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

NFI3
number of stems
development stage · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern 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	240	36	709	24	419	48	72	56	382	33	72	68	1895	16
	fir	90	43	174	51	47	63	40	80	8	71	0	0	359	30
	pine	4	100	0	.	0	.	10	100	0	.	0	.	14	76
	larch	0	.	4	100	0	0	8	71	23	55	16	71	51	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	219	36	172	69	43	80	0	.	4	100	51	78	489	31
	maple	0	.	30	55	30	53	0	.	0	.	0	.	60	38
	ash	23	85	24	61	0	.	0	.	0	.	0	.	47	52
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	41	60	9	100	37	59	30	100	48	100	0	0	165	40
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	0	0	0
	total	618	26	1122	22	576	42	160	44	465	33	139	54	3081	14
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1161	26	727	24	505	31	199	32	809	23	92	52	3493	12
	fir	356	22	249	34	121	38	0	0	32	69	0	.	758	17
	pine	74	37	0	.	0	.	299	77	42	79	0	.	415	57
	larch	25	46	14	100	24	85	317	30	66	43	254	44	701	22
	Arolla pine	0	.	0	.	0	.	45	66	10	100	0	.	55	57
	other conifers	30	58	0	.	21	100	39	100	51	60	0	.	140	40
	beech	993	15	77	37	155	37	36	73	0	0	131	72	1391	13
	maple	246	24	80	40	63	44	45	91	10	100	19	71	464	18
	ash	166	32	47	66	31	58	10	100	0	.	131	58	385	26
	oak	72	33	0	.	0	.	82	76	0	.	10	100	164	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	182	53	182	53
	other broadleaves	333	24	131	39	142	47	265	50	94	44	543	39	1509	19
	indeterminable	0	.	0	0	0	.	0	0	0	0	0	0	0	0
	total	3457	13	1325	18	1061	21	1337	26	1114	19	1362	24	9656	8
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17121	9	5216	17	4034	19	2659	25	6229	16	988	29	36246	6
	fir	2860	16	596	36	551	37	126	47	87	60	298	52	4517	13
	pine	830	28	386	51	34	67	1725	29	1603	26	0	.	4579	16
	larch	421	28	216	45	134	66	1972	23	1152	27	1686	23	5581	12
	Arolla pine	0	.	0	.	24	100	137	65	184	49	0	.	345	37
	other conifers	645	34	125	83	80	64	31	75	243	44	9	100	1132	24
	beech	4789	14	688	37	804	31	633	41	58	62	3886	19	10856	10
	maple	3305	14	870	23	923	31	342	27	96	60	663	30	6199	10
	ash	3000	15	523	28	970	31	367	36	171	42	1290	24	6321	10
	oak	1187	27	247	61	0	.	589	33	0	.	980	32	3002	17
	sweet chestnut	0	.	0	.	0	.	52	76	0	.	4354	19	4406	19
	other broadleaves	2912	16	1135	26	1427	30	3091	19	2715	24	8027	12	19307	8
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	37068	6	10001	12	8980	14	11725	12	12537	11	22179	8	102491	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	9248	10	5239	17	3571	20	3985	20	3193	20	1746	25	26982	7
	fir	3744	13	874	26	952	26	507	44	102	65	203	67	6384	10
	pine	821	24	52	59	116	67	1223	26	1134	33	106	100	3453	16
	larch	278	43	0	.	158	65	1882	21	432	37	804	26	3555	14
	Arolla pine	0	.	0	.	0	.	270	46	228	66	4	100	502	39
	other conifers	321	43	69	100	43	50	10	100	31	100	10	100	484	33
	beech	10280	9	1666	26	2300	21	615	41	411	42	3033	20	18304	7
	maple	1483	15	704	26	974	23	134	48	72	63	306	41	3673	11
	ash	2215	15	662	41	1012	34	182	42	235	46	832	30	5137	12
	oak	1890	23	127	74	22	71	130	42	0	.	841	28	3011	17
	sweet chestnut	0	.	0	.	0	.	30	100	0	.	3239	20	3269	20
	other broadleaves	2240	14	445	32	1118	27	689	29	430	33	3854	15	8776	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	32519	6	9839	13	10266	14	9659	14	6269	15	14977	9	83530	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	11385	7	9243	11	6091	13	4080	15	8251	11	1878	19	40928	4
	fir	7528	8	2651	15	1914	17	324	40	177	38	327	31	12921	6
