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

NFI2
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 1993/95
		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	24	100	212	48	86	48	87	50	179	51	9	100	597	25
	fir	0	.	4	100	17	72	0	0	0	.	0	0	21	62
	pine	0	.	0	.	4	100	10	100	0	.	0	.	14	77
	larch	0	.	0	0	0	.	8	71	4	100	21	73	33	51
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	37	71	11	100	78	71	0	.	0	.	45	72	170	41
	maple	15	100	0	.	6	100	0	.	0	.	0	.	21	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	54	100	54	100
	other broadleaves	0	0	0	0	13	100	20	100	0	.	0	.	33	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	76	51	227	46	205	40	125	44	183	50	129	57	944	20
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	784	24	748	22	462	25	138	40	870	21	166	47	3168	11
	fir	362	32	180	38	114	47	60	86	43	60	39	79	798	20
	pine	160	37	0	.	0	.	10	100	114	61	0	.	284	32
	larch	82	49	20	71	61	93	216	39	266	30	115	42	760	19
	Arolla pine	0	.	0	.	0	.	48	74	0	.	0	.	48	74
	other conifers	76	69	0	.	51	82	0	.	41	100	0	.	168	47
	beech	338	25	167	52	332	36	35	76	24	62	119	40	1015	18
	maple	196	30	90	42	147	32	83	56	26	72	73	57	615	17
	ash	173	34	76	58	71	47	27	73	19	100	14	76	379	23
	oak	61	53	0	.	0	.	48	70	0	.	0	.	109	43
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	19	82	19	82
	other broadleaves	301	34	177	44	183	34	230	38	139	44	318	31	1348	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2532	14	1459	18	1422	19	895	24	1542	17	863	22	8712	7
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	23019	8	9982	13	6271	16	2212	27	3885	21	2508	22	47876	6
	fir	4340	14	938	26	1351	22	447	54	108	66	275	44	7458	10
	pine	1248	22	524	54	357	54	3683	20	2198	24	19	71	8029	12
	larch	689	29	42	82	121	65	1615	21	869	27	1705	20	5039	12
	Arolla pine	0	.	98	100	24	100	429	42	389	52	0	.	940	31
	other conifers	877	30	31	58	61	56	154	87	317	44	4	100	1444	23
	beech	8646	10	2044	24	2178	23	875	35	235	59	6671	14	20648	7
	maple	2547	16	1112	23	1237	23	259	28	249	45	610	25	6013	10
	ash	3325	13	663	31	1496	24	384	32	275	40	1761	19	7902	9
	oak	3053	20	233	48	22	71	398	31	18	81	1673	22	5397	14
	sweet chestnut	0	.	0	.	0	.	125	52	4	100	7343	14	7472	13
	other broadleaves	3892	12	1437	21	2079	22	2719	18	2552	23	10716	9	23394	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	51633	5	17103	10	15195	11	13299	11	11099	12	33284	6	141613	3
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10544	9	11749	11	7109	15	3398	19	7373	12	913	32	41085	5
	fir	5922	10	3138	15	1262	19	225	39	191	39	212	62	10949	8
	pine	1508	17	175	46	327	41	868	34	1322	25	149	84	4348	13
	larch	298	39	46	57	50	56	1520	21	1258	19	758	30	3929	12
	Arolla pine	0	.	4	100	0	.	336	57	722	33	0	.	1062	29
	other conifers	208	47	40	81	20	71	0	.	20	100	0	.	289	37
	beech	16706	6	3991	16	3013	18	724	38	383	50	1684	27	26502	6
	maple	3142	10	1156	19	667	26	182	58	19	71	0	.	5167	8
	ash	2637	12	605	26	552	26	62	63	29	100	31	75	3916	10
	oak	1666	19	357	44	56	68	91	46	0	.	178	54	2348	16
	sweet chestnut	54	100	0	.	0	.	19	100	0	.	1285	30	1358	29
	other broadleaves	2577	13	1311	21	546	22	544	33	240	40	513	43	5731	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	45262	5	22571	9	13603	11	7968	13	11557	10	5724	14	106685	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15548	6	10708	9	6368	11	4545	14	8512	10	1731	19	47412	4
	fir	9387	7	3381	13	2132	17	302	45	147	39	382	32	15731	6
