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
number of stems
development stage · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
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
development stage	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	103	87	160	41	0	0	328	42	151	55	742	26
	fir	0	.	7	100	74	71	0	.	0	.	0	0	81	65
	pine	41	85	14	71	0	.	7	100	14	71	0	.	76	50
	larch	0	.	0	.	0	.	7	100	13	71	98	80	118	67
	Arolla pine	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	136	88	30	64	91	58	0	.	0	.	0	.	257	52
	maple	0	.	17	100	148	83	0	.	0	.	0	.	165	75
	ash	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	oak	0	.	16	72	0	.	0	.	0	.	0	0	16	72
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	0	0	7	100
	other broadleaves	0	.	7	100	0	.	0	.	0	.	0	0	7	100
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	177	87	201	56	481	35	14	71	355	40	249	58	1477	22
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	283	38	528	37	1947	19	86	66	791	27	310	53	3947	13
	fir	525	73	379	30	325	29	0	0	0	.	0	0	1229	33
	pine	14	100	46	55	14	71	0	.	82	73	64	100	219	42
	larch	25	76	86	100	44	86	69	62	218	50	59	47	501	31
	Arolla pine	0	.	0	.	0	.	0	.	126	100	0	.	126	100
	other conifers	0	.	47	61	51	76	0	.	55	100	0	.	152	48
	beech	233	31	371	24	683	27	0	.	14	100	26	100	1328	16
	maple	106	41	128	61	303	32	0	.	105	85	0	.	641	25
	ash	55	75	8	100	109	53	43	100	0	.	0	.	215	39
	oak	14	100	81	48	0	.	0	.	0	.	0	.	95	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	8	100	8	100
	other broadleaves	90	52	188	51	396	37	87	82	160	56	266	46	1187	21
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	0
	total	1346	38	1862	19	3871	15	286	42	1551	22	734	31	9649	10
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2398	30	9947	14	9063	16	1190	30	6495	20	2137	34	31230	8
	fir	1844	30	3225	22	2099	36	154	73	24	77	167	46	7513	16
	pine	108	53	322	51	255	67	1089	50	2330	31	0	.	4105	23
	larch	0	.	1116	43	387	49	1330	31	957	37	2222	26	6012	16
	Arolla pine	0	.	0	.	0	.	49	87	935	38	0	.	984	36
	other conifers	95	64	136	45	307	68	121	74	451	42	35	100	1145	27
	beech	3053	22	5888	15	2394	28	175	65	105	71	2908	28	14523	11
	maple	2163	25	2414	22	3181	17	1013	41	20	100	1126	39	9918	11
	ash	802	32	1571	19	1323	26	908	40	137	79	1058	31	5800	12
	oak	638	70	574	31	145	75	670	45	0	.	953	47	2980	24
	sweet chestnut	0	.	0	.	15	100	0	.	0	.	3858	28	3873	28
	other broadleaves	438	27	2801	19	3374	21	2569	24	2800	29	7810	18	19793	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	11540	14	27995	8	22544	11	9269	16	14256	14	22274	11	107877	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2396	25	6011	16	9720	17	4058	30	5168	21	1355	35	28709	9
	fir	1108	28	2155	26	1671	26	7	100	206	51	299	69	5447	15
	pine	375	47	235	38	138	65	1964	37	799	50	181	87	3692	24
	larch	14	71	146	43	70	100	1456	26	812	35	1134	40	3632	18
	Arolla pine	0	.	0	.	0	.	325	61	692	52	49	100	1066	39
	other conifers	126	78	290	33	264	86	17	100	35	71	0	.	731	36
	beech	3720	22	2326	17	3002	24	291	70	455	58	4666	25	14461	12
	maple	688	25	1387	21	2408	23	0	.	288	57	343	44	5114	13
	ash	312	31	683	30	1335	32	104	58	139	68	864	49	3437	19
	oak	329	77	760	47	68	66	140	56	0	.	633	36	1930	26
	sweet chestnut	0	.	0	.	0	.	68	84	18	100	2624	28	2709	27
	other broadleaves	862	42	1260	23	1021	28	686	36	586	44	4216	22	8631	13
	indeterminable	0	0	0	0	0	0	0	.	0	.	0	0	0	0
	total	9929	14	15254	11	19696	12	9116	19	9199	17	16364	12	79558	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3700	18	3259	16	9425	14	2238	28	5588	17	1969	29	26178	8