	pine	1485	15	120	52	288	48	496	34	1013	25	143	81	3546	12
	larch	288	30	83	56	31	67	1725	20	904	20	1156	24	4187	12
	Arolla pine	0	.	120	86	0	.	162	51	269	38	0	.	551	30
	other conifers	254	41	9	100	10	100	10	100	145	86	0	.	428	38
	beech	16020	6	3831	18	3693	15	198	63	370	31	1204	29	25316	5
	maple	3250	10	1330	20	1110	19	167	55	29	58	37	76	5922	8
	ash	2564	13	592	23	851	21	9	100	20	100	70	46	4106	10
	oak	1408	17	192	56	53	57	8	71	63	100	125	46	1849	15
	sweet chestnut	0	.	4	100	0	.	4	100	0	.	580	30	588	30
	other broadleaves	2447	12	983	21	497	23	499	33	43	50	867	27	5336	9
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	46628	4	19157	9	14539	9	7682	12	11284	10	6387	12	105678	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8636	7	8659	10	8067	9	3154	15	7106	10	2565	14	38188	4
	fir	7473	7	5422	10	2526	13	416	41	280	33	442	33	16559	5
	pine	771	15	67	100	104	71	219	47	302	39	46	71	1508	15
	larch	168	31	78	44	146	43	1880	16	1082	17	755	19	4108	9
	Arolla pine	0	.	0	.	0	.	194	35	413	31	0	.	607	24
	other conifers	198	33	25	85	42	61	0	.	0	.	0	.	265	27
	beech	11134	6	5058	12	3802	12	68	59	395	32	949	29	21407	5
	maple	2040	12	899	17	822	18	184	49	97	44	23	61	4065	8
	ash	1930	11	356	33	600	25	87	46	103	65	0	.	3075	10
	oak	1210	13	51	59	55	43	9	100	19	82	220	83	1564	16
	sweet chestnut	135	70	0	.	0	.	0	.	0	.	664	27	799	25
	other broadleaves	2043	15	667	23	564	27	227	41	186	44	506	25	4193	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	35737	4	21280	7	16728	7	6439	11	9984	9	6172	11	96339	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4954	12	13145	8	3735	15	4880	13	9339	11	3657	15	39709	5
	fir	4366	12	3687	13	839	27	370	35	273	33	402	37	9938	8
	pine	663	24	607	48	339	43	1118	25	1200	24	129	64	4056	13
	larch	37	48	31	63	28	63	2180	17	2033	15	2429	15	6738	9
	Arolla pine	0	.	9	72	49	100	1083	23	1612	21	29	74	2782	15
	other conifers	501	37	31	74	80	64	0	.	320	55	0	.	931	28
	beech	4854	11	3198	17	1008	26	539	41	517	37	3244	17	13360	8
	maple	1315	15	1666	21	439	28	277	32	303	42	387	29	4387	11
	ash	1056	19	690	28	421	32	346	56	315	62	449	32	3276	13
	oak	667	26	79	51	29	62	162	46	64	60	298	37	1299	17
	sweet chestnut	0	.	0	.	0	.	100	96	17	100	2126	18	2242	18
	other broadleaves	1806	17	1636	18	541	26	818	25	1005	37	2957	14	8764	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	20220	7	24779	7	7508	13	11872	10	16998	9	16106	8	97483	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	52745	4	42938	5	26422	6	19028	7	35310	5	10997	8	187441	2
	fir	26416	4	13653	6	6951	8	1784	19	960	17	1673	18	51437	3
	pine	4648	9	1233	30	881	26	5090	14	5294	13	424	43	17570	7
	larch	1216	16	426	27	521	29	9966	8	5692	9	7100	9	24922	5
	Arolla pine	0	.	129	80	73	75	1891	16	2717	15	33	66	4843	11
	other conifers	1950	17	258	50	275	30	90	53	790	31	18	71	3381	13
	beech	48289	3	14688	8	11805	8	2089	21	1754	18	12498	9	91123	3
	maple	11638	6	5579	10	4361	11	1149	17	608	25	1434	18	24770	4
	ash	10953	6	2893	14	3883	14	1002	25	844	29	2772	15	22346	5
	oak	6433	10	696	31	158	28	981	23	146	51	2473	18	10889	8
	sweet chestnut	135	70	4	100	0	.	186	58	17	100	11145	10	11487	9
	other broadleaves	11822	6	5006	10	4327	13	5620	12	4520	17	16754	7	48050	4
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	176246	2	87504	3	59658	4	48875	5	58652	4	67323	3	498258	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. 1356648. Birmensdorf, Swiss Federal Research Institute WSL

back