	pine	1766	14	121	62	180	57	460	31	699	30	135	84	3361	12
	larch	195	34	61	46	131	53	1901	19	974	18	555	27	3818	11
	Arolla pine	0	.	0	.	0	.	309	38	632	28	0	.	940	23
	other conifers	171	35	4	100	31	58	0	.	0	.	0	.	206	30
	beech	15349	6	4587	16	3155	16	56	56	362	33	655	29	24164	5
	maple	2165	11	912	16	829	21	89	60	44	51	0	.	4039	8
	ash	2493	14	553	25	517	29	124	66	55	62	22	84	3764	11
	oak	1616	13	47	54	58	48	10	100	105	83	34	75	1871	12
	sweet chestnut	30	74	0	.	0	.	0	.	0	.	261	42	291	39
	other broadleaves	2480	13	875	26	576	26	336	44	208	38	231	39	4706	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	51200	4	21250	8	13977	9	8132	12	11737	9	4006	13	110303	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6579	7	6391	11	3825	12	1674	18	4073	12	1388	18	23930	5
	fir	4432	10	2797	13	1066	18	395	44	205	30	106	53	9001	7
	pine	543	18	32	100	13	58	94	55	249	50	38	67	969	18
	larch	107	33	70	46	39	48	866	20	665	23	314	23	2061	12
	Arolla pine	0	.	0	.	0	.	23	85	219	34	0	.	241	32
	other conifers	135	44	10	100	0	.	0	.	0	.	0	.	145	41
	beech	7467	7	2165	17	1642	18	163	80	412	36	549	41	12400	6
	maple	1120	16	603	25	365	25	19	100	44	62	51	93	2201	12
	ash	730	19	181	44	90	39	28	74	4	100	10	100	1043	16
	oak	878	16	28	59	41	58	9	100	0	.	36	52	992	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	545	25	545	25
	other broadleaves	1097	19	233	31	177	41	188	57	73	61	445	24	2213	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	23090	5	12510	9	7257	11	3458	15	5944	11	3482	14	55741	4
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3456	12	8528	9	4323	15	5197	13	6063	12	2971	17	30538	5
	fir	2736	14	4002	13	1199	20	239	38	199	42	314	41	8689	8
	pine	261	29	214	48	204	51	507	34	637	29	117	78	1940	16
	larch	37	71	18	71	45	72	2585	17	838	21	1772	16	5295	10
	Arolla pine	0	.	4	100	38	100	572	22	512	32	13	77	1140	18
	other conifers	235	35	10	100	62	62	10	100	184	71	0	.	502	32
	beech	3044	13	2315	15	1816	17	116	55	319	44	1562	20	9172	8
	maple	798	18	730	18	763	24	121	46	112	55	78	50	2602	11
	ash	961	20	442	30	643	28	196	42	255	81	181	38	2679	14
	oak	182	36	47	79	14	100	156	62	34	100	272	52	705	27
	sweet chestnut	0	.	0	.	0	.	101	100	17	100	1845	21	1962	20
	other broadleaves	1287	19	1111	22	787	29	509	35	298	29	1816	17	5808	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12999	9	17421	8	9894	12	10309	10	9467	10	10942	9	71032	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	59954	4	48319	4	28443	6	17251	7	30953	5	9686	8	194606	2
	fir	27178	4	14439	6	7141	8	1667	21	893	18	1329	19	52647	3
	pine	5486	8	1066	30	1084	25	5633	14	5219	13	457	42	18945	7
	larch	1408	17	257	25	447	29	8710	9	4875	9	5239	10	20936	5
	Arolla pine	0	.	106	92	62	73	1717	18	2473	16	13	77	4371	12
	other conifers	1701	18	96	42	226	31	165	82	562	35	4	100	2754	14
	beech	51588	3	15280	8	12214	8	1969	22	1735	19	11285	10	94072	2
	maple	9984	6	4603	9	4014	11	752	21	493	27	812	21	20658	4
	ash	10319	6	2520	13	3370	13	821	21	636	37	2018	16	19683	5
	oak	7455	10	712	28	191	30	713	23	157	60	2194	18	11422	8
	sweet chestnut	84	70	0	.	0	.	244	50	21	83	11352	10	11702	9
	other broadleaves	11633	6	5144	10	4362	13	4546	13	3510	17	14039	7	43235	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	186792	1	92541	3	61552	5	44187	5	51528	4	58430	3	495030	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. 1356621. Birmensdorf, Swiss Federal Research Institute WSL

back