15	5491	13	476	40	16367	7
	larch	120	36	1214	20	834	24	9345	8	6682	10	8905	8	27100	5
	Arolla pine	0	.	0	.	241	54	2182	15	3180	14	116	55	5720	10
	other conifers	752	27	1022	17	927	28	71	63	840	27	69	56	3680	12
	beech	25040	5	22879	4	23590	5	1332	28	2474	16	13209	9	88523	3
	maple	6087	7	6690	8	10894	7	805	21	970	24	1724	17	27171	4
	ash	3140	10	7934	7	6009	9	952	27	920	25	3003	14	21958	4
	oak	2318	18	3422	11	669	29	1185	25	131	54	2090	17	9815	8
	sweet chestnut	10	100	89	86	193	72	40	77	0	.	10604	10	10937	10
	other broadleaves	4473	9	7750	9	9921	8	4877	12	4718	15	17261	7	49001	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	80272	3	96360	2	140630	2	43617	5	63829	4	72270	3	496978	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. 1327021. Birmensdorf, Swiss Federal Research Institute WSL

back