	fir	3049	18	2001	21	2296	20	120	72	77	91	204	62	7747	11
	pine	534	41	442	27	76	90	367	46	652	40	14	100	2085	19
	larch	7	100	41	41	0	0	636	37	592	32	1143	31	2419	19
	Arolla pine	0	.	0	.	0	.	24	100	181	49	0	.	206	45
	other conifers	174	81	184	40	18	100	0	.	0	.	0	.	376	43
	beech	7036	12	4634	14	3958	14	356	83	228	59	1583	34	17796	7
	maple	1386	19	727	21	1152	21	18	100	33	71	114	47	3431	11
	ash	619	28	1135	24	909	27	52	100	0	.	56	64	2771	15
	oak	256	35	703	31	26	77	68	62	0	.	119	51	1172	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	647	45	647	45
	other broadleaves	933	22	892	26	584	23	138	50	99	56	1310	29	3956	13
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	17695	10	14018	10	18444	10	4017	23	7451	15	7160	15	68783	5
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2293	15	3813	12	15448	8	3432	18	6789	14	3057	18	34833	5
	fir	4095	13	4294	12	6248	11	286	55	319	36	597	43	15839	7
	pine	150	40	305	26	240	63	25	77	250	37	0	.	970	21
	larch	21	57	102	39	56	53	1585	26	1282	20	814	25	3860	14
	Arolla pine	0	.	0	.	0	.	189	49	318	39	0	.	507	30
	other conifers	52	74	308	42	18	100	0	.	0	.	0	.	378	36
	beech	4255	12	7826	9	6317	12	18	100	705	30	1137	34	20258	6
	maple	1052	18	1287	19	1737	15	55	100	138	53	0	.	4268	10
	ash	324	28	1257	17	742	26	18	100	30	72	0	.	2371	13
	oak	113	46	642	20	30	62	0	.	35	100	46	61	866	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	1121	34	1121	34
	other broadleaves	516	37	1706	23	1023	23	177	46	231	53	668	35	4321	13
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	.	0	0
	total	12871	9	21539	7	31860	7	5787	16	10097	11	7440	13	89593	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5072	13	1676	20	18403	9	6470	15	10785	13	5127	24	47533	6
	fir	5625	13	2432	20	5639	13	502	47	283	38	369	44	14851	8
	pine	302	34	227	41	945	53	1086	32	757	36	211	68	3527	20
	larch	21	100	52	76	174	62	3010	19	1881	20	3476	18	8614	11
	Arolla pine	0	.	0	.	10	100	1203	28	1618	35	93	84	2924	23
	other conifers	191	40	52	75	185	55	0	.	121	77	44	70	593	28
	beech	4130	13	2462	18	6022	14	979	45	982	39	2404	23	16980	8
	maple	1442	18	749	24	2696	18	292	48	401	44	217	44	5797	11
	ash	502	32	743	24	1071	24	137	72	522	58	454	28	3429	14
	oak	541	47	465	33	214	38	231	82	7	100	485	36	1945	21
	sweet chestnut	0	.	0	.	149	100	7	100	0	.	2218	26	2373	25
	other broadleaves	1212	22	1533	21	2991	18	1087	24	576	31	3286	23	10685	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	19037	9	10390	12	38499	7	15005	11	17935	11	18383	12	119249	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16143	8	25337	7	64167	5	17474	10	35944	7	14105	12	173170	3
	fir	16246	7	14493	8	18353	7	1069	29	910	22	1637	24	52707	4
	pine	1523	20	1590	16	1668	34	4538	22	4885	19	470	47	14674	10
	larch	88	37	1543	32	731	32	8094	11	5755	11	8945	11	25155	6
	Arolla pine	0	.	0	.	10	100	1791	22	3870	20	142	65	5813	15
	other conifers	637	31	1017	19	842	39	138	66	662	33	78	59	3376	15
	beech	22563	6	23538	6	22467	7	1819	32	2490	21	12725	12	85602	3
	maple	6836	10	6709	10	11625	8	1379	32	986	27	1800	26	29336	5
	ash	2615	14	5403	9	5489	12	1262	30	828	41	2433	22	18030	6
	oak	1892	31	3242	15	483	30	1109	33	42	85	2235	23	9004	11
	sweet chestnut	0	.	0	.	171	88	75	76	18	100	10476	13	10739	13
	other broadleaves	4051	13	8386	10	9389	10	4745	15	4453	20	17555	10	48580	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	72595	3	91259	3	135395	3	43493	7	60843	5	72602	4	476187	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. 1283975. Birmensdorf, Swiss Federal Research Institute WSL

zurück